Introduction

I recently had the opportunity to discuss innovation policy with Charles Plant. Charles is an economist and serial entrepreneur, having led several ventures in medical and deep tech fields over an impressive career. On the side, he authors The Narwhal Project, a publication that provides evidence-based policy insights on a number of issues facing Canada today and advances hypotheses about what it takes to build and scale a company in the Canadian context.

If you are not already familiar with his work, I strongly suggest that you take the time to explore it. Charles’ research is grounded in statistically rigorous analysis of real data. Many of his conclusions challenge prevailing narratives about the issues holding back Canadian innovation, particular where private investment and startup performance is concerned. His thoughts should be required reading for public servants seeking to maximize the impact of the programs they administer.

In this interview, we explore a small subset of this work, examining and questioning the narratives around Canadian innovation and related policy.

Your email client will probably truncate this post. My key takeaways are presented at the end, so be sure to read the web version if you want to get the whole story. Many thanks to Charles for taking the time to share his insights.

Interviewer’s note: Charles Plant approved the final version of the section entitled “Interview with Charles Plant” and had editorial input on that section, with the option to rephrase and expand on the ideas discussed in the interview without changing or removing any intended meaning. The key takeaways presented at the end are my own commentary, and do not necessarily represent the views of Charles Plant.

Interview with Charles Plant

KB: Tell us about yourself and how you got to where you are today.

CP: I never know how to answer this, because it’s been such a long and winding path. Many years ago, I co-founded a software company and was its CEO. We built telecom software for phone companies in the US, England, and Canada, and I left after 15 years.

We’d built a moderately successful company. We were growing, we were profitable, but we weren’t a world-beater by any stretch of the imagination. And I realized I really didn’t understand how to scale a company to a large size. If you don’t work in one, you never see how it’s done. Remember, we started that company in the late 1980s. By the 2000s, companies were starting to scale, but it was a fairly opaque process, and it was hard to understand how people were doing it.

So I thought, okay, I have to learn how to scale a company. I deliberately set out to work with a bunch of different companies, and I acted as a fractional CFO for a number of them. I didn’t find the answers that way, because I never ran into any rapidly scaling companies.

I tried investment banking and saw a few moderate successes there, but nothing earth-shattering. I worked at MaRS and didn’t see any massive global companies coming out of Toronto through MaRS, although that was a few years ago and it may have changed a little since. I tried venture capital for a while too, and didn’t find it there either. I could figure out what it took to start a company, and what companies were doing right and wrong, but not what it took to scale.

So I scratched my head, and I ended up at the University of Toronto as a senior fellow at a new research institute. That gave me the chance to actually dig in, look at data on thousands of companies, figure out what was going on, and investigate. That’s when I really started publishing research.

Over the last 12 years or so, I’ve produced about 40 research reports, including a major one for the C.D. Howe Institute. I’ve written two books, with another on the way, and I’ve given tons of speeches. In that time I’ve talked to thousands more entrepreneurs, literally around the world. I’ve advised hundreds of them, and I’ve been an officer or director of about 25 companies. That’s given me a much better handle on what’s going on, and the work with entrepreneurs feeds the research. That’s where the latest batch of research you’ve seen came from.

Right now I’m co-CEO of a nanotech company, board chair of another, and CFO of a third. They’re all in quasi-medical, high-tech or deep-tech fields, and that’s proving interesting as well.

KB: Was deep tech part of the scope of companies you’d worked with before, when you were exploring the scaling question?

CP: If you call AI deep tech, yes. I worked with a couple of AI companies in the early days of AI, and I think it qualified as deep tech back then. Maybe not today. I never really thought of it as deep tech, though. It’s software. To me, deep tech means physical materials, medicine, drugs, things like that.

KB: In your research, you found that Canadian governments directly and indirectly contribute about 44% of the capital received by domestic tech companies, compared to about 4% in the US. Based on what you’re seeing, where are the actual gaps? How do we make more efficient use of the funds that exist?

CP: First of all, that 44% blew me away. I’d never considered it. You have to look not just at what governments are putting into venture capital, but at what they’re putting in through IRAP and SR&ED and all of these other programs. That’s really capital. It goes in different ways, but you still have to count it.

So the government has poured money into this, and people have been blaming the government for years anyway. I found one article from 1967 with a big headline: Canada introduces new R&D funding program because we trail the rest of the world in innovation. We’ve been telling the same story for nearly 60 years, as far as I can see.

What’s happened is that it’s become a mantra. Whenever productivity is low, everybody demands that the government do something, and as a result the government is spending billions. You mentioned $750 million, but when you add in SR&ED, they’re spending $5 billion a year trying to create an innovation economy in Canada. We’ll come back to that. But your question was about capital gaps.

I see three capital gaps that are real and valid. The first is angel capital. There’s a lot less angel capital in Canada than in the United States, if we use the US as a comparator. The second is early-stage VC. There’s a lot less of it, and if you take the government’s money out of the VCs, the amount looks really bad. And the third is a lack of late-stage money.

So there really are three gaps. But the first two, angel and early-stage VC, have been filled by governments. You ask later on whether early-stage companies here are getting as much money as they are in the US. Yes, they are, and they’re getting it out of that 44% government funding. So we’ve solved the first two gaps. I don’t think we’ve solved them in a good way, but in terms of dollars, we’ve solved them.

That leaves late-stage funding. My belief is that this gap exists not because of a lack of funding, but the other way around: there’s a lack of good companies, so the funding isn’t flowing into the system. The funding doesn’t come first. The companies come first, and companies attract funding if they’re good. So if we’re not getting funding in Canada, it isn’t because of Canada. It’s because we lack good companies.

I said that once in Colombia (the country, not the university), where they were complaining about a lack of capital. I told them, stop complaining about a lack of capital. You have a lack of good companies. When you have good companies, the capital will find them, because that’s what capital does nowadays. It goes around the world looking for opportunities. If you can show investors a better return in Kazakhstan, they’ll go to Kazakhstan. Capital follows companies.

And sure enough, I went back the next year (I visited regularly and worked with the tech community there) and Colombia had two unicorns while Canada had none. People remembered what I’d said the year before, scratched their heads and went, well, I guess that works. A lot of places have figured that out. Singapore is one. So yes, there’s a lack of funding, but really it’s a lack of good high-growth companies.

KB: For the sake of clarity, when you say “early stage” and “late stage,” what do you mean?

CP: Companies have three problems to solve. The first is, is there a market, and is it big enough? The second is, do we have product-market fit? And the third is, can we scale efficiently? You can roughly line those up with funding stages.

Pre-seed capital answers the question, is there a big market here? Seed capital answers the question, do we have product-market fit? By the time you get to an A round, you should be at about $200,000 a month in revenue, and that’s enough to judge product-market fit.

Series A money lets you experiment with going to market and figure out whether you can get an efficient growth engine going. After that comes later-stage money to scale. Call it Series B if you like. But I think of it as three questions that have to be answered in order: large market, product-market fit, efficient growth.

KB: How do you see the influence of Canadian public funding for innovation showing up in the incentives, risk appetites, metrics, and behaviour of Canadian private money for innovation?

CP: I don’t know that I’ve been able to test that empirically. But I know from experience what happens. People chase grant dollars, and they skew their spending toward things that will get them grant dollars, because they think it makes the money go further.

I can think of lots of companies I’ve worked with (ones that don’t listen to me) that are getting massive amounts of grants and tax credits. They’re spending massive amounts on R&D and almost nothing on sales and marketing.

Now, I’m not sure whether that’s caused by the grants or just associated with them. I think there’s a bigger problem than the grants alone. But the incentives accentuate it.

KB: What’s your take on the relative risk appetites of Canada versus the US?

CP: I’m going to attack the premise of the question. Two weeks in a row, I read the Sunday column by the head of The Logic (I can’t remember his name) talking about risk aversion and risk appetite.

I don’t believe investors are risk-averse. I believe they’re not stupid. People run around blaming angels for not investing and asking for tax credits. They blame pension funds for not investing in venture capital. But put yourself in the place of a pension fund. You have a choice of assets, a mandated rate of return, and you’re personally rewarded for hitting it. You can go to the United States, invest in a venture capital firm, and earn on average, let’s say, a 12% return. Or you can invest in a Canadian venture capital firm and earn 7%. (We’ll come back to that 7%.) What are you going to do?

You can invest in an airport that gets you 18%, or in Canadian venture capital that gets you 7%. Is that risk-averse, or is it just smart money?

Now let’s look at that 7%. I just discovered something. I haven’t blogged about it, though I should have. It comes from a Business Development Bank of Canada report. That figure is supposedly the Canadian rate of return, but there’s a little asterisk, and the asterisk says it includes Canadian funds and American funds with heavy investments in Canada. What does that tell you?

I think the 7% is inflated. I don’t think we’re getting that return. If the number looked better with just the Canadian funds, they wouldn’t include the Americans. They make the number better by including American funds, because you can’t separate the Canadian returns from the US returns inside a US fund. So obviously the US funds that happen to invest in Canada have better returns, and they’re boosting the Canadian figure. We’re deluding ourselves about what our real rate of return is.

You’re an angel, right? Has an angel group ever told you your expected rate of return? Or the historical rate of return? Can you even get an angel group to tell you how much each company raised, and from how many angels? Not unless you’re in the tent, so to speak. I’ve been in the tent. One angel group asked me to do an evaluation, and I said, give me all your data on who you’ve invested in. I ended up having to use proxies, because I never got a list of who they’d invested in.

So we have a very, very immature, unprofessional angel community in Canada. And I say that as an angel who has been just as unprofessional and just as immature in my time. I now realize that was a real mistake.

So take somebody who has just sold their company and give them two choices. One: invest in a few angel deals with a 10-year horizon, no liquidity, and an unknown rate of return, because we really don’t know what you should expect. Two: buy an asset you can sell tomorrow that has earned 17.5% a year over the past 10 years. Which would you pick? And do you know what that 17.5% is? It’s the Royal Bank. So not investing isn’t risk-averse. It’s just smart. Totally rational.

And there you’re back to incentives, which gets to your question about how the state’s presence alters the risk appetite, target metrics, and behaviour of Canadian for-profit fund managers. People are now blaming pension funds and saying Canada should mandate that they invest here. But what they’re really saying is that we should mandate pension funds to accept lower returns. It makes no sense.

KB: According to your research, once a company crosses a “micro-funding” threshold of $100,000, its chances of going on to raise anywhere from $1 million to $100 million don’t look all that different from the United States. How should policymakers interpret that finding?

CP: Well, there are two things. Number one, we do have a lower startup rate, 24% lower than the United States. I don’t know why. And there’s a problem with startup-rate statistics, because there are two types of entrepreneurship: necessity-based and opportunity-based.

Look at a country like Uganda. It probably has a really high entrepreneurship rate, but that’s necessity-based entrepreneurship. People can’t get jobs. Have you ever spent time in Bogotá? It’s delightful driving down the highways, because there are people standing in the middle of the lanes selling things. That’s entrepreneurship, but it’s necessity-based, not opportunity-based. You can’t use the stats to tease out opportunity versus necessity, so I don’t know what the real rate of entrepreneurship is. There are studies, but they confuse the two. Still, we do have a lower startup rate.

So you have to adjust for the lower startup rate first, and then look at how many companies get to each size. Otherwise it looks like we’re not doing as well per capita. Well, of course not. We don’t start as many. The next question is: once companies actually start, how many get to different amounts of capital?

Most other research has been based on funding rounds: seed, A, B, and so on. I’ve done that too. But there’s no definition of what those mean. What’s a pre-seed round versus a seed round versus an A round? There have been 114 Series A rounds in the United States this year alone that were over $100 million. So what is an A round? You can’t do the analysis that way. You have to look at how many companies raised a given amount of money. And when you do that, we’re pretty equivalent to the United States.

That’s something people need to analyze more. I’ve done the other type of analysis multiple times, but for this one I picked a particular window, and it may be the wrong window. We should go back, look at different points in time, and keep doing this kind of analysis to see whether the finding holds up. Right now I’m working with a petri dish, and I don’t have enough data.

KB: When you look at the amount raised, are you referring specifically to private money?

CP: It’s anything registered in Crunchbase, however people choose to register it. Sometimes they register grants, but they’re not putting SR&ED in. So there might actually be even more funding in Canada if you include SR&ED tax incentives. Typically it’s angel money, venture capital, a whole variety of things. But it’s whatever is registered there, because I can’t tease out the different sources.

KB: As you alluded to earlier, one of your findings is that Canadian firms spend less on sales and marketing than their American counterparts. Is that why we struggle to scale in Canada?

CP: First of all, it’s one of the reasons, not the only one. Incentives play a part. But there are also three other things. I say three, and I’ll probably come up with four.

The first is virtue signalling. There’s a strong belief in Canada that intellectual property is the most important thing. You get that from the Jim Balsillie school of building companies, and from the fact that virtually all of the programs in Canada are oriented to research and development. Part of that is the result of international agreements, but be that as it may, there are ways around those agreements.

The government has promoted STEM education for many years, for instance. But it doesn’t take into account that someone in marketing and sales is probably earning more than the average person in STEM. If you can do marketing and sales, those are better jobs than software development or something like that. So that’s part of the virtue signalling: research and development is good, and marketing and sales is something other people do; you don’t really need it. I’ve had entrepreneurs say to me, “I don’t believe in marketing,” as if it’s a belief system. There’s scorn heaped on commercialization. In Canada, the definition of innovation usually skews toward invention rather than commercialization. But the two parts matter equally. To me, invention plus commercialization equals innovation.

The second thing is that there isn’t a great base of experience for people to draw on. We’re at the early stages of developing a community here, and there just aren’t many people who’ve spent their lives in sales and marketing in the tech sector. So there’s nobody saying, hey, you’ve got to do more of this. The people giving advice typically don’t have that experience. The government isn’t saying it, and the entrepreneurs don’t have it. All of that adds up to people not thinking of sales and marketing as important.

Now, I did one really interesting piece of research in the last couple of weeks. I was looking at scale-up efficiency, trying to find what drives growth, and I found a 0.76 correlation between the rate of increase in sales and marketing spending and the rate of increase in revenue. General and administrative expenses and research and development aren’t correlated nearly as highly. So it’s pretty clear, and you can experience it in any company: if you want more sales, you spend more on sales and marketing, not on research and development. But we don’t have that conversation here, we don’t have that base of experience, and the government isn’t promoting it. All of those things, along with incentives, feed into this lower rate of spending on sales and marketing. And I’ve run that test multiple times. It comes back the same way every time.

KB: Is that a stage-independent finding?

CP: Yes. Now, it could partly be a result of the amount of capital we get, too. If you get less capital, you still have to build the same product, and if that costs the same in both countries, you have less left over for sales and marketing. That could be a factor. But the funding data says it isn’t true at the early stages. We’re actually close in dollar funding.

KB: Is that correlation as strong if you look at the American ecosystem?

CP: Oh, the 0.76 is from America. It’s from 160 software companies that went public.

KB: How would you approach remedying the issue? Is it a question of restructuring incentives, deprioritizing certain areas of grant funding, cutting grant funding, incentivizing talent retention, or something else entirely?

CP: Those are all little tweaks, and I don’t think a tweak is going to work. I think we have to go back to fundamental strategy.

First of all, we should stop comparing ourselves to the US and start comparing ourselves to France, England, Germany, Singapore, and places like that. The reason is that the US is special. It’s a country with an enormous amount of power in the world, and we’re all dealing with that power imbalance right now. But when you look at the opportunity for starting a tech company in the US, they have three things we don’t.

Number one, they have a large local market. Same regulations, same language; all of those issues are already dealt with. In Canada, we have to get big by exporting, and that’s a much more complex problem to solve. We watch the same TV shows, but that doesn’t mean we buy the same way or for the same reasons.

The US also has a higher degree of competition, and because of that, American companies tend to buy more innovation. We’re an oligopolistic country with a low degree of competition, and that doesn’t drive innovation. So these are very different markets. Their size, their nature, and how they work are substantially different.

I remember that most from the first sales call I made in the US. It was to the CTO of Cingular (at least, I think it was Cingular). He was a Texan, in an office in Baltimore, I think, with an enormous Texas flag behind his desk. And he practically jumped across the table and devoured me while I was trying to sell to him. I thought, oh my God, this is totally different. In the US, you don’t have to sell to them. They buy. They want innovation. Canada is different. So that’s the first issue: the market is different.

The second issue is that the talent is different. There may be one school in Canada that teaches sales. They all teach marketing, but not sales. A lot of people in the US see sales as an honourable career, and they have really talented people with lots of experience. In the early days of the tech industry, they didn’t have much experience either. Nobody gave salespeople big budgets, and nobody expected them to burn through a hundred million dollars from a Series A round. But starting about thirty years ago, the US began developing that talent base, and it’s now been a full generation. It’s replacing itself: the people who were young then are now retiring, and all of that knowledge has accumulated.

We don’t have that base of knowledge. We don’t have enough people here who’ve spent their lives selling high-tech products to build an ecosystem around. And if you’re trying to get big, you have to sell in foreign countries, so you should be hiring your salespeople locally in those markets. So we’ll never build that talent base here. We’ll never have the big market, and we’ll never have the big talent base.

The third thing is capital. They have enormous pools of capital, absolutely, that can write billion-dollar cheques to back wacky ideas. They didn’t have that thirty years ago either. They had small amounts. Look at how much money Apple got initially. We’re talking two to six million, that kind of thing. Not six hundred million. They built those pools of capital up through success.

We’ll never have those pools of capital, because of power-law distributions. To get a narrow range of outcomes on a large investment, you have to make lots of investments, say a hundred of them. We don’t have enough investments here for people to get a consistent rate of return, so the math is working against us. We’ll never have the capital. And besides that, capital here has all sorts of other places to go: oil and gas, mining, forestry, natural resources. Those sectors soak up large amounts of capital because the world needs what we’ve got, and they draw people away as well.

So fundamentally, what I’m saying is that our economy is structurally very different from the US economy, and because of that, we don’t have the raw materials to create what the US has created. When it comes to government policy, you can’t make tweaks to try to make us look like the US, because we will never, ever succeed at that.

We need a completely different approach to developing the innovation economy, built on a different set of premises. Instead of requiring a large market, maybe we figure out how to build big companies out of analogous small markets. Maybe we go into areas that depend less on great sales and marketing and lean more on the technology, deep tech being one. And we figure out how to be more capital efficient.

Shopify, for instance, is a great Canadian company and remarkably capital efficient. The average US company has $2.18 of capital for every dollar of revenue. That may be an old stat, but Shopify was one to one, and that’s why its stock has done so well. We need to create a different style of company in Canada, not keep doing it the same way. We have to stop comparing ourselves to the US and figure out what we can do with our resources that somebody else can’t. Why are we special?

I say to people, it’s like Japan deciding one day that it wants to be an oil and gas powerhouse. You smile, because that’s a stupid idea. Japan doesn’t have any oil and gas. How could it ever be an oil and gas powerhouse? Yet that’s what we’re doing. We’re being totally stupid about this.

KB: Have you seen ecosystems with a similarly structured economy to Canada’s that have been able to overcome this?

CP: Well, no. We’re doing better than France, Germany, and England in this game. We aren’t doing as well as Singapore or Israel. Singapore is a great case. They rely on partnerships; they’ve developed a way of partnering with everybody, I think. I don’t know enough about Singapore, and I should really do a bit of research before I talk about it, but from what I’ve learned, they took a different strategy.

Israel has a completely different set of issues. It has a highly educated population, which works to its advantage. It also faces an existential threat that forces it to make sure it has a local supply chain of innovation, and it has promoted that. So there are countries that have developed different philosophies from the US. But the first thing an Israeli company does when it goes big is open an office in Boston or New York. The number of Israeli companies with offices in Massachusetts is quite astounding.

The point is, we’re all looking at the US and going, I’ve got to be like the US. But the countries that have succeeded have succeeded for other reasons, because they’ve done something else. Now, I don’t have that thesis completely tested, so take it with a grain of salt, but that’s where I’m heading.

If you go back to economics (I don’t know if you’ve ever taken any), it always starts with: what resources do you have, and how are you going to capitalize on them? An analysis of countries might use Porter’s Five Forces or something like that, and it all starts from the premise that you have to build on your strengths. You can’t fix your weaknesses. In Canada, we’re trying to fix our weaknesses instead of building on our strengths.

KB: On the question of missing sales and marketing talent: is this talent we train that then leaves, or does it never develop in the first place? It sounds like you’re saying it never develops, because the kind of sales and marketing talent that has driven success elsewhere just doesn’t develop in a market with low competition that’s export-dependent from the get-go. Am I understanding that right?

CP: I did a piece of research a couple of years ago, which you can find online, that surprised me. It’s old, so I don’t know how well it applies today. Everybody talks about the brain drain to the US. But there’s more of a brain drain of sales and marketing people than of technical people, and it happens early in their careers. Once you’re pretty good at sales and marketing and you want a good career, you go to the US.

KB: In some of your writing, you posed a series of questions, some of which we’ve touched on already. The first is whether grant-heavy firms take longer to reach meaningful revenue. Do you see a causal relationship between easy access to grant money and delays in actually taking steps to commercialize and scale?

CP: I don’t know. I haven’t tested that. It’s logical. But I asked some of those questions precisely because I really don’t know why people do what they do, or exactly what they’re doing. That leads into something we’ll get to toward the end.

KB: What data do you actually need to answer these questions? And who should be collecting it?

CP: I’ve tried to research these things, and the only way to figure them out is by talking to entrepreneurs directly. Talk to the founders, ask them what they’re doing and why, and do a lot of it. I tried to get research money to do this and failed. Maybe I went about it the wrong way, but there’s no one in Canada who’s incented to research this area, and research follows the dollars.

This would be non-academic research. There is academic research on these topics, but it’s done by people who each see only one part of the elephant. If you have real experience, it leads you to the research. You know the problems you’ve had, and you can go look at them. Academic researchers aren’t doing that. They theorize about what the problems are and then research those.

Meanwhile, the entrepreneurs who have the problems don’t know what their own problems are. If they did, they’d have fixed them and they’d be doing better, right? You see it all the time in academic and government research that asks entrepreneurs what’s wrong. That’s a completely invalid way to do research. They don’t know. If they knew what was wrong, they’d fix it and nothing would be wrong. The fact that they’re saying something is wrong means (and we’ll get to this in your last question) that I need to go out and actually talk to entrepreneurs.

And there are some great sources of them. IRAP provides money to all the right companies. You could just go through IRAP and start asking questions, but IRAP isn’t funded to do that research. BDC isn’t funded to do it either, and it also has access to all of these companies. So you could go through government agencies to reach the people, but nobody is spending money on it.

KB: If you were given control of the $750 million envelope of funding right now, what would you do differently from what’s currently being done?

CP: I probably have some ideas, but I should say that I really don’t know, because I don’t have enough data to make decisions. And that’s part of the problem. I’ve advised provincial, municipal, and federal governments on exactly these issues and programs, and there’s no data being used to develop programs. Well, “no data” is a little loose. There’s a limited amount, frequently of the wrong type, being used to evaluate which programs are needed. Most of the time they’re just responding to the noisiest voices. So I don’t know what I’d do with $750 million. I’d probably hit pause, take the time to research what the real problems are, and then build something around that.

KB: Is there anything you’d like to get into that I didn’t ask about?

CP: The blame game. We’ve touched on it. You read in the press that people blame the government for not spending enough in Canada, and there are always articles saying the government should do more. It should be the first buyer. The hospital system is terrible; it doesn’t buy from Canadians. So the first complaint is that we’re failing because buyers aren’t buying.

Number two is that we’re failing because the right people aren’t here. Nobody has really said what to do about it. Oh yes, they have. This one really got me. Recently, people have said we’ve got to change the tax system to allow greater inequality, so entrepreneurs can keep more of the money they make and won’t leave for the US. Have you seen that one? So there’s the pattern again: blame the government; the government can fix this.

The third is that we don’t have enough capital. Everybody fights over that one. We don’t have enough capital; government, you must give us more.

What’s happening is that people are blaming external factors for the lack of success instead of looking inward and asking, what could I have done better? If I have limited resources, what could I do with them? We really have to stop the external blame game and have a serious internal conversation about what we’re doing and why it isn’t working. It’s not the government’s responsibility to buy our products. It’s not the government’s responsibility to make sure the right people are here. It’s not the government’s responsibility to provide capital.

Now I’m sounding like a rabid right-winger, aren’t I? I’m actually a highly woke, very left-leaning person. I believe in putting money in regular people’s pockets and cutting taxes for lower-income people, not making the wealthy wealthier. So this is partly a reaction to that, and I don’t want to come across in the article as a rabid right-winger, because I’m the complete opposite. It’s just that we’re being greedy in a lot of what we’re asking for.

We have to look inward and ask why we’re failing and how we can succeed in a different way, and have a serious conversation based on serious research. I may have done more research on this topic than anyone in the country. I haven’t found anybody with more, particularly longitudinal research. And that’s sad, because I do it for fun, on the side.

We need a much more serious conversation. The institutions sitting around blaming everything but themselves, and the entrepreneurs blaming everyone but themselves, need a kick in the pants. That’s not the way to succeed. It’s like the Maple Leafs blaming the crowd for their performance.

Key takeaways

It isn’t the amount of money

While Charles did not comment on it directly, the results of his analysis land awkwardly for the federal government, which last week announced the allocation of Growth VCCI funding into precisely the stage his data suggest is least in need of it. Adding up all of the programs run on public dollars, he finds that 44% of the money going into Canadian innovation comes, in one way or another, from the government, 10 times more than the equivalent figure in the US.

Despite this, the Canadian private sector keeps asking for more. As Charles puts it:

“[T]he government has poured money into this, and people have been blaming the government for years anyway […] We’ve been telling the same story for nearly 60 years.

What’s happened is that it’s become a mantra. Whenever productivity is low, everybody demands that the government do something, and as a result the government is spending billions.”

Clearly, the issue is not the amount of capital being spent by the government on innovation.

Funding gaps

Charles identifies three gaps in Canadian private capital: angel investment, early-stage VC, and late-stage VC, but he is careful to distinguish between them. The first two, he argues, have already been closed by the public sector, at least on paper:

“The first two, angel and early-stage VC, have been filled by governments. You ask later on whether early-stage companies here are getting as much money as they are in the US. Yes, they are, and they’re getting it out of that 44% government funding. So we’ve solved the first two gaps. I don’t think we’ve solved them in a good way, but in terms of dollars, we’ve solved them.”

The funding exists, but the way it is delivered determines who can actually use it and the impact it ultimately has. I’ve written extensively about the structural problems that divert public funding away from its intended recipients, or that are designed, through revenue minimums, matching requirements, and reimbursement after the fact, to prevent access by the pre-revenue companies that could make the most efficient use of them. A dollar that arrives as a tax credit eighteen months after it was spent, or that requires a cash match the company does not have, counts toward Charles’ 44% without ever reaching a company at the formation stage.

The late-stage gap is a different animal. Here Charles rejects the premise that more capital is the answer:

“My belief is that this gap exists not because of a lack of funding, but the other way around: there’s a lack of good companies, so the funding isn’t flowing into the system. The funding doesn’t come first. The companies come first, and companies attract funding if they’re good.”

This is where last week’s Growth VCCI announcement should be closely examined. The program is explicitly designed to address the late-stage gap: larger cheques for companies that are ready to scale. If Charles is right, that is the one gap in the Canadian capital stack that is a symptom rather than a cause. The shortage of late-stage capital reflects a shortage of companies worth funding, and no amount of growth-stage public money will create them after the fact.

A shallow pool

There is a mantra in Canadian innovation that Canada is great at starting companies but bad at keeping them. While Charles’ analysis certainly supports the view that Canadian companies have trouble scaling in Canada, it challenges both the first half of that idea, and the usual causal attribution to insufficient capital: he finds that on a per capita basis, Canadians start 24% fewer companies than Americans. Once he adjusts for that difference, he also finds a “micro-funding threshold” of $100,000: a Canadian company that crosses it is no less likely than a US startup to raise $1M–$100M in follow-on capital

Charles is as always careful to caveat the generalizability of his findings:

“I picked a particular window, and it may be the wrong window. […] I’m working with a petri dish, and I don’t have enough data.”

My own diagnosis differs slightly from that of Charles, but they are complementary: my assertion in much of my writing is that public money arrives early but in a form that excludes pre-revenue firms, while his is that it may skew survivors toward R&D over sales and marketing. The best investor on the planet will be unable to realize good returns in an ecosystem that does not produce a deep pool of investment opportunities from which to choose, and Canada’s pool is shallow. As Charles puts it:

“We don’t have enough investments here for people to get a consistent rate of return, so the math is working against us.”

No amount of government funding will change this if the money is delivered early in aggregate but structured so it doesn’t reach formation-stage, pre-revenue firms (and, per Charles, so that it distorts spending toward R&D).

Rational versus risk-averse

I have written often about risk aversion in Canadian innovation. Charles attacks the premise and highlights a critical nuance:

“I don’t believe investors are risk-averse. I believe they’re not stupid. […] You can go to the United States, invest in a venture capital firm, and earn on average, let’s say, a 12% return. Or you can invest in a Canadian venture capital firm and earn 7%.

[…]

You can invest in an airport that gets you 18%, or in Canadian venture capital that gets you 7%. Is that risk-averse, or is it just smart money?

[…]

[You can] invest in a few angel deals with a 10-year horizon, no liquidity, and an unknown rate of return [or you can] buy [RBC,] an asset you can sell tomorrow that has earned 17.5% a year over the past 10 years.

[…]

So not investing isn’t risk-averse. It’s just smart. Totally rational.”

He is right, and my own research backs it up: the earlier in a company’s lifecycle we look, the greater the disincentive for for-profit investors. Apparent private sector risk aversion is a symptom of rational investing in an ecosystem with a shallow pool of opportunities, and it is through taking steps to increase the quality of the pool of investable companies, not through further funding for late stage growth, that Canada can incentivize private-sector participation in Canadian private markets. He goes further, challenging the purported Canadian rate of return in venture capital:

“Now let’s look at that 7%. […] It comes from a Business Development Bank of Canada report. That figure is supposedly the Canadian rate of return, but there’s a little asterisk, and the asterisk says it includes Canadian funds and American funds with heavy investments in Canada. What does that tell you?

I think the 7% is inflated. I don’t think we’re getting that return. […] US funds that happen to invest in Canada have better returns, and they’re boosting the Canadian figure. We’re deluding ourselves about what our real rate of return is.”

I’ve previously written that to address this, we must embrace risk, recognizing that in the pre-revenue stages, before it makes sense for private capital to get involved, it is too early to pick winners. At first glance this contradicts Charles’ point, but this is not the case: my position is that the risk at the earliest stage can’t be carried by for-profit capital, so it has to be carried by public or venture philanthropic capital designed for that stage, strengthening the pool of opportunities so that it makes sense for private capital to get involved.

Context matters

A further issue he identifies relates to market structure and the importance of building policy for the context in which it will be applied:

“[W]e should stop comparing ourselves to the US […T]hey have a large local market. […] In Canada, we have to get big by exporting, and that’s a much more complex problem to solve.

[…]

The US also has a higher degree of competition[…] We’re an oligopolistic country with a low degree of competition, and that doesn’t drive innovation. So these are very different markets. Their size, their nature, and how they work are substantially different.”

I have summarized this idea in other articles simply as “context matters”. We cannot simply import American practices and expect them to work here. His prescription follows from that:

“We need a completely different approach to developing the innovation economy, built on a different set of premises. Instead of requiring a large market, maybe we figure out how to build big companies out of analogous small markets. Maybe we go into areas that depend less on great sales and marketing and lean more on the technology, deep tech being one.

[…]

Shopify, for instance, is a great Canadian company and remarkably capital efficient. The average US company has $2.18 of capital for every dollar of revenue. […] Shopify was one to one, and that’s why its stock has done so well. We need to create a different style of company in Canada, not keep doing it the same way. We have to stop comparing ourselves to the US and figure out what we can do with our resources that somebody else can’t.”

His offers insight into where we should direct our effort:

“You can’t fix your weaknesses… In Canada, we’re trying to fix our weaknesses instead of building on our strengths”

[...]

“[I]t’s like Japan deciding one day that it wants to be an oil and gas powerhouse. […] Japan doesn’t have any oil and gas. How could it ever be an oil and gas powerhouse? Yet that’s what we’re doing.”

Canada is a natural powerhouse in many areas: we are among the world leaders on quantum technologies, we have vast natural advantages in agriculture, and the recently announced Defence Industrial Strategy is an opportunity to establish a strong defence sector, to name just a few. Doubling down on these areas will do more work for us than yet another attempt to buy our way past structural weaknesses with late-stage capital.

Invention is not innovation

Having debunked risk-averse culture and a lack of capital as the source of the issue, Charles offers two further explanations as to what does contribute.

The first issue he identifies is that we over-index on invention over commercialization:

“There’s a strong belief in Canada that intellectual property is the most important thing. You get that from the Jim Balsillie school of building companies, and from the fact that virtually all of the programs in Canada are oriented to research and development.”

This has been the subject of lamentation in the Canadian ecosystem for some time: the idea that we are great at inventing things but poor at commercializing them. What differs between Charles’ take and the usual one is that the usual take concludes that we need to do a better job protecting IP. Protecting IP is a first step, not an end goal. In his words:“invention plus commercialization equals innovation.”

Funding only half of a commercialization strategy

The second issue he identifies revolves around sales and marketing. Canadian companies do not spend as much on these areas as their American counterparts, and the talent that does develop leaves early. Charles’ research found a larger brain drain of sales and marketing people than of technical people, concentrated early in their careers.

Part of the issue stems from the R&D focus of our public spending. By providing funding for research and IP but not for sales and marketing, our public contributions to private innovation only provide half of a commercialization strategy. While Charles is careful to note that it is not clear if there is a causal link between public R&D incentives and the under-spend on sales, his findings certainly warrant a more careful look.

Charles’ research points to the lost opportunity:

“I was looking at scale-up efficiency, trying to find what drives growth, and I found a 0.76 correlation between the rate of increase in sales and marketing spending and the rate of increase in revenue. General and administrative expenses and research and development aren’t correlated nearly as highly. […] if you want more sales, you spend more on sales and marketing, not on research and development. But […] we don’t have that base of experience, and the government isn’t promoting it.”

While he notes that this is specific to a set of American software companies and that this is a correlation rather than a causal attribution, it certainly suggests that we should be considering sales and marketing when providing public funding intended to help Canadian firms succeed, or at the very least, collecting the data to make it possible to disentangle the effects in play.

Conclusions

Taken together, Charles’ findings suggest a change in strategy. However, he is careful to note that many of the questions that we need to answer simply do not have enough data to allow the required analysis:

“I’ve advised provincial, municipal, and federal governments on exactly these issues and programs, and there’s […] a limited amount [of data], frequently of the wrong type […]. Most of the time they’re just responding to the noisiest voices. […] I’d probably hit pause, take the time to research what the real problems are, and then build something around that.”

Among the many things that Charles’ analysis makes plain, it is clear that addressing Canada’s innovation challenges will not come from pointing fingers, nor will it come from repeating, or making incremental changes to, that which has not worked in the past.

Charles objects to demands for more of the same public funding, and he is right to do so. There is no shortage of public capital for innovation, and no shortage of evidence that it is not working in aggregate. The issue is at least in part that it is delivered in a form that cannot reach the earliest stage, and for only half of what commercialization requires.

Growth VCCI is a case in point. It is a well-intentioned answer to a gap that Charles’ data suggest will close on its own once there are enough good companies to fill it. If the public servants administering the program take one thing from this interview, it should be his instinct to pause and ask whether the gap being filled is the one that matters.

Above all, his final thoughts ring true:

“We really have to stop the external blame game and have a serious internal conversation about what we’re doing and why it isn’t working.

[…]

We have to look inward and ask why we’re failing and how we can succeed in a different way, and have a serious conversation based on serious research.

[…]

The institutions sitting around blaming everything but themselves, and the entrepreneurs blaming everyone but themselves, need a kick in the pants. That’s not the way to succeed. It’s like the Maple Leafs blaming the crowd for their performance.”

Thanks again to Charles for taking the time to do this interview. You can find more of his work on The Narwhal Project.

Introduction

Since the spring of 2026, there has been debate on how best to use two pots of federal funding: the Venture and Growth Capital Catalyst Initiative, that everyone has started referring to as “Growth VCCI”, and a pot of $750M, with most of the debate centred on the latter. The Canadian Venture Capital & Private Equity Association (CVCA) wants this money to go to companies in series B and later, pointing to issues finding money to scale Canadian champion firms, while the National Angel Capital Organization (NACO) identifies gaps at pre-seed and seed and wants money set aside for these stages, arguing that Canada’s weak scaleup is a symptom of a weak early stage. The Canadian Startup Capital Association (CSCA) has been pushing for 10-20% of the $750M to be allocated toward capital activation in the earliest stages.

Yesterday, the government announced how the money is to be split, opening Growth VCCI up for expressions of interest between now and January 21. The $1B portion contained no surprises, and equally unsurprisingly the bulk of the $750M is being allocated to “later- and growth-stage” gaps at series B and beyond. However, the announcement contained something genuinely new: $75M of the $750M pot has been set aside for pre-seed and seed-stage companies. The details of how the $75M will be deployed remains a matter for future consultation.

This is a significant departure from previous iterations of the program.

$75M allocated for pre-seed and seed is very welcome, and a step in the right direction, but it does not go far enough: if it flows only through for-profit funds, it can’t reach companies that no for-profit fund will touch yet.

Since the details of how the $75M will be allocated remain to be determined, this article offers an argument for why we should put aside a portion of it for something completely different: nationally scoped venture philanthropy.

A brief history of VCCI

This is an evolution of a program that has existed in several forms over the years. Briefly, the government has allocated capital toward predecessors of this program several times, including:

2012: The Venture Capital Action Plan set aside $400M to help increase private sector participation in early-stage venture capital, broken down as $250M to establish new funds of funds, $100M to recapitalize existing ones, and $50M for existing “high performance” VC funds. $390M was actually deployed in 2013.

2017: Up to $400M was allocated, and $371M actually deployed. The fund-of-funds stream remained, while smaller pots went to supporting emerging and diverse fund managers, underserved regions, and alternative fund structures, as well as high-growth companies in clean-tech. Payout was structured to favour private investors: private investors get their capital back plus a 7% preferred return before the government gets its own capital plus 3%.

2021: $450M was allocated this time, and again the fund-of-funds stream received the lion’s share, but $50M was set aside for life science VC funds, and another $50M to increase access to VC for underrepresented groups.

2024: The Fall Economic Statement committed $2B across two envelopes: $1B for a fourth VCCI round with sweeteners for pension funds, and a separate $1B for mid-cap growth companies.

2025: Budget 2025 proposed to “realign previous venture capital and mid-cap commitments”, folding the two 2024 envelopes into $1.75B. $1B became the Growth VCCI, split into $700M for funds-of-funds, $200M for life sciences, and $100M for emerging managers. The remaining $750M was set aside for “Canadian firms facing early growth-stage funding gaps.” That phrasing is ambiguous, and it has fuelled the debate over which stage the money is meant for.

The $750M looks like the descendant of the mid-cap envelope, which helps explain why CVCA reads “early growth-stage” as Series B and beyond, while the word “early” gives NACO a strong case for the other end of the pipeline. In the end, the government mostly sided with CVCA’s reading, giving $675M to later and growth stages, while acknowledging the NACO and CSCA arguments by committing $75M to pre-seed and seed.

Building a house from the roof down

VCAP, and every round of VCCI since, was pitched as a catalyst, not a permanent subsidy. The program’s own performance measurement framework states the goal plainly: deliver positive returns, renew investor confidence, and help the Canadian VC industry become self-sustaining. Returns have been quite reasonable, at least in aggregate: VCAP’s funds-of-funds reported a 14.8% pooled gross IRR at the end of 2023, roughly double the 7% target (though both how this breaks down between private and public contributions and the methodological details behind the assessment are not clear).

Self-sufficiency is another matter. Thirteen years on, private investors still wait for the next federal round, and the same four funds-of-funds managers have anchored VCAP, VCCI 2017 and VCCI 2021 in turn. In 2022, then-CVCA CEO Kim Furlong said she would not lobby for another round: “This is not a crutch the industry should rely on”, a position that did not survive the end of her tenure in the role.

If the returns are there but the industry is still not standing on its own, that begs exploration.

Dan Gray of the Odin Times articulates what I believe to be the underlying issue better than anyone. In The Origins of Alpha, he explains why picking skill alone can’t save an investor drawing from a shallow pool:

“[T]he success rate of investments is essentially a combination of pool quality and picking skill. Pool quality acts as a ceiling or constraint - you can’t pick winners that don’t exist in your pool.

Thus, a small improvement in pool quality yields greater returns than a much larger improvement in picking skill, as picking skill compounds on pool quality to produce alpha.

Put another way, your ability to pick well only matters if there are good options to pick from.”

Growth VCCI and the earlier iterations of the program are, at heart, an investment in picking skill: better-capitalized funds, run by experienced managers, choosing among the companies that already exist. It does nothing for the pool of opportunities those managers pick from.

VCCI has at various stages been earmarked to cover the entire continuum “from seed and early stage funds through the capital formation of late stage and growth funds”. This phrase is a contradiction in terms, since it does not in fact cover the entire continuum. In current Canadian practice, institutional seed rounds increasingly require evidence of traction, a stage that might be years past founding and in many cases might be the second or third round of investment they receive, following angel stage or pre-seed rounds, if not both.

According to ISED, the mean time between company founding and the first angel cheque is 3 years (with the caveat that this data sources only from formal angel groups and does not capture individual investor behavior). The mean time for VC money (the earliest that VCCI money would reach a company in any iteration prior to the current one) is 6 years. Per my own research, when we consider specifically IP-rich university spinout companies bringing emerging technologies to market, the time between founding and the first dilutive cheque is longer than for the startup ecosystem at large, and can be as much as 5 years, even longer than the angel average.

Because VCCI only engages starting many years after companies are formed, the entire foundation of the pipeline remains untouched by the target recipients of the funding. The pipeline leaks opportunities early, not necessarily through visible events like acquisitions or failures, but more often silently, through companies that never get off the ground in the first place. The pool of opportunities in which VCCI-supported funds can invest is hollowed out before they have an opportunity to invest, leaving valuable publicly funded research collecting dust. This in turn drives a vicious cycle about which I have written extensively: a shallow pool guarantees weak returns, which discourages capital allocation, which further shallows the pool.

Support for scaling companies only works if there are companies worth scaling, and by neglecting the high-risk pre-revenue phase of company formation, before it makes rational sense for a for-profit investor to get involved, we miss out on opportunities that would otherwise form the basis for returns on VCCI-supported investments.

Put another way, most companies worth scaling were once startups, and most startups were once a founder with an idea. Until the foundation of the pipeline is actually built, it will not be possible to make VCCI funding self-sustaining.

Innovative financing and venture philanthropy

Since pool depth is a problem, the acknowledgement in today’s announcements of the importance of the earlier stages of the pipeline is an opportunity.

What CVCA and NACO have in common is that both identify problems that begin once a company is investable by someone with a profit motive. CVCA’s begins at Series B. NACO’s begins at the first angel cheque. That works where private investors are already willing to show up. It does nothing for the companies they won’t touch yet. That leaves the founding stage uncovered: the years between a lab result and a first cheque, when an IP-rich spinout has no revenue, no traction, and no investor willing to lead. A portion of the $75M needs to support this stage, and to do so effectively it will have to be deployed in a fundamentally different way.

The earliest stages of Canadian company development are a different beast entirely from the ones covered by for-profit investors. From my own research on IP rich university spinouts, the risk and reward profile of the pre-revenue phase makes it irrational for a for-profit investors regardless of the level of subsidy, being skewed toward extreme outliers and plays out over timelines that can extend well beyond the 10- or (rarely) 12-year lifetime of most VC funds. Combined with public funding programs that require matching funds or pre-spending for reimbursement that these companies simply do not have, startups in the formation and pre-revenue phase cannot access funding efficiently from any source.

$75M allocated purely to for-profit funds will not change this.

The evidence suggests this is where public money goes furthest. A recent working paper on European venture policy finds that shifting the same amount of public support earlier in the pipeline produces roughly the same economic uplift as doubling it. In the US, small early-stage R&D grant from the Department of Energy roughly doubles a firm’s chance of later raising venture capital, with the largest effects for the most financially constrained firms. Early public dollars don’t compete with private investors if deployed with careful consideration for context, instead creating the the companies that private investors later compete to fund. Subsidizing investors (rather than companies) has a weaker record. Across 31 US states, angel investor tax credits increased angel investment but had no measurable effect on new business formation.

In other words: how the money is deployed matters as much as where. Across 25 countries, companies backed by a mix of government and private investors raise more and exit more successfully than those backed by government alone, and public money in those markets tends to add to private capital rather than replace it, but this is not a given. Canada’s labour-sponsored venture funds of the 1990s and 2000s crowded out private venture capital: tax-subsidized vehicles competed with private funds for the same deals. (Measuring this is harder than it looks, and many studies get it wrong).

Public capital pulls private capital in when it goes where private investors aren’t yet willing to go. It pushes private capital out when it competes for deals private investors would have made anyway. Formation- and pre-revenue-stage investments in IP-rich Canadian spinout companies sit firmly in the former category.

The example I keep coming back to has its roots in the UK, and is based on venture philanthropy. In this approach, a not-for-profit (ideally a charity that uses a blended public-private finance model) makes many small, dilutive investments in pre-revenue startups bringing publicly funded IP to market, but instead of paying proceeds to limited partners, any returns are recycled into the next generation. The UKI2S fund is one of the best-established examples of this approach (though not charitable), and per their final impact report, 78% of the companies in which they invested attribute their existence to that first cheque. UCeed in Calgary is charitable and based on a similar model, pioneered at Oxford. My research gives strong reason to believe that provided a sufficient number of investments are made, venture philanthropy at the founding stage can actually be self-sustaining. More work by Dan Gray puts it into the context of a national innovation strategy.

My research explores the venture philanthropy model in detail:

“From the perspective of the public sector, which is concerned with economic growth more broadly, the value created by this approach goes beyond profits, and includes positive spillovers such as enhanced economic security and sovereignty through retention of intellectual property and entrepreneurial talent and creation of a more robust domestic economy based on the economic activity by scaleups that would not otherwise exist.”

Venture philanthropy addresses the problem of pool depth in a way that for-profit investment simply cannot.

Next steps

Finance Canada and ISED have indicated that consultations on how to deploy the $75M will happen in the near future. A defined portion of the envelope should seed a national, not-for-profit, evergreen, founding-stage fund. It would make small equity investments in pre-revenue companies built on publicly funded IP, matched at the fund level by philanthropic contributions, a presently untapped source of funding nationally. Returns would be recycled into the next generation of companies rather than paid out to investors.

Among many metrics relating to IP retention, the fund should be judged on three measures: how many of its companies say they would not exist without its First Cheque; how much private capital those companies go on to raise per public dollar; and how many later raise money from the VC funds that VCCI and the $750M support.

Every house needs a foundation. $15M of the $75M, allocated toward a not-for-profit venture philanthropy strategy which funds Canadian company formation before for-profit investment is rational, would be a first step toward building it.

Minister Joly stated that it is time to “be bold”. So, be bold: fund a novel-to-Canada approach to supporting the one stage of company development that for-profit mechanisms can’t reach, even if the money is earmarked for it, by backing nationally scoped venture philanthropy.

Disclosure: the author is a co-founder of the SAIL Fund, which intends to use the model proposed here and would be eligible under this proposal.

Canada’s recently-released Defence Industrial Strategy (DIS), the first of its kind in Canada, sets out several ambitious goals, not the least of which is to route 70% of Canada’s defence procurement spending toward Canadian firms. While the ecosystem has started a lively debate on what it means for a firm to be Canadian, the definition is far from the only challenge in the way of realizing this goal. As Canadian companies shift toward dual-use and defence application in response to the present funding environment, they face regulatory challenges in a system that is not ready to absorb the demand.

In this interview, I learn from Loki Jörgenson, CTO of Circle Innovation about the regulatory bottleneck that is fast approaching. We get into the weeds of Circle Innovation’s approach to supporting companies as they begin to scale, the imminent rush to meet regulatory requirements for defence procurement, and what it’s going to take to meet that demand.

Loki has lived everything Canada’s innovation ecosystem has to offer, and his insights are essential reading both for Canadian businesses considering a pivot toward defence, and policy makers gearing up to execute on the vision set out in the DIS.

Your email client will probably truncate this post. My key takeaways are presented at the end, so be sure to read the web version if you want to get the whole story. Many thanks to Loki for taking the time to share his insights.

Interviewer’s note: Loki Jörgenson approved the final version of the section entitled “Interview with Loki Jörgenson” and had editorial input on that section, with the option to rephrase and expand on the ideas discussed in the interview without changing or removing any intended meaning. The key takeaways presented at the end are my own commentary, and do not necessarily represent the views of Loki Jörgenson or Circle Innovation.

Interview with Loki Jorgenson

KB: Tell us about yourself. How did you get to where you are today?

LJ: I originally did my degree in physics, which is why you and I get along so well. Then I spent about seven years in mathematics and recognized that there were opportunities I could continue to drive into, but I got very curious about business. So I made a jump from the academic scene into business. To cut that short: FinTech, IT, MedTech, about nine companies over 22 years, primarily in the role of CTO, sometimes chief scientist doing algorithmic work, what we might call AI today. Back then, it was predictive systems of various sorts.

Through that, I was constantly coming into contact with NCEs, Wavefront, and funding programs like Mitacs. The challenge of innovation kept arising within the companies I was in, along with a sense that the larger ecosystem was also challenged to achieve what was intended with all of these initiatives. So when I thought I was more or less done with entrepreneurial life, I started thinking about the innovation problem as a problem to solve, learning how it actually works and what could be done differently. I subsequently encountered Circle Innovation, which has a specific methodology, so I was attracted to that. It sounded different, very direct, and very bottom-up. In finally approaching innovation as a problem needing a solution and reflecting back on my entrepreneurial experience, I ended up as the CTO for Circle Innovation and I run funded programs for SMEs, helping them overcome particular barriers to innovation.

KB: Tell us about the Circle Innovation model. How has it evolved?

LJ: Circle is a non-profit that spun out from Simon Fraser University. It was originally part of one of the NCEs, and its CEO, Sylvain Moreno, a professor at SFU and an entrepreneur (previously at U of T), has been heading up Circle Innovation for the last ten years. I joined about three years ago.

What caught my eye in conversation with him and the COO at the time, Tom Philpott, was that they were taking a very involved, bottoms-up approach instead of what I had been seeing in an ecosystem making a series of assumptions about what needed and how to address it. That was leading to companies, as I’d experienced it, saying, “Okay, you have this program with these requirements; I’ll try to take that shape or look like the candidate you’re looking for to get to the money.” I think we’ve trained a whole generation of entrepreneurs to get to non-dilutive funding that way.

At Circle, we inverted all of that and instead, sit down with the company and ask: “What do you need? What barrier are you facing?”. Note that we focus on companies at a certain stage. Ideally for us, they are facing just one barrier to growth, somewhere around Series A, ready to get into growth mode, but facing that barrier. We aim to help them rapidly overcome it, and off they go, creating an economic impact almost immediately.

Between incubators/accelerators and superclusters, there’s a gap, and we operate in that gap. From an investment standpoint, investors are often no longer supporting companies solely on promises and prospects; they want to see traction, sales, and revenue. That’s where a lot of companies can get stuck, and it can be a very dry period and a big challenge. The longer they sit there, the more likely they are to fail. So from that pool of companies at that stage, you can rapidly translate a lot of effective potential into something concrete.

That’s where Circle’s methodology, which is called SWaT (SME Workforce and Technologies), comes into play. We work with the company, figure out their barrier, collaborate with them to define a project that will overcome those barriers, get that co-funded, and then work with them throughout the project. We try to stay out of their way, since they know their business best, and when they need outside support, subject matter experts, or connections to academia or clinicians, we bring those to the table as quickly as possible, often during the project definition phase so we have perspective on what they need to overcome that particular barrier. We figure out who needs to be part of that, get that project going, and get it done. Six to nine-month quick-spin projects with products going into market is one of our constraints.

Over the 10 years, this approach has led to multiple multi-million dollar, multi-year programs funded both provincially and nationally. We’re in BC, so a lot of our work tends to be regionally focused if it’s through an RDA, but we also operate nationally.

KB: Can you give me a concrete example of a company and the barrier you were able to work through with them?

LJ: A lot of our projects are simply curating companies’ access and getting them to that barrier resolution. But when we do our best work, it’s unexpected. That’s why you can’t do a top-down program: every company has its own story, and you can’t predict what will be important to them in the moment.

For example, Human in Motion Robotics is an exoskeleton company based in Vancouver. It solution was originally intended for people with spinal injury conditions whose legs were no longer working properly, helping them walk earlier during rehabilitation as a recovery mechanism. Along the way, they’ve developed some amazing actuators and robotics technology. What they had as an opportunity was to take this into home use, which changed the game dramatically for them. They hadn’t really focused on the FDA and they were suddenly faced with validating as a Class II medical device. What did they need? They needed a mechanical engineer with Quality Systems experience, which is an interesting blend of skills. We were able to field three candidates for them, they selected one, and they were able to pivot their operations and get their QS in place rapidly.

That kind of pivot or change in direction can catch a company and slow it down at a critical moment. We felt like we were able to grease those rails, get them where they needed to go as quickly as possible, bring on that new person with those skills, and keep them moving forward. That’s the kind of project we like to cite as one of our better stories.

KB: In that example there’s obvious potential military applications. With an increase in the number of companies operating in the defence space, regulatory requirements become a serious consideration. What are the regulatory implications for Canadian SMEs seeking to move into the defence space?

LJ: Stepping back for a moment, the other thing Circle does when declaring a program is look around the ecosystem for major sources of friction affecting a whole ecosystem or necessary changes needed by that ecosystem. Working with our friends at Export Navigator, we recognized that this cybersecurity certification, Canadian Program for Cybersecurity Certification (CPCSC), though a relatively mundane requirement that companies in the defence market face, poses a critical challenge to ensure they are at an appropriate level in a timely fashion. The level varies according to how they handle and protect confidential or sensitive information, and that they have the IT infrastructure and procedures in place to protect it and demonstrate that protection.

You see these requirements in various regulatory spaces; healthtech and FinTech are similar. Companies that have been operating in a civilian market now considering themselves dual-use and looking at the defence market are faced with a set of regulations they may have never anticipated. Fair enough, that’s just part of making a change, and you can make that change. But we are faced with a unique moment right now. CPCSC is being implemented at a rapid pace with a hard set of requirements. Companies moving towards dual-use need to be properly certified so they are at least able to look at RFPs to determine whether they can deliver. Canada is not able to support that many companies moving all at once to get their Level 1s and Level 2s sorted out.

Looking to the US, Cybersecurity Maturity Model Certification (CMMC) has similar levels also based on the NIST 800-171 standard, so there are lots of similarities between CMMC and CPCSC. The Americans moved earlier in enforcing the need to have CMMC in place, whether you’re a Canadian or US provider. What they have seen are the prime contractors pulling on Tier 1, 2, or 3 suppliers to meet the standards and comply. Primes are ultimately responsible for their entire supply chain, and they get penalized if anybody in that supply chain doesn’t meet compliance standards. They were about a year ahead of us and we are seeing the first multi-million dollar penalties being applied.

The real challenge, oddly enough, is in the assessment step. Level 1 is self-assessed, which isn’t too bad and takes a month or two to sort out and make a self-attestation. Level 2, however, is third-party assessed. In the US, there are about 100 C3PAOs (third-party assessment organizations) and about 800 assessors for 80,000 companies.

What they are experiencing is a sudden rush. Level one takes a couple of months with about 13 controls to implement in the Canadian version. However, Level 2 requires about an order of magnitude more controls, around a hundred, and it can take 12 to 18 months to sort out and complete the 3^rd party assessment. There are simply not enough assessors in the US market, and we are even farther behind in Canada.

To get companies to Level 1, they need to be aware that they need it. A lot of them don’t know, or they think it’s out on the horizon somewhere and aren’t too worried about it. Level 1 for CPCSC was laid out in March of this year and is now starting to appear in DND contracts this summer; it will likely be fully enforced by next year. By next March, Level 2 will be in place, and everyone is waiting for those detailed requirements.

The companies already in the defence market are aware and have put processes in place to achieve their levels. However, any emerging dual-use companies will face a full pipeline with limited assessment capabilities. If they thought they were going to get their Level 1 or Level 2 on demand, they are going to be shocked and surprised.

You could say that’s simply bad timing and bad planning on the part of those dual-use companies. But federally, Canada wants to move from about 43% Canadian procurement to over 70% Canadian procurement by 2035. To do that, we have to significantly increase the number of Canadian companies participating in the defence market from its current level at around 600. So, if we want to achieve that goal, yet lack the capacity in the ecosystem to get all the companies aligned, just at the cybersecurity level, not to mention controlled goods, security clearances, and many other considerations, we are talking about one piece of a larger puzzle that will end up being a critical blocker. It will create a backlog, and everything will slow down, yet it is foreseeable.

We can do something about it now before it becomes a problem, and arguably we are already at that stage. We have to move quickly, but that’s the challenge. It sounds like regulatory complexity, but it is precisely that complexity which will introduce friction at the exact moment when we really need to be agile and get our Canadian sovereign defence strategy together.

KB: The Defence Industrial Strategy indicates intention to to buy 70% Canadian, and we have regulatory requirements that create a bottleneck. Where is the disconnect?

LJ: A lot of attention and money are going toward making this happen, so it’s not as if the federal government or the ecosystem is unaware of what needs to be done. Quite a lot of money is flowing specifically through BDC to SMEs who are trying to make this pivot. What is clear, however, is that there is a gap between what prospective emerging dual-use companies understand about how this works and the ecosystem’s response to the demand. That demand has not fully arisen yet. There is going to be a lag in the ecosystem’s response to build up sufficient capacity. We really have to look forward into the future and see this as an upcoming wall or blockage.

Most attention is being placed on SMEs already in the defence market or those with existing relationships with primes. What is being missed is that capacity has to work end-to-end. We must have sufficient assessment and certification capacity to get enough SMEs into and through that pipeline. That larger, end-to-end picture has been overlooked. Various pieces are getting attention, but the overall end-to-end effect hasn’t been sufficiently addressed.

KB: Early in the conversation, you noted that companies shape themselves to facilitate access funding. How do you distinguish between these and companies that legitimately have dual-use potential?

LJ: That is a great question because there is a lot of ambiguity. Part of the overall problem we face is an insufficient definition generated early enough for individual companies and ecosystem members to respond effectively. So that is an obvious problem we need to tackle: what does dual use look like, and how do you meet those requirements? Can any given company decide to modify its business model to make itself dual use, and how does it figure that out? That is a critical decision.

Anytime a company pivots like that, an awful lot is committed. The defence market is not like a civilian market in many ways; you don’t do standard sales and marketing, rather, it is all business development and relationships, and the timeframes are much longer. Does the company understand what it is going to take? Fortunately, various organizations are tackling that education problem and helping companies evaluate themselves.

In the meantime, while waiting for definitions from those organizations, the federal government, and supporting programs, we are taking a practical, functional approach. We look at the requirements to operate in that space and the readiness of companies to take them on; for example, ensuring that they understand the implications, costs, time, and business changes involved in CPCSC certification. We assess their readiness to take those additional steps and ensure they are appropriately educated by supporting organizations. This includes factors like Canadian IP, sovereignty, data residency, security clearances, and cybersecurity requirements. We can also sit down with a company and evaluate various aspects of that.

While many good programs do that, we want to make sure each company understands every aspect. If they claim they are ready for dual use and want to pursue CPCSC certifications, we ensure they have everything in view. An expert panel sits down in a rapid engagement with the company to go through all the requirements and confirm they have a clear view. If we see a gap there, we deselect that company as not ready to take that step and favour a company that has thoroughly examined it.

KB: Care to take a stab at what you think should be the litmus test for what constitutes a Canadian company?

LJ: That is a very active discussion as well. Everyone recognizes that when we talk about sovereignty, you instantly introduce tension. We cannot operate in a silo as a purely sovereign nation with no contact, interaction, or relationships with outside companies; we have those relationships and will continue to have them. So who do we favour? What do we call a sovereign Canadian company or infrastructure with appropriate data residency? That is really above my pay grade.

Much of the conversation right now focuses on governance qualifications: who owns how much of the company and how it is controlled. Establishing thresholds like 51% ownership, Canadian headquarters, and Canadian infrastructure not subject to foreign data access laws is a good start. However, we also need to know when to make exceptions or relax particular constraints for a company providing a specific role. We can consider that on a spectrum. Almost every company likely has some degree of non-sovereign character, and we look at companies individually based on the solutions they are aiming to serve.

Again, at Circle, we tend to work bottom-up, evaluating each company individually as opposed to applying bulk rules and filters across the ecosystem. It might sound a bit evasive regarding what we consider sovereign, but there is a lot of work for others to do to provide clear direction on that.

KB: Circle Innovation is proposing the Defence Readiness and Advanced Capabilities Cluster (DRACC) program aimed at addressing the CPCSC bottleneck. Give us an overview of DRACC and how you see this unfolding.

LJ: We always try to create handy acronyms. DRACC stands for Defence Readiness and Cybersecurity Certification, and it is primarily aimed at what I described as the CPCSC bottleneck. It is intended to identify companies in near-term need of certification so they don’t face obstacles entering the market simply because they don’t meet regulatory requirements. It builds awareness where necessary, selecting companies best prepared to take advantage of the defence market.

We have what we call Stage 1, where we rapidly take a company through a due diligence process to establish that they are positioned, have potential, and understand what they need. Some companies need Level 1, while about 40% overall will need Level 2 or possibly Level 3, and they must understand those heavier implications.

Once that awareness is in place, we aim to get them certified as quickly as possible through co-funding and guidance, working with compliance certification companies across Canada capable of delivering that certification. Additionally, I want to stress that we are developing means to accelerate beyond current capacity, increasing capacity by doing it faster and better. We are working or aiming to work with compliance companies capable of accelerating the process so that instead of taking 12 to 18 months to achieve Level 2, companies can reach it in under 12 months. We have done similar things with SOC 2 Type 2, typically a 12-month process brought down to six months, and we can repeat that here. We would develop a sovereign Canadian solution to enable that acceleration, which advances Canadian service providers while speeding up and expanding capacity overall for all Canadian companies pursuing these certifications. So it operates on two levels: getting individual companies through required certifications via co-funded, rapidly delivered projects, and improving existing processes to build overall capacity in the ecosystem. We also want to signal to others in the ecosystem that we need more assessors and certification capacity overall, but DRACC’s focus is to establish those processes and capabilities.

KB: Are you aiming at both CPCSC and CMMC, or is there enough overlap between their requirements that you can do them in parallel?

LJ: They can generally be done in parallel. There is a lot of similarity between the two of them, though they are not identical. Getting your CMMC is distinct from CPCSC, but if you have one, you are very close to having the other.

An awful lot of Canadian companies are looking at the American market as part or all of where they need to focus, as its size and capacity are attractive and often a necessary part of doing business. The programs we focus on would primarily be about CPCSC, but we should be able to support CMMC as well so Canadian companies can operate south of the border. More broadly, when we think about Europe and NATO, that is another set of processes. But again, the original NIST standard, 800-171, is the common base for all these different processes. So, you can get a company a long way down the path toward operating in any of those spaces with a primary focus on CPCSC.

KB: I understand that you are anticipating $50,000 per company as a rough cost of going through that level and certification. How do those costs break down?

LJ: Our current estimate is $50,000 for a Level 2. Level 3 is going to be more expensive, and Level 1 is much less expensive. For a typical project Circle operates in the $50,000 to $100,000 overall cost range and we partially co-fund that effort. So the company has a lot of skin in the game as it is not 100% covered.

What is typical for them is engaging consultants and processes to do the scoping for that particular company, identifying, for example, who within the company handles particular types of information, how that information is stored, where that equipment is, and who has access to it. That is really a company-by-company scoping process. Once that is in view, it is back to the company to start making those changes and implementing them properly, often with additional guidance but a lot of internal work.

Level 2 is externally assessed by third parties, so certification requires engaging third parties to do that audit and assessment. Those kinds of costs extend over a period that of 12 months or so, though we would like to see that period much shorter. It could be as much as $100,000 depending on the company and their scope. We are aiming to compress that in both cost and time, so maybe targeting $50,000 or less would be great.

KB: What is the status of the DRACC program now?

LJ: This has been a rapidly emerging problem that we have responded to. We are now putting this proposal in front of provincial, regional, and federal funding sources (regional in the sense of RDAs). There has been a change at the federal level where responsibilities related to defence have moved from ISED over to DIA, the Defence Investment Agency. Things are shifting, and it is a bit of a shell game at the moment, but we will get a clear response in the coming weeks as to whether this can be funded in the short term - it is very time-sensitive. Often, cycles for this kind of funding are over a year: putting in a proposal, refining it, and waiting until the next round of funding is available. This cannot wait; it needs an early response if we are going to avoid that bottleneck.

KB: Shifting gears a bit, tell us about TICAN and how it interacts both with Circle Innovation’s work and with DRACC.

LJ: To respond to how TiCAN applies to DRACC: probably not much, because DRACC is quite focused on defence-related certification. Where TICAN comes in, we are constantly looking around the ecosystem to see where there is friction or gaps that to overcome. TICAN is a very specific response to a problem similar to CPCSC, though not related functionally. That problem can be articulated as: how do we incentivize academia and private industry to work together optimally?

Both when I worked in academia with companies and then working in companies with academia, there was always a level of challenge to overcome. I ended up summarizing it as an impedance mismatch (in an engineering sense): a lot of energy is lost trying to communicate, coordinate, or meet each other’s agendas, which are not similar. To use another metaphor, the currencies are quite different: papers, students, and research on the academic side; products, revenue, and markets on the company side. They are not talking the same language. It is often difficult to get them coordinated on a given project, especially when aimed at achieving economic outcomes. TICAN recognizes that and motivates both sides of the equation properly, which is why we are involved and excited about it.

This has been 10 years in the making, based on the MScAC program (the applied master’s program out of the University of Toronto) which has been very successful. TICAN brings together companies and researchers to execute a research program defined by a company that is seeking to get R&D done, generate IP, and generate HQP. What makes it different is that the research program is not adjacent to the master’s or PhD student’s primary work – it is their research program. An industry project is often an interruption in the academic process; the supervisor or researcher is looking to get the student through their research program, complete their thesis, and grant their degree. Working with a company often temporarily derails that process, creating friction.

TICAN addresses this by making the project the core of the student’s work rather than an adjacent or parallel aspect. The work in setting up a TICAN project is getting the researcher and company to identify common interest in a research program that has the necessary high-quality research potential leading to a degree while meeting the company’s needs for IP and R&D leading to economic impact. It is a real challenge, but the opportunity is that everybody gets their needs met. There is no longer an impedance mismatch or derailment; the student stays on the common program all the way through to their degree.

Scenarios can vary: a company may know a researcher and need to find a candidate, or someone in the company doing R&D may want to get a degree for their work and can move into the academic environment to do work for the company. Large companies may also want to build clusters of activity. There are many ways TICAN can support a project like that, but this is the main component, overcoming this key challenge in activating Canada’s academic potential and bringing its impact into private industry.

TICAN is funded and operational. We are in the process of building projects right now. Sometimes the researcher is looking for the company, sometimes the company for the researcher, and sometimes they have known each other for quite some time and now have an opportunity to fund work within the company or within the university system with proper support. We are seeing a lot of interest in the level of funding for the candidate. To attract high-level HQP, the stipend is on the order of $75,000 to $80,000 annually ,in one-year or two-year scholarships. This funding is significantly higher than a typical master’s, PhD, or postdoc stipend, enabling companies and researchers to attract top talent.

KB: Does TICAN take an active position in terms of the ownership or licensing of IP that arises in the course of that research?

LJ: IP has been another source of friction in the past. Many universities have straightforward IP policies when working with them, while others are very restrictive. TICAN takes the view that IP from the project flows to the company, structuring it appropriately and establishing agreements with universities on how TICAN operates within the academic environment. This removes natural friction around IP by making clear how projects are built and where IP goes.

At the same time, there is always room to improve the IP process. We have talked about your program, SAIL, which I am very fond of, and want to implement alongside TICAN.

KB: If you were advising federal leadership at ISED, DND, or wherever decision-making authority ends up once the dust settles, what funding commitments or policy moves would you advise in the short term (aside from “fund DRACC”)?

LJ: In the context of defence, where CPCSC is one piece of a larger puzzle, leadership should look at critical capacities, such as the assessor requirements for CPCSC and beyond, ensuring sufficient attention, funding, and support are placed on all stages of needed ecosystem capacity. It is easy to overlook one, we identified one and are addressing it, but there will be others. In a time of rapid change, it is easy to trip over those pinch points. They should proactively identify and address innovation friction broadly, and not necessarily just in the defence picture.

The thing that comes back for me, both having worked as an entrepreneur within SMEs and also in this current role, is that we really need to go where the companies are. We need to meet them in their circumstance, understand what they need, and operate at their speed. Time is often of the essence. We need to work at the speed of business if the intention is to enable them. A lot of programs take too long or create new and additional work for companies, and that really burdens them in ways that work against what we are intending: innovation, economic impact, and growth. Really trying to meet companies where they are is one of the key admonitions.

KB: Is there anything I should have asked you but didn’t?

LJ: There are an awful lot of parts of the overall ecosystem that I think we could be talking about. I think one of the larger challenges we face is coherence as an ecosystem - there I don’t know what the answers are. Sometimes I start to feel like it’s cultural, where a lot of people have been ruminating about what it’s like to be Canadian, and maybe we don’t have what it takes, always shooting for second, and things like that. But what seems to be key overall is the need for leadership at various levels to maintain a level of risk tolerance, which, both as Canadians and as government specifically, is sometimes hard to come by - there is so much sensitivity to optics, how it looks and whether there’s a misstep. That’s often the end of the road for anyone who has failed even once. Entrepreneurs know that failure is often part of the game, but that’s not broadly how the ecosystem works.

If anyone is looking to find out more about Circle Innovation or DRACC, they can find us at circleinnovation.ca or contact us at industry@circleinnovation.ca or find me on LinkedIn.

Key Takeaways

When the DIS was first released, I was genuinely excited to see the commitments it made. If Canada has to increase its defence spending to such a degree, it only makes sense to use that as a way to strengthen our economy, and the 70% Buy Canadian commitment is an excellent means by which to do it. However, there are significant structural barriers to realizing this ambition, barriers that arise from the unique composition of Canada’s defence market. Loki makes clear that a purely top-down approach will not work:

“That’s why you can’t do a top-down program: every company has its own story, and you can’t predict what will be important to them in the moment.”

From the perspective of the public service, procurement processes are designed to reduce risk and avoid blame for failures, a process that naturally favours large incumbents. Picking a safe, established prime carries zero personal risk for a procurement officer, even if it undermines sovereign capability.

“Leadership [needs] to maintain a level of risk tolerance, which, both as Canadians and as government specifically, is sometimes hard to come by... Entrepreneurs know that failure is often part of the game, but that’s not broadly how the ecosystem works.”

In defence procurement, however, there is more to it. To some degree, there is structural necessity underlying the preference for primes. Across NATO, relatively few defence contracts go to SMEs directly, for the simple reason that relatively few SMEs produce an entire platform themselves at the necessary scale, and failures can cost more than just dollars.

For Canada, this poses a unique challenge to the ambition of the DIS, because 92% of Canadian defence firms are SMEs. Regardless of what we ultimately decide it means for a company to be Canadian, it will likely be that by any reasonable definition, it will be possible to count on one hand the number of large incumbent defence firms that will fit. (This is, in a nutshell, the problem that ITB policies are designed to solve, by requiring that foreign primes favour Canadian subcontractors, among other mechanisms).

However, the fact remains that engaging with SMEs is a more direct path to unlocking the economic benefits of defence spending. While the recently created Defence Investment Agency (DIA) is intended to streamline the process, its initial mandate limited it to contracts above $100M, which most SMEs still cannot service directly. The recent news that the head of the DIA is stepping down further suggests some challenges on implementation, and there remain disconnects between early programs like Innovative Solutions Canada and actual procurement contracts.

As much as I like to focus on the policy challenges, it is clear from the interview that in this case the problems are not limited to the public service. It is not a simple thing for a company that was not previously integrated with the defence establishment to achieve that integration, and with so much money flowing into defence, many companies that previously were not built to serve defence customers are eyeing the space. The systems through which onboarding happens are about to be overwhelmed, creating a bottleneck on the private sector side that will further frustrate the DIS if not proactively managed:

“[C]ybersecurity certification […] poses a critical challenge[…] Companies that have been operating in a civilian market now considering themselves dual-use and looking at the defence market are faced with a set of regulations they may have never anticipated. […]

Level 2[...] can take 12 to 18 months to sort out... There are simply not enough assessors in the US market, and we are even farther behind in Canada.”

In short: Canadian SMEs seeking defence contracts are caught between top-down procurement policies that exclude them due to size, and bottom-up regulatory requirements that stall them due to capacity. Together, they create a bottleneck to the vast majority of Canadian companies that could take advantage of the push toward defence. Loki highlights the urgency:

“Often, cycles for this kind of funding are over a year: putting in a proposal, refining it, and waiting until the next round of funding is available. This cannot wait; it needs an early response if we are going to avoid that bottleneck.”

I should be clear where I stand on the regulatory question before going further: while I recognize the challenge posed by the regulatory environment, I do not view regulation as an inherently problematic thing. Done well, regulation can actually be the basis for very effective predictive tools. Cybersecurity, sound security clearance processes, and compliance with export control regimes are not optional, especially in defence. Nothing in this post should be read as a suggestion that these requirements should be relaxed or waived.

Nevertheless, it is inevitable that regulatory burden favours large, incumbent firms over smaller upstarts, and in a market where most firms are small upstarts, we need to get creative. Per Loki:

“The companies already in the defence market are aware and have put processes in place to achieve their levels. However, any emerging dual-use companies will face a full pipeline with limited assessment capabilities. If they thought they were going to get their Level 1 or Level 2 on demand, they are going to be shocked and surprised.

[…]

What is being missed is that capacity has to work end-to-end. We must have sufficient assessment and certification capacity to get enough SMEs into and through that pipeline. That larger, end-to-end picture has been overlooked.”

There has long been a tension between public and private sectors in Canada: the public sector points at flagging private investment in innovation and calls on the private sector to step up, while the private sector reminds the public that their decisions are driven by profit. If the profit is not there, neither is the incentive to act.

The not-for-profit private sector holds enormous potential to assist Canada in mediating that divide, and Circle Innovation’s DRACC program is an excellent example of how.

“We really need to go where the companies are. We need to meet them in their circumstance, understand what they need, and operate at their speed. Time is often of the essence.”

While a for-profit entity may not be incentivized to take on the challenge of streamlined onboarding processes for SMEs (though ITB policy overhauls have potential to tip the scales here), a not-for-profit like Circle Innovation derives value from more than just direct returns.

Regulatory compliance through programs like DRACC could clear the path to market for existing technologies, but domestic capability creation starts much earlier. A major hidden friction in building sovereign defence tech is what Loki calls an “impedance mismatch” between academia and industry. Traditional R&D programs treat industry projects as interruptions to academic degrees, leading to IP disputes and conflicting incentives:

“To use another metaphor, the currencies are quite different: papers, students, and research on the academic side; products, revenue, and markets on the company side. They are not talking the same language. It is often difficult to get them coordinated on a given project, especially when aimed at achieving economic outcomes. TICAN recognizes that and motivates both sides of the equation properly”

Programs like TICAN are examples of exactly the kind of incentive-aware approach I advocate for generally: by aligning a researcher’s core degree directly with an SME’s commercial roadmap, guaranteeing that IP ends up somewhere it can be used beyond the lab, and offering competitive stipends to retain top talent in Canada, TICAN can be seen as part of the system that creates the companies that then need guidance through regulation by DRACC.

Looking Ahead

If Canada's Defence Industrial Strategy is to succeed, policy makers cannot treat defence research, procurement policy, and regulatory compliance as separate domains. Buying 70% Canadian requires not just opening procurement doors at the top, but actively pulling valuable technologies through the research pipeline and building the institutional regulatory capacity to get domestic technologies past regulatory hurdles. As with every part of the innovation pipeline, no part of this can operate in a silo and expect the whole to function.

This in turn involves recognizing the constraints and incentives at play and using those incentives to direct strategy. Programs like DRACC demonstrate how the not-for-profit private sector can bridge the divide, but without public investment in compliance capacity and assessor throughput, the 70% ambition will remain a policy aspiration stalled by regulation.

Many thanks to Loki for taking the time to share his insights. Anyone looking to find out more about Circle Innovation or DRACC, can find him at circleinnovation.ca, email industry@circleinnovation.ca or find him on LinkedIn.

Today’s interview is the third of a thematically connected series on purposeful research that explores the tools that we have at our disposal to identify research and innovation worth funding.

The first one explored the question from the perspective of a research funding agency trying to find ways to connect their work to downstream impact, using public funds to “push” research toward impact. The second entry provided the perspective of someone working at the interface of regulation, policy, research, and commercialization, and using economic analysis tools to situate nascent healthcare technologies in the broader context of Canada’s healthcare system.

This time, I sat down with Sarah McCullough, National Director for the newly established XPRIZE Canada Hub, to discuss the role of “pull” funding in directing Canada’s investment in research and innovation. The XPRIZE model is conceptually simple: identify a problem worth solving, create a set of target metrics through which a candidate solution will be judged, and create a prize pot for anyone who can meet those targets.

Sarah’s insights, grounded in a career path that spans startups, government, and innovation ecosystem development both in Canada and abroad, cover the rationale behind establishing a physical presence in Canada, how the XPRIZE model needs to adapt from its American origins to be effective here, the role of pull-based funding within the broader context of Canada’s innovation funding landscape, and more.

Your email client will probably truncate this post. My key takeaways are presented at the end, so be sure to read the web version if you want to get the whole story. Many thanks to Sarah for taking the time to share her insights.

Interviewer’s note: Sarah McCullough and the XPRIZE Canada Hub team approved the final version of the section entitled “Interview with Sarah McCullough” and had editorial input on that section, with the option to rephrase and expand on the ideas discussed in the interview without changing or removing any intended meaning. The key takeaways presented at the end are my own commentary, and do not necessarily represent the views of Sarah McCullough or the XPRIZE Canada Hub.

Interview with Sarah McCullough

KB: Tell us a little bit about who you are and how you got to where you are today.

SM: I’m currently the National Director for the XPRIZE Canada Hub hosted at the University of Calgary, which is the very first hub in what will be a network of global hubs around the world to support advancing the scale and impact of XPRIZE competitions.

I was reflecting on my journey before this conversation and thinking that I’ve always somehow ended up being in and around supporting builders and trying to understand what it takes to scale a technology company, whether that’s software or hardware, from multiple angles. I’ve had a lot of great opportunities to see it from multiple perspectives. I’ve had the opportunity to be inside really early stage, venture backed startups in the US and the early stages of what is now commercial AI. I’ve also been able to support, in Canada, founders exploring, growing, and developing tech startups, mostly on the software side, and those wanting to move here and grow and scale their company.

In between all of that, I’ve gotten to work in the provincial and federal economic development departments, trying to design these flourishing economic innovation ecosystems that for me drive inclusivity and sustainability in the area of economic growth.

Through all that, I keep coming back to this interesting challenge: we have great talent and we have abundant resources and we care about solving challenges and we want to build and grow companies, and yet when it comes to translating that research into innovation through the investments or the mechanisms that we have or the way the economic incentives and structures are set up, we often miss the value capture at the end. So we might start a lot of great companies, we have a lot of great R&D, but the ability to translate that into applicable solutions that solve challenges for us or being able to scale those solutions to the value capture stage in Canada really becomes a core challenge. This commercialization piece we see other economies do really well, including one just to the south of us.

When the opportunity came to lead the XPRIZE Canada Hub, I saw it as this great way to help solve this challenge by augmenting what we already have in the Canadian ecosystem and using this challenge-based innovation model and ultimately these global networks and pathways that have really been shown to know how to de-risk technology and also de-risk the market at the earlier stages so that you can really solve important and impactful problems, but you can also mobilize capital around those challenges and mobilize talent at the same time. My job ends up now being focused on working out how I can get more Canadian founders, creators, innovators opting in to solve those massive challenges and helping them understand and really build a local ecosystem behind them that’s moving along the direction with them so that they ultimately either have a pathway to win or stay in the competition as long as possible to capture the value of that in terms of de-risking the technology in the market, and then having really well-defined pathways for either pilot deployment or scale.

This is a challenge I’ve always been really fascinated with. My background’s in psychology and I’ve done a lot of work on customer development, product market fit, and I just find it fascinating that pathways to market are something we don’t talk about as deeply as access to capital, when it’s still a core challenge that we have in Canada.

KB: Tell us about the history and mechanics of the XPRIZE.

SM: It’s just over 30 years old, founded in 1996 with just one prize: the $10 million Ansari Space XPRIZE. It was based on this idea that we want to solve a big challenge and to do that, we have to incentivize innovators from around the world to come together with a particular prize purse or a cash prize at the end to make that happen. It was also based on the idea that ultimately, we do our best work in competition.

They’ve shown this by creating really structured challenge statement, by setting really well-defined milestones and metrics of what success looks like, and then working through what the prize purse or cash prize at the end needs to be to ultimately incentivize individuals across the globe to get excited and to dedicate their time and energy to trying to solve that challenge. You can actually end some of these problems and challenges that once were thought not to be possible.

XPRIZE has really built a well-functioning, what I call “de-risk and validation model,” which is really their process. It starts with a well-defined challenge, and they source individuals from around the world and across different domain areas and expertise to come together and help identify the root causes or barriers to progression in a particular industry or market. That focus and energy allows them to ensure that people going to market or focusing on that challenge are going to tackle the primary blocker that’s holding the market or industry or society back. They’ve done a really great job of defining success and ultimately being able to raise funds to support the incentives along the way.

The thing that I find the most interesting in terms of how the model now operates and some of the work they’ve done in understanding the impact and scale is that in these areas where you first thought you couldn’t solve a challenge, now at the end, you somehow have this collection of ventures or teams that have solutions to this unsolvable challenge. You also have market signals from XPRIZE’s model that show this technology and market is de-risked enough, and you start to see naturally both talent and capital galvanize around teams. Not just winners do well: semi-finalists to finalists go on to find pathways to market and ways to bring their solutions into society.

KB: This differs from traditional “demand-driven” or “purposeful” research in that in those cases it is usually a public funding agency that puts money in upfront and pushes towards a solution. XPRIZE flips this around, making the prize a post solution reward, and lets the market figure out how to get there: pull instead of push. In what circumstances or for what class of problems do you see that pull mechanism being more effective than more traditional research funding push mechanism?

SM: Push funding is obviously foundational. I do want to be clear: I think we need a nice product mix of funding opportunities and structures across Canada to continue to drive innovation and scientific discovery.

Pull has been shown to be really effective where you might need rapid or real world deployment, or when a problem is pretty clear, but the pathway to the solution is really not known. It’s when we really don’t understand the methodology required to get there through which we can align funding metrics and milestones or activities along that pathway that you’d see in traditional push funding or grants.

With pull funding, we have no idea how we’re really going to get there, but we know that this is a problem, we know how to define what a solution needs to be able to do, and we’d like to put some resources behind it. The pull models have worked really well there.

I think one of the reasons it works really well, and what research shows, is because you have a clear metric of what success looks like and you have a lot of people focused on how to solve that, it allows you to focus on finding the best solution, just the way an early stage tech company would. You focus on the problem, you co-develop along with customers. Early stage investors aren’t that concerned with how you get there, they just want you to find the best fit for the market so you can be competitive.

Innovation competitions and prize challenge-based innovation works very similarly: we really understand there’s a challenge here, and while we don’t exactly know what the mixture of success looks like or what a viable solution is, if you work alongside the market, we’ll reward you for that should you meet the criteria for success. You typically see better pathways to market, better uptake, and better solution deployment, than you would see necessarily in starting with a solution.

You could argue, although I have no research currently or data in front of me to back this up, but you could argue it’s more resource-efficient, as well, in those use cases where a problem is well understood. Where a problem isn’t well understood, some push funding could be quite valuable in just helping people understand what the root causes are

KB: A lot of work goes into defining the problems and in articulating the metrics that a solution has to satisfy. Tell me about the process of identifying problems worth solving and of designing one of these prizes.

SM: XPRIZE has definitely honed this process, and it’s the part that I get the most excited about. We’ll talk through in this conversation about how this applies in Canada and how we really leverage what this means.

The overall process is referred to as “global visioneering” . It’s a multi-stage year-round process that’s definitely not an internal decision. What makes XPRIZE challenges and competitions so successful is the fact that they source ideas, understanding, and barriers from across the globe. They bring together individuals from across various domains. So the mix could include business consultants, or perhaps sci-fi writers, or deep experts in, let’s just say, vaccines.

We have a vision for 2040, which is set by the domain lead at XPRIZE, organized by domains. For example, one domain is health. If they have a vision for what in 2040, an abundant and equitable future looks like in relation to health, we might identify a number of different tracks we want to explore where we think there’s some paradigm shifts that we can drive or opportunities for the evolution of society and technology, and from there, they’ll go out and source individuals.

So if they’re looking at something like cancer vaccines and we think that’s the next stage, they might bring in some vaccine experts and then you might bring in people from across other areas that can get together and challenge each other on what the core root causes are and what makes an XPRIZE viable. They go through this rigorous design process where they ideate, and then they do a series of what you could imagine as design sprints, both in-person and virtual.

They then accumulate that by bringing some of those top concepts where they think there’s both good market potential and value as an area of focus, and where there might be alignment with individuals in philanthropic funding or other areas that might want to fund and support the development and launch of these prizes, and they’ll take them out to that community.

That process starts with what they call their “brain trust workshops”. Now that we have the XPRIZE Canada Hub here, we’ve been able to host the brain trust workshops in Canada, both in 2025 and in 2026, first in Calgary, and then this past year in Montreal. Being able to bring over a hundred experts, both from within Canada and from across the globe, to Canada to talk about what the future is and where XPRIZE should be focusing on has been a very beautiful benefit of having a regional hub and a regional team here, as well as being able to source some of the brain trust experts that they’re looking for.

They ultimately start that process and then they’ll move to a lot of virtual work and then it’ll culminate in a global visioneering summit, which is where they bring those ideas and concepts to their benefactor communities. If any of those get taken up (which we would call “moving on to prize design) then they’ll go through a more primary and secondary research process to really define the right metrics of success and the right fit of the cash prize to incentivize individuals across the globe to really tackle this issue.

KB: Are XPRIZEs usually philanthropically funded, or do you get funding from other sources as well?

SM: We (prizes) are funded by both private dollars and private philanthropy, but you see a mixture and it really is challenge or prize dependent. Sometimes you see family offices, you see other philanthropic groups or high net worth individuals where a challenge really aligns with something that they care about or something that they want to reinvest in. But there’s also arms-length governments. For example, the Water Scarcity Prize is a more government-orientated prize because that’s a huge regional challenge, and they think this is another way to augment work that they’re doing and ways they’re trying to find solutions to these global challenges.

KB: XPRIZE is clearly a globally-scoped effort, both in terms of the expertise that goes into the design and the intended impact of the challenges. Given that global focus, what’s the value of the regional hub?

SM: There’s a couple of key points, but it really started a number of years ago when the CEO of XPRIZE was starting to think that now that we have this methodology and we’ve really seen what the impact of an XPRIZE competition can be on creating emerging markets, de-risking technology, de-risking markets, and enabling growth and scale for particular businesses or ventures and teams that have gone through the competitions, they started to just ask the question of where are competitors coming from and what is the impact post-competition.

At that time, XPRIZE did not necessarily have any impact support following a competition. Typically it is simply that the winner wins the money and then everyone moves on to the next challenge. They looked at it and they saw although they are primarily based in the US and so they have great reach there, there were a significant number of competitors coming out of other regions like Canada and Europe, and actually those teams were winning at a higher rate than some of the US-based teams. So they were very curious to explore what the incentive model looked like and how these competitions were showing up in other regions.

The other thought, and really what led to beginning to build out regional hubs is that at the end of the day, the goal is to end a challenge. The more people you get and the more creators, builders, and innovators are turning their time and energy towards developing a solution for a set challenge, the greater chance you have of solving that, and potentially the more viable solutions you have at the end. The thought for the hubs is if you had a small regional team that was self-funded, self-managed, and very well-connected, could they actually enhance the number of teams and individuals recruited into competitions? Ultimately, could they improve their chances of moving through the competition to get to a viable state, or to some solution with applicability in the market, by developing strategic partnerships around those teams in challenge areas.

Again, at the end of a challenge, the problem might be solved, but the application and scale isn’t really solved in any of those competitions, because it is still at a really early stage. It’s really in that zero to one area. So, how could a regional hub improve pathways to market so you see more pilots, more deployment, and ultimately more scale of the impact of the solution? It is nice to talk about the fact that we solved it, but are we actually applying it in the market on a regular basis?

That’s the core idea behind the regional hubs, and I’m really excited to have an opportunity to see how we can build out that blueprint so that it actually works in the way that it’s been envisioned.

KB: In the relatively short time that the Canada hub has been operating, has that hypothesis been born out? What lessons have you learned from the Canadian expansion so far?

SM: I feel like every day we are definitely learning a ton. I think particularly it’s interesting to think about how to recruit across challenges. Often competitions are somewhat similar, but custom designed for the challenge. Depending on where it starts, what the prize purse is, and what we’re incentivizing individuals to do, how can we create a repeatable scalable model or a process workflow that you could apply to every challenge, so that the resource management within the hub is effective and we can be as impactful as we want with as small and nimble of a team as we have, while still prioritizing maybe not every competition, but definitely those that are strategically impactful for regional and national priorities in Canada.

For example, you could think of the application of destructive wildfires. Obviously, some of the solutions coming out of that competition are very interesting to Canada, and a lot of funding and resources are heading towards that direction. There’s other things like ovarian health, prizes in the pipeline that I think would also be interesting, things like energy efficient AI and thinking about how we reduce energy expenditure across that technology development. All areas I think Canada really wants to play in. So for me, the big thing has just been defining how you can not treat each challenge as a custom approach, but rather identifying the processes, scalability, and foundation we are building within the hub that can be replicated. From there, with that in mind, also considering what pieces of that could translate to other global regions: what stays stable and repeatable and, ultimately, what assets need rebuilding, that we can share with other global hubs so that they start ahead of the game without having to rebuild everything from scratch.

That’s the biggest thing I think about on a daily basis beyond just making sure that this augments what already exists. Obviously every region that a hub will go into will have a different set of ecosystem players, a different set of financial and capital mobilization available, potentially different market opportunities or applications. And are we adding value without being duplicative? Are we connecting within the other prize networks and institutes that exist?

Luckily, challenge-based innovation isn’t new to Canada, it hasn’t previously been that mainstream. We talked to groups like Impact Canada about their model, how they’ve supported prizes, how we partner with them when it comes to using some of the regulatory sandboxes they have to make sure that there’s opportunities for teams. We really think a lot about that partnership, what that looks like, and how we’re additive and not duplicative.

KB: Many of the challenges you mentioned have a very strong regional component: regulatory frameworks differ, and there are considerations relating to engagement with Indigenous groups, among other regionally-specific challenges. On the other hand, the XPRIZE is global in scope. To what extent are prize metrics and problem definitions taking these regional considerations into account upfront versus handling them after a general solution has been proposed to the core problem?

SM: This is my favorite part. I think it’s a really strong example of why regional hubs can be quite impactful.

On the long-term horizon, having a hub means that we get to source experts to support prize design. So you ultimately are building in Canadian perspectives, Canadian understanding of regulatory, the complexity of the multi-nation network we have with Indigenous groups, and all of this feeds into the front end of prize design. Competitions naturally will have some of those global considerations and that’s the idea when you have other hubs too: they’re also putting forward experts, there’s better understanding of who’s doing the work because we have this regional team that just is more active and obviously more aware of the individuals in various groups and can also support that through prize design.

In the short and medium term, there are things that no matter what you do in the prize design area, as you start to move through testing, as you start to move through really understanding how this is going to operate and apply, even with the set metrics in place of what a successful technology or solution would look like, challenges come up.

The Wildfire prize is interesting as an example because with Wildfire, they realized that we can’t just start fires for testing. We have to work with regulatory groups, and we’ll have to find areas where we could do this in partnership with cities.

So we think a lot about, as prizes are prepping to launch now, how we want to be involved? Can we be a partner in competition testing? Is there a way we can build that in? This helps not only position Canada to build capacity in emerging markets and opportunities, but it will also attract global competitors to Canada or at least raise awareness of our people and their capabilities. For example, we’ve been exploring this in a prize that has yet to launch – the Ovarian Health Prize. Because it’s at that right stage to be thinking about looking at testing partners and starting to think about what solutions could integrate with the public healthcare system in Canada, which is obviously quite different from a private one or others globally.

The biggest thing though, and what is our absolute focus at this point in time, is no different than how you think about developing a product with a customer along the way. We just have a few more stakeholders and a lot more ecosystem partners to bring to bear, but we imagine being able to wrap partners around teams and competitions and challenges areas that then allow various types of individuals to be providing real time insight to teams that are developing in the markets in which they plan to potentially deploy or pilot their initial solutions.

Ultimately, even if their solution isn’t directly related to the structure and focus of an express competition within the market, if the solution has application, they’ll have maybe had a city of Calgary or a city of Ottawa individual giving them guidance on things they need to be thinking about, early relationships and partnerships they should be thinking about if they want to pilot a solution. Hopefully, we’ll be able to play a role in facilitating some of those discussions, because again, we’re all moving towards the same de-risking metrics.

KB: In the US, there’s lots of risk tolerant capital available to fund the early stages of an attempt to solve ambitious problems. In Canada, not so much. How are you adapting the XPRIZE model to take into account the availability of resources for the first steps?

SM: I’ll talk about two different levels. At the XPRIZE level, have definitely, over the last few years, started redesigning prizes with milestone payments. So to your point, it used to be all for nothing. If you’re the winner, great. If not, we’re glad you came along for the ride. The prize is solely for first place.

Now they have it built out where in various prizes, depending on the technology development or the risk tolerances (required in development), there will be milestone payments at various points. So for example, in the HealthSpan XPRIZE, they’re about to award the top 10 teams. Each of those teams will get a million dollar payout for the next phase of development, which includes a clinical trial. A million is not likely to even get them through their clinical trial, but it’s meant to help advance and galvanize other grants towards that end.

They’ve also added on an impact year. So a year of support is a mixture of introductions to capital allocators, access to customer’ resources, and introductions for teams everywhere from semi-finalists upwards that are still working in the solution area or around that challenge. So at an XPRIZE level, that is what they’re trying to do to level out global access.

At a regional hub level, we’re not specifically at this time looking at any additional funding support or funding through the hub itself. We’ve thought about venture philanthropy, syndicates, and other opportunities unique in the ecosystem where there’s maybe people coming together and at various milestones they get an injection of cash to allow them to stay in the competition or if they want to progress that potential solution.

But at this time, we’re really focused on the macro level, there is capital available, but to your point, it’s risk averse. It’s a little less risk tolerant. It can be administratively challenging to access. But if you bring those groups around teams from day one, or at various points of interest, you can show funders the metrics to prove it’s actually de-risked. So we’re trying to give people the same framework and model that you’d see in a venture capital approach and help them understand where the de-risking moments are so that we can mobilize more capital.

I think there is capital, but to your point, it just sometimes doesn’t show up. We really want to figure out ways in which we can leverage the validation and de-risking that comes in participating in a competition and being a part of a global challenge and the XPRIZE network and all of the resources that come with it to enable more capital to feel comfortable coming to the table.

KB: Is XPRIZE actively involved with these teams during the development process?

SM: At XPRIZE, they always have a couple of individuals on a prize team. So when a prize team gets set up, they always have someone who’s leading the prize operations in terms of what the flow and experience of the prize will be. They always have someone who’s liaising with all the teams to follow progress, help address challenges that might pop up, and answer questions they might have. So XPRIZE is very focused on ensuring that teams feel supported and that they have a key point of contact within XPRIZE.

Of course, many things happen over the course of a prize for an individual team. Some are in research institutes, some are within universities, some are in startups, some are in corporate innovation teams, they really come from everywhere.

Then they typically have a technical prize lead within a prize as well. So someone who really understands the technical aspects. So they might have been in emissions reduction in the past and now they’re supporting the Carbon XPRIZEor the Carbon Removal XPRIZE.

So they’re at a broad base of what support they can provide to the team as a whole. XPRIZE always wants to run a fair and transparent competition. They don’t get involved in the judging. They bring in an external panel of judges, and they work on what can be accessible to all teams. They do at various points, depending on the competition, bring in other supports. So they might run a mini accelerator in partnership with, for example, CDL or Avatar Innovation.

When it comes to thinking about other regions, we are now considering what it looks like for regional hubs to support teams in a fair and transparent way. That process is being built in real time in terms of how we are aware of Canadian teams that are opting into competitions. Right now the thought is that we want to create a local and active supportive community. What we’ve heard from alumni who’ve competed is that, as much as the competition added a lot of value through the structure of the process, being in a community of other people who are solving the same challenge, but probably from a very different angle, was one of the most valuable parts of the experience. We very much want to engage with teams by continuing to surround them with the right people at the right time. We’ll be working very closely with prize teams to understand who’s involved, who’s opted in in Canada, where are they located, and maybe there are ways in which we can have meetups, community events, or even activate our ecosystem to make sure that they’re connected with the startup and innovation communities.

If it’s their first time ever thinking about taking research or their work into the market, they might not be aware of all the communities and supports that already exist.

KB: As you’re observing the teams entering the Canadian hub, are you seeing particular structural challenges that are faced by teams from different regions? What do you think needs to change in order to be able to address those differences?

SM: Within the time frame of the hub, it’s been a lot more active competition. When I joined to set up the hub last August, they hadn’t yet launched, they only recently launched a couple of new competitions and they’re actually smaller ones. I feel like we’re very much in the active stages of understanding what some of the structural differences might be.

What we learned or heard from alumni is that sometimes there is capital available at various points and then it, but it doesn’t really flow through the entire process.

So for example, there’s a couple of companies that have come out of the Carbon XPRIZE that are still active in Canada and they were able to get through the first barrier of deployment and implementation, and now they’re experiencing a gap in the funding for scale.

There’s still huge opportunities, I think, to bring people along and help them understand the value of investing and understanding what metrics or criteria they would need to de-risk the investment on their side, and communicating the market opportunity.

One of the things we’ve been thinking a lot about is the question of what it looks like to tell the stories and help communicate the impact of competitions, the impact that’s already been created in Canada, and ultimately how we’d like to grow that. What is then needed for us to come together as an ecosystem? That’s including governments, that’s including funding agencies, that’s including individual institutions and how they allocate capital within their research groups, to make sure that if we all say we want more startups and we all say we want more economic impact, and we want companies to stay here and grow, well then we also have to ensure that they have access to markets and they have access to capital at those particular periods of time.

We’re really early in understanding where exactly, but I am fairly certain that a good amount of the storytelling and support and helping amplify some of the work that’s being done by other individuals in this space is a benefit that we can help provide to adjust and maybe move capital to places where it needs to be.

I’m a big proponent of the idea that we don’t have to play in every space, because that would be pretty risky, but maybe there are areas in which Canada has growing challenges, or we know this is an area that we want to really be leading in, where we can help push for a little bit more risk capital to be allocated there and have a strategy that allows for that across a commercialization spectrum. That’s a reasonable thing: to be able to hold public funders accountable and for them to feel like it’s politically and financially safe for them to do that.

KB: Do you have prizes currently in construction that you can tell me about?

SM: There are some prizes in development. The most I can say is that XPRIZE is hoping to launch three to four prizes over the next four to five months. One I can say will be in interspecies communication, thinking about how technology might be able to advance the communication and understanding of other species here. Another will definitely be around AI energy efficiency. The others I say are probably too early to confirm.

We’re also really focused on understanding ways in which we can recruit and support teams throughout the Build with Gemini XPRIZE, which is active right now. It’s a 90 day hackathon focused on building a viable, sustainable, AI driven company in 90 days with revenue and potential for scale. We’re really excited to see how that turns out for us, and which of those we can convert into our recruitment process.

You will absolutely see a real focus on understanding how we bring together people who are curious that are builders, creators, innovators. How do we bring them together early on and start creating those natural collisions and helping them understand what the value of a competition could be or like what it looks like. We’re hoping to bring even some alumni to the table that are continuing to work in the commercialization space in these emerging markets and impact areas to help communicate that value. You’ll also see us across Canada this fall and winter very actively sharing how the Hub can support you and how we’re working with stakeholders and ultimately starting to bring the ecosystem together just so people are aware of the competitions and so that the first time they hear from us they understand what XPRIZE is, understand its potential, and of course then they know who they can contact if they have any questions.

Sometimes just joining a competition can have a huge amount of benefit like in the build with Gemini you get access to all these free tools, there’s some credits, there’s the ecosystem. Every week they’re running like a customer discovery workshop with an expert. If you’re already in the space and curious, there can be just sometimes a ton of benefits to joining a competition that are just wrapped around you.

KB: Is there anything you wish I had asked you but didn’t?

SM: Why an XPRIZE Canada hub and why does it exist in Canada? Canadians have never been excluded from XPRIZE competitions, so why do we really need a hub?

I think we’ve highlighted a lot of unique things in the economic structure of Canada that are very different and create very different incentives for technology development and commercialization or that are very structurally different from the US in terms of how capital could be allocated, the risk tolerance that exists, our interest in even starting ventures and growing and developing them versus maybe working inside of a research institution and optimizing for early stage discovery.

I’m someone who really believes there is an opportunity to merge business and technology to solve really important challenges. Something that really always resonated with me years ago when I heard it, was that some of the biggest market challenges are some of the biggest market opportunities. Often there’s a lot of resources at what’s very tangible and ready, where the market already is, but what is driving the creation of our next medium and long-term markets and ultimately driving Canada’s economic future?

I think XPRIZE competitions show what is possible, but without bringing people along the stages of commercialization in a challenge, it’s hard for them to opt in and it’s hard for them to justify the risk, or to see how it’s being de-risked. I think there’s a huge opportunity for us to help build those pathways to market so that we do see some of these solutions that are developed here actually being applied and that we have the capacity to improve our lives and our communities, and also feel like we have great economic opportunities. I know many people who consider moving to other countries, other regions when they’re interested in commercialization versus thinking about doing that here in Canada. And I’d love to see the hub play a role in changing that.

Key Takeaways

Push and pull

Most of the discourse around funding for innovation in Canada revolves around push funding: money that is given upfront with the intention of delivering some goal. In the case of research, that usually means publicly funded grants being used to advance whatever was written in the grant application, for example.

XPRIZE (mostly) flips the model around: instead of providing funding upfront, money is given after the target has been achieved. Importantly, the reward is for a solution, any solution, which satisfies the metrics. The precise form that solution takes is (mostly) irrelevant. It just has to work.

Both types of funding are necessary, and which one is needed at which stage of delivering innovation depends largely on the kind of problem being solved. As Sarah puts it:

“Push funding is obviously foundational. […] [W]e need a nice product mix of funding opportunities and structures across Canada to continue to drive innovation and scientific discovery.

Pull has been shown to be really effective where you might need rapid or real world deployment, or when a problem is pretty clear, but the pathway to the solution is really not known. […] [W]e have no idea how we’re really going to get there, but we know that this is a problem, [and] we know how to define what a solution needs to be able to do […].”

Pull funding is less prevalent in Canada to date than push funding. There is a reason for this, and it boils down to context. The gaps in Canada’s innovation funding pipeline mean that it is much harder to deliver the process that leads to success in Canada than in an ecosystem like the US, where risk-tolerant capital is relatively abundant. In other words, without funding for the process, there can be no chance of successfully delivering the end goal.

Sarah is cognizant of this. XPRIZE Canada has started to build prize programs that involve partial funding for milestone achievements en route to a solution, creating a sort of hybrid model that is probably necessary for pull funding effective in Canada, given aforementioned risk capital access issues. The fact that this hybrid approach is necessary tells an important story about Canada’s innovation ecosystem and reinforces the need to accept the risk involved in funding the process if we are to benefit from whatever we build at the end.

Focus on the problem

As with all of the previous interviews on this theme, the key question to be asked is how to identify problems worth solving. Unlike (most) grants, where it is often a single panel making a funding decision based on the plausibility of the path to the solution, XPRIZE convenes a multidisciplinary panel that goes beyond just traditional subject matter experts, allowing them to pitch ambitious problems in need of solutions, rather than the other way around.

Their “visioneering” process seeks to articulate exactly what it means to solve the problem, rather than evaluating the plausibility of a particular proposed path to the solution.

What makes XPRIZE challenges and competitions so successful is the fact that they source ideas, understanding, and barriers from across the globe. They bring together individuals from across various domains. So the mix could include business consultants, or perhaps sci-fi writers, or deep experts in, let’s just say, vaccines.

A critical element of the difference between pull funding and push funding is that in its purest form, pull funding does not care at all how a solution is delivered. All that matters is that it solves the problem, by some (ideally) well-defined measure of “solved”. Per Sarah:

[W]hat research shows, is because you have a clear metric of what success looks like and you have a lot of people focused on how to solve that, it allows you to focus on finding the best solution, just the way an early stage tech company would. You focus on the problem, you co-develop along with customers. Early stage investors aren’t that concerned with how you get there, they just want you to find the best fit for the market so you can be competitive.

[Y]ou typically see better pathways to market, better uptake, and better solution deployment, than you would see necessarily in starting with a solution.

This is a critical lesson, and it generalizes to another critical area of innovation policy and Canadian paths to market, an area that suffers from precisely the problem that problem-focused pull funding is designed to solve: public procurement.

While procurement is pull funding in principle, the Canadian approach to it is overly prescriptive, prescribing the exact solution to be purchased rather than defining the problem that the solution seeks to address. The success of the XPRIZE to date at delivering solutions to audacious challenges should be used as a blueprint for procurement, refocusing on defining problems and the metrics against which we will measure solutions, rather than prescribing the solutions themselves.

Derisking the path to market

To be effective, purposeful research policies must consider paths to market: explicit mechanisms for market de-risking, regulatory integration, and local adoption. Sarah’s thoughts on the ecosystem echo my own experience:

“[…] I just find it fascinating that pathways to market are something we don’t talk about as deeply as access to capital, when it’s still a core challenge that we have in Canada.

[W]e have great talent and we have abundant resources and we care about solving challenges and we want to build and grow companies, and yet when it comes to translating that research into innovation through the investments or the mechanisms that we have or the way the economic incentives and structures are set up, we often miss the value capture at the end.

For-profit capital follows de-risked market pathways and moves into areas where risk-adjusted returns are sufficient to justify it, and a key part of de-risking innovation is in the work that XPRIZE does to include ecosystem partners (cities, public health systems, regulators) around teams early so that pathways to adoption exist when the technology is proven.

“I think there is capital, but […] it just sometimes doesn’t show up. We really want to figure out ways in which we can leverage the validation and de-risking […] to enable more capital to feel comfortable coming to the table.”

Lessons for Canada

There are three key lessons in the XPRIZE approach that can be generalized to other areas of Canadian innovation.

1. Where purposeful research is concerned, the fact that we have necessarily identified a societal problem opens the door to changing how we evaluate success, shifting from input metrics (patents filed, papers published, students trained, etc.) to output metrics (problems solved) or concrete milestones that demonstrate progress toward solutions. While not all research fits this approach, Sarah’s commentary above makes clear when pull funding is appropriate, and that both push and pull funding must be part of an effective innovation ecosystem’s toolkit.

2. We must consider paths to market (or more broadly, paths to purpose) in all types of innovation funding. At present, Canada invests in research and expects (with decades of evidence to the contrary) that economic development will follow. It will not, unless we are thoughtful and deliberate about connecting the people building solutions to the people who actually have the problem and ensuring that we are working to de-risk adoption pathways to the point that it makes sense for the private sector to take over.

3. As has been the theme of countless other articles on this blog, we have to make strategic risk-taking safe politically. This means establishing clear governance frameworks that evaluate success at the portfolio level rather than the project level, allowing public funders to concentrate public capital on well-defined national priorities (e.g., wildfire mitigation, healthcare system sustainability, energy-efficient computing) without fear of administrative penalty for project-level failures.

Last word to Sarah:

“I’m a big proponent of the idea that we don’t have to play in every space, because that would be pretty risky, but maybe there are areas in which Canada has growing challenges, or we know this is an area that we want to really be leading in, where we can help push for a little bit more risk capital to be allocated there and have a strategy that allows for that across a commercialization spectrum. That’s a reasonable thing: to be able to hold public funders accountable and for them to feel like it’s politically and financially safe for them to do that.”

I have heard often that some of Canada’s issues with innovation stems from the fact that we do not celebrate our entrepreneurs and innovators. Through the process of building CanInnovate I have had the opportunity to meet many impressive founders, investors, and supporters of innovation across the country, and I have found that this holds true. We have an incredible base of domestic talent building in spite of the obstacles, quietly contributing their time and energy to improving the place they have chosen to call home.

Today’s interview is with one such person: Ehsan Mirdamadi, a serial entrepreneur who immigrated to Canada in 1998 and who has been building and supporting other builders ever since. He was involved in the early days of cloud computing, worked with Ron Davis at Stanford on DNA sequencing, worked in university tech transfer, co-founded angel networks to pay forward his early successes, experienced innovation in both Canada and California, and chose to come back to Canada, building in the AI space with Codalio and contributing thought leadership around related policy. His experience, earned from such a wide variety of perspectives inside and outside the Canadian innovation ecosystems, offers an invaluable look at both the potential and challenges Canada offers its innovators.

While some previous interviews have explored similarly themed stories, I intend to make this a more consistent element of CanInnovate reporting, through a series that tells the stories of Canada’s builders. If you know someone you think should be celebrated for their contributions to Canada’s innovation ecosystem, please tell me how best to get in touch with them.

Your email client will probably truncate this post. My key takeaways are presented at the end, so be sure to read the web version if you want to get the whole story. Many thanks to Ehsan for taking the time to share his insights.

Interviewer’s note: Ehsan Mirdamadi approved the final version of the section entitled “Interview with Ehsan Mirdamadi” and had editorial input on that section, with the option to rephrase and expand on the ideas discussed in the interview without changing or removing any intended meaning. The key takeaways presented at the end are my own commentary, and do not necessarily represent the views of Ehsan Mirdamadi, Codalio, or any of the investor networks of which he is part.

Interview with Ehsan Mirdamadi

KB: Tell us about yourself, how you got here, and where you are today.

EM: As an immigrant, I landed in Canada when I was 18, in August of 1998. Less than a year later, I started a computer science program at York University. That same year, I had this bug in me that I wanted to build a company. It was in the early days of the high-speed internet, so I ended up playing around with the Rogers @ Home service to see if I can actually run a server on it.

Before I knew it, I had a company. Even before I graduated, I had a few employees for it. That turned out to become one of the first—maybe the very first—Canadian cloud computing company. I essentially bootstrapped the company to about 100,000 clients, through partnerships and channels, and eventually sold.

One of the biggest questions I had from myself as a Canadian entrepreneur, was that: given that we started before almost everybody else in that environment, why were our American counterparts able to scale similar companies to billions of dollars, but we were only able to get to millions?

Obviously, I was an inexperienced entrepreneur at the time. I was more of an engineer and computer scientist. So I went on a journey to find that answer. The most shocking learning in that experience was that there were companies that were coming into our space 10 years after, and they were still getting to the billion-dollar mark very quickly. So as you can imagine, in the technology world, 10 years in cloud computing is essentially a century. So there were these companies that were coming into our space a “century” later, and they were still able to build billion-dollar companies in no time. I think the lesson, for anyone trying to build today in the AI space especially, is to not be afraid of missing the boat. These things are going to be around for the long term. I think the impact of AI is probably way bigger than the cloud in terms of the way that it will impact our lives.

In search of the answer, I moved to California, to San Francisco. I worked with a few scholars at Stanford University in the Biotech Engineering Department. I co-founded a company with a bunch of scientists that were actually part of the team that invented pyrosequencing back in Sweden, at KTH. My advisor was the head of biotech engineering at Stanford, Dr. Ron Davis, who was amongst the top 10 most influential scientists in the world at the time. I was trying to help them to commercialize a short DNA sequencing technology.

To make a long story short, I learned a lot about IP commercialization at Stanford. I realized that they were doing a lot of IP commercialization, including the IP that later became Google. A shocking realization for me was that the invention disclosure form that led to Google was just two pages, in which they discussed how they could assign ranks to nodes in a linked database. With only that, they were able to start to build one of the largest companies in the world.

Back to DNA sequencing: we raised a couple of rounds of financing. In the course of that, I realized that working directly with scientists that still maintained their affiliation with the university and the research attitude wasn’t easy. And so when we took a quick exit.

At the time when I decided to come back, Trump was about to get into power. I had just a very short amount of time, maybe a few months, before I could have transitioned my H1B to a Green Card. I made that choice that I didn’t want to do that and I made the deliberate decision to come back to Canada.

So I gave that up, came back to Canada, and for about four years, I got myself engaged with university-linked incubators. So at University of Waterloo, Toronto, York, Ryerson, and McMaster. I was amazed to see the depth and the breadth of the companies and the ideas that were coming out of these universities. Obviously, in contrast to what I was seeing in San Francisco, it was eye-opening. The quality of the IP was amazing. The passion, the energy, the government support, the ecosystem support, all of the ingredients needed to build a successful early-stage ecosystem were actually there.

I started joining some of the angel networks. One in particular, GTAN, the Golden Triangle Angel Network out of Waterloo was, in my opinion, doing a fantastic job. Through them and through my own affiliation with these university-linked incubators, I invested, coached, and advised about 20 companies and was very lucky that at least two of them became Canadian unicorns.

I took some interim positions with some of the companies that I invested in to really support them in getting to the next level. By 2017 when I had my first kid, I realized that there is a big gap between what’s happening in the university settings and the R&D space, and what was happening in the early-stage investment and angel space. So I decided to do something about it.

With the new baby, I didn’t want to just go right back into entrepreneurship. So I took a job at MaRS Innovation, now called Toronto Innovation Acceleration Partner (TIAP). This entity was a grantee of a program that was called the Centres of Excellence for Commercialization and Research (CECR) program. They received about a hundred million dollars in funding from the government, with the mandate to support commercialization of the IP that was being generated at York, Ryerson, U of T, and the 11 hospitals in the GTA.

When I joined they were already a mature entity. They were very strong in commercializing life science-related intellectual properties, but there was a gap in supporting the 3 Universities under their mandate - York University, University of Toronto and Ryerson (at the time) University. So I ended up taking over part of the representation of MaRS Innovation with the three universities. I had offices at the three universities all at the same time at the VPRI level. I was working with their own in-house commercialization specialists. I think we actually made it even worse for them.

A typical process was that somebody would come into our office with a disclosure form filled out, and we would have a couple of meetings to understand what exactly their invention was. We then gave them the results of an IP search using PatSnap, essentially bombarding them with a list of about 200 to about 700 past filed patents. We handed all of those files, PDF files to them and we said, “please figure out if your IP has any overlap with any of those.”

Many of the inventors never came back, because they didn’t have any answers for that. The ones that came back, continued these long meetings. We started to identify the main value of those IPs, using university-retained IP lawyers to get opinions. We then delved into understanding the competitors, doing our own internal research about the use cases. That process usually took about six months.

A very small percentage of those innovations actually made it to the point where we said, “Okay, there is something there.” At that point, we sent them back again, for the third time, saying that the inventors should run a pilot, supported by intros that we would make. We didn’t have a very good Rolodex, but IRAP also gave us a good Rolodex at the time. We were able to connect them with some industry experts. We did a lot of hand-holding those processes. Then maybe they could find an interesting use case.

Again, now for the fourth time, we sent them back to grant writing teams and to write a grant for collaborative research with an industry partner. If they would reduce it to practice, then they could come back and only then would we decide whether or not to pay for the patent filing.

Because of that process, the IP was often actually encumbered, because the industry partner would have some claims to it, and then there were the background IP and the foreground IP involved, all of which made it even more difficult to figure out who owned what. Maybe the industry partner proposed to license it, maybe not. Sometimes we found other entities that were interested in licensing. Finally, after we had some validation of the use cases, we then moved ahead with paying for the patents.

At this point, there is commitment from innovators to commercialize the invention, and they had absolutely no idea. Based on my exposure to the whole angel community, I can confidently tell you that majority of the angels never knew how tech transfer offices operate.

I think at best we were commercializing under 1% of all of our intellectual property. 52 billion dollars of our tax dollars goes into R&D. Obviously not a lot of it goes to universities, but I think about 16 billion of it goes into the universities and colleges [Interviewer’s note: per the previous link, in 2022, $8.4B of federal dollars went to research, with a further $8.9B from higher education institutions, which may include other public sources]. We do not have a big share of commercialization resulting from this.

There were a couple of interesting programs there that relatively few people knew about. There was a program called NSERC I2I (Idea to Innovation). It’s like a seed capital for innovators trying to commercialize their IP that effectively lets them develop a product while still in the academic lab before they spin out a company. It is delivered in several trances, starting with 125k. It’s a very competitive grant program.

To my surprise, all of my colleagues in the university settings advised against fighting for those. I didn’t listen, and I got two of them approved in my first year. At the time there were only 30 of them awarded nationally. If you validate some of the assumptions and if you have the entrepreneurial background, you can actually write good proposals and the government is actually listening.

The projects that I actually filed for were stuck in the never-ending back and forth interactions with the university commercialization specialists. I won’t name names, but there was this one inventor whose work had real potential for Canada’s national interest, and I put a lot of energy behind it, but the people I was working with at the time kept telling me to just drop it, that he was some kind of crazy scientist not worth my time. And you know what validated my instinct? When I decided to apply for an I2I grant for his project, and despite the fact that the program is insanely competitive it got approved, which pretty much confirmed that the work was worth taking seriously all along.

This inventor spent eight years going back and forth with the university, begging for them to file. He was actually correct about the potential value, in the end. That one got an I2I. The other one was for autonomous vehicles, a routing algorithm. But the first one, the inventor, was actually from the industry. He previously worked at Hydro One. He let go of his job. He went back to university, doing research to solve the problem that he knew the industry had. Yet the commercialization officers were still pushing back because they didn’t understand it. These commercialization officers are actually PhDs. They are academic people. For the most part, they have no idea about the entrepreneurial experience and entrepreneurial world.

Obviously, everybody hated me, and I knew that I had to get out. After about 18 months, I decided to try to fix some of these challenges from the private sector because those environments were so rigid that I realized there was nothing I could do from inside.

From there I created two entities. One of them was the Archangel Network of Funds. We were a consortium of five or six super-angels from GTAN. We knew that we were going to do a better job by distributing the risk amongst many deals rather than each angel doing their own due diligence and taking risks on individual deals. We wanted to raise 10 million. We ended up raising 25 million. We actually launched the entity right as the COVID lockdown was announced. So we took the pictures, and then it was quiet.

We decided to use our own capital rather than raising from others, and to fund companies that were doing something about the COVID or otherwise alleviating the health or its economic impacts. We ended up investing in four companies. We created those stories and without even trying to raise capital, people basically came to us, and ultimately we did raise a significant amount of capital. So far we’ve invested in over a hundred companies. In the majority of them, we were the first check. We had a few different buckets for investment, including one that does an automatic follow-on to angel networks. Another bucket B2B SaaS. Another bucket for minorities, women-led companies, and LGBTQ.

The fourth bucket, which is the one that I was most passionate about, was Axion, which had the mandate to accelerate commercialization of IP generated in Canadian universities and colleges. Because of the fact that there was so much noise and momentum in Southern Ontario, we wanted to pay our own dues as private citizens. We had a bunch of passionate angels from Northern Ontario through which we decided to have a secondary angle for anybody who wanted to take their own innovation to Northern Ontario. We found many good companies in those corners.

Another entity that I started at the same time was a company called NuBinary, a fractional CTO service. As an angel, I did a study amongst over 80 companies in angel network communities, and I realized more than 50% of all of the failed companies in their portfolio were failing not necessarily because they didn’t have enough capital, or they were not passionate about what they were doing, or not finding product-market fit. It was because of the fact that they couldn’t scale the technology, which for the most part was an issue anchored around software.

So with NuBinary, I wanted to bring senior expertise in scaling to the early stage deep tech. Within about four years, we grew from 4 to about 50 people. We serviced over 125 companies with a big majority getting to the next round of financing or growing the company. Over time, half of our companies got to series A and above.

We saw a lot of inefficiencies in software development. A lot of inefficiencies in the ways that people were prototyping and building MVPs. A lot of wasted capital. A good 80 to 90% of whatever capital early stage companies for pre-seed, seed, and post-seed rounds went into technology development, and they revamped their software at least three times on average. Even for the ones that eventually succeeded, they wasted a good chunk of their capital just trying to build their software. It was shocking for us because as a computer scientist, best practices in software architecture and coding all existed already, yet people were not adopting them because in university settings, they teach you how to code, but they don’t teach you how to write a good application. There’s a huge difference between the two.

I think we solved that problem, and in doing so we learned a lot. I decided to sell the company, sell my own shares, and then buy that IP, bring it out and build my current company, Codalio. We essentially use the same methods, but augmented it with AI. So we brought the expertise of a multitude of senior engineers from UI/UX designers, senior developers, and CTOs into the pipeline and we are now able to accelerate the whole software development process for scalable, expandable, high quality enterprise grade software by a good 90%.

KB: What you are building at Codalio?

EM: Codalio is a product and software development augmentation tool that allows you to build high quality software, not just a prototype, that scales and expands as needed. To do this, we’re using that tool to first build MVPs, and then we have an automated process and workflow that allows you to quickly iterate and grow it from there without having to scrap any of your code.

We’re positioning it as an MVP builder engine. We hear a lot about vibe coding these days, which is a way for a non-technical person to put a few prompts into these coding tools l and generate some code. But if you do not have the experience and the expertise to understand how the software should be architected, you can only go so far. Those platforms may not scale.

There is now an industry category of coding assistant tools that are primarily designed for the developers. They give you maybe 20-40% productivity gains. Then you have vibe coding tools that are designed for the product managers and non-technical people that actually give you a good 70% of your UI and maybe 5-10% of your backend.

We thought we needed a major new category that could actually get a non-technical person from zero to 80 or 90%, with the last mile going back to the developers. We do that by translating the business objective into a thorough technical scope, and so from that point on, it’s much easier to properly instruct AI to produce better code.

KB: Part of Codalio’s core value proposition is avoiding vendor lock-in and having a sovereign tech stack. That word is being thrown around a lot these days. What does “sovereign” mean when you say it?

EM: A lot of these AI and coding tools, especially the ones like Replit or Lovable, have a certain way of producing and deploying your code that gets you hooked into their infrastructure. If you unplug, you probably can’t even take your deployment with you to. So that’s one of the major parts.

The second major part of it is that all of these tools obviously are using LLMs. We make sure that we design an architecture that allows us to avoid some of the publicly hosted LLMs to generate the code.

The third part of it is that we do everything based on the traditional software development lifecycle. So we’re not breaking that lifecycle, we’re just augmenting it. That was an intentional choice, because to remain platform agnostic you need to do that.

The last part is that at the end of the day, people may decide to go with many different technology stacks in their builds. We chose one that had more of a Canadian root. For the front end, everybody is now for the most part using React, but for the backend, we chose to go with Ruby on Rails, and we added our own layer of abstraction on top of it that essentially commoditizes a lot of the components that goes into any software because 75% of all code is essentially just a repeat of existing code and components.

As an aside, Shopify is one of the biggest supporters and the sponsors of Ruby on Rails. The Shopify stack is actually on Ruby on Rails. We have one of the largest talent pools in Canada, in Southern Ontario especially, around that tech stack. So we made that deliberate choice to go with Ruby on Rails. That makes it very Canadian.

KB: You have a unique and diverse set of perspectives: you’ve built companies, you’ve invested in them, you’ve worked in tech transfer offices trying to get technologies out of the universities and into startups, and now you’re now actively involved building out use cases for AI. Based on this breadth of experience, what’s the main bottleneck to AI in Canada, both from the perspective of companies that are adopting it and customers that are using these tools that you’re building?

EM: I never thought of it this way. It’s very hard to be accurate on this because there are so many things that are happening at a much faster pace these days. Even as someone that spends a lot of time in these environments, it’s very hard to catch up. So I’ll leave that as a disclaimer to start.

With that said, I think one of the biggest challenges is that a lot of the current grant programs that support commercialization do not consider you eligible if you are building AI tools or platforms that leverage LLMs. The assumption seems to be that no one can build AI anymore, and that AI belongs to the big guys, Google and Microsoft, etc.. They will provide funding to use their infrastructure, but not to build something new. I can’t say it’s a problem across the board, but there are some significant programs that think that way. I think the problem is mainly inexperience. I have been called a “ChatGPT wrapper” by someone that has never written code.

The second issue is that Canada was maybe one of the biggest contributors to the AI technology to begin with, with the work of Geoffrey Hinton, and we’re amongst the first nations that invested heavily into AI, but the base of the LLM, but the first version of a transformer, came from Google Translate.

Even though we had a strong foundation, we still do not own any of the LLM infrastructure. We need local LLMs infrastructure to avoid sovereignty issues if the government, if the military, if anybody wants to build on these AI systems. They should not be required to run them on foreign hosted LLM platforms. I think that capability is coming.

We neglected local infrastructure to the point that the majority of Canadians are using foreign cloud infrastructure. Now that we’re trying to catch up, I’m hearing that for the next 10 years, we won’t be able to launch any defined data centre project because we don’t have enough power for that. Shortage of energy and resources and infrastructure is impacting our adoption of AI.

Even for the government, most of our email and baseline cloud services are actually running on American servers. Everybody’s either on Gmail or MS Office. We’re training their LLM models with our own data no matter how we look at it. That’s another big bottleneck that will take a long time to catch up, because then we need the local infrastructure, all of those baseline cloud tools, and then finally LLM training.

My focus is on thought leadership around our lack of confidence as a nation. I think we’re always looking out to our counterparts in the United States and elsewhere, and I think that we should try to gain the self-confidence that we can build globally scalable companies just like our counterparts.

The last piece relates to an old mentality when it comes to VC funding. The old SaaS model is dying off, and we’re waking up to this reality too late. When you look at some of the thought leaders in our ecosystem, we’re still basically saying that we should fix the scale-up challenge here, forgetting that we have massive amounts of intellectual property that we have to commercialize. We’re still betting on growing big companies, whereas you see companies like Replit or Lovable, in the span of eight to 10 months, growing from zero revenue to hundreds of millions of dollars of revenue. We’re neglecting that. We’re again missing the boat on AI because we’re not realizing you can actually move things a lot faster, and instead we’re trying to double down on companies that grew on a decade-old technology SaaS mindset.

I think that’s the wrong mentality. You need both. You need to pay attention to the early stage and you need to pay attention to the later stage all at the same time. You need to identify the companies that can actually have a global impact and then if they have all of the ingredients of success, invest in them and make sure you can actually grow those companies. But in terms of the talent pool, know-how, and the market, we have all of that. We have one of the best Ai talent pools because of entities like Mila and Vector, but we are not properly leveraging them.

For Codalio, I can tell you that a year before Lovable existed, we had the same pitch, but nobody believed us, and they still don’t.

KB: All of this should be feeding into Canada’s AI strategy. Are the issues you raise here reflected in that report?

EM: We engaged with the AI strategy talks back in 2023 through the Canadian Council of Innovators. They basically were, in my opinion, instrumental in putting together a bunch of CEOs that had some really sound ideas around AI and AI technology. We liaised obviously with the government officials around the program, and we ended up submitting “A Roadmap for Responsible AI Leadership in Canada”.

We put forward 10 recommendations in total. The core idea was to build out a tiered approach — one that could actually work across a wide range of use cases, rather than a one-size-fits-all kind of policy. So on one end, you have areas where you want to actively encourage and promote AI adoption, give people the tools, the guidelines, the frameworks they need to do it responsibly. And then on the other end, you have higher-risk use cases where you need actual regulation — clear rules around how AI gets used, what the potential impacts are, and who’s accountable when things go wrong. The goal was really to match the level of oversight to the level of risk, so that innovation isn’t stifled where it doesn’t need to be, but there are guardrails in place where they genuinely matter.

We wanted to have rules and standards that innovators should comply with, regulatory sandboxes and so on. A new entity was created just in November 2024, so I think the government actually listened and reacted properly.

KB: Having seen the pan-Canadian AI strategy, are there issues that you have been pushing on in your own thought leadership that were missed in that report? What do you think that strategy gets right, and where is there still work to be done?

EM: I think the strategy has had some great success in these areas:

• Talent Magnetism: It successfully turned Canada into a global “brain magnet.” By funding CIFAR and the three national institutes (Amii, Mila, Vector), Canada secured a first-mover advantage in AI research.

• Ethical Leadership: Canada was one of the first to prioritize “Responsible AI.” This has given us a “brand” of trust that is valuable in a global market wary of black-box algorithms.

• Ecosystem Clustering: Moving from research to commercialization shows the government understands that “papers published” don’t equal “GDP growth.”

Talent magnetism has been very important for us as a nation. It has the potential - or already has - to reverse the brain drain. The collaborative research projects undertaken by these institutions are a great way to train and retain talent. Engineers that were to receive world class training in real use cases didn’t have to fly to Palo Alto or go to other countries. I think the global credibility that we created through those entities was just amazing.

Having said all of that, there has not been much of a focus around collaboration with the startups or even supporting the startup mindset that could quickly commercialize some of the great IP they produce. In early 2026, though, they pivoted toward venture scientists, and Mila launched the Venture Scientist Fund in partnership with Inovia Capital. They finally recognized that research for research’s sake isn’t enough to sustain the national economy.

The accessibility of Sovereign AI Compute infrastructure remains a bottleneck for companies and researchers alike. The federal government’s introduction of the $300M Canadian Sovereign AI Compute Strategy was a significant step forward; its “heavily oversubscribed” status within just a few months is a clear testament to the unmet demand in the ecosystem.

While early federal investments have helped some companies build impressive GPU clusters, these entities often prioritize academic and research-based outcomes. As a result, we are doubling down on pure research rather than the rigorous commercial MVP testing required to scale. Currently, demand is far outstripping supply, making a robust Sovereign AI Strategy the only way to prevent our most promising firms from migrating to the U.S.

Canada has a long history of supporting the science of AI through programs like SR&ED and IRAP, as well as world-class hubs like Mila, Vector, and Amii. These institutes provide invaluable validation for deep-tech R&D and early-stage use cases. However, there is a disconnect between scientific validation and market readiness.

Under our current structures, it is difficult to imagine a company like Anthropic emerging from Canada. While we excel at the “Deep Tech” phase, we lack the infrastructure to support the transition to market. To bridge this, we must:

• Shift Focus to Commercialization: Move beyond academic validation and prioritize how complex AI technologies reach the market.

• Fund the Development Gap: We need to double down on pre-traction funding, specifically for high-tech ventures.

• Support the Long Tail of R&D: AI companies often require extensive development cycles after the research phase but before generating revenue.

Without a dedicated focus on the commercial “middle ground,” we risk being a nation that discovers the future but never owns it.

KB: What does a junior developer role look like in a world where well-architected code can be produced from an AI system? Conversely, what do AI systems look like if humans stop writing the code that trains them?

EM: I don’t think it’s going to be that different. I’ll give you some examples from the dot-com era. Early on, when HTML and CSS came out, people were just editing HTML and CSS in Dreamweaver. Then WordPress came, and now they’re doing drag-and-drops. Then Wix and Squarespace came and made it even easier. The number of web developers did not go down. It actually went up.

Tools like this help you to augment your process and abstract away the manual work, but you still have that last mile. For websites you have to add tags and forms, connect to other tools, etc.. You’re just shifting the work higher into the abstraction layer.

I think AI will drive the same kind of shift. But, for anybody to really be able to be good at what they do, I think they need to really understand the fundamentals of those other abstraction layers. As LLMs improve—and I have no doubt today, based on what we’re seeing in our own internal data—I think the LLM is going to be able to do 90-95% of our future coding. But that last mile still requires expertise.

Because of the fact that we are now going to be doing more of the development, we can accelerate digitization of industries that haven’t had that much penetration. You can actually expand into many different environments, and that will also require experts. I think projected numbers are showing that we’re going to move from 28 million software developers to about 40 million in the next 10 years.

The good thing that is going to happen there is that even if you don’t know everything, you can use an LLM just like a senior developer or a CTO to guide you. If we try to extrapolate out maybe 20, 30 years from now, I think it’s evident that a lot of things are going to be automated. But you still need that layer—I want to call it an “intent layer”—that requires human intent as in input to AI. That intent layer is going to be the realm that everybody or at least new businesses are going to be operating on. If you want to win this race, try to see how you can win in that intent layer.

Key Takeaways

On Canada’s innovation pipeline

When we seek to build a system that requires multiple sequential successes for overall success, we must support every stage appropriately. If we do not, then what we build will only be as strong as the weakest link in the chain, a chain that spans years of work, often starts with tech transfer from universities, and ends with scaled domestic anchor companies. Ehsan has been involved in every part of it.

His experience with tech transfer highlights core challenges with the Canadian tech transfer process that I and others have tried to surface in related debates: the need for validation before filing and funding, at a stage when this is often premature.

I won’t recreate Ehsan’s entire description, except to note that his story is far from unique. Ehsan’s experience supports an idea that comes up again and again on this site: where emerging technology is concerned, it is too early to pick winners, and we are better served by broadly enabling innovators to build than attempting to eliminate the risk of failure before we start.

In fairness, this is improving in general. Many universities have now established or are establishing investment funds focused on research commercialization, several of which use venture philanthropy as the basis for their approach. While streamlined and consistent tech transfer remains a bottleneck, the conversation has moved significantly in the right direction in the past few years.

Ehsan’s work with the Archangel Network of Funds demonstrates how founders recycle capital and expertise into the startup ecosystem. From his early success, he has gone on to be an investor or manage funds that invested in more than a hundred companies, and has been a mentor for many more.

His experience has important implications for the debate in the Canadian ecosystem about where funding should be allocated in the innovation pipeline. Ehsan’s answer is simple:

“When you look at some of the thought leaders in our ecosystem, we’re still basically saying that we should fix the scale-up challenge here, forgetting that we have massive amounts of intellectual property that we have to commercialize. […] You need both. You need to pay attention to the early stage and you need to pay attention to the later stage all at the same time.”

Without a strong pipeline of effective tech transfer, we miss out on potentially valuable startups created with the IP that results from publicly funded research. Without a strong early-stage investment ecosystem, there’s nothing to scale up. Without scale-up capital, companies will not get past the startup stage or arrive at that stage disadvantaged. This creates a self-fulfilling prophecy of under-performance that reinforces the very issues that cause it.

On building with AI

While Canada is too late and too small to compete with the hyperscalers, this is not to say that we cannot capture value.

Ehsan pinpoints where Canada can compete, suggesting that we “can win in that intent layer.” Just as the emergence of the infrastructure that enabled Silicon Valley gave way to value creation on the application layer, and just as that same shift will happen with quantum computing, so too with AI, where successful application depends on accurately capturing intent.

In my view, there are two primary classes of problems to which an LLM can be effectively applied and where it will be possible to capture value in the intent layer:

1. those whose solutions would take a human a long time to produce but that would take an expert only a short time to verify correctness; and

2. those that do not necessarily have a “correct” answer, but rather that have many useful answers from which an expert can make an effective choice.

Critically, AI works for neither class of problem without expertise.

A good example of the first class of problem is code generation. Creating good code takes time, but (for relatively simple snippets) it is relatively simple and straightforward for someone who knows what they are doing to test if it works as intended.

A good example of the second class of problem is choosing a toolchain to use in developing something new. There’s no objectively “right” answer, and you can make anything work if you are determined enough, but understanding the options available and being able to make an educated choice between them is non-trivial.

Codalio’s approach hits both classes of problem.

“[I]f you do not have the experience and the expertise to understand how the software should be architected, you can only go so far […] [W]e needed a major new category that could actually get a non-technical person from zero to 80 or 90%, with the last mile going back to the developer.”

Regardless of what we build with AI, however, a nation cannot fully secure the benefits of success if it relies on foreign cloud providers and servers. These are not built overnight:

“We neglected local infrastructure to the point that the majority of Canadians are using foreign cloud infrastructure. Now that we’re trying to catch up, I’m hearing that for the next 10 years, we won’t be able to launch any defined data centre project because we don’t have enough power for that. Shortage of energy and resources and infrastructure is impacting our adoption of AI.”

This reliance creates sovereign data risks across both the private and public sectors:

“Everybody’s either on Gmail or MS Office. We’re training their LLM models with our own data no matter how we look at it. That’s another big bottleneck that will take a long time to catch up, because then we need the local infrastructure, all of those baseline cloud tools, and then finally LLM training.”

For policymakers, the key takeaway is that energy, technology, innovation, and industrial policies are no longer things that we can keep in siloes. Addressing this requires whole of government leadership that cuts across multiple files, a subject that I have explored in more detail in previous interviews and articles.

On the path forward

Ehsan’s career is a firsthand exploration of a fundamental policy question for Canadian competitiveness:

“…why were our American counterparts able to scale similar companies to billions of dollars, but we were only able to get to millions?”

Despite seeing the problem, Ehsan actively chose to build in Canada, giving up a clear path to U.S. permanent residency:

“I had just a very short amount of time, maybe a few months, before I could have transitioned my H1B to a Green Card. I made that choice that I didn’t want to do that and I made the deliberate decision to come back to Canada.”

Unfortunately, his story is more an exception than the rule, as there are more Canadian-founded unicorns in the US than there are in Canada. This state of affairs need not persist. On America’s present trajectory, Canada’s attractiveness as a place to build will only increase relative to our southern neighbours, but to capitalize on it, we will have to address the frictions that plague those trying to do it.

Rebuilding and streamlining the Startup Visa program should be high on the priority list. Just as Canada has a generational opportunity to attract scientific talent fleeing the United States, so too do we have a generational opportunity to attract talented entrepreneurs and innovators through immigration; people who, like Ehsan, make an active choice as to where they build and who commit to the prosperity of the place they choose to call home.

Ehsan’s thoughts on what Canada needs as a nation are grounded in first-hand experience of almost every part of the innovation ecosystem:

“My focus is on thought leadership around our lack of confidence as a nation. I think we’re always looking out to our counterparts in the United States and elsewhere, and I think that we should try to gain the self-confidence that we can build globally scalable companies just like our counterparts. […] For Codalio, I can tell you that a year before Lovable existed, we had the same pitch, but nobody believed us, and they still don’t.”

I hope that by highlighting the stories of talented builders across the country, I can contribute in some small way to building that confidence. Many thanks to Ehsan for taking the time to share his insights.

If you know someone you think should be celebrated for their contributions to Canada’s innovation ecosystem, please tell me how best to get in touch with them.

The common theme of most of Canada’s innovation challenges is captured in Goodhart’s Law:

Misaligned metrics that have since become the targets underlies most of the issues of policy fragmentation and risk intolerance that are the subjects of so much of my writing, creating negative feedback loops that reinforce the underlying problems.

In this article, I get into the weeds of how Goodhart’s Law manifest in several closely-connected areas of Canadian innovation policy, including our approach to risk management, how we think about the relationship between government and competition among emerging technology firms, procurement, and the ever-fraught issue of foreign acquisitions.

Risk Management

Within Canadian public funding for innovation, there is a deeply entrenched idea that sound governance of taxpayer dollars means that every expenditure of public funds has to create a success story.

On the surface, this is an admirable and rational goal: if the government is going to spend taxpayer dollars on anything, it should be able to point to clear benefit to the taxpayer arising from that specific use of public funds.

For an infrastructure project that will be delivered over years by a single lead bidder, this approach makes sense. Technical risk is low, we understand everything that needs to happen, and no new inventions are required. All the risk is in the execution and timeline, and there is only one attempt being made. We either get it right, or the money is truly wasted.

Emerging technology, has a completely different risk profile. Many attempts can be made at low cost, most attempts will fail for reasons completely unrelated to the technology, and it is effectively impossible to predict winners early. A majority of such projects ultimately fail, and the minority that succeed create so much value that they more than offset the cost of the failures. The risk management approach used for infrastructure projects is not fit for purpose in this case but we continue to apply it anyway, treating both risk profiles as nails to be driven in with the same risk management hammer, which leads us to the logical but wrong conclusion that we should simply not invest taxpayer dollars in high-risk innovation.

The need to tell a success story for each individual investment actually creates aggregate under-performance as under-investment guarantees missed outliers, resulting in exactly the outcome the policy exists to avoid.

Technological innovation requires that risk be managed at the portfolio level, considering the aggregate value created by many projects together, rather than evaluating the success or failure of each individual project in isolation. Through this lens, the decision of whether or not to invest becomes less about avoiding blame for the failure of individual projects, and refocuses on the outcomes that matter: developing domestic technological capabilities and broad socioeconomic impact.

Tech Transfer

One of the many challenges that stands between technologies developed in federal labs and commercialization of those technologies is concern over accusations of favouritism by the government when licensing technologies to spinout companies. The thought process is that if a spinout of a government lab competes with an existing company in the market, any support for that spinout represents an unfair advantage, and may become the basis for lawsuits. Internally, there is also potential for conflict of interest when public servants are involved in supporting or founding that company.

These are rational fear that reflects the realities of the trade policy constraints under which the public sector operates, but together, they mean that the government often shies away from directly funding or supporting its own spinouts, or does not deal with the commercialization step that should follow some research at all. As a result, grants and funding are spent on research, but little goes to the early stages of turning that research into economic impact, in part to avoid the possibility of accusations of favouritism or the possibility of failure. Nobody gets fired over projects that don’t happened.

This is problematic, since domestic spinouts are more effective vehicles for disruptive technology than incumbent firms, and not engaging means we are leaving intellectual property (IP) collecting dust on federal shelves.

The United States solved this problem in 1980, but their approach is not the only one that has worked. China’s approach to the problem is also interesting. Instead of shying away from challenges related to competition, they make it a central feature of their approach to technological innovation. When the Chinese government wants to develop national capability in a technology area, they deploy funds toward many competing companies at the same time and let them fight it out in the domestic market. As puts it in his recent article:

“[T]he centre sets objectives, localities experiment with methods. The result is dozens of parallel experiments running at once. Survivors scale. The rest disappear, and no one writes their obituary.”

Most of those companies fail. In Canada, this would be viewed as a failure and a waste of taxpayer dollars. In China, it is considered the cost of developing domestic technological capability. Far from being wasted, the talent and IP developed by companies that fail are simply absorbed by their competitors, which become more economically fit in the process, until the handful of survivors are internationally competitive. ITIF refers to this approach as “economic Darwinism”, and state that:

“China’s subsidies are still on steroids, in part because of the intense competition between cities and provinces, which shell out massive funds to support local champions. In 2022, 99 percent of listed firms in China received direct government subsidies.

China’s extreme approach does not translate directly to Canada, for many obvious reasons. We operate on an entirely different scale, and cannot afford to fund hundreds of competitors in the same space, and our approach to government/industry relations is fundamentally different and much less hands-on than that of China. However, we can still learn from it. The way to avoid being accused of favouritism is not to avoid funding anyone, it is to adopt a portfolio approach that funds multiple parallel competing experiments in the domestic market, accepts failure as a feature, and allows projects to fail when it becomes clear they are not keeping up.

The other signatories to our free trade agreements do this regularly.

This is also something that can only be effectively done by the public sector, despite lamentations that the Canadian private sector is not investing enough in innovation. It makes little sense for a private investor to back multiple companies competing in the same space, but the public sector, being interested in economic development broadly while being disinterested in the specific outcome of a battle between competing companies, is actually able to engage in economic development in a way that the private sector never will.

Money spent on projects that ultimately fail will not be wasted if we support a vibrant ecosystem in which talent and IP can be absorbed when companies fail. If one company fails in such an ecosystem, most of the talent and IP will simply add to the strength of the winner.

Foreign Acquisition

While Canada presently leaks IP on exits to the United States and other jurisdictions, this is a direct consequence of a lack of early stage support, not a fundamental truth of Canadian innovation.

One of the many causes of this issue is, like most of our innovation challenges, the result of a self-fulfilling prophecy. There is a persistent concern that taxpayer funding for domestic companies just goes to supporting companies that later exit, subsidizing foreign benefit with Canadian taxpayer dollars. As a result, many Canadian funding programs for innovation wait to see signals that a company is viable and intends to stay in Canada before investing.

A good recent example is the change in eligibility requirements for the CanExport SMEs program. Previously, companies could access this after having $100,000 in revenues in the previous year. This year, that threshold was increased to $300,000, further freezing out the very pre-revenue companies that could be the basis for the next generation of Canadian innovation.

This fear itself contributes to the rate of exits: by denying companies that might exit access to domestic support, we force them to seek support abroad for their first cheque, greatly increasing the probability of the very outcome we are ostensibly trying to avoid.

It’s also worth noting that not all foreign acquisitions are bad outcomes. Given the neighbours, some attribution through exits is probably inevitable, but that does not mean it is necessarily a problem. Supporting companies that later exit is only a waste if the net benefit created in the process is negative. As with the commentary on competition above, a vibrant ecosystem that allows companies to start, grow, and scale domestically before exit is one that will retain the talent that made that company an attractive acquisition target and recycle the capital that resulted into the next generation of companies.

Procurement

Government procurement of emerging technologies is a powerful tool to direct innovation when used effectively. However, Canada’s approach has long been criticized for being overly prescriptive and rigid, typically following a waterfall model that involves fully specifying a solution before beginning the project.

Needless to say, this is not an effective way to engage with emerging technologies, which generally require iteration and experimentation to reach a final useful form, and for which full capabilities may not be clear from the start. (In fairness, the government recognizes the problem and has signalled that it intends to start addressing it).

Other jurisdictions recognize this. The American SBIR and DARPA programs, for example, both fund multiple competing, high-risk innovations that address the same problem, expecting 80–90% to fail, and are recognized as cornerstones of American technological dominance.

Canada has Innovative Solutions Canada, which is a structural clone of the SBIR program, but we lack the scale of demand of the American government, which is central to its success,. While Canadian government departments are technically mandates to spend a portion of their budget on innovation through ISC, most departments missed those targets without consequence and instead continue to buy from safe, established foreign multinationals. Instead of penalizing under-spending departments we simply changed the targets, a pattern that has repeated itself several times in other under-performing programs. It remains to be seen whether proposed changes to ISC can overcome these issues, but they are certainly a step in the right direction.

Outside of ISC, the fundamental difference between the American model and the approach taken in Canada is focusing on defining the problem that needs to be solved, rather than detailing how the solution must do it. The rationale is the same as that which I discussed in the risk management section above: the government uses long, detailed Request for Proposals (RFPs) as a shield against risk. If the project fails but the vendor followed the exact specifications, nobody gets fired. The misaligned metric for success is process compliance, whereas procurement systems that effectively engage with emerging technology and innovation instead measures problem resolution and domestic economic value creation.

The solution, clearly born out in other jurisdictions, is the same as with the other two issues above: take a portfolio approach to procurement risk management, specifying problems rather than solutions, and funding multiple competing attempts to address it. Allow some to fail en route when it becomes clear that they are not going to deliver. By incrementally engaging with emerging technology as it progresses toward a solution, the cost of early failures is minimized while the chance of actually achieving a solution is greatly increased. The Canadian government as an anchor customer would go a long way toward keeping Canadian innovation at home.

A good example of such an opportunity is in the development of quantum sandbox to help retain our world-class quantum expertise and IP.

The Path Forward

In risk management, public servants are evaluated on fiscal responsibility, and a costly failure can end a career. The target naturally becomes avoidance of failure at the project level, which does not allow for public engagement with high-risk, high-reward innovation.

In federal tech transfer, the intention is to create a fair and impartial playing field. The target becomes avoidance of the risk of a lawsuit or accusations of favouritism or conflict of interest, which can only be achieved by not engaging at all. This leads to starving domestic startups, and IP developed in federal labs never making it off the shelf.

In procurement, the goal of the framework is supposed to be fair and transparent sourcing. This devolves into measuring process compliance rather than problem resolution, which ironically leads to favouring large companies, often foreign multinationals, who know how to fill out the paperwork over less bureaucratically-inclined domestic startups.

In foreign direct investment, we decry foreign exits without considering the context, nor what happens after the acquisition, painting with the same brush an exit that simply loses research IP before it can be developed and one that seeds the next generation of domestic companies. This leads us to avoid supporting our own companies for fear they will later leave, creating an incentive, if not an existential requirement, for them to do exactly that.

The people who administer these programs are doing what the metrics on which their performance is evaluated incentivize them to do, guided by perfectly rational goals. The issue lies with the metrics themselves, metrics that are embedded in rigid policy frameworks that punish exactly the type of risk-taking Canada needs to move forward.

This structural gridlock creates a market failure. Private venture capital flows to jurisdictions where public policies supports the best balance of risk against reward. Because Canada’s policy frameworks hinder technological innovation, private capital looks elsewhere and domestic companies defect early to places with policies better suited to supporting them.

In short, Canada’s innovation challenges are self-inflicted. Changing this must start at the structural level in the public sector, by shifting from project-level to portfolio-level risk management, from avoiding complaints to funding parallel and competing experiments, from specifying rigid solutions to articulating the problems, and from fearing foreign exits to creating conditions in which those exits can become the basis for the next generation of innovation. Only when the government acknowledges its role as an investor of first resort and a disinterested anchor customer for homegrown innovation will private investment follow and allow Canadian innovation to stay and build at home.

Learning from failure

Veterans of both the American and Canadian innovation ecosystems will often attribute the central difference to a single factor: how each approaches failures and exits. In the US, failure is celebrated as experience, and a failed founder can easily raise money for their next attempt. In Canada, the opposite is often true. In the US, an exit is the launch point for the next project. In Canada, exits, especially foreign exits, are viewed as losses.

While I’ve written in the past that Canada’s challenges with entrepreneurship and innovation are structural rather than cultural, I’ve come to realize that the two are inseparable. This shows up in Canadian risk aversion, which dictates that we bet only on outcomes we believe are "safe". This has two consequences: we miss the asymmetric upsides generated by outliers, and when failures do occur, the damage is severe.

In this article, I examine two high profile cases: the collapse of Northvolt in Quebec, and the acquisition of Skype in Estonia. By looking at the long-term and ongoing evolution of what followed those events, we can see that failures and foreign exits are only disasters if we fail to retain the resulting talent and capital. Canada must learn to offset downside risk by building a supportive ecosystem that recycles experience as a hedge against failure.

Northvolt: a tale of two ecosystems

The failure of the Northvolt battery plant in Quebec is well-documented. You can read some of the details here. In a nutshell, Quebec invested about $270M into a battery factory in partnership with Swedish company Northvolt. Northvolt ultimately collapsed into bankruptcy, wiping out the investment. Canada’s best-case scenario is a lengthy effort to recover a fraction of the original funds, which will certainly be net-negative.

In Sweden, the outcome looks very different. An ImpactLoop analysis tracked the Northvolt talent after the collapse. Among the former employees of the battery manufacturer they were able to track down, 20% have founded new companies, almost all of them in Sweden. 15 are building in the open while 8 more are in stealth mode. Many of the other 80% have been snapped up by deep tech countries both in Sweden and abroad. While there was some brain drain (some of the talent ended up in the US, UK, and EU), most of the talent stayed to build again.

The damage to Sweden was minimized because Sweden captured and recycled the “exhaust” of failure. The highly skilled talent that tried and failed to build Northvolt mostly stayed local, and many were enabled to try again. It is far too early to know to what degree the economic activity created by these new companies and the long-term contribution of the talent left behind by Northvolt will offset the cost of the failure, but the lesson is clear: by creating an ecosystem that enables second chances, Sweden ensured that Northvolt’s collapse was not a total loss, and created conditions in which, long term, it might even be net-positive. That the ecosystem supported the employees in their next attempt served as a hedge that offset some of the loss, and laid the foundations of a new generation of Swedish tech companies.

Re-evaluating acquisitions

Skype was founded in Tallinn, Estonia in 2003. It was almost an overnight success, years ahead of its time as compared to the modern equivalents born out of the pandemic. 3 years later, it was sold to American eBay for $2.6B, eventually finding its way to Microsoft, where it was finally killed off in 2025.

As Skype wound down, the founders and early employees (now known as the “Skype Mafia”) reinvested their new wealth and experience building a globally connected company back into the local ecosystem. They became angel investors and venture capitalists, funding a wave of startups that are now household names.

At the time, Skype was Estonia’s only global tech success. To put that acquisition in perspective, imagine Shopify being sold to the Americans tomorrow. As with most acquisitions of Canadian technology, such a thing would be mourned as a “hollowing out” of the Canadian tech sector. In Estonia, that acquisition became the foundation of a movement that, over 20 years, turned Estonia into one of the most vibrant tech ecosystems and digitally connected economies in Europe.

It’s true that foreign acquisitions of Canadian tech companies have historically done little to stimulate Canada’s economy. Skype proves that this is a problem arising from the structure of our ecosystem, rather than a fundamental truth of foreign exits. As with everything, context matters. A lack of domestic support post-exit dictates that it is not just the company that moves during an acquisition, but also the talent and capital that could otherwise be compensation for the IP.

Given the weight of our neighbours, some attrition through acquisition is unavoidable. It is what happens next that dictates whether the event is net-positive for Canada.

Changing the narrative

Sweden and Estonia are not anomalies: Israel similarly focuses on ensuring this recycling. If Canada hopes to correct its productivity challenges and drive entrepreneurship and innovation at home, it will need to embrace the idea that an outcome is only truly a failure if we fail to capture the byproducts of talent and capital to seed the next generation of Canadian innovation.

An exit is only a problem if the capital isn’t reinvested locally and if the value is not commensurate with the value of the IP sold. A failure is only a total loss if the talent leaves and the IP is lost.

When Canada punishes failure (or fails to fund the next venture of a failed founder), that talent moves to the U.S. or elsewhere, taking their hard-won experience with them. This dynamic partly explains why there are more Canadian-founded unicorns in the US than there are in Canada.

Investing public funds into foreign branch plants may appear low-risk, but because these operations are not fundamentally Canadian, they leave no foundation behind when they close. The tragedy of the Northvolt Quebec failure isn’t just the lost battery plant, it’s that Canada won’t get the consolation prize of a new set of homegrown tech startups founded by its ex-employees.

Changing this requires that we rethink our approach to support for early-stage founders to make Canada a place that they want to try to build twice, incentivize exited founders to reinvest their success into the next generation of Canadian innovators, and foster a culture that celebrates a failed attempt as a valuable experience rather than a black mark. We can never completely eliminate the risk of failure, but we can position ourselves such that when it occurs, it makes us stronger.

This week I interviewed Daniel Munro and Creig Lamb. Among their several hats, Daniel and Creig co-direct Shift Insights, where they work to “cut through complexity to provide clarity about the social, technological, and economic challenges and opportunities facing Canada”. In that role, they recently released “Northern Potential: Advancing Canada’s AI Ambitions”, a report that examines AI adoption in both the public and private sectors in Canada that is well worth a read.

Canada’s history with AI should be familiar to most readers: from leading the world in the research phase, to taking a backseat on bringing the technology to market, and now considering how best to adopt and use the technology in the context of a push for technological sovereignty after the fact. Daniel and Creig raise several key questions that Canada must answer as we work to adopt AI: asking what we hope to achieve, and why. They share insights into how Canada can begin to answer these questions as a guide to driving effective AI adoption in a way that is consistent with Canadian societal values, and make concrete suggestions through which the Canadian public sector can overcome practical challenges standing between Canada and the efficiency gains promised by AI. The values-based, human-centred approach they suggest in the report should be required reading for policy makers involved in any aspect of AI adoption.

While our conversation touches on several aspects of the report itself, we cover more ground, including issues of data sovereignty, intellectual property, and the “low-innovation equilibrium” in which Canada finds itself. The interview is not a substitute for the report, and I suggest reading both together to get the full benefit of their contribution.

Your email client will probably truncate this post. My key takeaways are presented at the end, so be sure to read the web version if you want to get the whole story. Many thanks to Sasha for taking the time to share his insights.

Interviewer’s note: Daniel Munro and Creig Lamb approved the final version of the section entitled “Interview with Daniel Munro and Creig Lamb” and had editorial input on that section, with the option to rephrase and expand on the ideas discussed in the interview without changing or removing any intended meaning. The key takeaways presented at the end are my own commentary, and do not necessarily represent the views of Daniel Munro, Creig Lamb, or Shift Insights.

Interview with Daniel Munro and Creig Lamb

KB: Tell us about yourselves and give the audience some context for where your perspectives are coming from.

DM: I do three main things. I’m co-director with Creig of Shift Insights, which is a policy research shop that examines the social, technological and economic challenges and opportunities facing Canada with a view to helping us become a more prosperous and just society. We look at a wide range of policy issues related to innovation, education and training, skills, and technology development and adoption.. It’s in that capacity that the two of us put together this report for the CSA Public Policy Centre. I’m also Director of Research and Innovation at Actua, which is a youth STEM outreach organization. We deliver STEM camps, clubs, and workshops to youth all across the country. And I’m a senior fellow in the Innovation Policy Lab at the Munk School of Global Affairs. I’m actually a political philosopher by training. The innovation and skills policy work came more recently, if 15 or 20 years ago could be called recent. That’s ancient history to someone Creig’s age. In any case, in the first part of my career I was a political philosopher and I spent a little bit of time in academia. I still keep the political philosophy hat in my back pocket and I keep one foot in academia thinking about innovation and technology ethics.

CL: My main policy hat is shared with Dan. I’m the co-director of Shift Insights and the chief economist. I come at it from an economics background primarily. I do a lot of the data analysis and all of those components. Before that I was at the Brookfield Institute which is now The Dais. That’s where Dan and I met and worked on a bunch of projects together. I was leading a lot of their tech innovation skills and automation research at the time. And in my spare time, I design lamps.

DM: That’s the humble way of putting it. The real way of putting it is that you’re a designer and entrepreneur focused on lighting solutions.

KB: Let’s start with a little bit of context on the report you published on AI adoption in Canadian public and private sectors. How did that come about, and what are the goals of the report?

DM: When the government appointed a minister for artificial intelligence and digital innovation and gave him the job of developing an AI strategy for Canada, Creig and I thought that given what we had done on innovation policy and especially technology adoption in both the public and private sector, it might be useful to distill what we had learned in the form of helpful guidance for the minister to develop the strategy. In talking with the CSA policy group, the scope expanded to offering advice for diffusion and adoption in both the public and private sectors, since our previous work has given us some insights into what does and doesn’t work in those areas. And so what was initially supposed to be something like a moderate-length letter to the minister with some helpful hints turned into a longer report that came out a few weeks ago.

Initially, we thought about releasing something shorter much more quickly, in part because it wasn’t going to be based on new research. It was going to be a curation of the kinds of things that we already knew that might be helpful. We actually anticipated a four to six week turnaround, but organizations move at the speed of organizations, and as we got into the work we realized something more robust would be more helpful, so it took us months instead of week and we produced a longer report. We’re grateful to the CSA group for supporting the work and shepherding the review, design, and publication with us. We think it turned out to be better than it would have been had we just thrown a letter up on our website.

CL: I would add that while the intention was for quick advice, we quickly realized that the pieces that we were summarizing and contextualizing are challenges that the government has been tackling for decades in some form or another. It remains relevant even in the new context of AI. I think even as different mandates and different policies come out, these will probably continue to be challenges that the government will be tackling in some way, shape, or form over the next decade.

DM: The core question that occupied our thoughts and organized the writing was that in light of difficult innovation performance in the private sector and a string of challenges with technology adoption in the public sector, what is the minister going to do differently such that we don’t get the same result that we’ve seen time and time again? AI has features that are different from previous technologies, but it’s similar enough as a technology, and in terms of the background conditions that drive or block adoption. We’ve seen this story before and we don’t want to repeat it. But we’ve got to do something different in order to get a new story. That was the orienting question for us.

KB: You suggest in the report that Canada has this window of opportunity to translate early leadership in AI research into sustained economic and social benefits. What do you think is a realistic niche in which Canada can lead in AI, given that we don’t have the capacity to compete head to head with the American hyperscalers and others in the space?

DM: Creig can agree or disagree with me, but I’ll start by saying that I share the pessimism. We’re not Pollyannaish about this by any means. We share the skepticism in that we’ve seen this story time and again. In this case we have moved about as slowly as we always move, so the window might be closing.

To zoom out a little bit, just to put it into context, when we’re thinking about AI in Canada, we need to make a couple of distinctions. We’re thinking about it in two ways. One is from a “tech-making” perspective, we might call it, and the other is a “tech-taking” perspective. Tech-making focuses on the technological innovation, commercialization, and sales side of things, and what can be done to spur better performance. The tech-taking side of things itself has two dimensions: adoption in the public sector for the sake of productivity and efficiency gains (as well as pursuit of key public sector values), and adoption in the private sector. So there’s actually three moving parts here.

We’re less pessimistic and less skeptical about the prospects for tech-taking and using technology, both in the public and private sector. We are probably a little bit more skeptical about the tech-making side of things. I do think that there might be some niche areas where we can offer things. One that really comes to mind is AI for supply chain management. Think about the Scale AI innovation cluster. We might ask whether we can develop ways of using AI to spot possibilities and challenges in global supply chains and then use that information to manage those supply chains better. We might also be able to export AI for supply chains solutions for use by other companies and countries. That’s an area where I think the window may be closing, but we might still have some opportunity to do things there - especially given ongoing disruption and changes in global supply chains.

I also think that there are probably opportunities to do niche things with a Canadian focus. Even if we don’t develop things that have global export potential, there are some things that we can do domestically around health technology. Given concerns about health data and privacy, “Canada first” solutions might be the way for health tech. You may not be able to export what you develop in that area, but there’s probably enough both technical know-how to do Canada-focused things, and political will that would be quite receptive to Canadian made solutions for health. There’s probably some defence related things as well.

CL: To bolster your point on the tech taking side of things, looking at it from a broad economic perspective, we’re really thinking about how to improve things like productivity with the goal of gaining the widest benefit in terms of wages, equity, distribution, etc. Canada has had a particularly deplorable track record on adopting technology writ large both in the private and public sectors, but there are some signals that AI with its ease of use and subscription-based models that require relatively low upfront investments may be able to break some of these trends. So we do think that there is an opportunity to facilitate the adoption of AI and promote diffusion in a more concerted way across the private sector that might look a bit different than some other forms of digital technology.

I think the opportunity, and where most of our effort should lie, is focusing on other sectors in which we have a competitive advantage or that are strategically important or that simply employ a lot of people and focus on making them more competitive through the use of AI. While I will always say that the development of technology in our homegrown tech sector is important we will never out compete the giants. There are places on the margin that we can grow, but I think really it is about helping the average firm recognize how they can compete more internationally and hire more people and just be more productive. Hopefully that wealth gets distributed in the form of wages and things like that.

KB: That leads into a tension that you explicitly call out in the report, which is the idea that the minister has a mandate to ensure Canada has the enabling infrastructure, data storage, compute, energy, etc., to use AI effectively. You acknowledge in the report and in the preceding discussion that we are probably going to be dependent on foreign hyperscalers for AI service provision, both in the public and private sectors. At the same time, there is a lot of talk about “technological sovereignty” as a guiding principle of policy development. What does sovereignty mean in this context, given our external dependencies?

DM: You’re asking a political philosopher to define sovereignty. Do you have two hours?

I’ll give you a really pithy answer. My understanding, or my way of thinking about sovereignty, centres on whether or not you can meaningfully shape your own future. When we’re thinking about meaningfully shaping our Canadian future, that doesn’t necessarily exclude all external influences or inputs. It just means that when you pull everything together, you need to be able to say that you had or have the power to make decisions about where you want to go. I think there’s a tendency to think that sovereignty requires absolute control over all components, products and uses, which is simply unrealistic given the reality of fragmented global value chains, how components are pulled together to produce products and services, and how no country can produce everything itself that it might require. But that’s not resignation to an uncomfortable reality. Relying on others for inputs can in many cases enhance our power to shape our own future, by allowing us not to get bogged down in things that we don’t do well. Ultimately, the core question should be: “can we meaningfully shape our own future in this space.”

That means that on some things, it may be okay to rely on the external hyperscalers. In other cases, it might not be okay to rely on external hyperscalers. It comes down to what kinds of data you’re using, where that data is stored, and the privacy requirements around it. It depends on whether someone can push you in a direction you don’t want to go because the control some vital component or input you can’t get elsewhere. In some areas, I’d lean more towards the “tech nationalist” approach – when it comes to health applications and health data – versus other areas where we’re simply crunching public data for economic reports or trends.

I think the second question that I would ask is simply “what is it that we want to achieve with AI or with any other technology”?

In the tech-taking space, we want to achieve efficiency, productivity, maybe some other goals like more accurate answers to questions that people have about government services. What are the tools that we need in order to do that, and can we maintain meaningful control over what the future of that activity looks like?

When it comes to tech-making, that’s where it gets a lot trickier. If we want to have homegrown firms that develop solutions both for domestic consumption and for global export, how do we maintain IP? What data do we draw on, and to what extent, if at all, can we use the products of the hyperscalers?

That’s how I would think about AI sovereignty, which is not an answer. It’s more like principles for how to think through what you want to do in any given case. I’m going to do that to you again on your other questions. I’m not going to give you an answer. I’m going to give you criteria.

CL: This is how we think about things: we come up with a framework and then send it out to the world. I would add, though, that I often wonder, from a public policy lens, how much we also impose our views, and views around things like tech nationalism, on individual firms, and whether they actually coincide with the views of the individual firms. Firms are probably mainly concerned with making a go at it and making a living and being competitive. The question of whose infrastructure they’re using probably matters less for the individual firm than the public sector writ large. So I wonder sometimes how much we impose those viewpoints on specific firms, and in doing so, to what degree do we put them at a competitive disadvantage in an industry in which they were already starting 10 steps behind.

That doesn’t answer anything, either, it just adds another question.

KB: I want to go back to a point raised by Daniel around intellectual property. Part of the minister’s mandate is to maintain broader societal values and democratic norms as we adopt AI. Large language models ingest massive amounts of data. They,, and for the most part ignore copyright to get it. On the other hand, if you’re going to build an AI system that has any kind of chance at competing with what’s out there already, you pretty much have to play that game. Is there a path to using LLMs that is consistent with tIP rights, or does this new technology require us to fundamentally rethink what copyright means?

DM: I’ll give you half an answer and tell you why I won’t give the other half answer. The half answer is that if we take an application or use-specific perspective on some things, I think there are ways to develop AI solutions that don’t necessarily violate copyright or expose intellectual property or personal data to risk.

I’ll make it more concrete. If you’re designing a chat bot for the Canada Revenue Agency that’s intended to answer questions about Canada Revenue Agency procedures, rules, and that kind of thing, you will be drawing on data that doesn’t require IP protection: CRA policies, procedures, and things like that feed into that. It could help answer questions that users might have about how to file their taxes or what exemptions would be permitted. So, on one hand, for certain kinds of applications, I think we can develop those without violating these other things. To be sure, the base model on which a CRA chatbot is built may have been trained on copyrighted data and thus have antecedent IP violations built in. That’s a problem. I’m not sure how you disentangle those things, to be honest. It’s certainly something that the government will need to keep in view.

I will say I lean more towards protecting copyright and intellectual property than I do towards changing rules simply to facilitate development of some new technology. That might be a personal bias. Creig is the second most creative and stylish person I know. The most creative and stylish person I know is my wife, who’s an artist. In the arts community, the attitude towards generative AI is deep skepticism. The pervasive view in the arts community is that AI is inextricably built on IP theft. And in those specific use-cases, I think that view is right. . I think both analytically, but also for personal reasons, I lean more towards the idea that, if there is a tension between these two things, between protecting IP and developing AI, we should protectIP rather than allow violations to facilitate the development of AI services and products. That might suck for AI developers, but we all have to innovate under constraints.

CL: I concur with that. I think this report came at it from the lens that what already exists is a given, that we can’t dial it back, and then considering what we can do in the current environment in terms of adding additional measures and protections. That’s the subject for a whole other report that I would be willing to dive into.

KB: You gave a few examples of applications, and the mandate given to the minister is to push towards efficiency and effectiveness in the public sector. On the other hand, LLMs match patterns and produce likely text, not necessarily correct text. How do you balance the drive for efficiency against the reality that AI systems hallucinate and have built-in biases? Is it possible, in the framework suggested by the guiding principles that you develop in the report, to identify the kinds of tasks that can be handed off to AI versus the stuff that should remain in human hands?

DM: This is the other question where I’m to give you two criteria rather than an answer.

In the public sector, when we’re thinking about whether or not to adopt AI to make some government function or service more efficient, yes, there’s the concern about whether or not the technology will hallucinate, and produce inaccurate answers with an enormous amount of confidence, which is actually the real problem. It’s not just that the outputs are wrong sometimes, it’s that they’re wrong and there’s no function for admitting they’re wrong. So one principle or a question that I would ask is whether, for any given task, the use of AI is more or less accurate than the current way of doing that task. Again, take the CRA example. Is a CRA chatbot any more or less accurate than getting a CRA agent on the phone? Because we know they’re not perfect. Let’s say that 4 or 5% of answers from a chatbot are inaccurate. If it turns out that this is more accurate than the people who were doing that job, we should be cautiously OK with that, and deal with reality rather than perfection, given that existing systems are also imperfect.

There is one caveat, before I give you another principle. Human-centered approaches are better at learning from error and improving than chatbots. If you tell a human agent that they got something wrong, they have a capacity to integrate the better answer into the mix pretty quickly. By contrast, getting a chatbot to acknowledge and learn from errors is a much more involved process. And if it produces a better answer later, it’s still not doing that because it has selected the right answer as the right answer, it selects the right answer because it’s the most likely answer, or it’s been given a specific instruction to produce a specific response even if its underlying model doesn’t recognize it as the most likely response. In short, I think the human capacity for learning from error is still superior to that of chatbots.

The second principle for thinking about this is to reject the idea that one is replacing the other and think about whether there are ways that the technology can complement the existing human approach to collectively produce a better outcome. To beat this example into the ground, maybe what you get is a CRA agent who answers a question, but before actually giving it, checks it against what the chatbot would produce. If the answers align, then your likelihood of accuracy is probably much higher. If they don’t align, then the CRA agent has to either think about whether they’re wrong or whether the chatbot is.

The funny part about that is that it would be a lot less efficient, but it might be more accurate. So this is one of these cases where you have to step right back and ask what it is that we want to achieve through the adoption of technology. We often say we want better efficiency, but we might also want to improve public sector accuracy. Those might not always go hand in hand.

CL: When you were saying in terms of supplementing existing human tasks and increasing efficiency, we’re really talking about fundamental automation, and that is how we’ve gained efficiencies and productivity growth throughout the entirety of technology adoption throughout all of history. There’s a famous example of the ATM and the bank teller where everyone thought the ATM was going to replace the bank teller completely, but all it really did was remove the transactional, basic, routine-oriented handing of cash as a job task for the bank teller. Their job became actually more complex and value added in terms of customer relations and financial advice and things like that. Automation allows people to remove some of the routine things and do tasks above and beyond what technology can do, which overall leads to efficiency gains. The more we can see people eliminating some of the routine tasks that would take them all day so that they can do more complex things that day, the more we will see efficiency gains throughout the government.

I think it would be a useful exercise for people to go through within government and look for areas where the most efficiencies can potentially be gained with like an eye to some quick wins to gain momentum with the least amount of potential risk.

I think we cannot separate the skills question from this area. As we’ve learned with AI, and the more I talk to folks who are using AI and employing AI within their companies, the more I realize it actually takes quite a bit of labor to verify the output and check and ensure that hallucination hasn’t occurred and actually understand the mechanisms, to some degree, of how AI is working, how to ask it questions, and how to query it properly. I think there’s going to have to be a lot of effort put toward building that skillset within the government. That’s been a shortcoming of the government for a long time.

DM: I’ll add one thing to not the last thing that Creig said, but the one previous, riffing off the example of introducing ATMs and how that actually allowed tellers to move up the value chain. (James Besson, Learning by Doing). I 100% agree that when done well, you introduce a technology that can automate the lower value tasks and thereby liberate employees to do higher value tasks. This is good for them, good for the business.

Here’s where I’m going to get myself in trouble: I’m not convinced that that’s the perspective or orientation that the government is taking around AI. What we’re seeing is an AI strategy at the same time that we’re seeing an attempt to reduce the size of the public sector. There’s this hyper focus on the number of public servants we have, or the ratio of public sector employees to population. And there’s a sense among many people that the number is too high or the ratio is out of whack, so we need to fire a bunch of civil servants. My sense is that the government isn’t thinking in the sort of James Besson “ATM-moving-employees-up-the-value chain” way. They’re thinking about how AI can fill the roles of public sector workers that they want to take off the payroll, and that concerns me.

As we note in the report, the public service has many values that it must uphold. These include respect for people, respect for democracy, integrity, stewardship, excellence, equity and accessibility. Reducing the size of the public sector by adopting AI might get you some efficient gains or cost savings. Maybe. Big maybe. We also need to think about the impact on these core values. For what it’s worth, upholding these values likely requires a lot of inefficiency and redundancy. The public sector isn’t the private sector and we shouldn’t treat technology adoption in these sectors as equivalent processes.

CL: The automation side of things is kind of where I got my start in public policy and innovation research. I took a historical perspective on this, and throughout history, the most successful and long lasting versions of automation have been labor-augmenting, not labor-replacing. I think looking at technology as augmenting human capacity rather than replacing it is actually the smartest way to do it, and clearly the most ethical, and that’s the most important piece, but even if you’re looking at it from the perspective of pure efficiency gain, you should think about augmenting tasks, not replacing entire jobs.

KB: Let’s dive into the skills question. Clearly there is a difference between the stated intention and the approach that’s being taken to public sector adoption of AI. Is this the result of missing skills in the government to properly utilize this technology? What gaps do you see?

DM: I think Creig can probably speak to this better, but I’ll take a swing at it anyway. With respect to using AI in the public sector, there’s obviously some skills required in order to do that. In the report, we lay out three categories of skills that need to be considered.

One category is technical skills: you need people who have the technical and technological skills to develop, implement, and maintain AI solutions.

Then you need user skills. To go back to our CRA example, the CRA agent might not have to be an expert in how the chat bot actually works, but needs to be able to make the right queries, offer the right prompts, and have the capacity to critically evaluate whatever comes out the other end.

Finally, you need management skills: the skills to properly manage technology transformations, which is partly, if not largely, what a lot of this is about. Management skills to procure solutions, to manage the transformations, to ensure that employees have the other categories of skills, and things like that. So you need technical skills, user skills, and management skills to do all of this right.

One of the things that we’re concerned about is that at the same time that the government has shortages, especially in the technical skills side of things and to some extent on the management side, they’re actually cutting the public service. The Treasury Board says 30% of digital roles in the government are currently vacant. There’s already a technical skills gap. At the same time that they have those vacancies, they’re letting people go. What I’m concerned about is the signal that sends to people who might fill those roles that jobs in the public sector are precarious. The government already faces challenges around whether they can pay the same rates to technical people as the private sector. If you’re on the technical side of things, you might have good software skills, and you’re thinking about where to go. If the government says they need some technical people, but you’re also reading that the government is just getting rid of people, seemingly left, right, and center, why would you go work for the government? My concern is that they’re almost shooting themselves in the foot here, where stated need does not align with the signals they are sending.

CL: I think that’s a good point. You’ve seen it time and time again. As the government increases its focus on sort of digital technical talent they’re increasingly competing with the private sector. What’s the value proposition for people with either highly technical digital skills or just general digital literacy, who could work in all sorts of private sector roles that would pay more, probably have more flexible hours, all sorts of benefits, maybe even in cities that they want to live in or remote. So why would they work for the government? Part of the answer is job security, but if they’re sending the signals around a lack of public sector job security or a shrinking workforce, that undermines the value of that component.

I think the management side is another really big piece, and building up skills around procurement and oversight for digital transformations. We’ve seen so many challenges with respect to that. How can they improve their capacity and skills in areas that enable them to both identify areas where digital transformation should happen and then manage the procurement and development and diffusion of the results.

KB: Your report discusses how Canadian firms have been stuck in a “low innovation equilibrium”. Can you elaborate on what that means? Given that this issue predates the recent AI explosion, has the situation been changed at all, or could it be changed, by AI?

DM: My first experience with the phrase “low innovation equilibrium” came from a paper written by Peter Nicholson in which he tries to explain why business innovation is so low in Canada. His answer to that question is that businesses are only as innovative as they need to be, because, he says that a lot of Canadian businesses have been able to maintain reasonable profits without having to innovate. Canada has the conditions, whether it’s market conditions, the cost of labour, or what have you, in which they’ve been able to maintain reasonable levels of profit without innovating. If you can maintain profits without innovating, why rock the boat? Firms simply don’t have any incentives to innovate.

Creig and I take this a step further. We have a conceptual model that breaks a couple of these things apart. We agree with the Nicholson line of thinking, but when we think about why firms do or don’t innovate, one side of the equation is whether or not they have incentives to do so, and the incentives in Canada tend to be weak. There is that reasonable profitability. There’s weak competition. If you’re not facing a lot of competition, why would you innovate? So there’s weak incentives.

The other side of the equation is limited capacity. Even if you’re a firm that wants to innovate you might lack the resources: financing, expertise, etc. In the past, a lot of government efforts to spur innovation have focused only on that limited capacity issue, for example by offering financing or tax credits or expertise around entrepreneurship and digital technology adoption or whatever. I think this is changing, but I think in a lot of cases, government policy doesn’t spend a whole lot of time thinking about the incentive side, or at least if they think about it, they haven’t taken any sort of strong measures to address it.

There is some noise around competition and improving the competitive environment, but the actual steps to do that haven’t really been as robust as I think they need to be. My worry is that to the extent that AI might contribute to innovation and productivity , a lot of firms are facing these weak incentives and in some cases, limited capacity to actually do things. If you want to spur adoption and innovation in AI, you have to begin thinking about improving those incentives before addressing the limited capacity issues. Focus on the drivers before we focus on the supports. We haven’t done that in the past, and I think we’re making that mistake again with AI. We’ve used the same conceptual framework for a number of things that we’ve done.

CL: While their AI has a particularly strong hype around it, the available data on adoption would suggest over the past few years that AI isn’t much different in terms of incentives and capacity challenges. We’re facing similar issues in terms of adoption. Adoption is relatively low and dispersed across the select few industries and large firms, those with the incentives and capacity to do so, so it doesn’t feel particularly different than other versions of digital technology.

DM: With one caveat though. When you look at the Canadian survey of business conditions, across the economy, 12% of firms say that they have adopted or will be adopting AI over the past 12 or next 12 months (which is up from 6% so it’s accelerating, but it’s still just 12%). This is what you get when you ask people who are authorized to speak about what firms are doing. When you talk to employees, though, the numbers are over 50%. Officially, 12% of firms might have plans and strategies and be thinking about this intentionally, but meanwhile something like half of employees in the economy are using AI, whether their firms are on board or not. This “shadow AI use” is a source of risk, obviously, so there’s actually a funny thing happening with AI, or at least certain kinds of AI, where firms are as conservative as they have always been with every other digital technology, but employees seem to be racing ahead in ways that we haven’t seen with previous technologies.

CL: This is a really good point. We’ve also seen a corresponding thing that does change the incentive structure. Companies are realizing that this is happening and are deciding to permit the use rather than developing a more deliberate strategy. The incentive is to let adoption happen by itself and just pay for the subscription or whatever else needs to happen to make it possible. That’s an interesting point of differentiation from previous technologies.

KB: In the report, you allude to fragmentation in the approach to public sector AI adoption: you identify vertically siloed departments, thousands of legacy systems, and poor application health, and you suggest a centralized body as a means to deal with it. What drives that fragmentation, and what does the centralized approach that you suggest look like practically?

CL: I think what drives the fragmentation is just the nature of the bureaucracy and the risk aversion and lack of communication and all of these sorts of bureaucratic limitations and rules. I have personally noted, on projects where I was working with two departments, that often government departments that are working on very similar things don’t know anything about the efforts of the other.

I think another piece is just the size and the mandates of these departments and the number of things that they’re doing. It’s really difficult for them to continue to keep on top of what everyone else is doing and coordinate and consolidate efforts.

There also tends to be a tendency to reinvent and build the same thing over and over again, as opposed to checking who’s doing what and partnering.

DM: For context, I think the fragmentation in the public sector predates any technology adoption. I mean, that’s just a feature of bureaucracies that are oriented to different functions and given different lines of power. So the fragmentation predates AI. The fragmentation or differences in technology adoption and systems and things like that is just a reflection, a symptom, of that deeper fragmentation.

The upshot is that what you get is six different departments that are trying to do the same thing end up with six different ways of doing that same thing because they don’t talk and because they have their own unique preferences about what exactly it is that they want a technology to do or a solution to be, and so you end up with these thousands of legacy systems that are different across the different departments.

An AI strategy isn’t going to solve the underlying problem of public sector fragmentation. Our hope is that if you can create or establish some kind of central body that will support and incentivize communication across departments, across ministries on uses of technology that appear to be similar, that you might get something that is a little more consistent across the different parts of the public sector. There is a suggestion about central coordination, information sharing, shared systems, and procurement of solutions that can be used by different parties in the report where AI is concerned. There’s a hope that that kind of thing can be done, but we’re adults about this. We know that this isn’t going to address the underlying problem. That’s a much, much bigger problem.

CL: You’re right. I do recognize that in a lot of instances, funding mechanisms for departments drive that kind of siloed approach. I also understand that there are different privacy issues across departments that require these sorts of siloed and fragmented legacy tools and systems, or at least explain this kind of thing. I think it is the hope that a central body could cut through a bit of that noise and identify common areas and common issues across departments and identify certain areas where it makes sense to adopt AI and ways to mitigate the risks that are common across departments, allowing insights from one department to inform another.

The issue is that we’ve also seen this happen and seen this done a number of times with central bodies and it often doesn’t play out in the way we think it will. Sometimes we end up with something that lacks the teeth it might need to be effective, and so it ends up being ignored.

KB: It strikes me that something that has an overarching awareness and oversight of all of the various is an ideal use case for an AI system. I’m curious if that’s something you thought about: is there a path to having an AI system actually do the convening work, or would we just be creating an Ouroboros of fragmentation?

CL: I don’t know why I didn’t think of that. It would be great for the central body to use AI.

DM: I think AI might be able to help with this, but having a well-staffed central body with the right mandate and authority to develop policies, share ideas and best practices, coordinate procurement and implementation, is the key piece. I can think of a couple of things that are already in place that suggest some precedent or pathways for a central body. We’ve got the Canada Digital Service. They should be a candidate for being part of something like this. But they’re wildly understaffed. They will do a project here and there and they can’t keep track of the whole. If you overlay some kind of function that has that sort of broader perspective across the whole public sector and not just individual projects that might be helpful.

You’ve also got the Treasury Board, which has developed the policies on automated and algorithmic decision making, so it’s not like it’s an idea that comes out of nowhere. There are things that could be transformed into this role. Maybe AI becomes part of this as well to help with fragmentation – but I think we need the agent and authority before we bring in the technology to help.

KB: Your report talks about Innovative Solutions Canada and compares it to the SBIR program in the US. This is a comparison I’ve made in other contexts. What creates the divide between the technological driver that SBIR has been for the US for the last 40 odd years versus the relatively lower impact that ISC has had in the Canadian ecosystem?

CL: Kyle, I know you’ve written about this. I like your take, so I’m going to mirror back some of your takes to you. I have a few things that I will add to what you’ve said.

The Innovation Solutions Canada was obviously modeled after the SBIR and there have been recommendations to adopt an SBIR-type of program floating around, both provincially and federally, for a long time. SBIR is one of the mainstays of successful innovation policies out there, along with DARPA and others.

You can see that they replicated some of the foundational principles with Innovation Solutions Canada. 21 departments are mandated to spend 1 % on procurement and R&D. As you’ve seen, these departments were consistently underspending by two thirds of their actual mandate. In 2024 the budget got slashed to match what departments were actually spending to about a third of what it was before. ISC didn’t really have the teeth of the SBIR, which is another kind of pre-commercial procurement where departments across the U.S. that spend $100 million or more on R&D have to spend roughly 3% of their R &D budget on small and medium sized enterprises.

I think there are three explanations I have for this. One is just fundamental risk intolerance. You can just see in governments across Canada that there’s just a lower tolerance for the kind of risks that comes with pre-commercial procurement and working with small innovators. You can see that in the kinds of companies that the Canadian government works with and our preferred vendor lists. They really skew toward major corporations that handlea wide array of government activities and can provide a turnkey solution. Our ability to work with one-offs and tolerate project failures is fairly low. I think there’s a bit of a cultural thing and a bit of a government culture thing that would need to be addressed somehow.

There’s also the issue of governance of the SBIR versus the ISC. Why would a program that’s being mandated to spend a certain amount not actually achieve its mandated spend. If you’re being forced to do something, why would you not do it? Why can’t it be enforced? I think this comes down to both a governance issue and a capacity issue.

Canadian government departments just have very low capacity for even basic things like moving away from “waterfall” procurement to more more agile procurement. So working on pre-commercial procurement with smaller firms on risky projects is not within the realm of knowledge and expertise of most departments. The string of big failures on the procurement side of things is kind of evidence to this.

It’s not like the US doesn’t have this issue, but you can see a slightly different governance structure: ISC is a central body that’s mandating government departments. SBIR has that central body, but then it has 12 other bodies within individual departments that manage and facilitate the actual guidance and oversight of the actual day-to-day of the SBIR implementation within the department. The DOD, the Department of Health, etc each have their own SBIR bodies that help facilitate this. I think that helps these departments actually work with smaller firms and commit to spending on the pre-commercial procurement.

You would have to not just mandate. You have to have these disperse networks of people with expertise embedded within the organization working on a day-to-day basis with the people that are managing their programs and the projects and the individual procurement to actually reach the numbers that you want.

And finally, as you’ve pointed out, there is the issue of scale. SBIR funds more than 5,000 projects in phase one and phase two. ISC funded something like 30 in the same timeframe. ISC is 1% of R&D spend and SBIR is 3.2%.

Altogether, ISC is a sort of “uncanny valley” type of program where on the surface, it looks like it was the SBIR, but as you start to dig, you realize it is not, and the differences turn out to be really important.

KB: Is there anything I should have asked you but did not?

DM: The only thing that I’d add or emphasize, that we say in the report, is that we’d really like to see not a technology first strategy on AI, but a values first approach. Rather than taking this technology and trying to find places where it might add value or advance something that we care about, instead ask “what is it that we want to achieve, and to what extent and how can this technology help us do that?”

Whenever people ask my friend and colleague, Danny Breznitz, what Canada’s innovation policy or strategy should be, he always says that the first thing we need to do is answer the question “what do we want from innovation?” Only then can we talk about an innovation strategy. I think that applies here as well. What is it that we want? What is it that we want to achieve?

We’re going to disagree about that, obviously. We have different values, we have different priorities. But once we come to some kind of answer or conclusion about that in the public sector and in the private sector, only then can we figure out what role AI could play in getting there.

A values-first rather than a technology-first approach to this is the way to go.

CL: That’s a really good point. Even on the low innovation equilibrium piece, I think sometimes we assume that it’s a problem because people think that innovation is intrinsically good. They want to bolster their R&D numbers so that they look good on an international stage or something. But if the end goal is to have people earning incomes and companies earning profits to facilitate those incomes, if that’s happening with or without innovation? Does it really matter?

DM: Innovation is just a mechanism. It’s an instrumental good. We have to ask the more fundamental question about what it is that we want to achieve, and then figure out how innovation fits into that.

Key Takeaways

The report by Shift Insights, and the commentary in the interview above, paints a complex picture of AI adoption in Canada. Aside from the usual challenges of fragmentation and siloed attempts to move forward that characterize pretty much all of Canada’s attempts to innovate, the core of what I took away from our conversation was a need to take a step back and answer a few fundamental questions before trying to do anything else.

What are we trying to achieve?

Daniel describes himself as a political philosopher, while Creig is an economist. It’s a powerful combination: throughout their report and this interview, they use a values-based framework to inform their recommendations, grounded in the realities of the incentives that dictate behaviour. Instead of prescribing a particular solution to Canada’s AI adoption challenges, their approach is to ask thoughtful questions that help us understand why we care about AI adoption in the first place and how the incentives might be misaligned, and only then to make concrete recommendations as to what needs to be done. It is an approach that I deeply appreciate, and use in my own work anywhere that systemic change is part of the path forward.

While this approach shows up throughout both the interview and their report, it appears most explicitly in our discussion of sovereignty. There is a huge amount of commentary making its way through the innovation space on what it means to have technological sovereignty and data sovereignty, and while these concepts seem straightforward on the surface, they are fairly difficult to define in practice. Daniel gets to the heart of what sovereignty means in the context of AI in just two questions:

“can we meaningfully shape our own future in this space?”

and

“what is it that we want to achieve with AI, or with any other technology?”

In other words: part of the reason for the difficulties Canada faces in its AI adoption journey is because we have not agreed on what is important. Daniel makes clear that he does not necessarily have answers for these questions to suggest; rather, that these are “principles for how to think through what you want to do in any given case.”

This idea generalizes far beyond AI, a lesson that I learned more slowly than I care to admit. Early in building my own company, mentors and advisors tried hard to educate me on the importance of clearly articulating “mission, vision, and values”. As a physicist who had not yet made the transition to business thinking, I mostly ignored their advice as tech-bro nonsense. In the end, it turns out I was wrong, and the tech-bros were right (broken clocks, etc.). Daniel and Creig’s questions invite us to articulate a vision (what we want to achieve long term), a mission (how we plan to get there), and values (the principles we agree are important to uphold in making decisions en route). Only once we have agreed on what these are does it make sense to decide how to go about adopting AI, or building Canadian data sovereignty, or, frankly, almost anything that requires significant change.

It is a framework that is particularly effective for complex decision-making, since values, in particular, provide guidelines that allow us to make decisions with ambiguous implications, secure in the knowledge that the decision was made with purpose and that we have guardrails with which to course-correct when things get off track enroute.

Shaping our future

I am personally skeptical of Canada’s ability to lead in the AI space, and have expressed as much in contributions to the innovation discourse. As far as I can tell, AI is just the latest in an often-repeated story in Canada: we contribute foundational, groundbreaking research to the world, and then sit back and watch as someone else makes it practically useful.

That being said, Daniel and Creig make a strong case that there are still opportunities for AI to create value for Canada and to direct our own future in the space, even if those opportunities may not involve setting the global agenda. They see models that are purpose-built for Canada as being an important area of focus, built using proprietary datasets that are specific to the society they serve. Health data is a key example, as is anything that similarly depends on sensitive, personally identifying data for an associated AI model to be useful:

“[T]here are probably opportunities to do niche things with a Canadian focus. Even if we don’t develop things that have global export potential, there are some things that we can do domestically […]. Given concerns about health data and privacy, “Canada first” solutions might be the way [….]”

The suggestion from the interview is to focus on models trained on Canadian data, geared toward solving domestic challenges.

While the suggestion is sound, it reinforces the importance of addressing issues of control over data that have plagued the space to date. Our ability to shape our future in AI hinges on being able to control the data on which that AI is trained.

Challenges around data sovereigty go beyond personal information, however. As I have previously contributed to writing, our ability to shape our future depends on control over our intellectual property, and AI does not have a great track record of respecting it. A key issue that arises when considering Canadian specific data is the need to fine-tune existing models rather than training them from scratch, given the volume of data involved. Even if the data that we use to train a Canadian-specific model is managed in a way consistent with our values, the base models almost certainly have not been. Per the interview:

“the base model […] may have been trained on copyrighted data and thus have antecedent IP violations built in. That’s a problem. I’m not sure how you disentangle those things, to be honest. It’s certainly something that the government will need to keep in view.

I will say I lean more towards protecting copyright and intellectual property than I do towards changing rules simply to facilitate development of some new technology.

Despite this preference, Creig takes a pragmatic view:

“[T]his report came at it from the lens that what already exists is a given, that we can’t dial it back, and then considering what we can do in the current environment in terms of adding additional measures and protections.”

Is productivity the end goal?

Canada is stuck in a “low-innovation equilibrium”, an idea that has been around for some time. Unlike most commentators on the issue, however, Daniel and Creig do not take for granted that an innovative Canadian economy and the associated productivity are the end goals, asking that we go one step further and first ground ourselves by articulating why this matters. As Creig puts it:

“I think sometimes we assume that [low levels of private innovation is] a problem because people think that innovation is intrinsically good. […] But if the end goal is to have people earning incomes and companies earning profits to facilitate those incomes, if that’s happening with or without innovation, does it really matter?”

Daniel echoes the point:

“Innovation is just a mechanism. It’s an instrumental good. We have to ask the more fundamental question about what it is that we want to achieve, and then figure out how innovation fits into that.”

Especially where the public sector is concerned, the answer to these questions has practical consequences for what it means to adopt AI at all. Daniel and Creig observe that we need to go through an additional layer of reflection on what we want, since there are tradeoffs to be made even after we have decided to proceed with AI:

“[Suppose a CRA agent] answers a question, but before actually giving it, checks it against what the chatbot would produce. If the answers align, then your likelihood of accuracy is probably much higher. If they don’t align, then the CRA agent has to either think about whether they’re wrong or whether the chatbot is. The funny part about that is that it would be a lot less efficient, but it might be more accurate. So this is one of these cases where you have to step right back and ask what it is that we want to achieve through the adoption of technology. We often say we want better efficiency, but we might also want to improve public sector accuracy. Those might not always go hand in hand.”

In other words: efficiency may come at the cost of accuracy, and vice versa. There is not necessarily a “right” balance, and even if there is, it will be highly context-dependent. A clear goal and values to guide us toward it are required to arrive at an answer that will serve Canadians, and getting these right has real consequences:

“There’s this hyper focus on the number of public servants we have, or the ratio of public sector employees to population. And there’s a sense among many people that the number is too high or the ratio is out of whack, so we need to fire a bunch of civil servants. […] They’re thinking about how AI can fill the roles of public sector workers that they want to take off the payroll, and that concerns me.

Only when we come to a consensus on how we want AI to benefit Canadians will we be able to move forward with AI adoptions, grounded in the values we have collectively decided are important. I do not think that blindly chasing productivity for productivity’s sake is the answer. The report by Shift Insights makes several recommendations on how to approach the necessary questions that should be required reading for anyone in the public service that is involved in AI adoption efforts.

Many thanks to Daniel and Creig for taking the time to share their insights, and for their contribution to the national conversation.

This week I interviewed Sasha van Katwyk, Senior Health Economist and Managing Principal at the Institute of Health Economics (IHE). As Sasha describes it, “IHE is a not-for-profit research institute that […] sits at the intersection of academia, government, and industry”, providing insight to policy makers and innovators alike on the value and potential of new health technologies.

This interview is the second instalment in a series on “purposeful research”, focused on finding ways to identify research projects that address real societal needs. My goal in this interview was to learn how health innovation is valued and evaluated by the different stakeholder groups in the adoption process, and to better understand what tools can be applied early in the innovation process to focus healthcare R&D investment where it can achieve the greatest impact.

Sasha’s unique perspective at the intersection of academic research, private sector health innovation, and healthcare policy will be of interest to anyone operating anywhere in that continuum.

Your email client will probably truncate this post. My key takeaways are presented at the end, so be sure to read the web version if you want to get the whole story. Many thanks to Sasha for taking the time to share his insights.

Interviewer’s note: Sasha van Katwyk approved the final version of the section entitled “Interview with Sasha van Katwyk” and had editorial input on that section, with the option to rephrase and expand on the ideas discussed in the interview without changing or removing any intended meaning. The key takeaways presented at the end are my own commentary, and do not necessarily represent the views of Sasha van Katwyk or the Institute of Health Economics.

Interview with Sasha van Katwyk

KB: Tell us about yourself and give readers some context for where your perspectives are coming from.

SVK: I’m an economist by training, specializing in risk assessment to inform social protection policy. I use simulation modelling to allow for the combination of disparate evidence sources to inform economic decision-making and forecast outcomes. I first used that skill set while working in South Africa to do simulation models for cash transfer programs; think welfare payments, anti-poverty initiatives, and taxation policy, things like that. When I moved back to Canada, I got into health research because there’s a growing area of simulation modelling in health economics. That’s where I first got involved in health policy, about 10 years ago. I worked within a research institute that was academically oriented, doing in-hospital trials and hospital-level policy-making. I then moved to the Institute of Health Economics (IHE) where I’m working on policy analysis and research for provinces, innovation investment centres, and formulary committees, all asking questions about health system capacity and decision analysis.

KB: What does IHE do, and how do you fit into it?

SVK: IHE is a not-for-profit research institute that we often say sits at the intersection of academia, government, and industry because those are the three sets of collaborators that we usually work with. Our work is a mix of grant-funded research and service-specific consulting. We also provide independent third-party analysis and advice for health policy and investment choices.

In my role as Managing Principal, I’m involved in a couple work portfolios; Health Systems Modelling, where our clients are either provinces or specific health condition foundations, Innovation Support, where we work with SMEs and independent researchers to define their value proposition and identify the best evidence generation strategies to build their economic case for system-level adoption, and Methods Advancement, where we just nerd out on the latest mathematical modelling methods and service delivery strategies in order to keep the organization at the forefront of quality analytics and delivery.

KB: Sitting at the intersection of those three stakeholder groups, what kind of disconnects and misunderstandings do you see in terms of how they are approaching health policy?

SVK: Everybody has their blind spots, which lead to misunderstandings and incorrect assumptions about what others’ priorities and needs are.

With researchers, the main blind spot is that they don’t understand the set of constraints under which policymakers operate. Researchers tend to present problems and solutions in a way that’s very “blue sky” policy-oriented, that imagines system-wide change. Even when the changes seem relatively modest to them, they’re often not aware that implementing what they are proposing—what they identify as the optimal solution—would require coordination and collaboration across many agencies within a government body and would consume an enormous amount of political capital to achieve. Because researchers don’t fully appreciate the policy environment they are working within, and the political and operational pressures within government agencies, there’s a disconnect between how they frame problems, how they present potential solutions, and what is actually practical from a policymaker’s perspective.

From the policymaker’s perspective, in my experience, they often don’t have access to the operational or technical details of the problem and aren’t operating with a deep understanding of the environment they are regulating. They don’t know these problems in depth. Often this is because, at the level where decision-making actually happens, policymakers think in terms of the policy levers at their disposal. If a problem can’t be addressed by pulling one or some combination of those levers, it’s very hard for change to happen without legislation, and now you’re demanding a lot of the agency or department you’re engaging with. As a result, there is a strong institutional gravity toward existing options. If the problem is not solvable through a relatively straightforward policy lever, it is difficult to sustain policymaker engagement.

For innovators, especially independent or small-scale groups, I think the main blind spot is that they don’t understand the decision-making process and what policymakers are using to assess the value and viability of a technology that they might want implemented. In part that’s not their fault, but it’s still a significant blind spot. Innovators often envision that the system works one way, but in reality it operates very differently. As a result, innovators are often unaware of what actually drives meaningful change at the system level.

KB: In IHE’s role at the intersection of all those blind spots, what have you found to be effective at convening all of those viewpoints and getting them onto the same page?

SVK: We are most successful when there is alignment with policymakers who know they want to resolve a problem and come to us with an understanding of the levers at their disposal, and are looking for solutions that fit within those levers. Often, we have to get them to that place before any math can actually happen, and it can be where the real intense work has to happen. Once we have this clarity, it is easier for us to then look at the research, do the deep dive, develop a nuanced understanding of the nature of the problem, bring in the researcher perspective on the true nature of the problem and the available solutions, and assess where those solutions fit within what is legislatively possible, what is practical, and what is financially feasible at the policy level.

We’ve definitely produced analyses that are missing one of the major actors, and we’re trying to speak to that actor as part of the process. That can be useful, but it’s much more challenging if there isn’t already a high level of clarity about what is possible, who is already invested, and who is willing to commit capital to the effort. When you don’t have that buy-in to actually implement change, it makes the whole process significantly more difficult.

KB: Walk me through the decision-making process that occurs when a policymaker is evaluating a novel health technology. Where are these policymakers sourcing problems, and once a possible solution is identified, what does the evaluation process look like?

SVK: That’s a really big question. There are a couple of caveats that need to be made as part of answering it. One is that not all health technologies are pitched as a solution to a clearly defined problem. It is often the case that there are technologies that have industry champions or policy-oriented champions who have identified a specific health technology and have the necessary network within government to drive a decision forward somewhat independent of a formal problem-identification process. That absolutely happens. So an important caveat is that when policymakers already have a solution in mind, the process is very different than when they are recognizing a problem, or appreciate that a problem exists but don’t necessarily know what the solution is or what technologies could be implemented. It can also be the case that even though a particular problem has not been formally identified as a policy priority, an innovator approaches the government with a health technology solution. You’re running into very different versions of the government decision-making process in each of those cases.

The other caveat is that when we say “health technology,” we need to be more specific. There are different processes for different kinds of health technologies. If we’re talking about pharmaceuticals, for instance, there’s a relatively well-established federal process for regulatory approval and health technology assessment. That process then filters down to the provincial level, which is where coverage and reimbursement decisions are actually made. Broadly speaking, it involves a national-level process of going through Health Canada for regulatory approval, then the Canadian Drug Agency for clinical and health technology assessment, and then the pan-Canadian Pharmaceutical Alliance (pCPA) for price negotiation, and only at that stage do provinces begin making reimbursement decisions.

There are other technologies—for example, digital technologies or medical devices—that might or might not go through Health Canada, depending on how they are classified. I’m not going to pretend to be an expert in how different health technologies are classified, because it can be a gray zone in some cases.

Another unfortunate complication in answering your question is that the means by which decisions are made about whether and how to adopt a health technology are often quite ad hoc. Every province has its own process, and the level of development and maturity of that process is highly variable. In some cases, the decision is not even made at the provincial level; instead, it may be made at the health system or regional health authority level, with organizations making adoption decisions independently. Even when it is a provincial decision, the kinds of evidence being presented and what the pathway toward adoption looks like can vary substantially, and sometimes the process is not especially well defined, even within a given province or territory. It might not even be clear to policymakers themselves what the exact process is in a given case.

Basically, every part of your question has a number of asterisks attached to it. It depends what you mean by health technology, it depends what you mean by decision-maker, and it depends what you mean by adoption.

KB: Let’s simplify by using concrete examples of the kinds of analysis that IHE does — walk me through the inputs and outputs of your simulation models, and how they help inform decisions downstream.

SVK: The primary work that we do that is relevant here is what we call “health technology assessment” (HTA). Health technology assessment is a set of methods used to evaluate the clinical effectiveness, applicability, and economic impact of a health technology. As economists, we are acutely aware that every dollar spent on one patient is a dollar we can’t spend caring for another, so we’re ultimately interested in knowing how every dollar can achieve the most health benefit, or what we call cost-effectiveness. So, when we do HTA, we are trying to identify what of several competing options is going to be the most cost-effective to the purchaser. HTA doesn’t have to be conducted in a single-purchaser health system environment, but that is typically how we apply it.

Within an HTA, we first need to understand the current standard of care that the health technology in question would affect. Specifically, we want to understand what the current standard of care costs and what the associated health outcomes are. From there, we then need to know where the new health technology fits within that standard: is it supplementing the current process, or is it replacing some part of it? What are the implications for total costs to the health system and to society, and what differences are expected in health outcomes for patients, caregivers, and other parts of the health system?

For example, we might see benefits in terms of reduced resource use within a hospital setting, which might not necessarily be fully captured within a health system costing model, but would still be highly relevant to hospital operations. As another example, if you can improve ER wait times, that doesn’t necessarily change the overall cost of running that ER in a substantial way, but in terms of efficiency, throughput, and staff well-being, it might be very influential. So those are additional outcomes that might be relevant. Again, that all falls into the broader categories of measuring costs, measuring health outcomes, and measuring functional or system-level outcomes that may be important to decision-makers.

From there, we build a simulation model, which is a mathematical representation of the current standard of care, in which we simulate patients moving through that process and through the healthcare system. We capture variability in health outcomes and variability in costs based on patient characteristics, health system characteristics, differences in physician practice patterns, and differences in patient response to treatment or diagnostics. In other words, we account for heterogeneity within the population and within health system performance. We then create a base-case model that represents the current state of affairs—the current version of the world. This allows us to estimate what it currently costs the health system to deliver care and what patient outcomes look like under the status quo.

Starting from that base case, we then introduce the new health technology into this simulated version of the world. To do this, we draw on a variety of data sources: clinical trials, pilot studies, observational data, or in some cases early effectiveness estimates. We run the model and observe how outcomes and costs change relative to the base case.

We run the model many times to account for heterogeneity in outcomes and uncertainty in the treatment effect and other model parameters. This gives us a full picture of the expected health outcomes, system outcomes, and costs. From there, we conduct the economic analysis. At this point we get into cost-effectiveness analysis or cost–benefit analysis, which are structured ways of translating those differences in costs and outcomes into decision-relevant metrics, based on what we understand the priorities of the decision-maker to be. This allows us to quantify the trade-offs involved in adopting this technology versus not adopting it, and to more clearly understand which version of the world we would prefer to implement. Of course, this usually involves uncertainty, so we need to explicitly account for that as part of the decision-making process.

That overall process is the health technology assessment. We present that information to the decision-maker, and the decision-maker then makes a decision. Sometimes the cost-effectiveness results are clear and it’s a relatively straightforward decision, but more often the decision is complicated by evidence uncertainty, heterogeneity, and data limitations, all of which introduce decision risk. Part of the process is helping the decision-maker understand that risk, understand their options, and identify whether there are ways to mitigate that risk—for example, through phased implementation or conditional adoption.

KB: How do you define “optimal” when you evaluate standards of care?

SVK: It depends on what kind of intervention we’re trying to model. Let’s take a really simple example: a diagnostic tool. There’s no shortage of new health technologies that aim to improve diagnostic efficiency and diagnostic accuracy. There’s also a ceiling on how effective a diagnostic tool can be—you can only be so accurate and you can only be so fast. So that’s a good example of what we mean by “optimal”: the upper bound on achievable performance, given technological and clinical constraints.

A more complicated example is something like diabetes, where there are multiple points in the care pathway where you can intervene. Once somebody has diabetes, there are many ways we can treat and manage those patients, and we can model that standard of care. But we could also intervene earlier through prevention, screening, or better self-management to prevent cases from occurring or to delay progression. The optimization question becomes: what combination of interventions across prevention, management, and treatment produces the greatest health benefit for the population we have and with the resources available?

When we model this, “optimal” usually means the most efficient allocation of resources across those different parts of the pathway—where an additional dollar spent produces the most health benefit. Preventing a case entirely often represents something close to an upper bound on potential benefit, because you avoid all downstream costs and health losses associated with that case. But in reality, the optimal solution usually involves some mix of prevention, monitoring, and treatment, and the model helps us understand where along that pathway investments have the greatest value.

We can’t always calculate a perfectly optimal system in a literal sense, but we can usually identify which interventions are likely to produce the greatest benefit at the margin, and that’s usually what decision-makers actually need to know.

KB: Clearly the process you’re describing is only as good as the data that informs it. Understanding that the quality and quantity of available data will reflect the stage of development of a new health technology, where is that data typically coming from?

SVK: Evidence collection is the biggest part of any health technology assessment, and usually we’re combining data from many sources. At a minimum, we are collecting evidence about the health technology itself, which ideally is coming from trials. From that, we might get data on the effectiveness of the technology. We also need data comparing those results to some standard of care or alternative technologies that are already in use.

That can get complicated, because sometimes a trial conducted in another jurisdiction, like the United States, may compare the technology to a standard of care that is different from the standard of care in Canada. They might be using different drugs, different technologies, or different clinical pathways. In these cases, there is some extrapolation and statistical adjustment required to make sure we are comparing apples to apples and that the results are applicable to the jurisdiction where the decision is being made.

Then there’s the system-level data, which we try to collect from administrative data bases or fee schedules. We then have to evaluate that data, clean it, and transform it into something that allows us to simulate the care pathway and the patient population. Often, administrative data has gaps that we need to fill—we may need to impute missing data, forecast certain parameters, or better characterize populations that are not well captured in the data but still need to be represented in the model. Sometimes this involves making assumptions informed by alternative data sources, including qualitative or observational data about those populations.

Finally, we may also use data from the published literature that looks at things like patient experience, physician practice patterns, and health system implementation in different settings. Health systems are not implemented uniformly, and outcomes can differ significantly depending on factors like urban versus rural settings, socioeconomic characteristics, and real-world challenges like differences in clinician or patient adherence to guidelines. We need to account for those factors if we want the model to reflect the real-world population and to support equitable decision-making.

The fact that there are so many different sources of data that need to be brought together is actually why simulation modelling is used in the first place. If all of the data came from a single source, we could often answer the question using a simpler statistical analysis. It is because we have to combine clinical data, administrative data, literature, and assumptions about how the system operates that we build a model of the care pathway and the population, and then run that model many times — what we call probabilistic sensitivity analysis — to generate a range of possible outcomes. That allows us to understand not just what we think will happen, but what could happen under different assumptions and accounting for uncertainty in our data, and only then can we draw conclusions for decision-makers.

KB: I understand you’re building an early healthcare assessment tool to be able to start making predictions about the potential value of these healthcare technologies earlier in the process. How you mitigate the inevitable data quality issues that come with the early technology in general?

SVK: I would describe early health technology assessment (eHTA) not necessarily as a single tool, but rather as a set of methods. We’re taking the same HTA framework but applying it earlier in the product development process, recognizing that at earlier stages we have less data available, which means there will be higher levels of uncertainty in our conclusions. That introduces a unique set of challenges, but when we’re talking about early-stage innovation, there’s a bunch of valuable insights eHTA can still offer.

We still assess the current standard of care and the current care pathway. What we don’t necessarily know at that stage is the true treatment effect of the new health technology. Often, the technology is early enough in development that we don’t even know exactly where it will fit in the health system. For example, it might ultimately be best suited to a specific sub-population, a specific indication, or a specific point in the care pathway.

With that in mind, what we do in eHTA is develop analyses that are not meant to definitively conclude whether the technology will be cost-effective — it’s too early for that. Instead, we focus on answering a few key questions.

First: given the current standard of care, what is the unmet need?

Another way of asking this is to what extent the current standard of care is not optimal compared to best achievable outcomes or ideal patient health. If we are already achieving very good outcomes, then there may be limited room for improvement, and therefore limited value in introducing a new technology. We can quantify this as “headroom” — both therapeutic headroom, how much health improvement is possible, and economic headroom, how much are we willing to pay for that improvement. This helps determine whether the unmet need is large enough to justify the cost and complexity of introducing a new technology.

Second: given the current standard of care, how much more effective would a new technology have to be, at the point it is ready for adoption, for it to be considered cost-effective?

At this stage, we may not yet know how effective the technology actually is — it may not have been trialled yet, or it may still be at the prototype stage. So instead, we ask: given the current standard of care, what level of improvement would be required for any new technology to be considered cost-effective at a given price? This is what we call threshold analysis. We essentially map combinations of cost and effectiveness and identify the boundary at which the technology would be considered cost-effective. That allows developers to see whether the required improvements are realistically achievable and whether the technology is likely to be economically viable before they invest heavily in development and trials.

The third type of analysis applies when a technology is closer to the clinical trial stage. Clinical trials are extremely expensive, and it is not necessarily sufficient to run a trial, publish the results, and present those results to a decision-maker. A purchaser may say: “You’ve shown that the technology is effective, but you haven’t shown whether it is cost-effective, and you haven’t estimated the budget impact.” In other words, decision-makers require evidence that goes beyond clinical effectiveness alone.

So the question becomes: what evidence does the decision-maker actually need in order to make a decision, and what data should be collected during the trial to support that decision? For example, we might know that a technology improves life expectancy, but we don’t yet have good data on quality of life gains from better patient outcomes, which means we cannot calculate cost-effectiveness. That tells us that collecting quality-of-life data during the trial is critical. In other cases, quality-of-life data may already be well established, and instead it may be more important to collect data on adverse events, diagnostic accuracy, or resource use. Wherever the biggest evidence gaps are that lead to evidence uncertainty usually lead to the highest decision risk. We can price that risk.

We call this value of information analysis — estimating the value of collecting additional information, in terms of how much it improves decision-making. It helps determine whether it is worth the cost of collecting certain data as part of a trial.

So those are three broad types of eHTA, and they correspond to different stages of technology development. At the earliest stage, when you may not even have a prototype yet, eHTA is mainly about determining whether there is sufficient unmet need and potential value to justify investing in development at all. At the next stage, when you have a technology concept, threshold analysis helps determine whether it is economically feasible — whether it could be effective enough and inexpensive enough to be adopted. Finally, when you are preparing for trials and evidence generation, value of information analysis helps determine what data you should collect so that, after the trial, you have the evidence needed to support adoption. That helps ensure that you only need to run one trial, not two or three, to generate the evidence required by decision-makers.

KB: I am seeing a lot of interest from policymakers in identifying research that addresses a societal need. Could eHTA be flipped on its head and used to map out where there’s lots of economic or performance headroom? If so, it could be a key input into how health research funding could be more efficiently allocated.

SVK: Exactly right. These methodologies can be, as you say, flipped on their head: instead of starting with a solution and asking what problem it solves, we can start by identifying the biggest problems for which we need better solutions. The same methods can be applied in that direction.

We can use headroom analysis, threshold analysis, or value of information analysis to assess a health domain and identify where the largest sources of unmet need are, where the largest performance gaps exist between current care and best achievable outcomes, and where the biggest areas of uncertainty are that currently limit decision-making in the health system.

In the case of value of information analysis, we can actually quantify how much a health system should be willing to invest to close the gap between the current standard of care and the optimal standard of care. By quantifying that, we can help guide funding agencies, innovation accelerators, and health systems toward the areas where even small improvements in health technology or care delivery would translate into large health or economic gains. In other words, it provides a way to prioritize research and innovation funding based on potential system-level value, rather than just scientific interest or technological feasibility.

We’ve definitely seen excitement from policymakers on that front. The challenge from their perspective is that there are many competing health priorities, so the real question becomes where to start and how to prioritize across disease areas and interventions.

KB: Based on what you’re seeing from results of applying this process so far versus how you’re seeing actual healthcare research spending being allocated, do you have a sense of the kind of efficiency improvements we could see were our research funding being more effectively allocated at the front end?

SVK: It’s a complicated question. I think the simple answer is that, in my experience, when I look at how health system priorities are set, it’s largely done by looking at the biggest ticket items. This usually means that conditions with the highest prevalence and the highest healthcare system cost get the most attention. Which may seem logical. But once you look more closely, you realize that there are significant gains to be had in these less common health conditions that are currently given much less attention. There is enormous headroom in these areas.

Note that these are not necessarily rare conditions, because rare conditions are often already very expensive to treat and therefore do receive attention. Rather, I’m talking about things like preventive interventions and population health measures aimed at improving the overall health of the general population. Those areas are often under-invested in, even though they can have substantial downstream effects: they prevent late-stage disease and delay disease progression, which can produce very large long-term benefits. From an economic standpoint, that is often where some of the largest gains can be realized, but in practice I see fiscal pressure and immediate budget impact driving a lot of prioritization.

I’ve seen that there’s a lot of patient advocacy that informs priorities. This is a good thing — it’s important to a democratic health system. But it’s often the case that when a new technology is developed, suddenly there’s a tremendous amount of advocacy about that solution and about the importance of implementing that solution, and so all of a sudden a disease gains a level of attention and a level of prioritization because we because we found a new solution to it, which puts priority onto a condition based on the existence of a new technology rather than prioritization technology development based on where the most need for health system investment actually lies.

KB: Coming back then to the innovator and the researcher side of the equation, what should innovators and researchers be thinking about before having a conversation with you and with IHE?

SVK: The most common blind spots we see when working with innovators are, first, that the innovator doesn’t know who their purchaser is, and second, that they don’t know what their purchaser values. It’s not really the innovator’s fault that they don’t know these things. Our health system isn’t particularly transparent about how decisions are made. The process differs by province, sometimes by health authority, and the regulatory and decision-making processes are evolving, with new approaches being introduced over time. We don’t make it easy, so it’s understandable that innovators often don’t know these things.

The main thing I would say is that every innovation is different, every pathway into the health system is different, and the decision-making environment is often difficult to navigate from the outside. That’s really where we see our role — helping innovators and researchers understand that landscape, understand what evidence is going to matter, and think through how their technology can realistically fit into the system. We’re always happy to have those conversations early, because the earlier you start thinking about these questions, the better your chances are of generating the right evidence and ultimately getting your innovation adopted.

Key Takeaways

Recent changes to provincial research funding mandates make clear that Canada is starting to reorient its investment in research toward projects and technologies that respond to a clear societal need. This is a non-trivial task, and funding agencies have mostly used the existence of a partner organization willing to co-fund the research as the primary signal of societal need. This is reflected mainly in grant eligibility criteria, for example NSERC Alliance and I2I. However, this approach leads to a very narrow definition of societal need, as there are many challenges for which no champion organization exists yet. Focusing entirely on private sector involvement leaves opportunities for valuable and impactful research on the table. Sasha’s commentary on eHTA makes clear that we have a much larger toolkit at our disposal for identifying research worth funding than basing it entirely on existing private sector involvement. Where health technology is concerned, it is actually possible to optimize our investment of research resources in a way that maximizes impact. As Sasha puts it:

“[I]stead of starting with a solution and asking what problem it solves, we can start by identifying the biggest problems for which we need better solutions. […] In other words, [eHTA] provides a way to prioritize research and innovation funding based on potential system-level value, rather than just scientific interest or technological feasibility.”

Part of why this works in the healthcare space specifically is that the process of adoption of novel technologies is (at least somewhat) systematized, through the regulatory pathways that govern whether and how new technologies become part of the public health system in Canada.

Health regulation is often viewed by early-stage innovators and investors primarily as a hurdle to be overcome. It is expensive, complex, and disproportionately impacts new startups that do not yet have the internal processes experience to navigate it effectively. Sasha’s commentary makes clear that it can also be the basis for effective decision-making and can inform optimization of both public and private sector health tech investment. By creating predictable pathways to impact, regulation contributes to making it possible to assess the potential value of technologies much earlier, in ways that simply would not be possible in a less regulated space.

Starting from the idea stage, eHTA can be used to progressively refine the direction of research and decide at each step whether and how to move forward:

“At the earliest stage, when you may not even have a prototype yet, eHTA is mainly about determining whether there is sufficient unmet need and potential value to justify investing in development at all. At the next stage, when you have a technology concept, threshold analysis helps determine whether it is economically feasible — whether it could be effective enough and inexpensive enough to be adopted. Finally, when you are preparing for trials and evidence generation, value of information analysis helps determine what data you should collect so that, after the trial, you have the evidence needed to support adoption. That helps ensure that you only need to run one trial, not two or three, to generate the evidence required by decision-makers.”

This is not to say that regulation is inherently a good thing — simply that, if it is consistent and well-understood, it can be inform high-quality decision-making.

It is apparent, though, that Canada’s regulatory landscape in the health space suffers from the same fragmentation that plagues every other aspect of its innovation pipeline:

“Every province has its own process, and the level of development and maturity of that process is highly variable. In some cases, the decision is not even made at the provincial level; instead, it may be made at the health system or regional health authority level, with organizations making adoption decisions independently. Even when it is a provincial decision, the kinds of evidence being presented and what the pathway toward adoption looks like can vary substantially, and sometimes the process is not especially well defined, even within a given province or territory. It might not even be clear to policymakers themselves what the exact process is in a given case.”

Obviously, this is not a landscape that can be reasonably understood by innovators and researchers that have not navigated it before. However, it is one that can and should be used by research funding agencies to identify health research that responds to a societal need, and one that can be the basis of a tight feedback loop between research funders, the researchers applying for funding, and the innovators taking that research beyond the lab:

“If we are already achieving very good outcomes, then there may be limited room for improvement, and therefore limited value in introducing a new technology. We can quantify this as “headroom” — both therapeutic headroom, how much health improvement is possible, and economic headroom, how much are we willing to pay for that improvement.

We essentially map combinations of cost and effectiveness and identify the boundary at which the technology would be considered cost-effective. That allows developers to see whether the required improvements are realistically achievable and whether the technology is likely to be economically viable before they invest heavily in development and trials.

[…]

Wherever the biggest evidence gaps are that lead to evidence uncertainty usually lead to the highest decision risk. We can price that risk.”

This kind of analysis is critical both for funders and innovators. I cannot count the number of times I’ve engaged with an early-stage health tech company excited about a new technology that have already started to build a company without having given any thought to the economics of what they are trying to build.

On the funder side, the alternative is a more reactive approach in which research breakthroughs lead research funding investment decisions:

“[I]t’s often the case that when a new technology is developed, suddenly there’s a tremendous amount of advocacy about that solution and about the importance of implementing that solution, and so all of a sudden a disease gains a level of attention and a level of prioritization because we because we found a new solution to it, which puts priority onto a condition based on the existence of a new technology rather than prioritization technology development based on where the most need for health system investment actually lies.”

While not a problem in and of itself, it is unlikely that a reliance on serendipity will lead to economically optimal use of scarce resources. Effective research funding policy must consider both focused discovery and opportunistic engagement with unplanned breakthroughs.

Not only does eHTA help decide what is worth pursuing at the research funding stage, it makes what is pursued more likely to succeed at each step of the process while reducing cost and risk en route. It’s not about picking winners (many early health technologies can and will still fail to achieve their potential impact for all kind of reasons unrelated to their economic and technological merits), it is about being thoughtful in matching spending to real need so that when there are successes, they are impactful at a societal level.

As we shift our research priorities toward addressing unmet Canadian challenges, analysis like eHTA should inform the investment of at least a portion of our research funding. The existence of a private sector company is not the only signal of demand for innovation — and with a reasonable expectation that technologies will be cost-effective ahead of time and a clear understanding of the risks, research funders allocating resources informed by eHTA can be confident that the private sector will step into the niche at the appropriate time in the development process.

Many thanks to Sasha for sharing his insights.

A recent study of what the authors call “university venture capital” (UVC) in North American and Europe provides an interesting point of convergence for several threads that have been floating around the innovation ecosystem over the past several weeks. Before reading the rest of this article, I suggest at least skimming “University venture capital in Europe and North America: Evidence, models, and EU policy implications”, an article by Thomas Crispeels, Emiel De Buyser, James P. Gavigan, and Ramón Compañó. This work contains a wealth of data and policy insights from across a large subset of OECD countries, and is well worth a read on its own.

They frame the problem clearly:

“[University spin-offs (USO)s] often encounter significant barriers in accessing early-stage investments by Business Angels and Venture Capitalists (VC). Their dependence on intangible assets, early-stage technologies, and the absence of a commercial track record make it difficult for investors to evaluate their viability. This information asymmetry leads to a persistent funding gap, as traditional VC firms tend to favour more mature ventures with clearer market potential.”

The results strongly support a recent report by the National Angel Capital Association (NACO) that demonstrated that Canada is missing 141M and $181M USD in pre-seed and seed-stage investment capital, respectively, relative to the United States, resulting in $66B in foregone economic value creation. Contributing to that gap, Crispeels et al. show, is that Canada sits in last place for total UVC among countries surveyed (see Figure 7, reproduced below).

This total number hides a heterogeneous domestic UVC landscape. Several Canadian universities have investment arms that are of a size consistent with the global medians in terms of total assets under management, as discussed in detail in my article covering such funds. Acknowledging the existence and importance of those outliers, the fact remains that Canada lags the world in this area, a problem I believe is foundational to our lagging productivity in general. A careful read of this work, informed by the recently published simulation model on effective investment strategies for emerging technology, offers clues as to structural reasons for the gap.

In this article, I connect the dots between this paper, my recent simulations of venture philanthropy, my survey of Canadian university deep tech funds, NACO’s findings of missing Canadian early-stage capital, and several university-related topics about which I have written in the past. Combined with NACO’s report, this work is confirmation of several “weak signals” that I have been writing about for some time, and a call to action for Canada both to address these gaps and to commit to collecting the data needed to be able to proactively identify them in the future.

Weak signals

Innovation arises as the result of accurately identifying and acting early on weak signals of impending change. One such signal exists in this paper, as well as in my own previous research on the topic: Crispeels et al. observe a trend toward larger and larger structures in a small but growing minority of UVC funds:

“[W]e observe that the amount of UVC available in Europe is growing, with some notable trends: the emergence of multi-university UVCs attracting larger investments, the occurrence of international multi-university UVCs, and the evolution towards a broader investment scope (e.g., investing in companies that collaborate with universities or in-license university intellectual property rights).”

Figures 9 and 10 in particular, reproduced below, show a small but growing minority of multi-university investment funds. It appears that several universities, across Europe in particular, are pooling their investment resources.

As I was conducting the interviews for my previous article, it came quickly clear that in cases where universities had iterated at least once in terms of their approach, the second attempt usually involved an entity that was at a greater structural distance from the university, and that had an expanded investment scope, as compared to the first.

By way of example, the fund with the longest track record that I surveyed (Velocity Fund in Waterloo) has actually gone through several iterations, ending up with an almost entirely separate entity from the university (the university is an LP in the latest fund) that invests globally. UCeed in Calgary, (built and led by John Wilson, who previously had a hand in building the UK approach), is managed by a wholly-owned but arm’s-length subsidiary of the university, and has a provincial scope for some of its sub-funds. While not a multi-university approach in most cases, the trend toward larger and larger investment scope is the same.

Scale matters

In light of recent results from simulations of investment in university-affiliated companies, this trend should not be all that surprising. The key takeaway from that work was that the single most important determinant of long-term success is scale. Where emerging technology is concerned, it is more important to make sure that you do not miss the homeruns than it is to ensure success with every investment - median returns are dictated entirely by the presence of outliers in your portfolio. Combined with the fact that it is all but impossible to predict which attempts to commercialize research will be successful at the founding stage, and it becomes clear that UVC, like any emerging technology investment play, is primarily a numbers game. Seen through this lens, the trend toward larger and larger investment scope that we are seeing in Canada, and the trend toward multi-institution and even cross-border UVC funds in Europe, makes perfect sense.

Canadian universities are at a crossroads, with recent shifts in policy applying pressure toward delivering the “third mission” of universities to demonstrate direct societal impact (and specifically, economic impact) of their research. All of these pieces of evidence point in the same direction: a broadly scoped fund with pooled resources can make more investments, while most single universities alone will struggle to achieve the necessary scale through no fault of their own.

There is a subtle nuance here that is worth spelling out. While the combined effect of many disconnected UVC funds would be the same at the ecosystem level as a pooled fund of the same total size, individually those 10 disconnected funds will experience extreme variance and will individually perform very differently, with some succeeding and some failing, exposing individual institutions to a much higher degree of risk and jeopardizing the long-term impact of the while enterprise some universities drop off, leaving their spinouts without support and driving a heterogeneous landscape of research commercialization effectiveness. A larger, combined fund approach serves both to smooth out this variance, both geographically and at the level of fund performance, to increase the median investment performance, and thereby reduces individual university risk exposure.

The lessons for Canada

This tendency toward consolidated UVC is rational. It is reflected in a recommendation also made by the European Commission. As noted by Crispeels et al.:

“The European Commission […] contains a number of measures to boost the creation of innovative firms in the EU, targeting universities. One measure refers to support cross-border networking and collaboration between leading hubs rooted in strong university ecosystems.”

Of course, this is not the whole story. If emerging technology and research commercialization is a numbers game, then dealflow is critical, and the lack of focus on UVC in Canada also reflects a “chicken and egg” problem, wherein researcher-led entrepreneurship is not necessarily built into the culture of all Canadian universities (though there are some obvious exceptions), resulting in lower dealflow that in turns reinforces the difficulty involved in building effective single-institutions funds. This vicious cycle is examined through different lenses in my article on the importance of embracing risk in early-stage innovation, as well as on embedding entrepreneurial culture in university faculty. The European Commission also addresses this issue:

“A second [measure recommended by the European Commission] is to develop a blueprint for an academic career development framework that rewards research commercialisation [sic] activities, including considering staff mobility between the university and industry in academic staff evaluation and promotion criteria. Rewarding faculty members involved in the commercialization of research results and spin-off creation increases their propensity to contribute more to spin-off formation and knowledge transfer activity.”

The PTIE movement in the United States, which is readily adaptable to Canada, gives us a concrete roadmap for addressing this point.

While great progress has been made in several Canadian regional and provincial ecosystems with respect to university spinout investment, there remains an enormous amount of Canadian research that lacks reliable pre-seed funding for commercialization, as evidenced both by this paper and NACO’s findings.

Canada has a lot to learn from our European allies, and the findings of this paper will be an important consideration as Canadian universities (and Canada generally) responds to the early-stage capital gap and to shifting societal expectations of its universities. The important takeaway from the point of view of the broader Canadian innovation ecosystem is that weak signals are critical to getting ahead of global trends, and detecting them is impossible if we are not collecting and critically evaluating data on our innovation performance, at every level.

As we enact sweeping changes to our approach to innovation, now more than ever Canada needs to make collection and coordinated analysis of effective, long-term performance metrics a matter of course across all levels of our innovation ecosystem. This will enable us to detect these weak signals early so that we can proactively address gaps, rather than react to their revelation after the damage is done and the signal is no longer weak.

Simulating venture philanthropy

This post presents a project that has been in the works for a long time, and one that combines everything I’ve written about risk tolerance, venture philanthropy, and the Simple Agreement for Innovation Licensing (SAIL) into a single, cohesive framework for investing in emerging technology. Using data from PitchBook, I built a simulation model that enables me to back-test the performance of venture philanthropy as a means to invest in emerging technology. A pre-print of that article is available at the link below.

Read the pre-print

The results in the paper validate the thesis that investing in emerging technologies is primarily a numbers game, and that attempting to pick winners actively drives underperformance. It shows that there is good reason to believe that venture philanthropy will be self-sustaining if (and only if) it operates at the right scale, and it explains clearly why neither the public nor private sector can address the problem alone—risk must be shared for this to work. It also shows that investing in emerging technology is worth doing even if it fails more than 96% of the time.

Putting risk tolerance under the microscope

It’s now well-established that Canada is great at research but struggles to realize socioeconomic value from the results. For as long as CanInnovate has existed I have argued that this problem is structural, and is rooted in risk-intolerance. The thesis is not all that complex, and relies on three assumptions:

1. that emerging technologies follow an extremely skewed distribution of value in which a minority of technologies produce a majority of the return;

2. that it is practically impossible to predict which technologies will ultimately be in the valuable minority in the early stages; and

3. that the value of the small number of successes more than offsets the cost of the majority of failures.

The first two assumptions are, I think, well-established as truth at this point. The third one requires validation. The purposes of this paper were to demonstrate, using real-world performance data from university-based startups around the world, that the third assumption holds true, and to identify precisely why neither the public nor private sector alone have yet addressed the failure to bring emerging technologies to market.

If these assumptions are sound, it follows that the only “winning” move is to invest in almost everything, accepting the risk that most will fail. A number of previous simulation models have demonstrated convincingly that in these conditions, all that matters for investment performance is that you do not miss the valuable minority. You can find a simple model that demonstrates this here, and a more sophisticated one here. Their conclusions are broadly the same: performance depends heavily on the number of investments made, requiring a large number of investments and patience to cut through the noise of high failure rates and long development cycles.

Risk intolerance is a self-fulfilling prophecy of underperformance: risk-intolerant investors try to pick winners and as a result make only a few bets, increasing the chances of missing a homerun. Given the rarity of truly valuable technologies and the second assumption above, this undersampling effectively guarantees that they are missed, which causes their investment portfolios to underperform. This then becomes the rationale for risk-intolerance in the future, trapping an ecosystem into a negative feedback loop that results in a shallow pool of investment opportunities. Recent research featured at capture the problem simply:

Pool quality acts as a ceiling or constraint - you can’t pick winners that don’t exist in your pool.

Thus, a small improvement in pool quality yields greater returns than a much larger improvement in picking skill, as picking skill compounds on pool quality to produce alpha.

Put another way, your ability to pick well only matters if there are good options to pick from.

The only way out of this cycle is to embrace risk and accept that a majority failure rate is a positive indicator that we are investing broadly enough to make sure we are not missing the valuable opportunities. Risk and failure are key elements of strategy, rather than things to be minimized.

The need for Canadian data

Aside from the conclusions and caveats noted in the paper, there is one task that remains: creating a version of the simulation model that is specific to Canada. The model built for this paper is based on startup companies from the US, Europe, the UK, and the Nordic countries, with a majority of the companies being based outside North America. While the innovation ecosystems in the EU and UK are less different from Canada than, say, that of the United States, this still presents an obvious issue when trying to make quantitative predictions about the Canadian ecosystem.

To address this, more Canadian data is needed. Specifically, data on the founding date, the complete fundraising history, dilution taken in each round, and the eventual outcome (if it has been resolved), of Canadian startups that set out to commercialize publicly funded research. If you know of a source for any of this data (or even just a subset of it), please get in touch.

Many thanks to Innovation Support Services at the University of Ottawa and to Intellectual Property Ontario for funding this research.

This week I interviewed Stefan Leslie, the CEO of Research Nova Scotia (RNS). RNS is an arm’s length, board-governed organization that deploys research grant funding on behalf of the provincial government. Notably, RNS was recently given a new mandate to fund research that will drive provincial economic growth, and they have just finished their first call for proposals under the new strategic plan they developed in response.

My goal in this interview was to learn how Stefan Leslie and the RNS team approached rebuilding their strategic plan to address their new mandate, in the hopes of coming away with a clear articulation of what it means for research to serve the needs of society that can be generalized to other Canadian funding agencies. Stefan’s commentary is insightful, and reflects a deep understanding both of the research he supports and of what it takes to move the results of that research beyond the lab.

While Nova Scotia may be leading the charge, I strongly suspect that similar changes are coming to the rest of the provinces in the near future. The strategic plan developed by Stefan and the RNS team will no doubt serve as a useful blueprint for both provincial and federal policy makers that are tasked with connecting research to societal impact.

Your email client will probably truncate this post. My key takeaways are presented at the end, so be sure to read the web version if you want to get the whole story. Many thanks to Stefan for taking the time to share his process.

Interviewer’s note: Stefan Leslie approved the final version of the section entitled “Interview with Stefan Leslie” and had editorial input on that section, with the option to rephrase and expand on the ideas discussed in the interview without changing or removing any intended meaning. The key takeaways presented at the end are my own commentary, and do not necessarily represent the views of Stefan Leslie or Research Nova Scotia.

Interview with Stefan Leslie

KB: Tell me about yourself. How did you find your way to leading Research Nova Scotia?

SL: I actually came into this from managing fisheries: 12 years in Canada and 4 in New Zealand. When I say managing fisheries, I mean for the government authority, in Canada for DFO and in New Zealand for the Ministry of Fisheries. This included stock assessments and meetings with fishermen in the local fire hall and all that kind of stuff.

What was interesting about coming from that direction was that it is a very science-based activity where you have a very direct connection between the research and science that’s being done and resulting management and policy decisions. That includes fundamental research work around how stock dynamics operate, and then very applied stuff like figuring out how we can exploit this stock while remaining sustainable. I also recognize, having worked in that environment, that you cannot optimize just based on the outcome of a scientific process. People are involved, and they have to come up with the trade-offs and develop the normative choices that come with competing interests around science and research. I also came to recognize my own limits as a public servant and both the limitations and the advantages of working in that kind of environment.

That background then led me to lead a Network of Centres of Excellence, which is a program that no longer exists in Canada as it was cancelled four or five years ago. This was a federally funded program embedded in a host organization, typically a university, that did very purposeful science. It was to pursue certain outcomes that were of importance to the country, develop high-quality work, and also train the workforce, engage in knowledge transfer, and all the rest of it. But it got me into the academic environment and illuminated the value that academic research, or research that happens in the universities, can bring across a whole bunch of different dimensions of Canadian society and the economy. It also highlighted for me the value of an arm’s length, non-profit-style board-governed world.

When Research Nova Scotia was started, and the conversation in the province of Nova Scotia really kicked off around 2015 or 2016 and had about a 3- or 4-year gestation period, the idea was to create an arm’s length organization devoted to funding “purposeful research” with research as the basis for this pathway to prosperity and societal improvement. That was the sentiment in that time, in the pre-pandemic world. So as they wished to launch this organization, I was very interested because I thought that it was an unexplored dimension in the provincial interest in science.

I wanted to explore what this meant that’s different from straight economic or federal interest, because the dominant theme of research is often driven by federal considerations, but the provinces are actually interested in different things. They of course are managing universities, but also research hospitals or healthcare systems where health research happens. So there’s a provincial jurisdiction element. Provinces are responsible for the delivery of the majority of public or social services that you and I and everyone else need and enjoy and comment on on a daily basis: the education system, the health system, transportation, economic development, and so on. As a result, provinces have a real interest in the outputs of research, and are asking the question as to what are we actually learning from research that we can then apply in a direct way.

KB: It sounds like part of the goal of Research Nova Scotia, even before the recent mandate change, was to fund research that would have a direct socioeconomic impact. Is that accurate?

SL: From the outset, I should probably say that in Nova Scotia, and probably other provinces, there have been various approaches to supporting research since at least the 1930s. Research Nova Scotia, although it was created in 2019, is at the end of a long train of a variety of different options and selections and ideas around how the province can support research. This is attempt 7 or 8 at this particular problem, so we’ve tried every sort of model. The immediate predecessor organization had really focused on health research and leveraging federal funds primarily through the Canada Foundation for Innovation. So there really always was this purposeful element of connecting the public investment to issues that matter here. What needed to matter was the research outputs: what we learned, beyond just what we were funding, research excellence, and the people trained. What value could we actually extract from it?

For the first five years more or less of our existence, the province had established research priorities in regulation itself. They were fairly broad and enabling: inclusive economic growth, a healthy population, and a strong healthcare system. Quite broad, but nevertheless indicative of a direction that they wanted to take. Our first approach within that purposeful or intentional model was to develop an outcomes- or missions-based outlook in which we would define fairly broad but meaningful outcomes and we then invited researchers to come and creatively articulate how they could deliver those outcomes. We were quite open for researchers to provide insight into how they could devote their skill and craft in service of achieving that on behalf of the province.

There are diverse ways you can divide up the types of research that exist. You can break it into basic or fundamental and applied and experimental design, like the way the OECD does. Sometimes you can throw in curiosity-driven or investigator-led research. I don’t think necessarily that these are all that helpful as distinctions. We were never in the game of funding discovery for the sake of pursuit of knowledge, as that was not the intent at the provincial level. There was a desire to use scientific tools to fill a particular need, however, and that can call upon the need to engage in fundamental research. If that’s the limiting factor to achieving whatever societal outcome you want then that’s what you ought to be pursuing. We looked at it from the perspective of identifying the “pull” and then deriving the research need from that, regardless of whether it is applied or fundamental by that set of definitions.

As you pointed out, though, there has been a significant shift in what Research Nova Scotia has been asked to do, which has been underway now for the past about eight months. It was initiated by a change in the legislation that underpins what Research Nova Scotia is here to do, in the spring sitting of the 2025 legislature. Many things are still the same: we’re still an arm’s length organization, we’re still independent, we still have a board of directors, we’re still researching areas of provincial importance, and we are not, consistent with iterations of the research endeavour in this province, performing research itself. Instead we’re coordinating and funding others. We are not intervening on choices around methods, nor are we controlling outputs, none of that stuff that flared up during the Harper years that has sparked discussion around political control over science, none of that is part of it. But there are a number of really important changes that have happened.

The first is that under this revised legislation, the Minister of Advanced Education, which is the part of the crown to which I have a reporting relationship, now has the discretion to establish research priorities. So it’s been taken out of regulation and given to the minister. You could say that that’s just taking your coins from one pocket and putting it into another, because it is still a government process, but the regulatory process and a minister’s process is really quite different, or at least it can be, since the minister now has in their sole discretion the ability from time to time to determine those research priorities. That’s a critical difference for us.

The second is that the minister did exercise that discretion very early on under the revised mandate by giving us an overtly economic focus. So we now have to demonstrate advancements across 7 key outcomes which are grouped into two economic areas: economic growth and economic productivity. More specifically, provincial economic growth, and provincial economic productivity. So everything we do, and our authority to spend public money, depends on our ability to connect the research we fund to the achievement of outcomes within those two areas.

Third, which is related but quite important, is that there is an expectation of our ability to connect research outputs to those outcomes in a very direct way. The expectation from the minister is to show how research is contributing to measurable changes in those outcomes. A lot of weight that is held by the word “is” and “measurable”. “Is” really does imply that there has to be that obvious connection and “measurable” means you have to be able to point to some evidence.

The fourth major change is that we have had an adjustment of the priority sectors.I mentioned before that it was inclusive economic growth and healthy population and a healthcare system, and now the effort that we must devote ourselves to has been diverted to three different sectors:

1. Natural resources, climate change and clean energy;

2. Life sciences and health sciences; and

3. Construction and transportation.

So that has been the shift that happened on the 1st of May of last year of 2025.

KB: You’re one of the first if not the first provincial research funding agencies that has been asked to reorient towards economic impact, but I don’t think you’re going to be the last. Tell us about the process you undertook to develop your strategic plan in response, as a guide to those who may be asked to do the same in other provinces.

SL: There was no transition period. It wasn’t as if we were advised that we needed to change our priorities and get there in 12 months. We had the new set of priorities, and our research activities needed to respect them, effective immediately. We devoted the summer to developing a new strategic plan, and in fact the act specifically directed us to create and implement a strategic plan to deliver on our new mandate. That’s good practice anyway, but nevertheless it was helpful to have that pointed to us.

We went through a relatively formal iterative process where we engaged with insiders, got some drafting done, sent material out, and gradually over the period of about three months refined the ideas into a strategy that then went to the board for further refinement and then was approved in October. So approximately six months from creation to adoption phase.

It has three different areas of focus. The first, which almost sounds comical to state, but I think is actually really important, is that we still fund research. The reason I dwell on this is that it matters what people call “research” because it’s actually a term that carries a lot of weight in a lot of different ways depending on the circumstance. If you go back to the Frascati definition of the OECD, it’s got these aspects of novelty and creativity and uncertainty and all the rest of it, and so for us it has to be more than a phrase, which I thought was a really useful distinction point that I learned from this engagement process.

A colleague organization in the province described what they did within a particular sector as “managing scientific activities to resolve knowledge gaps”, and that is a perfectly reasonable definition of what research is, but for us it had to be a little broader. We needed to see absolutely the connection of the research outputs to realizing those outcomes, but we take broader view of research. For RNS, the discovery or the knowledge that’s gained through that research process has to be applicable beyond the immediate circumstances by people who weren’t involved in the initial investigation. This differentiates ourselves from simply supporting R&D, that may happen either in a company or an industrial participant or a government department that has to deliver on a policy mandate.

The second, consistent with that interpretation of what research is for RNS: we adopted a sector-level view. So that our support for solving problems, removing barriers, pursuing opportunities, whatever it is, would be applicable or relevant to a number of participants.

Senator Deacon, back in October or November, put out a report talking about all of the different federal level programs that support R&D, everything from tax credits to direct support, and there’s hundreds of them. Our view was that there’s no point in us simply being number 135 where there had been 134. Rather, we asked how we could incentivize collaboration within industry and make sure there was a connection between industry needs, and other indications that the research will produce economic value, and the research performers, whether they’re at a university or a not-for-profit or even within industry. So we were looking for opportunities to incentivize this collaboration where there are common challenges that have an economic benefit but are broader than just what one company needs in order to sell a particular product, improve a particular process, reduce their cost structure, or whatever it might be.

That leads to the third main takeaway from our strategy. Consistent with the first two parts, it behooved us to create a couple of different pathways to research support. First, we have a convened model which we call Focused Research Investments, which are large, they’re actively coordinated, there’s multiple component or constituent projects that fall out of it, in which we are looking for active engagement with other funders to really advance in an area over a period of multiple years. So you pick a sector for which there is an opportunity to manage a risk, or pursue an opportunity, and you say that, by virtue of systematic and consistent application of money, time, and talent, that you can really advance things along. Second, this is matched by a more responsive model, which enables those who have an idea, or their own challenge that they can cogently describe, to get funding over a shorter period of time toward resolving that challenge. You don’t have to have all that other bells and whistles around coordination here. You do need to be able to articulate how it fits our definition of research, but you don’t have to coordinate a larger scale effort. This is the opportunity for which our first intake round just ended. With that first round, applicants are being invited to propose what they think is important for us to pursue over the long run.

In both cases, whether you’re coming through that convened larger model or the responsive model, you need to be able to demonstrate a plausible pathway to impact. We can discuss the economic model later, but the summary point here is that applicants do have to demonstrate that the need of whoever’s going to apply the research, which has to be outside the research community, shaped the direction of the research that’s occurring. Applicants need to understand that particular pathway. This is different from promising success. Rather, applicants need to demonstrate that they have thought about who and where and when information moves through hands or through different parts of the value chain, to go from the research sphere to the application sphere. Just as important is the demonstration of an effective mechanism or governance structure within the research component that can make adjustments or accommodations as you learn to ensure that your focus is always on that outcome.

We have lots to learn on whether this is going to work, but by taking these two different models and keeping our eye on that application phase, we’re hoping to back that into a model that will allow us to identify and then track success of projects over time.

KB: Your convened model, and in particular the idea of removing the separation between research and industry, has some parallels to a more agile version of DARPA. Was that a source of inspiration?

SL: Certainly DARPA, or Skunkworks is another one that’s commonly brought up, are really great for engineering challenges where it’s easy to identify a measure of success or failure. To use the Skunkworks example, if you are designing a spy plane, you can tightly specify the design and success criteria: it must be able to fly at 90,000 feet and get a resolution of X, for example, and then you have either a spy plane, or you don’t, that meets those design criteria and you can design a research agenda around that. There’s certainly an element of that.

I think what’s different here is both the need to find a way to integrate the existing organizational structure of how research actually happens in this country. Nova Scotia has 10 universities, a community college with multiple campuses, and two or three health authorities delivering healthcare, but we have to recognize that there’s a strong social component to whether something actually happens. It’s not just an engineering challenge.

I’d say the inspiration was partly that DARPA model and the various other ARPA-[add-a-letters]. There are certainly other innovation models, and the US, for all of its challenges around science these days, are extremely creative in trying different models and different ways. Philanthropically supported research is considerably bigger there than it is here (although I think it’s growing here), convergence research, developing the Focused Research Organization model, speculative technology, working on deep tech challenges. There’s the UK version of some of this the Advanced Research and Invention Agency or ARIA that’s been around for maybe two or three years, Deep Science Ventures, various other organizations, etc. I’d say that what is unifying all of these is that they’re experimental and adaptive. It’s not just that they have this connection of a societal or economic or engineering need into the research community. I think what is kind of more special about them is that they are recognizing that you need to adjust the model according to what you’re trying to achieve, be honest about or or look carefully at what it’s taken to succeed, or not, and adjust going forward without trying to determine what the model is that you’re going to hold true to forevermore.

KB: Given the number of unknowns between research and economic impact that may have nothing to do with the quality of the idea or the researchers involved, how are you planning to connect the research you fund to the downstream impact?

SL: This has been an area which has obsessed us since the economic mandate for RNS began last May. But more broadly, this has proven to be a vexatious problem for at least 70 or 80 years. It has been the subject of considerable attention for probably 50 of those years. It is not one that is easily cracked. A few general comments before we get to the more specifics, because I’ve got a couple of different ways to answer that question in a more concrete way.

I think it is well established, and probably beyond contention, that there is a public or a social benefit to public investments in research and development. There’s been a variety of different papers on this, and probably the most recent or maybe best known was by the National Bureau of Economic Research in the US, where Lawrence Summers and Benjamin Jones looked at the social value of research. It is very difficult to find a study that doesn’t give you a multiplier of somewhere on the order of 4-10. For every dollar spent on research, you get four back in social value. The issue is that it really matters where that accrues.

This is the challenge around R&D: you have a high degree of uncertainty in where those benefits are felt, but you have high certainty over where those costs originate. So you have what you could probably characterize as benefits that are diffuse; distant; hard to attribute to an individual supporter, and that are often disconnected from the original investor or research performer, but the costs have all the opposite characteristics. They are coming from a very specific pot of money. Even when R&D is done to improve a very specific thing, say a manufacturing process, it will be on the order of years before you see the application of that knowledge to the improvement of that particular process.

There was a study done not too long ago that looked at the timeframe between publication in a scientific journal, where the research has been “concluded”, and the application for a patent. Not the use of that patent, mind you, just applying it. That’s on the order of three to six years So, if you’re looking for evidence of economic benefit from the activity of research, you have to think about the timeframe of years. The more fundamental the research question, the longer that timeframe is. For basic research, we’re talking probably 20 years.

[[interviewer’s note: Stefan’s numbers line up with the NBER study linked above, in which they suggest that “studies of R&D, product introductions, and product sales suggest quite rapid linkages between up-front costs and peak market payoffs. A total delay of 3.6 years appears reasonable, and a 10-year delay appears very conservative.” (p.14)” and that “”citation network analysis suggests that even more remote basic research investments begin paying off within 20 years.”]].

You can always point to, say, the connection between someone studying the spit of a Gila monster and the production of Ozempic, or you can point to research into how bird beaks were formed through an evolutionary process connecting to methods to improve the aerodynamics of high-speed trains. That all happens, but it is a very tortuous path, and you can make those connections only in retrospect. It’s very difficult to do it looking forwards.

Now I want to answer your question in a more specific way, with two different perspectives. The first is rooted in evaluation. Looking back, how do you demonstrate that research you’ve funded has resulted in value. As I’ve just spent a lot of time describing, you need something that goes well beyond the timeframe of the research project itself. You need to be able to reach well beyond the end of the research, and probably coordinate with others to be able to track it back, and you need to focus as much on efforts and intent as on results. You can’t hold a researcher to account for a failure to realize economic return because there are so many aspects that are going to either frustrate or amplify creation of that economic value over which they have no control, but you can certainly expect that there is effort and intention behind what they do. I think that the metrics you need to look at need to allow you to develop a sensitive understanding about why something worked, or why it didn’t work, so that you can apply those learnings to future investigations.

This is where I need to get to the second area that represents a very different way of thinking about the problem. How do you actually select research projects that have the prospect of economic benefit, given the timeframe and the lack of attribution. Basically, how do we decide what represents the best bet? If you line up 10 ideas in front of venture capital, they’re going to have about a 10 percent success rate. They know what the product is, who the principals are, what the market is, and what the financing looks like, and the success rate is still that low. So, how can you provide information to someone like us, that is being asked to select research, which is well before you get to the point where VC would ever take a sniff, that signals a chance at success?

I think the only way you do that is you look at this in a different way. You have to completely change what you’re looking for. You need not focus on evidence or expectation that whatever comes out of the lab is going to be applied, but instead look for indicators of how the problem within society that needs to be solved is influencing what goes on in the lab. We need to move away from the idea of moving knowledge out of the lab, this “knowledge translation” language we hear so much about, and instead flip it around and consider knowledge translation in the opposite direction: how information from the social side informs the research sphere.

So we need to completely change what we’re looking for, and focus on evidence of how the needs of society or industry shaped the research endeavor. It’s more than “I had a question, I know how something happened and I set about to understand it and hopefully it’ll be applicable”. Instead, it needs to be “are the questions that I’m asking directly inspired by or

The patron saint is like Louis Pasteur. Abraham Flexner, the guy who who started the Institute for Advanced Study at Princeton, doing the purest exploratory research, had all the time in the world for Pasteur, even though he even wrote an article on “The Usefulness of Useless Knowledge“. He talked about Pasteur taking inspiration from the real needs of people and converting those unknowns into fundamental challenges.

We need to take needs and convert those into real problems. Transform the applied into the fundamental rather than take the fundamental and try to apply it. So we need to out that process. I think that the many researchers who do applied research do this in an intuitive sense. They are not cloistered and kind of just come up with stuff in which they’re interested. Even for curiosity-driven research, that curiosity comes from somewhere. The challenge for us is to shine a light on the process transforming an applied problem into a research challenge that can give us a solution. It’s difficult to answer as far as specific metrics go, but I think what we need to start with is to clearly articulate what we are asking people to show evidence of, and engage in that conversation first.

KB: Building on that reframing of “purposeful research”, how do we go about identifying the problems that should inform the research we support? If there’s an existing company with a problem, it’s easy. What about broader societal goals that may not have an existing industry champion?

SL: Yes, the obvious case is where you’ve got a technical challenge. You know, if you’re Louis Pasteur, it’s fermentation in wine - it’s an economic disaster if they can’t sell their wine, and so that lends itself to an obvious set of challenges. The genius there is to broaden that into a wider set of applicable ideas.

What we need to be able to do here, and in fact what we’re required to do in order to give due response to the instructions we’ve been provided by our minister, is to begin with the three sectors that are the areas of focus within this economic model, and extract from that the research challenge components, because not everything should be treated as a research problem. Research is the right instrument to use when there is something we need to learn to better understand what to do. Under those circumstances, the application of scientific methods will get us closer to better, more informed choices. But there are many challenges where research is not the limiting factor. What’s preventing the improvement is something else: making the trade-off between competing benefits; devoting sufficient time or financial resources to something; or management or administrative competence. In those cases, calling for research is a diversion. There may be good things that come from that research, but it won’t be to improve the immediate problem at hand.

In an economic environment where the economy grows by industry participation, you could also look at through the lens that saving public investment means that you are reducing demand on public resources. But typically those are simply then drawn into other areas of public investment so it doesn’t necessarily produce cost savings. But if you accept the premise that you need industry to make use of what is produced, then the question of what should be the public investment versus what is a private investment opportunity becomes important. I think the appropriate area for public investment is where you have broad need, across a number of participants, where there is insufficient incentive for any one individual organization to pursue that particular work. You cannot expect an industry participant to take on that particular cost, because the spillover benefits will be to others. That makes a very strong argument for why the public sector ought to be involved at this stage, because it is going to provide broad sustained support to industry in that particular sector.

KB: Under the new strategic plan, does Research Nova Scotia take an active position on governance of the intellectual property coming out of the research you fund?

SL: One of the outcomes that we have been asked to produce, one of the seven that the minister’s given us, is to enhance commercialization of research, including retention and deployment of Nova Scotia IP in the province. So in the minister’s mind, the province is already thinking about this connection between who owns the IP and how it’s deployed in service of economic growth in the province as opposed to kind of the simpler question of what’s going to create jobs or whatever. So that was already part of the broad context in which Research Nova Scotia operates.

So as a result, we evaluated whether it made sense for us as an organization to take some sort of equity position or ownership of IP as a result of the research in which we were going to invest. We had a hard look, a really hard look, at a UK program which ran for about four or five years, where the model that they had was to invest public money in R&D programs executed by industry, but the public money retained the right to take ownership of any resulting IP if it wasn’t used within a particular timeframe. So if IP is created, if nothing’s done with it in that timeframe, then the public owns that IP. They went through three or four funding rounds and they were in an experimental and adaptive mode as we were discussing a few minutes ago. The reason we didn’t go down that path was that, in discussion with the developers of that program, they kept changing and trying to get closer and closer to delivery of a better program, and I realized that they had never actually exercised that opportunity because they didn’t know what to do with it. Just because they owned it wouldn’t necessarily then result in improved deployment of that IP in service of the ultimate outcome. Holding the patent is nothing, you have to actually then build it into something that then creates value.

I arrived at the conclusion that we need to stick with what we’re good at, and instead turn IP management into an area that an applicant for our research funding has to think through and shape and articulate to us. I think that this speaks to a difference between prescribing a set of rules that everyone has to work within, or developing an enabling framework with some flexibility that acknowledges that we might not know what the IP environment has to look like, but make clear to applicants that that part of our job is to ensure that there’s increased deployment of IP in Nova Scotia for its economic benefit. So we ask applicants to demonstrate to us how their approach is going to meet that outcome. Rather than us claiming IP or trying to manage it, applicants know they will need to develop a results distribution plan and that plan may be to share it broadly, it may be to hold it close, but it needs to be the right approach in context to deliver on the target outcome. It shifts the burden over to the applicant but it allows it to be flexible depending on what area they are working in. It’s going to be very different for deep tech versus process improvement. What IP actually means is going to be very different, and what’s patentable, and so on.

I have every interest in ensuring that the research we support creates commercializable value that is then picked up either by the performer or by whomever ownership passes to in order to mobilize it in service of society. But I’m very aware of my limitations and I do not assume I know how to do that better than them.

We need to be clear about what actually creates economic value. Is it the invention itself -- the output of R&D? Or is it the application of that invention to industrial activity, or cost-saving public service delivery? In my view, it is the latter. There is some economic value to R&D activity -- researchers are paid – but also there is typically greater opportunity to build economic activity adjacent to the R&D efforts. This is not an automatic process. Some clear-eyed assessment needs to be done about what core ingredients determine where R&D-driven economic activity will actually occur. If, in Nova Scotia, we have few to none of those ingredients (e.g. labour profile; domestic market; proximity to export; low carbon energy, etc.), then being the R&D centre is unlikely to result in the economic activity that comes with the application. I like to think about where we have a competitive advantage on applications (e.g. manufacturing or other forms of R&D deployment) that already exist or could be built, and focus R&D efforts there. These can be small niches (that’s inevitable with a population of 1M), but that’s where the opportunities lie. I like the concept of ‘conversion’: converting R&D results to application.You need to actually pay attention to not just your research strengths, but also the path through to converting that into value, in whatever form it may take.

KB: A central theme of your approach to all of this is “intention”, understanding and being tolerant to the fact that intention will not always translate to reality. Having researchers articulate that intention leaves lots of flexibility, but shifts the burden onto you in that your reporting systems have to reflect that flexibility. How are you thinking about making that open-ended reporting practical given the variety of approaches that you’re going to encounter?

SL: I should start by saying that like every publicly funded or taxpayer funded organization, there is an expectation from the people who fund us that we will be able to report convincingly that we are managing those funds responsibly and to further their aims. Nothing of what I’m saying is trying to shirk that sense of responsibility. But what I report has to be real. I don’t want to go to them and say that we can come up with a set of metrics that will allow you to track the dollar to the benefit when that is not a realistic expectation, and moreover I don’t think that’s really what they think is feasible either.

What’s really important is that, in everything we do, we understand what they’re trying to achieve, we take it seriously, and we adjust our processes and the people with whom we work and the type of research that we’re supporting, and we follow up wherever and however and in whatever creative ways we can to ensure that we’re able to report on outcomes and to take corrective measures. That is always going to involve a combination of two approaches. The first is a formalized approach in which we require certain reporting or provision of data that we can then assemble to a report. The easiest part of that is based on having a financial relationship with someone because the research process is underway, where they provide us with information and we give them funding in return–we can compel them. That’s easy.

The second part, and the only way this is going to actually work in the long term, though, is to maintain that reporting relationship over the much, much longer term, longer than we’ve yet been on this earth as an organization. How that’s going to happen precisely is difficult to prescribe at this point in time except that we have been doing this in a slightly different form for the first six years, and I can tell you that the way it actually happens is by cultivating a productive and trusting relationship with the researchers that you support over the long term, through which we build confidence that our role is not to provide oversight and pass judgment on their quality, but that our role is to enable their success.

“Success” here does not mean that I particularly care whether they’re going to get tenure or promotion, or publish in Nature or Cell, or whatever– those are the accoutrements of success. By success, I mean that the research that they are passionate about, and really want to see happen, is able to proceed through the system to its logical conclusion. That doesn’t mean it necessarily always works, and so cultivating that personal relationship means that they’re comfortable that when a challenge occurs, when supplier doesn’t have something, when something’s going to cost more than planned, when students who they thought were going to come can’t because the federal system changes and they can’t bring in a foreign student who was going to be the postdoc working on the project, or whatever the issue is, we want to be their first call. We want to be able to ask them how we can help overcome the issue. I think that’s how you build an understanding over the longer term of how productive work happens. I think mapping the journey is almost as important as how many people they hired, whether they filed a patent, and where that patent went. It’s going to be part of it but it can’t be all of it.

So yes, it shifts the burden to a more qualitative or narrative model, but I think ultimately that becomes more meaningful in terms of actually giving insight to the people who have given us the funds to further this kind of work as part of the many things they could spend their money on.

KB: You are leading the charge on a priority shift that I expect is going to come to the rest of the provinces, and probably the federal research agencies as well, in the near future. Having done this, do you have advice for your counterparts in other provinces that are asked to similarly reorient their research funding toward economic impact?

SL: You’re right that this is happening everywhere. Conversations we have with other provinces, at the federal level you talk about the Capstone Agency, mission-driven research, and dual-use tech etc. are all part of the conversation here, but in Australia, UK, the US, New Zealand, etc. everyone’s thinking about how granting agencies operate and and I think it’s important to consider that research ecosystem is very complex.

The shift is not just toward an economic focus. It is also in recognizing that there’s room for and a really powerful role for active coordination of research rather than just simply passively running a competition to see who applies, picking the best based on some research excellence metrics, and hoping for the best. I think that’s part of how research beauty happens, but it’s not the full thing. You need to round it out with a recognition that certain societal challenges need a different model. All the countries I just listed, and many of the different provinces, are faced with this challenge as well. I think maybe we had to confront it a little sooner in a real way than others and so I think, to revisit maybe some earlier thoughts and give emphasis, this idea of experimentation and adaptation is crucial.

The model that is has been used the dominant model that’s been used to identify research – say the peer-review system and all of that – has been relatively stable but that continues to undergo experimentation as well just in more subtle ways. We should expect, as we move toward a direct connection between economic activity and research outputs and a more coordinated model, to have a higher degree of adaptation or or experimentation early on.

The second observation is that for research funders and coordinators like us—which it to say, relatively small ones—are pulling macro-level levers. We are designing research funding programs, we’re engaging with universities as almost conceptual organizations, as groups of scholars, but this is going to work or not work by marrying those macro-level concerns with the micro reality. The micro reality involves, in academic settings, things like tenure and promotion systems, university senates, and the various decision-making structures within universities. In industry there’s the reality of supply chain, the cost of borrowing, and the competitive environment that’s changing. People are managing micro-forces and we’re pulling macro-levers. I think that understanding the dynamic of how those two aspects work together is going to be a determining factor in whether programs that we design and engage with are going to be successful or frustrating, or maybe how much they have to change.

I think that this has to be more than just the simple discussion around leveraging funds. A lot of the conversation revolves around the fact that when there is a new federal program, what’s going to be the percent of federal investment versus provincial investment. I think a more nuanced view of it is to consider what the federal role is, versus the provincial role. The outcome of a proper partnership-based discussion will be a funding model. But what matters when building R&D funding models that have both federal and provincial components is true reflection of what the different jurisdictions’ needs and capacities are. When I say ‘needs’, I mean that within a purposeful research program, those purposes are legitimately different. That is a good thing: let’s draw out where those purposes align, and where they don’t. The idea of ‘capacity’ is bigger than just financial: it includes things like decision-making structures and timeframes. This may express itself in a different matching formula when it comes to money but a partnership model actually looks at what each of the partners wishes to achieve and then finds something that in combination strengthens the position of the component parts.

I think that identifying and managing the spillover benefits and where they go really matters. This is bringing back the point that, at the provincial level, when we are directed explicitly to increase provincial economic growth and provincial economic productivity we really do need to pay attention to where that conversion into value is going to happen, and where R&D mastery converts to economic opportunity. We have to care about that. That’s not because we’re Philistines and don’t care about other issues in the world, it’s that we’re dealing with provincial dollars from provincial taxpayers. It’s the same person that’s a federal taxpayer, but in that sense they are contributing to a different set of interests than at the federal level.

The final thing, because it is something that we have really grappled with over the past eight months and I can’t imagine anyone doing this kind of work would fail to grapple with these two issues, is that coordination is really difficult. Coordination amongst funds that have different decision-making structures, timeframes, and mandates just takes time. Second, a lot of where research value can exist is in potential savings of public investment, say in healthcare. It’s really important to look at what those kinds of innovations can do to simply reduce demand in other areas. If you’re really focused on economic growth you actually have to look at what is going to grow the economy, not necessarily displace one expenditure for another. On the surface it seems somewhat simple to say “we are now devoted towards economic growth”. Understanding what economic growth actually looks like does matter even when considering what kind of research projects are best suited to achieving that kind of outcome.

KB: Is there anything I should have asked you but didn’t?

SL: I always ask people if they are optimistic. I’m personally optimistic even if I can be somewhat globally pessimistic. I think it’s a very challenging time, but I think that constraint is often a precursor to creativity. So this is, I think, the source of my optimism.

People ascribe this quote to either Ernest Rutherford or Winston Churchill but it goes something along the lines of “We have not got any money, so we have got to think”.

I’d love to have lots of research dollars to dispense. There’s lots of good work, there’s endless amounts of work and endless numbers of people who are doing great stuff, so I’m not saying that we need to be restricted in that regard but nevertheless it is to a certain extent those restrictions, which are not just financial but directional, and the reality of how the system works, that do offer us the opportunity to be creative and try something new.

I think there’s a reason it’s happening or it can happen here in Nova Scotia because we are in somewhat of a unique position. We don’t have the complexity that comes with the size of some of the other provinces. We are not nor will we ever have the budget of Ontario, Quebec, BC, or even Alberta. But we do have a substantial research history and research capacity that we can mobilize. We’re at that mesocosm level of having some of the elements that enable really high-quality, functional research to occur, but it is a population of about 1 million people that’s it, so we can connect on almost a personal direct basis to all the people that we’re working with. This is what gives me optimism: we can be and we should be and should be seen to be by others to be worth watching as an experiment to see what can be drawn into other jurisdictions as well. I’m an optimist in that regard.

Key Takeaways

Research with purpose

In the debate around Canada reorienting its research infrastructure toward the needs of society, I often come across the perception that funding research with the goal of socioeconomic impact is somehow incompatible with research conducted for pursuit of knowledge. As a result, the various words we use to describe the distinction between different research goals have picked up some baggage. Various commentators, including myself, tend to use simplistic labels that divide research into one of two categories variously called “fundamental”, “basic”, or “curiosity driven” and “applied” or “demand-driven”.

Stefan argues that this is too simplistic a categorization to be useful. Instead, he adopts a more nuanced framing around the idea of purposeful or intentional research.

“We looked at it from the perspective of identifying the “pull” and then deriving the research need from that, regardless of whether it is applied or fundamental by that set of definitions. […] For RNS, the discovery or the knowledge that’s gained through that research process has to be applicable beyond the immediate circumstances by people who weren’t involved in the initial investigation.”

In other words, it does not matter whether the research is fundamental or applied, what matters is that it responds to a clearly identified societal need. If the barrier to addressing that need is basic research, then that is what should be funded.

The challenge, then, is two-fold: identifying the need, and articulating clearly what kind of research needs to take place to address it.

If we consider the various existing research funding programs that focus on research commercialization (NSERC Alliance, I2I, and others), we find that they typically require an existing industry partner to co-fund the research, based on the idea that this signals clear private sector demand for the research. This is “demand-driven” research, which, while a subset of purposeful research, misses opportunities to address societal needs that do not yet have an industry champion. The RNS approach is more inclusive:

“You need not focus on evidence or expectation that whatever comes out of the lab is going to be applied, but instead look for indicators of how the problem within society that needs to be solved is influencing what goes on in the lab […] we need to completely change what we’re looking for, and focus on evidence of how the needs of society or industry shaped the research endeavor.”

In other words, funding agencies should look for clear evidence that their applicants are proposing research that is responsive to a need that they can clearly identify, and that the need has informed their research plan.

Acting on this involves a degree of flexibility on the part of the grant funders: while it is very easy to use the existence of an industry partner as evidence of societal need, we will have to do better if we are to build research funding systems that are more broadly responsive. Not all worthwhile problems have a company trying to solve them already. This has been a significant challenge for any public sector granting agency that seeks to fund research broadly, since it is impossible for public servants to be technical experts in all the areas of research they will encounter in grant applications. Stefan freely acknowledges the challenge:

“I have every interest in ensuring that the research we support creates commercializable value that is then picked up either by the performer or by whomever ownership passes to in order to mobilize it in service of society. But I’m very aware of my limitations and I do not assume I know how to do that better than [the researchers].”

Stefan’s reframing of what constitutes purposeful research provides a clear blueprint on which a more responsive research funding system should be based. The applicant is the technical expert. Instead of attempting to evaluate a proposal primarily on its technical merit while using industry demand as proxy for societal need, our research agencies should look for evidence that a proposal responds to a clearly identified need, and that the research plan is informed by a nuanced understanding of those needs and can articulate how the proposed research seeks to overcome the immediate challenge to addressing them.

Measuring success

Where research is concerned, intention will not always (or more accurately, will usually not) translate to the desired outcome. RNS has been handed a significant challenge in being asked to connect the research they fund to economic impact, since there are an enormous number of steps between the lab and societal impact that have nothing to do with the quality of the idea or the expertise of the researchers involved.

You can’t hold a researcher to account for a failure to realize economic return because there are so many aspects that are going to either frustrate or amplify creation of that economic value over which they have no control, but you can certainly expect that there is effort and intention behind what they do.”

The path from lab to market is often convoluted, and is only ever clear with the benefit of hindsight. This makes tracking outcomes extremely difficult, and leads to the typical proxy metrics that are used in most Canadian research programs: ;number of patents files, number of jobs created, etc. In my view, these should be considered input metrics rather than indications of output. Impact must be tracked on a much longer timeframe, one that is sensitive to what is reasonable in the context of different types of research. Stefan’s commentary supports this view:

“There was a study done not too long ago that looked at the timeframe between publication in a scientific journal, where the research has been “concluded”, and the application for a patent. Not the use of that patent, mind you, just applying it. That’s on the order of three to six years So, if you’re looking for evidence of economic benefit from the activity of research, you have to think about the timeframe of years. The more fundamental the research question, the longer that timeframe is. For basic research, we’re talking probably 20 years.”

Stefan notes that even when research eventually does achieve impact, the impact may not be felt in the same place the research was conducted.

“This is the challenge around R&D: you have a high degree of uncertainty in where those benefits are felt, but you have high certainty over where those costs originate. So you have what you could probably characterize as benefits that are diffuse; distant; hard to attribute to an individual supporter, and that are often disconnected from the original investor or research performer, but the costs have all the opposite characteristics.”

To respond to their mandate of provincial economic growth, RNS has to be clear-eyed about what it means to succeed, and they must be able to track the outcome of their projects over the long term, and that intention is just as important to outcome tracking as it is to identifying research worth funding:

“.[…] you need something that goes well beyond the timeframe of the research project itself […] and you need to focus as much on efforts and intent as on results.

Given the variety of research projects being funded and the diversity of ways in which research can respond to societal needs, there is no one-size-fits all approach to outcome tracking that will be effective. Stefan makes clear that rather than attempting to rely on a simplistic set of proxy metrics, their approach will be based on relationships:

“By success, I mean that the research that they are passionate about, and really want to see happen, is able to proceed through the system to its logical conclusion. That doesn't mean it necessarily always works, and so cultivating that personal relationship means that they're comfortable that when a challenge occurs […] we want to be their first call. We want to be able to ask them how we can help overcome the issue.”

Finally, iteration is key. Given the complexity, and the high probability that not all intention will translate to outcomes, the value of a tracking system is less about reporting on impact and justifying the money spent as it is about making sure that lessons are learned and incorporated into future funding calls.

“I think that the metrics you need to look at need to allow you to develop a sensitive understanding about why something worked, or why it didn’t work, so that you can apply those learnings to future investigations”

Outlook

Public funding for research has the potential to be enormously valuable to Canada. Just as not all research has an existing industry champion, not all societal needs can or will be addressed through private initiatives. In Stefan’s words:

“I think the appropriate area for public investment is where you have broad need, across a number of participants, where there is insufficient incentive for any one individual organization to pursue that particular work”

The commentary to this point makes clear that the debate should not be over what balance we should strike between funding fundamental or applied research, but rather that we should be thoughtful about ensuring that research is performed with intention and purpose. The words we choose to discuss policy challenges matter. I will be moving away from the idea of “demand-driven” research in my own writing and focusing on the more inclusive idea of purpose-driven and intentional research going forward.

Stefan’s closing remarks are particularly impactful. We are in a time of geopolitical upheaval, and it is more important than ever before that Canada learn how to translate its research excellence into real-world impact. Stefan comments that “constraint is often a precursor to creativity”, and it certainly feels to me that out public sector is waking up to the fact that doing more of the same thing we have been to date will not work, and that creativity is needed. This need for creativity in the face of constraint is the basis of my certainty that the change enacted by RNS will come to the rest of Canada in some form or another.

The strategic plan developed by RNS should be a central point of reference for all Canadian research funding programs that seek to achieve real-world impact from the research they fund. It reflects precisely what they ask of their researchers: a clear connection between the actions they are undertaking, and the needs of the society that they serve.

This will be the last CanInnovate post of 2025. Happy holidays!

A few months ago, the Conference Board of Canada (CBoC) published “Intellectual Property in Canada: Technology Specialization and Competitive Advantage”, the results of an impressive research process led by Zafer Sonmez. In this report, the CBoC team categorizes Canadian competitiveness in a variety of technology sectors through a careful examination of patenting trends, benchmarked against the world (and OECD) averages. Their results paint a bleak picture of Canadian competitiveness and IP ownership, but provide actionable insights for policy makers seeking to change this. I strongly encourage everyone to read their report, which is available freely at the link below.

Access the report

This wake-up call is perfectly timed, coinciding with a paper co-authored by myself and David Durand entitled “Intellectual Property is Economic and National Security”, in which we make the argument that sound IP management is critical for economic security and sovereignty. Where we provide the context that articulates why intellectual property is so important, the CBoC report highlights precisely where Canada can capitalize on existing strengths, and where we must improve.

In this post, I provide a brief overview of the report’s framework and methodology, and pull on a few threads that I think deserve specific attention. I will not cover the entire report, which contains a wealth of information and should be required reading for policy makers on the innovation file.

I would like to commend Mr. Sonmez and the CBoC team for an insightful and actionable contribution to an urgent debate about Canadian competitiveness and sovereignty.

A brief overview of methodology

The CBoC report is built on the basis of patent ownership data. Their conclusions revolve around two key metrics: the Relative Specialization Index (RSI) and the National Shift (NS). RSI, in a nutshell, measures the degree to which Canada holds a concentration of patents in a particular sector relative to that of the rest of the world, while the NS measures the pace of new patenting in that sector relative to the rest of the world. A high value of RSI indicates existing specialization, while a high value of NS indicates that Canada has a strong rate of invention in the sector, pointing to the possibility of emerging specialization. They use these metrics to categorize each sector considered as one of:

1. Leading inventive strength (RSI > 1, NS > 0), meaning that Canada has both existing specialization and a strong rate of invention;

2. Slipping inventive strength (RSI > 1, NS < 0), meaning that while Canada has specialization, the rest of the world is catching up;

3. Lagging inventive strength (RSI < 1, NS < 0), meaning that we are both behind and falling farther behind; and

4. Emerging inventive strength (RSI < 1, NS > 0), meaning that while Canada is currently behind, we are catching up the rest of the world.

The report further examines policy aimed at technological advancement and ranks each sector according to whether or not Canada’s inventive strength reflects stated policy priorities (spoiler: it mostly doesn’t).

Strengths, weaknesses, opportunities, and threats

It is interesting to note the mismatch between the typical language around Canadian specialization as reported in the various national innovation strategies and the realities exposed by this report. For example, Canada is generally regarded as having a strong biotech sector, and while we do indeed have specialization here, the report makes clear that we are resting on our laurels and in danger of losing this distinction.

Nanotechnology stands out among the surprising opportunities in the report. While we lack strong specialization, it appears that we are on track to change this. This is a trend on which we should double down.

Probably the most striking result was the near-complete misalignment in technology sectors relating to agriculture. As was highlighted in my previous interview with Mark Olson, Canada has the potential to be the world’s bread basket. We have unmatched potential in agriculture, with abundant natural reserves of nearly everything necessary while being one of the few countries on the planet whose capacity for food production will not be negatively impacted by a warming world. Canada could project power through food export over the next several decades if we chose to use our myriad natural advantages.

At FORPIQ 2025, a panel discussion on Canada’s potential as an agricultural powerhouse highlighted that the second largest food exporter in the world (after the US) is the Netherlands. This is based entirely on highly efficient and innovative agricultural practices, given that their available arable land area is miniscule compared to Canada’s. Their examples demonstrates just how much room we have to improve. Our failure to secure competitive advantage in this area represents an enormous wasted opportunity for sovereignty-focused economic development.

One of the most concerning issues highlighted in the report relates to the distribution of patent ownership. CBoC shows that Canada lags far behind where organizations with large patent portfolios are concerned - but patent ownership tells only a part of the story. Where access to or control over patents is granted through licenses instead of assignment, it will not show up in the ownership data. In other words, the reality is even worse than what the report shows.

The fact that licensing details cannot be extracted from public data points to a systemic failure to collect that data rather than any weakness of the report. Rather, this highlights the importance of ensuring that data is collected on control over and access to Canadian IP, especially in cases where the IP is the result of publicly funded research. The recent submission by the SAIL team to the House of Common Standing Committee on Science and Research, as well as the guidance we have previously built around the Simple Agreement for Innovation Licensing (SAIL), give some more detail on the kind of data needed to address this.

What’s next

While it is to be expected that Canada lacks incumbent competitive advantage in many sectors, the number of sectors that are deemed national priorities in which we lack even emerging inventive strength should be a wakeup call for policy makers that current fragmented efforts are not effective.

This is not just a matter of economic competitiveness, but also one of economic security. Priority sectors in that Canada funds heavily but in which we lack emerging inventive strength in particular should be reviewed carefully, as these sectors are likely to be leaking IP. The CBoC report and our paper on the connection between IP and economic security combine to form a roadmap to address these challenges: Allocate funding to the early stages of research commercialization in sectors of national interest in which Canada lacks competitive advantage, and use slipping and lagging inventive strength as bellwethers for weaknesses in Canada’s approach to IP management.

Above all, recognize and prioritize investment in Canada’s natural strengths. If Canada is to secure its economic and technological sovereignty, we will need to be strategic in our approach. We are not big enough to be leaders in every sector, but we are well positioned to dominate a few. This report shows clearly how our inventive resources can be allocated to achieve maximal impact.

In our work connecting IP to economic security, one of our core recommendations was to create an open-source intelligence agency that continuously monitors emerging technology trends through analysis of patents and research funding. This CBoC report shows us just how valuable that capability would be.

Last week I appeared as as witness before the House of Commons Standing Committee on Science and Research (SRSR) to discuss ways to overcome Canada’s challenges turning research into economic impact.

I was encouraged to note that I was not the only voice in the room calling for risk-tolerant capital to be deployed in support of research commercialization. Other notable points made by my colleagues (in particular Dr. Gerry Wright and Kevin Dahl from ElevateIP Alberta, with whom I am in full agreement), relate to a pressing need for

1. funding specific to early-stage startups commercializing research;

2. funding for IP creation, protection, retention, and strategy;

3. IP-based valuation tools and IP-backed lending mechanisms; and

4. moving away from input metrics like research spending as indicators of impact and toward actually measuring the resulting outputs.

My presentation is shown in the excerpt below, and you can watch the full proceedings at the link underneath.

Watch full session on ParlVu

Transcript of Opening Remarks

Thank you Honourable Chair Zahid, Vice-chairs and Committee members for the invitation to speak to you on commercializing innovation emerging from publicly funded research at Canadian universities.

To briefly introduce myself, I am a physicist and the Entrepreneur in Residence at the Faculty of Science at the University of Ottawa. I was the CEO of Northern Nanopore Instruments, a nanotechnology startup built from my PhD research that we sold in Fall 2023. I am now the author of the CanInnovate blog on innovation policy and, alongside TJ Misra and David Durand, co-author of the Simple Agreement for Innovation Licensing (SAIL) and co-founder of the SAIL Initiative, through which we aim to streamline how new technologies get from lab to market.

The value of research follows a power law distribution: a small minority of new technologies eventually create most of the economic value, but it is impossible to reliably predict which ones will succeed or be valuable when the research is transferred to the private sector. Because of this impossibility, it is more important to make sure that we do not miss the valuable minority than it is to make sure that every attempt is successful. It follows that the most effective strategy for commercializing research successfully is to invest relatively small amounts of capital early in almost everything. Mostly, Canada does not do this.

Attempts to commercialize research have failure rates above 90%, but the successes create more than enough value to offset the cost of the failures. Countries that “invest well” have three things in common, namely, the public sector:

1. funds innovative startups before they have revenues,

2. usually favours new startups over existing companies as vehicles of innovation, and

3. is willing to let previously-funded projects fail when necessary.

This approach to funding serves to derisk innovative companies to the point where they can attract private sector investment, and ensures that valuable technologies do not slip through the cracks.

Ironically, systemic risk intolerance in the Canadian public sector mostly prevents it from funding pre-revenue companies in this way, and Canadian VCs cannot address the gap because of the long timelines involved. We must reframe how we evaluate and manage risk. We must understand that most investments will fail, and that this is acceptable as long as the combined return over time is positive. So while it may be true to say that our public sector has become too risk averse, I argue that “investing in almost everything” is in fact less risky (or at least more likely to produce a positive outcome).

Another element common to effective commercialization elsewhere is harmonized innovation policy. The United States has the Bayh-Dole Act, for example, that guides how universities transfer technologies to the private sector. In Canada, we lack even an attempt at national coordination. Senator Colin Deacon’s office recently found 134 different innovation funding programs at the federal level alone, and the tri-council agencies provide no top-down guidance on how universities should manage research IP. As a result, every research institution has a different IP policy, licensing negotiations with most research institutions are slow, no two licences are alike, and there is no standard for data collection on licensing or outcomes.

To address these challenges, my colleagues and I developed SAIL. SAIL is a licence framework designed to support harmonized and streamlined Canadian tech transfer from research institutions to startups. After consulting with a national community of innovation stakeholders, we designed SAIL based on 6 axioms of tech transfer specific to Canada’s unique challenges. It asks universities to play the role of “first investor” in research commercialization and rewards them with a pre-defined amount of convertible debt in exchange. The framework can be easily amended (with legal advice) to support a variety of startups more efficiently and effectively than building a new licence from scratch every time.

We are also working to adapt a risk-tolerant funding mechanism that has been highly successful in the UK, where it created an estimated $7 of economic value for every $1 of input. This model uses venture philanthropy delivered through a public-private partnership, combining public funds with private donations and university contributions to create a charitable investment fund that reinvests all returns to ensure that it is self-sustaining. Variations on this model have been implemented at a handful of Canadian research institutions, most notably the UCeed fund in Calgary, and we propose to implement it at the national level.

Policies to promote and grow private sector investment in research can only be effective if we increase the pool of investable, innovative companies. To do that, we must first build a better bridge from lab to market. I recommend that Canada:

1. embrace strategic risk-taking: deploy public funding toward pre-revenue startups commercializing Canadian research by trialling venture philanthropy delivered as a public-private partnership with a national scope, and

2. nationally harmonize a Canada-first approach to management of the IP arising from publicly funded research.

Thank you distinguished members of this Committee for your time. I look forward to your questions.

About a year ago, I was asked by the Canadian Council of Academies (CCA) to write an evidence synthesis paper on the state of deep tech in Canada. The paper that resulted, which I submitted to CCA in February 2025, is 1 of 8 that informed a report entitled “The State of Science, Technology, and Innovation in Canada 2025”, commissioned from CCA by Innovation, Science and Economic Development Canada (ISED). The completed report, as well as the paper I contributed entitled “Challenges and opportunities for Canadian deep tech commercialization”, are now available publicly.

In this post, I provide a brief overview of my contribution, since it synthesizes in one place many of the key themes that have informed by writing on CanInnovate since I started this project. The other papers commissioned to support the CCA report are also well worth a read.

Many thanks to CCA for the opportunity to write this paper, and to the peer reviewers whose commentary resulted in a much stronger contribution.

What is Deep Tech?

In the course of building my own deep tech company and in all my subsequent writing, I realized that the phrase “deep tech” means something different to everyone who uses it. For this reason, someone watching carefully might have noticed that I have moved away from using it in my own writing, because it can easily be the basis for misunderstanding unless one takes the time to carefully define it every time. This became the first task of the paper.

After careful review, I adopted the definition presented in an earlier review by Romasanta et al., who come to the conclusion that deep tech is:

“Early-stage technologies based on scientific or engineering advances, requiring long development times, systemic integration, and sophisticated knowledge to create downstream offerings with the potential to address grand societal challenges”

There is a lot of complexity buried in this definition, which the paper attempts to unpack and use to inform a detailed discussion of the challenges and opportunities for Canadian deep tech commercialization. I will be using this definition in my own writing going forward wherever deep tech comes up.

Challenges and Opportunities

In the paper, I focus on articulating challenges with deep tech commercialization that revolve around a few key themes. The first is IP governance and harmonized innovation policy. I compare the approaches used by the United States via the SBIR program, the approach used by the Israeli Innovation Authority, and Canada. As with previous articles that touch on these themes, I stress that while Canada has a lot to learn from other jurisdictions, it is not possible to directly import policy frameworks that work elsewhere—we must adapt them to the Canadian context first. As I have written in other contexts, Canada must harmonize its approach to innovation generally, and to intellectual property governance in particular, if we hope to become effective at commercializing deep tech.

The second main theme of risk tolerance winds through all the commentary in the paper. Risk-tolerant public funding, delivered to startups and small companies commercializing deep tech, is a common theme of innovative ecosystems that are effective at extracting economic value from deep tech, and lies at the core of Canada’s problems with commercialization deep tech and research. I draw on the example of the American Small Business Innovation Research (SBIR) program and compare it to the recently cut Canadian Innovative Solutions Canada, arguing that Canada will need to rely more heavily on active coordination between market demand for deep tech and the research that generates it than our southern neighbours.

The third main theme is data, and Canada’s systemic failure to collect metrics of performance that are useful for policy reform. In the paper, and drawing on work by others, I suggest that we must improve our data collection practices in any new initiatives aimed at improving our situation, acknowledging that intervention and impact may be separated by many years where deep tech is concerned. I also point out that university-focused investment funds that deploy patient capital are uniquely positioned to collect that data, provided that the approach can be standardized, and that long-term tracking of control over and access to research IP assets should be prioritized as an input to future policy updates and mandated as a condition of research IP licensing.

Finally, I spend some time debunking the idea that Canada’s challenges with deep tech commercialization are cultural.

The report concludes with reviews of three sectors with overlap in deep tech: quantum technologies, cleantech, and artificial intelligence (AI). The lessons that I present from review of related literature strongly support the ideas that

1. Canada’s quantum advantage provides a blueprint for deep tech dominance that can be replicated,

2. Greater coordination is needed between the various moving parts of the innovation ecosystem in areas (like cleantech) in which regulatory complexity is high, and that

3. Canada’s approach to AI is a cautionary tale of what happens when we fail to act sufficiently quickly to secure a deep tech advantage, but that all hope is not lost.

As I note in the conclusion of the paper:

“Canada has all the raw ingredients it needs to be a globally relevant force for deep tech commercialization.”

The CCA report is a strong contribution to the debate around how best to combine these ingredients, and I am grateful to have been given the opportunity to contribute. This paper represents a synthesis of many of the themes and ideas that have informed the articles on CanInnovate to date. I hope you enjoy reading it as much as I enjoyed writing it.

This week I interviewed Bruno Couillard, the CEO of Crypto4A, an engineer who has spent a decades-long career building the hardware security solutions that have underpinned trust on the internet for decades. Through Crypto4A, he seeks to do the same for a world where quantum computing is a reality and where legacy cryptographic solutions are no longer an option.

My goal in this interview was to better understand Canada’s contribution to this space and the opportunities before us as we reorient our economy toward security, sovereignty, and defense. Bruno’s insights touch on the foundations of what digital sovereignty means practically and how security and trust on the internet will evolve as we move toward a world in which quantum computing is a reality.

Bruno also makes clear that Canada has made globally leading contributions to cybersecurity that it has mostly failed to benefit from economically to date, and that we have a rare second chance to get it right as quantum computing becomes practical and the world shifts its priorities toward digital sovereignty.

Your email client will probably truncate this post. My key takeaways are presented at the end, so be sure to read the web version if you want to get the whole story. Many thanks to Bruno for sharing his experience and insight.

Interviewer’s note: Bruno Couillard and the Crypto4A team approved the final version of the section entitled “Interview with Bruno Couillard” and had editorial input on that section, with the option to rephrase and expand on the ideas discussed in the interview without changing or removing any intended meaning. The key takeaways presented at the end are my own commentary, and do not necessarily represent the views of Crypto4A or its employees.

Interview with Bruno Couillard

KB: How’d you find your way into quantum cryptography?

BC: My journey into cryptography and key management began in 1988. I had just finished my military officer training at the Royal Military College, and I was assigned to a fascinating project in Ottawa called the Integrated Data Network (IDN). It was essentially a Canadian Forces–wide internet, connecting all bases across Canada and even the two we had in Germany at the time.

The IDN relied on a mix of link and end-to-end encryption, securing both classified and protected information. Fresh out of engineering school, I was eager to contribute, and I got assigned to the cryptography and key management side of things. Back then, these concepts weren’t taught in school you had to learn them on your own. I dove in, and it was love at first sight. That set the course for my career: cryptography, key management, and public key infrastructure have been my focus for 37 years now.

From IDN, I moved to the Communications Security Establishment (CSE) in 1992. Then, just as the Internet was taking off in 1994, I left to co-found Chrysalis ITS in Ottawa. As CTO and co-founder, I transitioned from theory into practice designing and evolving hardware products. That’s where I created the Luna HSM, which became a leading Hardware Security Module worldwide. I also helped shape standards like PKCS#11 and contributed to best practices around root key ceremonies. After almost a decade, I left Chrysalis in 2003.

Editor’s note: A Hardware Security Module (HSM) is a dedicated physical device designed to securely generate, store, and manage cryptographic keys. HSMs perform operations such as encryption, decryption, digital signing, and key generation within a tamper-resistant environment. They are foundational to the security of systems that require strong encryption and regulatory compliance such as banking, cloud operations, and secure communications, and are key elements of public key infrastructure (PKI).

At that point, I wanted to recharge and spend more time with my family. My wife and I have three sons who were busy with school, sports, and music. I rebranded myself as a consultant and returned to projects with DND and CSE. It felt like going back to my roots, but with new perspective: instead of focusing on commercial HSMs, I was working on high-grade security systems.

Around 2003, one of the major efforts was modernizing cryptography and key management across allied security systems. The idea was to migrate algorithms quickly enough to withstand advances in general-purpose computing. I was heavily involved in Suite A and Suite B crypto right from their inception.

Then, in 2009, the NSA published a pivotal statement: it wasn’t general-purpose computers we needed to worry about anymore, it was quantum computers. Practically overnight, post-quantum cryptography (PQC) became the main focus. That announcement grabbed my attention. I began watching closely, wondering if anyone in the commercial HSM market was preparing for this challenge. To my surprise, no one was.

That’s when I started thinking about building a new HSM from scratch something designed from the ground up to be quantum-safe and ready for the fast-evolving digital economy. Together with some long-time colleagues from Chrysalis, we decided to take the leap. And that’s how Crypto4A was born.

From day one, our vision was clear: Crypto4A would create the HSM built to survive and thrive in a quantum era. These devices matter because they sit at the very center of the digital economy, which now represents more than a third of the global economy. This all traces back to 1995, when Netscape introduced SSL for secure browsing. Behind that were authentication certificates issued by Entrust or Verisign, and guarding those authorities was the Luna HSM. That triad, SSL, PKI, and HSM remains the foundation of digital trust today.

Over the past 30 years, we’ve only layered more services onto that same foundation: banking, government, identity, communications, everything. And behind it all, somewhere in the background, an HSM protects the root keys of trust. Having been part of the team that created the first successful generation of HSMs, I felt it was our responsibility to build the next generation this time quantum safe.

Life has given me incredible opportunities to be at the right place, right time, with the right people, participating in one of humanity’s greatest transformations: digitizing a third of the global economy in just three decades. Now, we’re entering the next major transitions; AI today, quantum tomorrow. But none of it will be possible unless the foundations of digital trust we laid down in 1995 are made quantum-safe and continue to hold strong for decades to come.

KB: Where are Canada and the rest of the allies in terms of replacing their hardware stack to make it quantum resistant?

BC: If you had asked me this question six to eight months ago, I would have said the United States was clearly leading the world in this push, with Europe and Canada following behind.

But today, I’d say Canada is really stepping up to the plate and starting to take a much more significant leadership role. What’s interesting is that this is happening at a critical moment for the digital economy. Two major themes, trust and sovereignty, have now joined the conversation alongside quantum readiness.

If we’re going to build the capabilities and technology stack for the future, its foundation, cryptography, must be quantum safe. That means protecting the root of trust while also ensuring sovereignty of its operations. Put simply, there is no digital sovereignty without cryptographic sovereignty. And this is exactly where HSMs come in, and where Canada has a real opportunity to lead.

On the political front, our leaders have been making bold statements over the past few months. We’re hearing strong, visible messages from the highest levels of government about the sovereignty of our information and the urgency of preparing for the quantum era. Even at the recent G7 meeting in Canada, quantum realities were flagged as a concern on the global agenda. Data sovereignty is no longer a niche issue; it’s now front and center here at home and around the world.

Of course, the real test will be whether these political commitments can make their way through the machinery of government to create meaningful action. That’s never easy. Cryptography underpins so much of our world that it’s almost easier to list the things that don’t use it than the ones that do.

The good news is that Canada has an incredible foundation to build on. We’ve been investing in cryptography and key management since the early 1990s, when PKI and HSM technologies first took root here. That’s when both Entrust and Chrysalis-ITS were born out of the Nortel/BNR era. Those companies became the backbone of the modern digital economy, and their DNA is still very present in Ottawa today.

The Luna HSM design team, now part of Thales, still works out of Ottawa South. Entrust’s PKI team is still in Kanata. Crypto4A itself was founded by former Chrysalis-ITS and Entrust engineers. Taken together, Ottawa, and by extension Canada, likely represents the highest concentration of cryptographic and key management expertise anywhere in the world.

For some reason, we became very good at this discipline. And, true to Canadian form, we don’t brag about it. But we should. Because the reality is: Canada owns some of the most critical pieces of this global puzzle. And given the scale and complexity of what lies ahead, I’d much rather be tackling this challenge here in Ottawa, Canada, than anywhere else on the planet.

KB: Beyond encryption breaking, what’s the business case for quantum cryptography? What’s real and what’s hype in the current thinking around capabilities relating to quantum cryptography?

BC: Before I dive in, let me refine the question a little. The real capability we’re worried about isn’t just “quantum cryptography” in the broad sense, it’s the arrival of quantum computers powerful enough to run Shor’s algorithm. Once that becomes possible, whoever controls that capability will be able to break virtually all the classical public key cryptography we rely on today. That’s why there’s a global race underway to migrate as quickly as possible to post-quantum cryptography, or PQC.

Now, when people hear “quantum cryptography,” they often confuse two very different things. What I just described is PQC developing new, quantum-resistant algorithms to replace the ones we use today. But there’s also a field properly called quantum cryptography, which uses the principles of quantum mechanics, things like entanglement and superposition to achieve new forms of security.

One of the best-known examples is Quantum Key Distribution, or QKD. It allows cryptographic keys to be generated at two different locations in such a way that both parties can be confident the key is identical, and that it hasn’t and in fact can’t be copied or intercepted anywhere else in the universe. This concept was first introduced in 1984 by Charles Bennett and Gilles Brassard in what became known as the BB84 protocol. Since then, several variations have been developed, but they all rely on the same unbreakable quantum mechanics principles.

We’ve already seen some remarkable demonstrations. China, for instance, has deployed advanced QKD networks and even tested satellite-based systems where secure keys were exchanged between Earth-based stations and satellites in orbit. These are fascinating achievements and hint at some powerful niche applications in the future.

That said, it’s important to keep perspective. QKD isn’t a wholesale replacement for the cryptography we use today. It addresses certain use cases very well, but it won’t solve the broader challenge. Most of our digital systems from banking and government to cloud and communications will continue to depend on PQC. In practice, QKD should be seen as a complement to PQC, not a substitute.

So, to your question of what’s real and what’s hype: the urgent and unavoidable business case is defensive replacing our current cryptography with PQC before Shor-capable quantum computers arrive. QKD is real as well, but more specialized, and won’t replace everything. The hype comes when people conflate the two or imagine QKD is going to single-handedly secure the digital world. PQC is the foundation we must address first, and QKD is an exciting complement that will play a role in specific scenarios.

KB: Can you speak to the limitations that prevent QKD being a replacement for more traditional cryptography methods?

BC: The limitation with QKD is really about practicality and scope. For example, if you and I wanted to set up a QKD-secured session between our two locations, we’d need a dedicated fiber optic cable connecting us. If the distance was too long, we’d have to rely on multiple QKD segments or even use a satellite-based system. That works well for “real-time” sessions securing data in transit, but it doesn’t solve long-term security needs.

Take something as simple as buying a house. If I sign a digital contract today, that signature has to remain valid not just tomorrow, but 20 or 50 years from now, so I can still prove I own my home. That requires a digital stamp that will stand the test of time. QKD can’t do that. Only PQC algorithms can provide the kind of durable digital signatures needed for those use cases.

The same applies to countless everyday examples. If you buy a smart car, the manufacturer will send firmware updates for years. Each update must be digitally signed to guarantee authenticity and integrity. In the quantum era, PQC algorithms will be the only way to achieve that. The same holds true for critical medical devices like pacemakers or insulin pumps, where firmware updates must be trusted without question.

That’s why there’s such urgency around migrating these systems now. Once a satellite is launched, or a medical device is implanted, or a long-term legal contract is signed, the cryptographic signature tied to it has to remain secure for decades. Those are “long-lived signatures,” and they absolutely must be quantum safe.

If you look at the landscape, there are thousands upon thousands of use cases where we’ll need to authenticate, verify, and preserve the integrity of digital artifacts in our lives. All of those still exist in the digital world of ones and zeros. We haven’t moved into a pure world of photons and light yet and we likely won’t in our lifetimes.

So, for now, and for many years to come, our security will continue to depend on PQC. QKD is exciting, but its use cases are in the dozens, not the thousands. It’s worth experimenting with, but it’s not “the next security solution.” It’s a complement. The heavy lifting will remain with digital cryptography zeros and ones for a long time.

KB: “Quantum” as an umbrella term has entered pop culture and often means different things to different people. What are some common misunderstandings and misconceptions that you encounter?

BC: Early on, there was a lot of hype around quantum. People talked about it as if it were a panacea that it would solve every problem and usher in a perfect future where we all lived on a cloud. The reality is much different. Quantum technologies are extremely hard, very expensive, and although they will eventually be amazing for many applications, we are still far from realizing all their promises. One day, yes, we’ll use quantum computers as naturally as we use classical computers now. But today, we’re still in the early stages.

This hype phase has a downside: customers get bombarded with messages that range from the realistic to the completely ridiculous. That noise makes it difficult for those of us focused on a secure transition to the quantum era to cut through especially when it comes to post-quantum cryptography. Too often, people think the solution is as simple as deploying quantum key distribution everywhere.

Another challenge is language. Many of the terms are not yet fully standardized, so people use them interchangeably even when they mean very different things. Fortunately, if you read Canada’s National Quantum Strategy, or other national strategies, you see a clearer framework emerging for the different “buckets” of quantum technology.

The first bucket is quantum computing. These are machines that replace today’s ones and zeros with photons or other quantum particles, and that can run true quantum algorithms. One of the most powerful, and frightening, of these algorithms is Shor’s algorithm. Discovered in 1994, it can break every classical public key cryptosystem: RSA, DSA, ECDSA, Diffie-Hellman, and more. This is what launched the global race to build such machines, with billions invested so far. Roughly half of that investment has come from China, and it would be naïve to think their priority is only better materials science or drug design. The more immediate goal could be breaking our current cryptography. If that happens, the consequences would be catastrophic: our banking system would fail, our digital economy would collapse, and society itself would grind to a halt because the Internet could no longer be trusted. Every sector of our lives, from mining to farming, already depends on secure digital connections. Without trust in the Internet, modern society doesn’t function.

The second bucket is quantum sensing, which I find fascinating. By harnessing quantum effects, you can detect incredibly faint signals: mapping mineral deposits deep underground, exploring the oceans, reading subtle electrical signals in the human brain, even probing signals from the far reaches of the universe. Compared to these advances, our classical sensors will look primitive. Of all the quantum technologies, I think sensing may have some of the most profoundly positive impacts.

The third is quantum communication, and this is where confusion tends to multiply. It includes things like quantum key distribution, which we’ve already discussed, as well as quantum teleportation, the idea of transmitting particles across distances to create new forms of communication. Another element is quantum random number generation, where we harness inherently unpredictable quantum processes, like radioactive decay or photon behavior, to create true randomness.

Finally, there is post-quantum cryptography. Strictly speaking, PQC is not “quantum” in that, it is not strictly speaking based on quantum mechanical principals at its core. Instead, it uses advanced mathematical algorithms designed to withstand both classical and quantum attacks. PQC exists for one reason: to ensure our digital infrastructure remains secure in a quantum world. At Crypto4A, we’ve built a new generation of cybersecurity products around this principle. Our solutions still operate with ones and zeros, but they use the new algorithms that NIST has been standardizing after an eight-year global competition. Three were standardized in August 2024 and more will follow in the coming years. From now on, it’s not enough for systems to be quantum-safe they must also be crypto-agile, able to adapt as algorithms evolve.

This is where misconceptions really take hold. Some vendors will say that because they have a quantum random number generator, their products are “quantum ready.” Others claim QKD or teleportation will solve every security challenge. Some even suggest they can magically make a classical device quantum-safe with a quick patch. None of that is true. To be truly quantum-ready, you have to redesign your entire stack to survive in a world where quantum computers exist. Every element has to be tested against that future reality. The litmus test is simple: imagine your design in the year 2040. Quantum computers are everywhere. Could a hacker in that world break your system? If the answer is yes, then you’re not quantum ready.

KB: Taking that one step further, what should governments and businesses be doing right now? How long do they have to get it done?

BC: We already solved this kind of problem once before. Thirty years ago, November 1995, we figured out how to make the Internet a trusted environment for business. That was when Netscape introduced SSL, completing what I call the “digital trust foundation.” SSL secured communications between browsers and servers, PKI technologies from Verisign and Entrust allowed Certificate Authorities to issue SSL certificates, and the Luna HSM that we had developed at Chrysalis-ITS here in Ottawa, underpinned the cryptography at the core. Those three pieces, SSL, PKI, and HSM, are still the same foundation the Internet rests on today.

To get to a quantum-safe world, we need to repeat that process. First, deploy quantum-safe HSMs. Then, make PKI quantum safe. And finally, update Internet standards and protocols so they can use quantum-safe certificates. You start at the foundation and work your way up the stack. Whether you’re a bank, a government, a manufacturer, a transport system, or an energy grid operator, the first step is the same: begin deploying quantum-safe HSMs.

At Crypto4A, we’ve built these next-generation HSMs. They’re quantum-safe by design, highly specialized machines that we can produce at scale. But like any physical component, supply is not infinite. Think back to COVID, when everyone suddenly needed masks and gloves, the lineups were endless, and prices spiked. The same will happen if organizations wait until the moment a breakthrough in quantum computing arrives. If you start now, the migration can be steady and manageable. If you wait, it will be chaotic and costly.

There’s no downside to moving early. I always recommend that governments, banks, and anyone providing trusted services get started right away. Canada has a chance to turn this into a national advantage. We’ve always punched above our weight in cybersecurity. If we make quantum-safe migration a major national project, Canada could cement itself as a global leader for decades to come.

And this isn’t just about technology. The digital economy, the intangible economy, is already outpacing the tangible one. In that reality, leading the world in securing cyberspace is the equivalent of having the strongest defense forces. It’s a matter of national strength and sovereignty.

KB: In a military context there is a need to build systems that allow for interoperability with allies, which points to the need for standards that are agreed internationally. Does Canada have a role to play in defining those standards?

BC: I’d say Canada is already very involved in shaping these standards. Over the past 30 years, much of cyberspace has been built through the work of international standards bodies groups of volunteers who dedicate their time and expertise to advancing the protocols we all depend on. One of the most important of these is the Internet Engineering Task Force, or IETF, which was founded more than 60 years ago and continues to evolve the core protocols of the Internet today.

Crypto4A actively participates in this work. We’ve contributed to efforts at the IETF, and we’ve been deeply involved in the post-quantum cryptography standardization process led by NIST. Our team has consistently pushed the envelope on quantum-safe design, because our mission is to ensure cyberspace remains secure and trusted for generations to come.

We’re also proud to be the leading HSM vendor in the quantum migration project run by the National Cybersecurity Center of Excellence (NCCoE), a lab under NIST in Maryland. That project brings together the world’s top cybersecurity companies to demonstrate interoperability of quantum-safe cryptography and related standards. Since its launch in 2022, we’ve led the HSM interoperability testing and have consistently delivered fully functional, standards-compliant products some of which are already deployed in live operational systems today.

And it’s not just us. The Government of Canada itself has a strong presence in these efforts, with experts participating in NIST’s standardization process, the evolution of IETF protocols, and other international bodies. Taken together, Canada’s contributions are significant. We’re not just observers we’re helping shape the very standards that will define how the world transitions to a quantum-safe future.

KB: Following the Luna HSM over the years, it has been the target of 4 acquisitions, the first of which caused Canada to lose control of this IP. It is still in service now, decades later, as part of the Thales Group IP portfolio. Tell us the story of what led to the original decision to sell the IP. What needs to happen differently this time around if we want Canada to lead in the post-quantum HSM space?

BC: After coming out of a military and government career, I wanted to build something to put into practice all the theory I had developed on how to ensure trust in the digital world. I co-founded Chrysalis, focusing on the technical side of things while my co-founder ran the business.

From 1994-1998 we designed the Luna HSM, and by 1998 we were selling it to the US, Canada, a few European countries, and Japan. A lot of our sales were alongside Entrust sales or directly to Verisign, forming the foundations of public key encryption (PKI). In 1998, the dotcom bubble was well underway, though, and chip-design was all investors wanted to hear about. Our investors decided that we needed to design a chip. We tried and failed to find a contractor that could design it, and eventually hired some very expensive in-house expertise to do it. We almost did, but in 2001, the board decided to pull the plug on all the semiconductor work. My co-founder was let go on August 15, 2001, along with 100 other people, and I was told to stay on as CTO and to go back to our roots with the Luna HSM and a number of other projects. September 11 happened a few weeks later, and the company almost disappeared. In 2003, after two years of intense efforts, we’d managed to turn things around with the introduction of the world’s first network-attached LunaHSM with very promising sales results. At that point, I decided to refocus my energy on my young family with my wife, left Chrysalis and transitioned into a consulting role with the Canadian Government. Shortly thereafter, Chrysalis was sold at a bargain price, basically just to acquire the IP.

That Luna HSM IP went on to become the global standard for its purpose and is still in use today, but it was lost to the Canadian ecosystem in 2003. It has since been sold several more times - from Chrysalis it went to Rainbow Technologies, then SafeNet, then Gemalto, and most recently to Thales. Given the value that IP has created in the world since it was first sold, I think that a Chrysalis that had stayed fully focused on the HSM instead of getting side tracked by semiconductor hype might still be alive and Canadian today. If there was a mistake made, aside from the semiconductor mess, it was simply not recognizing the value of the IP that we had and being relatively junior to the process of building a company.

Crypto4A is a second chance to do the same thing for a post-quantum world what LunaHSM did for the early internet: ensure the future strength, security, and trust of the internet in the upcoming quantum era. Someone is going to do it, because otherwise the internet stops working.

This is especially critical as we start to talk about data sovereignty. The only way you enforce security capabilities is by controlling the keys used for the cryptographic processes that secure your data. Without that control, all bets are off. You cannot ever allow data to be sent abroad and let someone else control the keys: no key, no sovereignty.

I believe what we are building is the world’s best technology for its purpose and that it will have the same longevity as the Luna HSM. I can assure you that I know far more today than I did when designing the LunaHSM - we will not make the same mistakes twice.

KB: What should I have asked you but didn’t?

BC: One of the biggest challenges right now is clarity, separating truth from noise. There’s so much misinformation, hype, and fearmongering out there that it becomes very difficult for people outside our “inner circle” to focus on what really matters.

Decision-makers today are bombarded with messages and marketing claims, many of them contradictory. That’s why your role as someone who listens to these different voices and distills them into something understandable and neutral for policymakers, lawmakers, and planners is so critical. You may not be an expert in quantum technologies or cryptographic algorithms, but you still have to make sense of it all and present it in a way that drives sound decisions. That’s not easy.

It’s something I think about a lot in my own organization. We recently brought in our first marketing lead, and one of my requirements was that they not come from a cryptography or key management background. I didn’t want someone who would immediately slip into the jargon we use among ourselves. I wanted someone who could translate our work into clear, accessible language for a broader audience.

It’s hard to do that when you’ve been immersed in the technical soup for 35 years, as I have. If you’re starting a quantum computing company, chances are you’ve just finished a PhD in physics and you’re ready to build machines. If you’re starting a cryptography company, you’ve probably been steeped in algorithms and key management your whole life. And when that’s your world, it becomes difficult to step outside of it and explain your work in terms your mom or dad would understand.

That’s the challenge we face, and it’s the challenge you face as a journalist. Taking these incredibly complex, technical topics and rendering them into something that makes sense to the people who need to make critical decisions for our collective future. It’s not just important; it’s essential.

Despite the complexity, I see this as one of the most exciting times to be alive. We’re navigating unprecedented technological transitions, and we have the chance to ensure that the world of tomorrow is secure, trusted, and resilient. We really are living in one of the greatest eras.

Although you did not directly ask me this question, let me share what I would do if I could make one critical strategic decision for Canada today.

I would launch a national initiative called “Quantum-Safe Trust Infrastructure.” Like Churchill Port or the Trans Mountain Pipeline, this would be a nation-building project—except this one secures the cyberspace underpinning our digital economy and critical infrastructure.

This initiative would include:

• Establishing a strategic reserve of ready-to-deploy quantum-safe hardware.

• An immediate transition of all federal IT systems to quantum-safe standards.

• A mandate for the Bank of Canada to lead the migration, followed by the broader banking sector.

• A national program to work with provinces and critical infrastructure owners to migrate their IT and OT systems without delay.

• A workforce training and incentive program to ensure Canada has the talent required to deploy and maintain quantum-safe technologies at scale.

The foundation for this project already exists. It is 100% Canadian, and it represents the world’s most advanced capability in this domain. We have the technology, the people, the manufacturing capacity, and the expertise to act—right now.

By treating cyberspace as a new dimension of national defense—alongside land, sea, and air—investments in this project would also count toward Canada’s NATO commitments. More than that, Canada could position itself as the Quantum-Safe Trust Infrastructure provider of choice for all our allies.

This is a “now” project. One that secures Canada’s leadership, sovereignty, and trust in the quantum era.

Key Takeaways

A rare second chance

As I explore the Canadian innovation space, I often find myself surprised, not only at the pioneering contributions that Canadians had made to the technologies that quietly operate in the background of the modern world, but at how unassuming Canadians are when they discuss their contributions. Bruno tells the story of contributing core intellectual property (IP) to the foundations of digital security, IP that has been in productive use for three decades and counting, and he tells it without any pretentiousness.

I was less surprised to learn that Canada no longer controls the IP he developed. The LunaHSM IP has been sold 4 times over its 30 years in service, first to US-based companies, and finally to Thales in France, generating enormous value in the process, value not enjoyed by Canada. In a bit of a break from the usual culprit, though, Bruno’s story doesn’t point to a specific policy failure as the reason for the original sale, simply a combination of timing that coincided with the initial semiconductor hype cycle, relative inexperience as a first-time founder, and investors chasing hype.

Unlike most similar stories, however, Canada has second chance to get it right. Crypto4A is trying to do for the internet what Chrysalis and the LunaHSM did for the nascent internet: provide a hardware level foundation for digital trust, but in a world in which quantum computers are the norm.

Digital sovereignty and nation-building

There is a lot of talk of “digital sovereignty” in response to the realization that American law trumps Canadian law for data controlled by American big tech. PM Carney has promised major investments in a Canadian sovereign cloud. The Canadian sovereign cloud could be a nation-building project or a campaign slogan, depending on whether or not we get the details right. Bruno’s commentary makes it clear that for this to work, Canada must control the keys through which all of this is secured. In his words: “no key, no sovereignty”.

While quantum computing is not yet here, Bruno’s commentary (and 30 years of real world demonstration) make clear that the hardware solutions that are deployed today to secure these keys must be ready for its arrival. Like the LunaHSM, many of the HSMs deployed now will operate for decades and be secure not just against today’s threats, but also those of the foreseeable future. No matter where you fall on your prediction of when quantum computing will be a reality, not even the most pessimistic estimates put it outside the operational lifetime of HSMs that are deployed today. The HSMs that secure the Canadian sovereign cloud must be quantum resistant.

Transitioning to quantum safe standards will have to happen sooner or later, and there is no downside to “sooner”. In Bruno’s words: “Someone is going to do it, because otherwise the internet stops working.”

The benefits are many-fold: getting ahead of a security threat that we can clearly see coming, if not exactly when; procuring solutions from a Canadian company that has the track record to prove their value while directly supporting a key nation-building effort focused on digital sovereignty; and providing opportunities for upskilling of Canadians across every sector of the economy that connects to the internet (i.e. all of it). All of this fits under the umbrella of increased defense spending mandates, and positions Canada as a leader among NATO allies with respect to quantum-resistant cryptography. As Bruno puts it:

“Canada has a chance to turn this into a national advantage. We’ve always punched above our weight in cybersecurity. If we make quantum-safe migration a major national project, Canada could cement itself as a global leader for decades to come.”

Last but not least, it provides a direct path for Canada to secure the economic benefits of world-leading IP in a technology will sooner or later be needed by the entire world. We already lost the LunaHSM, let’s make sure we don’t let that happen a second time.

Geopolitical and technological necessity set the stage for economic and security opportunity. Bruno’s commentary provides the roadmap for Canada to reap the benefits.

Many thanks to Bruno and the Crypto4A team for taking the time to do this interview and share their insights.

In the year and a half I have been ranting writing about innovation policy, I’ve had many opportunities to learn. A comment on one of my older posts prompted me to reread some of my early content, and it is clear that some of it is overdue for an update to take into account how my thinking on these issues has changed.

In the article in question, I came to the conclusion that inventor-owned IP policy in universities is correlated to better outcomes with respect to entrepreneurship than policies in which the institution takes ownership. In hindsight, this was an overly simple take. It came primarily from my own experience with tech transfer and was based on an overly Canada-centric view. At the time I wrote that article I had not yet explored how tech transfer is done abroad, which suggests a more complex interaction between policy and outcome than can be inferred from Canadian examples alone. For example, the United States has almost uniformly adopted institutional ownership of research IP, and has led the world on tech transfer over the past several decades. Clearly, IP policy at the level of universities is not the only thing at play.

David Durand, TJ Misra, and I recently published a related op-ed for IRPP Policy Options that presents a much more nuanced view of the issues at play. Focusing on the Bayh-Dole Act, an inspired piece of American legislation which shaped American tech transfer, this op-ed serves as a spiritual successor to the post linked above. I suggest reading the op-ed (it is not very long) before going any further.

Read the Op-ed

This is the first, but certainly not the last, old article that will get a refresh to take into account what I have learned through the debates and conversations in which this blog has enabled me to take part.

I am going to leave the old article up, warts and all, with a link to this one and a note that it is outdated. I welcome and encourage public debate on these topics, and I think that having a public, written record of the thought process behind these debates is both useful and important.

Context Matters

The op-ed makes a key point that is the core of where my original post went wrong: that we cannot generalize the outcome of a policy framework without also considering the context in which it occurred. While we point to the Bayh-Dole Act as a key example of successful policy on emerging technology commercialization, we also acknowledge that attempts to recreate that success in other contexts has mostly failed: Finland tried, with almost an opposite result, and other attempts have had mixed results, none of which come closes to the American version. New Zealand, inspired by Waterloo, just went in the opposite direction and changed its universities’ IP policies nationally to favour inventor ownership of IP. It will be interesting to see how that plays out, but my prediction is that it makes little practical difference, given that there appears to be little consideration of context in the language around the change.

In other words, the key correction to my original post is that while policy can set target outcomes and provide guardrails with respect to how they are achieved, that alone is not enough. What determines how intention translates to outcome is the context in which that policy is enacted. Canada has a lot to learn from Bayh-Dole, but it has just as much to learn from previous failed attempts to replicate it, and from thinking carefully about the differences that exist between Canada and the United States and how to compensate for those differences in any attempt to recreate its outcomes.

The United States has risk-tolerant private capital that is happy to invest early in the development process, understanding that most of those bets will fail. Canada does not. The United States has a private sector willing to take big swings on new technology, fuelled by decades of proof that technological innovation is the key to economic prosperity and security. Canada does not. The United States has whole-of-government leadership in the form of federal frameworks that support emerging technology (the SBIR, DARPA, Bayh-Dole, and others) which align all the stakeholders toward common goals. Canada does not. Most places where policies similar to Bayh-Dole have been attempted lacked these key ingredients, as well. The lesson here is not complicated.

Kevin Carmichael at the Logic suggested recently that because the Canadian government is making big bets, the private sector should follow suit. He’s right, but private sector investment in Canadian innovation is probably a few years away even if the Carney government delivers on these promises. The private sector will invest when the innovation and competition created by embracing risk and actively supporting emerging technologies forces them to adapt, and that will take time. If Bayh-Dole-like policies are to be adopted here, and I believe they should, we must actively fund and resource the initiative with risk-tolerant public capital, at least long enough for the effects of a more innovative ecosystem to spur Canadian private firms to act in self-preservation, or to fail and make room for those that will.

Industrial policy recommendations from IRPP

The op-ed linked above is a small part of a set of points I made in a round-table discussion hosted by the Institute for Research on Public Policy (IRPP) about a year ago. The round-table was part of a much larger effort to synthesize evidence relating to industrial policy into a set of recommendations for the Canadian government to address Canada’s productivity challenges.

The findings of the discussion series are now summarized in their report entitled Building for the Future: How Industrial Policy Can Strengthen Canada’s Economy and Sovereignty. It is full of gems and is worth a full read through, and I have to commend IRPP on coming up with a report based on a research process that straddled the second coming of Trump while maintaining any kind of relevance.

The report is a solid starting point for debate around industrial policy initiatives that will be necessary for Canada to remain globally competitive, and gives careful thought not just to implementation, but to ongoing evaluation and governance. As with every single remotely related report ever produced on Canadian innovation, it calls for strategic coherence across all levels of government combined with rigorous evaluation and an ability to course-correct as basic requirements of effective industrial policy.

Probably my favourite quote from the report is from Dani Roderick:

“What determines success in industrial policy is not the ability to pick winners, but the capacity to let the losers go”

Substitute “emerging technology” for “industrial policy” and you have the core message of CanInnovate in a nutshell.

What is implied by the quote above, and what I try to get across in some form in most of my writing, is that successful industrial (and innovation) policy requires a high tolerance for risk and the ability to place many bets, accepting that some will fail. In other words, the report makes clear that none of this works without capital, and suggests numerous means by which productive investment can be encouraged.

What’s next?

The lesson about context generalizes: in seeking to learn from any policy framework that has seen success elsewhere, including industrial policy, we must consider the context in which that policy worked and then either adjust the policy to compensate for any differences, or work eliminate those differences. Successful policy import requires beginning with the end in mind and working backwards to identifying the how. The path to similar outcomes may not be the same.

As Canada follows the rest of the world toward a more active industrial policy, we would do well to keep this in mind. We have no shortage of evidence, studies, and good ideas. What remains is action. From our own op-ed:

“We know exactly why we’re failing and we can’t let ourselves off the hook.”

Last year the Trudeau government began a consultation process on reform to the Scientific Research and Experimental Development (SR&ED) tax credit program that incentivizes private sector investment in R&D through use of refundable tax credits. The program is among the largest such incentive in Canada, amounting to some $4B in credits annually over the past few years.

At the time, I responded to the consultation through an initial open letter, and then again in response to the government’s follow up that synthesized what they had taken away from feedback in the first round.

On August 15, the Carney government released draft legislation toward enacting some of the updates, with no substantive changes from the original plan. While the changes were mostly positive, they were mostly low-hanging fruit and did not engage in a substantive way with what seem to be fairly straightforward structural changes to the way the credit is delivered that could make SR&ED more efficient at delivering value per dollar. My view on this has not changed much.

In this article, I reiterate part of what I proposed last time as additional measures that could be considered to maximize the impact of SR&ED and reduce inefficiencies.

What Stayed the Same (Pretty Much Everything)

Everything proposed in December is still there, and as far as I can tell, there is nothing much new. The changes announced in December 2024 indicated that expenditure limits on which the enhanced 35 per cent rate could be earned from $3 million to $4.5 million, as well as increasing from $10 million and $50 million, to $15 million and $75 million, respectively, the taxable capital phase-out thresholds for determining the expenditure limit. These threshold updates are unchanged in the new draft legislature, and represent only an adjustment of the limit to account for inflation since these thresholds were previously set. There is still no measure to continuously update these thresholds to keep pace with inflation. The December announcement also included a provision for extending eligibility for the enhanced 35 per cent refundable tax credit to eligible Canadian public corporations, and allowance for capital expenditures to be claimed for deduction against income and investment tax credit components of the SR&ED program. These are still there, and you can read my thoughts on it in my previous article on the topic.

In other words: the Carney government is moving forward with SR&ED reform as originally planned by the Trudeau government. While I have no objections to any of it, the inefficiencies that exist within SR&ED can only be addressed through changes to how the program is accounted for and delivered. Updating the base numbers is welcome, if overdue, but leaves on the table opportunities to increase the impact of the funding without increasing the cost.

What Could Still be Done

Anyone who has gone through the rigmarole of claiming SR&ED will tell you that the process is deeply inefficient. Claims take months to review, and payment of the credit can be delayed significantly. Because of the complexity of the rules around qualifying expenditures, most companies hire dedicated consultants to prepare their claims, which are usually paid on contingency, taking anywhere from 10-18% of the credit as payment and reducing the capital that flows back to the innovative companies that are being supported.

These structural issues disproportionately impact small companies. While an established company can weather a denied claim, for a small company, especially a pre-revenue company focused on the R&D required to commercialize something truly novel, a denied claim could be an existential threat. The cash-flow problems arising from the timelines and lump-sum nature of SR&ED payments require that innovative companies do one of two things: either they take fewer risks and spend less on R&D in the early stages to ensure that a denied claim will not end them, or they turn to SR&ED lenders to offset cash-flow, paying high interest rates in exchange while increasing the potential damage of a denied claim. Between contingency-based SR&ED preparation fees and interest rates on SR&ED lending, these cottage industries siphon off a large percentage of the credit. If this could be addressed, and if SR&ED could be better aligned with cash-flow requirements, the impact of this credit as an enabler of intelligent risk-taking would be dramatically increased without increasing the cost to taxpayers at all.

The means by which this could be achieved are also not complicated in principle.

SR&ED claims work in most cases through what is called the “proxy method”: instead of accounting for and claiming specific R&D expenses, companies can simply estimate the total salary paid in service of R&D, add 55% overhead, and claim that total as eligible expenditures (the calculation is a bit different for contractor costs, but we will focus on salary). The obvious change to SR&ED delivery is baked into the proxy method: because SR&ED credit is directly proportional to R&D-related payroll, SR&ED should be integrated with the payroll tax system.

Payroll tax is paid monthly. By incorporating a simple calculation of the percentage of each payroll tax submission that is SR&ED-eligible, the SR&ED credit could be used to first reduce the amount of payroll tax paid each month and then credited on a rolling monthly basis. Companies could then be required to submit the usual annual report that provides an overview of the past year of expenses as a means to verify these payroll tax deductions, but because this would be a correction to payments already made, the cash-flow implications would, assuming no major errors, be minimal.

This change alone would completely eliminate the SR&ED lending industry.

The second structural change should be focused on reducing uncertainty and the chance of a denied claim. Instead of post-vetting of research expenditures, pre-vet them. The annual report mentioned above should include not just verification of the payroll tax deductions claimed in the past year, but also a research plan for the following one. CRA could then provide advance notice to companies of ineligible expenses, which would allow for proactive adjustment to correct for any issues before they arise. The work required by CRA remains effectively the same, since they are approving these research plans regardless, but moves from a reactive, post-approval model that risks unexpected denials to a proactive, pre-approval model that reduces instances of denied claims. Increased confidence in expense eligibility and predictable cash-flow in turn promotes intelligent risk-taking and R&D-based innovation by Canadian firms, eliminates entirely the need for SR&ED lending, and reduces the value-add of SR&ED consultants, ensuring that more of the tax credit is used for its intended purpose. Together, these are potentially cost-neutral reforms that increase the fraction of the SR&ED credit that ends up where it belongs: in the hands of innovative Canadian companies.

Next Steps

While an exact estimate is somewhat challenging given public data on the topic, it is reasonable to estimate (as discussed in my previous posts on the topic) that upwards of 20% of SR&ED spending is not ending up where it should: in the hands of innovative Canadian companies,. Parts of the credit are being diverted through SR&ED consultants that exist only to navigate the complexity of the related administration and lenders that exist only because SR&ED is paid out as an annual lump sum rather than a monthly payroll tax credit. Addressing these through relatively simple changes to the way the credit is paid out could greatly increase the overall impact without changing the cost of the program.

I stand by my original recommendations as well, especially those relating to ensuring the SR&ED does not go to supporting subsidiaries of foreign firms. I will not reproduce them in full here, simply suggesting that you take a moment to review my previous article on the topic.

I intend to submit suggestions along these lines in response to the feedback invitation in the hope that the current administration is a little more ambitious than the last.

Several months ago, the office of Senator Colin Deacon produced an excellent report reviewing federal innovation funding initiatives, finding nearly 140 programs that aim to support innovation in Canada. As someone who makes a point of staying on top of Canadian innovation trends, I had heard of less than half of them.

While I am happy to see additional resources being earmarked for innovation via defence spending, the search for efficiency through deep cuts to the size of public sector before first finding it in underperforming innovation programs suggests that we have somehow still not learned the critical lesson.

Recently, the Information Technology & Innovation Foundation (ITIF) picked up the thread in the form of an excellent op-ed by Lawrence Zhang. Today I draw your attention back to this critical debate, highlighting his analysis and re-examining a well-established issue in light of the priorities of a new government.

Policy fragmentation

Lawrence Zhang’s assessment of the federal Canadian innovation ecosystem is blunt, and more or less echoes themes that thread through most of the articles in my own work:

“For decades, we’ve treated innovation as a policy file, not a system. We’ve staffed it with generalists and managed it through siloed programs and compliance. The result isn’t progress, it’s paperwork. No agency is mandated to steer strategy across departments, sequence support, or commercialize. […] These programs matter, but they operate in isolation—stranding firms, fragmenting funding, and stalling technological advancements.”

This is a dense paragraph that points to many problems. “Staffed with generalists” references a perennial challenge with the public service arising from a mismatch between the experience of many program administrators and the realities of the programs they administrate (a topic for another day). Endless paperwork bogs down the system, causing innovation support programs and to move too slowly to effectively support innovation. A lack of cross-cutting mandate and “whole-of-government leadership” leads to isolation and fragmented funding

This fragmentation has many consequences. As Lawrence points out, it often leaves firms, especially early-stage and pre-revenue firms, stranded in the gaps at precisely the stage where support is most needed. Even when funding is available, the complexity of access stretches timelines beyond the timescale required for innovation Canadian technologies to be globally relevant. I recently wrote about the opportunity costs of these issues: a prime opportunity for Canada to reverse its brain drain and benefit from America’s misguided recent research policies, likely to be completely missed because we are not prepared to capitalize on their mistake fast enough to capture any of the value.

I would add one more core issue to the list: an unwillingness to cut existing programs.

The result is what we have today: 134 fragmented programs. While there are no doubt gems among them (I have written previously about the good that Lab2Market, Mitacs, and organizations like IPON are doing for emerging technologies, for example, and while there is much that could be improved about SR&ED, I suspect it does more good than harm), many of the existing programs do not collect or report data that correlates specific programs to long-term outcomes, and an overreliance on job and revenue-related metrics prevents an objective assessment of program efficacy.

As Lawrence points out, numerous proposal have been tabled to address the issue, with the current thinking around the issue being established in 2011 with the Jenkins Report and repeated in the Bouchard Report. In an echo of the innovation policy frameworks it sought to change, the report generated mostly paperwork and little progress, though not for lack of excellent recommendations. Over the subsequent years many solutions have been proposed: the CIC was floated as an agency intended to streamline and coordinate scientific funding across the three main siloed research funders (NSERC, CIHR, and SSHRC), but was delayed and is now unlikely to see the light of day, even though I still hear about it occasionally on conversation with policy makers. The ambitious CARPA (modeled after DARPA and proposed in the 2021 Liberal platform) met a similar fate. The proposed capstone agency for tri-council funding appears to be in bureaucratic purgatory.

Shortly before the ITIF op-ed was published, the Carney government announced the Bureau of Research Engineering and Advanced Leadership in Innovation and Science (BOREALIS), which appears to be the latest in a long line of ambitious announcements. I am reserving judgment on the follow up for the time being.

Lawrence proposes a combination of past attempts:

“What’s needed is a combination of the two proposals—an institution that takes responsibility for the system itself. That’s why we need a Canadian Innovation and Industrial Transformation Agency with the authority to coordinate strategy, steer delivery, and close the structural gaps that hold back deployment.”

Lawrence does not directly address BOREALIS, but much of what he advises can be directly applied to any attempt to execute, especially his admonition that any such program aimed at delivering long-term innovation must be at arm’s length from the government:

“It must be a Crown corporation. Not a secretariat or special office. Only a Crown can operate outside standard government HR, IT, and procurement constraints while maintaining strategic accountability to Ministers.”

He suggests that the new agency should coordinate delivery of (at least)

“National Research Council’s Industrial Research Assistance Program (IRAP) and its 350 staff; the Strategic Innovation Fund (SIF); Innovation Solutions Canada (ISC); the Department of National Defence’s Innovation for Defence Excellence and Security (IDEaS) program; and design and appeals functions related to the Scientific Research and Experimental Development (SR&ED) tax credit”.

This would be an excellent start, and would fit the definition of “do more with less” that has been the hallmark of what PM Carney has asked, directly or indirectly, of the various programs under funding review.

Demolish first, build after

While I agree with pretty much all of Lawrence’s advice on what Canada needs to change about its approach to innovation, There is perhaps only one point on which I disagree. Lawrence asserts that “This isn’t about replacing programs. It’s about governing them coherently, strategically, and at speed. ”

Whatever is built next, be it the CIC, CARPA, BOREALIS, or Lawrence’s Canadian Innovation and Industrial Transformation Agency, I think it should be about replacing programs. 134 federal innovation programs are not something to preserve for the sake of avoiding stepping on toes, and very little in the current system is able to “govern coherently, strategically, and at speed” in the current siloed approach. It is abundantly clear, and has been for at least a decade, that what we are currently doing does not work.

Answering the question of what to cut, what to reform, and what to merge is of course much harder than the suggestion to do it.

We should not treat any program as sacred. My litmus test for what should be cut or repurposed revolves around the quality of the data on which an objective assessment can be based, and the degree to which it is explicitly and intentionally connected to other, complementary programs.

A program for which value cannot be demonstrated quantitatively (or at least concrete progress toward value creation, given timeline considerations) from metrics that same program collects should probably be slated for at least reform of how it collects and reports impact. A program that does not obviously connect to both upstream and downstream support should probably be restructured to explicitly align with what comes before and after to ensure no gaps. A narrowly scoped program that provides funding for just one piece of the puzzle without working hand-in-glove with complementary programs should have their mandate updated to be an active participant in ensuring that support recipients have access to the rest of the resources they need. A program that is unwilling to engage with pre-revenue companies should have its risk tolerance recalibrated.

We are being asked to balance conflicting goals that will play out on different timescales: build a self-sufficient Canada powered by innovation, invest in defence and the NATO Alliance, and at the same time tighten our belts to weather the short term economic storm caused by our neighbors to the south. As defence spending ramps up and Canada seeks to build economic resilience and reduce economic dependencies, we must take a long view of what it will take to achieve this. Innovation today lays the foundation for economic resilience and security tomorrow.

If we work to increase cohesion and demonstrable impact across the board, efficiency will follow.

While at QUANTUM NOW | ICI QUANTIQUE, I had the opportunity to interview senior representatives of three leading quantum computing companies, including Rafal Janik of Xanadu, Paul Terry of Photonic Inc, and Allison Schwartz of D-Wave about the future of Canada’s quantum computing ecosystem, and the implications of DARPA’s Quantum Benchmarking Initiative (QBI) program.

My goals with this article are three-fold. First, I aim to set the scene and highlight the urgency with which Canada must take action if we are to remain relevant in quantum computing. Second, I try to turn these conversations into actionable recommendations for policy initiatives intended to ensure that Canadian quantum computing companies are able to stay Canadian as they cross the finish line. Finally, I hope to make clear the value of actually following through.

While Photonic and Xanadu are both currently Canadian quantum computing companies, D-Wave is no longer. Founded in 1999 in Vancouver, D-Wave operated as a Canadian company for 23 years before going public on the NYSE in 2022, while maintaining an active presence in Canada. In my view, their move represents a failure by Canadian policy makers to act sufficiently quickly and signals the beginnings of a trend that must be proactively halted if Canada is to realize the benefit of its decades-long investment in quantum.

Since this article draws on multiple interviews, I am going to switch up the format a bit and present the key takeaways first, relying more heavily than usual on pull quotes. The full interview transcripts for Photonic and D-Wave are presented in separate sections below, while Xanadu requested that I limit publication only to pull quotes. As with all my interview-based posts, it is too long for email and will likely be truncated by your email client. For the full effect, you will need to read it online.

Many thanks to all of the interviewees for their generosity with their time and for sharing their insights.

Interviewer’s note: All interviewees reviewed and edited the the interviews and quotes posted below, with the option to rephrase and expand on the ideas discussed without changing or removing any intended meaning. The key takeaways are my own commentary, were not reviewed or edited by interviewees, and do not necessarily represent the views of any of the interviewees or their respective companies.

Key Takeaways

The DARPA Quantum Benchmarking Initiative

DARPA QBI intends to deliver a “utility-scale” quantum computer in 8 years, or prove that it can’t be done. This particular DARPA initiative is odd in that it is focused on delivery of a commercial technology for civilian use, in contrast to previous DARPA programs that were defense-focused and in some cases actively discouraged or forbade commercial translation. This is consistent with recent trends toward a reversal in the order of operations with respect to emerging technology development, with a recent NATO report highlighting that, in contrast with the historical approach that has characterized military technology development since World War II, emerging technology development is increasingly occurring in the commercial private sector first.

DARPA is focused on gate-model systems, which excluded D-Wave from consideration, as its commercial offering currently is a quantum annealer. As Allison describes:

“[…] quantum computing is not monolithic. There are different modalities or different qubit architectures. Each of these are advancing on a different timeline, and each of these have different strengths and weaknesses and capabilities. Annealers are best for optimization problems, which are ubiquitous across industry. That's what's commercially available [via D-Wave] to now […]”

Among the 18 companies globally that were selected to participate, four are Canadian (Xanadu, Photonic, Nord Quantique, and 1QBit). The program is divided into three phases (A, B, and C). Stage A is a 6-month process in which the companies present a large amount of information relating to the details underlying their delivery plan and the technology on which it is based. Stage B lasts 12 months and involves technical due diligence, with the DARPA team going through the underlying research and the proposed execution plan with a fine-toothed comb. Stage C involves execution of the subset of plans deemed worth funding, and, eventually, actual delivery of a utility-scale quantum computer.

The DARPA Dilemma

Regardless of the success of the program and acceptance into Stage C, participation is a risk for the companies themselves. Paul explains the dilemma:

“For the people that don't go through, is that a statement that their technology doesn't work? Is that a statement that, politically, they are people that are not meeting the US requirements? Those requirements could be wide-ranging. So for example, those requirements could have IP constraints and restrictions. They could have US domestic considerations. They could require you to move to the US. […]

It could be that the technology is not why they do or do not get into DARPA. If you speak to Rafal or Julian, they’ll tell you the exact same thing: that we worry about the signal of getting in, or not, to DARPA-C. Politically, you could take the view that people going to DARPA are pandering to the American stuff, or you could take the view that the technology wasn't worthy. It's a “Sophie's choice” from our perspective, we can't actually make the right decision.”

In the end, the decision comes down to the value of third-party validation and the opportunity to learn in turn from DARPA, and use what Paul refers to as a “forcing function” on the technology, using the questions posed by the DARPA technical team to learn and advance in turn.

What came through clearly, both in panel discussions at the conference and in the interviews, is the depth of due diligence being conducted by DARPA, with weekly half-day meetings between dedicated DARPA technical teams and senior company leadership, hundreds of DARPA analysts, and reams of technical documents changing hands. The program's depth of inquiry suggests that the Americans are benefitting already. As Paul puts it:

“They're learning a lot, actually. I can say from personal experience, things that they took to be true, they're learning are not necessarily true. It’s actually accelerating, not decelerating, the perception that quantum is going to be here sooner rather than later.”

While the information changing hands in stages A and B does not constitute details of the IP required to make current systems work and related IP terms were uniformly viewed as low risk, to my mind the main risk to Canada lies in DARPA Stage C. Performance requirements under Stage C have yet to be defined, but Paul’s comment above indicated that requirements to be physically located in the US are not out of the question. This is supported by indications that testing of the results delivered under the QBI will be conducted at least in part in facilities in construction in Maryland, and the investment of up to $300 million per successful company through DARPA—on par with Canada’s entire National Quantum Strategy Budget—combined with the possibility of anchor customers and third-party validation, will exert a strong pull.

Building a Quantum Sandbox

In 2022, Allison addressed Canadian Parliament with a proposal to build a “Quantum sandbox,” a set of infrastructure, hardware, and tools on which to base quantum and hybrid computing education, application development, and value demonstration. As Allison describes it:

“I testified before Parliament almost four years ago. I pitched the idea of a quantum sandbox and everybody thought it was a great idea. It was actually a recommendation from Parliament to the administration and we're still waiting for it to be implemented.”

The testimony became a recommendation in the resulting report, and subsequently went nowhere. In the meantime, the Americans are on it.

This is unfortunate for many obvious reasons, and a few less obvious ones. One of the highlights of the interview set was a comment by both Allison and Paul about the importance of the application layer and the value of everything that is enabled by quantum computing hardware, as opposed to the hardware in and of itself:

Allison:

“Where Canada can grow is on the application layer of seeing where these actually can do useful things. We need both. We need both the investments in the hardware advancements as well as the software, and we need investments in all the different modalities that are out there.”

Paul:

“We have an opportunity here to create what happened in Silicon Valley 30 years ago in Canada. I'm actually more excited about the surround, the quantum companies in the application space, around the quantum computers that we have here, more than that than the quantum computers themselves. Quantum computers themselves are, to a large extent, a solved problem..”

Their observation is critical: quantum computing could catalyze Canada’s Silicon Valley moment. If you have read any of my previous articles on ecosystem building, or Dan Breznitz’s book on innovation, you will find a lot of discussion of the magnetic pull of established innovation ecosystems that draw value away from smaller ones. The existence and gravity created by Silicon Valley is one of many reasons it is difficult to build something similar in Canada. Where innovation ecosystems are concerned, first mover advantage is everything, with a caveat: to some degree, the effect is sector-specific. With quantum technologies, Silicon Valley has limited gravity. It has no critical mass of quantum computing talent nor an established base of research, which represents Canada’s best, if not only, chance at creating that gravity here. We may not be able to compete on a pure cash basis, but we can use our current critical mass of expertise to balance some of the difference.

The value creation of Silicon Valley is much less about the anchor companies themselves as it is what was built around them. The software boom that has been the core of VC for the last few decades was built up on infrastructure. A quantum sandbox, built on Canadian quantum computing hardware, would have wide ranging knock-on effects, starting with the infrastructure required for a generation of hackers to develop quantum algorithms and create value by enabling practical applications.

For those of us still hung up on job numbers, Paul makes clear that this is where the vast majority of job creation potential lies, as well:

“I often get politicians saying that, ‘in Photonic you're creating 300 jobs. 300 jobs isn't interesting.’ Well, it's interesting to me because these are super PhDs. These are the best in the world, people. They create five jobs for every job I put in here, but more importantly, the application surround around this is going to create thousands of jobs and change the GDP of a nation, which is not necessarily a function of the number of jobs that you have.”

Execution will require large, public procurement projects aimed at Canadian quantum computing hardware companies, thereby providing them with anchor customers and signaling robust commitment from the Canadian public sector. Procurement need not be limited to the actual hardware. The federal government could also commission a Canadian quantum company to develop a dedicated quantum-optimized logistics solution for a major crown corporation, secure quantum network for a defense department facility, or optimize logistics for allied military operations. These projects are already well within the capabilities of present-day, commercially available quantum computers.

No matter the details, it has to be led by the public sector, a theme that comes up in many of my articles. As elaborated later in this post, this is not a request for grants, but for anchor customers, customers that are still overwhelmingly in the public sector globally. As Rafal points out, quantum computing is heavily subsidized everywhere:

"You'll see that this is not a free market. This is very heavily subsidized, everyone has a thumb on the scale. Historically Canada has said it's not going to pick winners, ‘there's going to be a free market on this, so we're not going to do it’ It just means that Canadian companies are fundamentally at a disadvantage."

Given the dual use nature of quantum computing, all or almost all of these expenditures could fall under the recently announced increased defense spending under BOREALIS, Canada's strategy for critical minerals and Arctic sovereignty, which directly relates to advanced technology infrastructure, including quantum.

Security Considerations

There are economic security arguments for doing this as well.

While there are real IP risks associated with the DARPA QBI program, they are not the only risks. Globalized supply chains, in particular where chips are concerned, represent key points of vulnerability. As long as critical components are being manufactured abroad, there is a risk of IP theft.

Lisa Lambert of Quantum Industry Canada recently wrote an op-ed in the Globe and Mail calling for industrialization of quantum technologies in Canada and “fabrication, integration, secure supply chains and deployment at scale.” Having Canada as the NATO epicenter of quantum technologies allows us to begin the onerous and expensive but increasingly necessary process of deglobalizing supply chains while making ourselves invaluable to the alliance.

Paul also highlighted a key issue relating to export controls. Quantum technologies are an export control nightmare, and the country that hosts related services will be in a powerful position to dictate how regulatory development unfolds:

“The growth of Quantum and the understanding of what Quantum is going to do to nations has created quite an export control problem for everybody. […] That's if you're selling hardware, lasers, semiconductors and stuff between countries. What if you're not selling that and you're selling Quantum services? […] I imagine that the Canadians or governments will have something to say about releasing Quantum services of this power to anybody in the world,”

We have been here before: in 2017, Canada was a world thought leader in AI, and the Montreal Accords were recognized at the time as being a valuable contribution to the regulatory debate of global value. Today, I doubt many people outside of Canada even remembers them. With quantum technologies, and quantum computing in particular, it is not too late to replicate for this sector what we tried and failed to achieve with AI, and from across a much deeper moat. Establishing Canada as the centre of quantum computing hardware, application development, and the manufacturing supply chain needed to make utility-scale quantum computing a reality will also allow us to lead the global debate on regulation with respect to export controls, quantum service provision regulation, and standards development, all of which are critical elements of economic security.

Now or Never

Time is limited. Paul points out that a consolidation of the market is coming:

“There will be a consolidation at some point in the next four years, I would think, of Quantum companies in the world.[…] There's two hundred and something Quantum companies in the world. In three years there may be four.”

This is supported by a recent raise by D-Wave of $400M in preparation for acquisitions. Between impending consolidation and the 18-month timeline for DARPA Phase C to kick off, Canada has about 12 months to get its act together. To do this, the federal and provincial governments need to buy quantum computing hardware from Canadian quantum computing companies, and keep buying more as it improves. Use it to build the sandbox we should have built five years ago, and enable Canadian startups, researchers, and hackers to start building the application layer on hardware that is ready to use today, building the application while at the same time training new talent, providing a reason for existing talent to stay, and for talent abroad to immigrate. Establish Canada as the centre of quantum gravity for NATO, and use all of that as impetus to industrialize as Lisa Lambert suggests. Be the anchor customer that keeps the ecosystem here.

Either we act before DARPA Phase C commitments are made, or we can assume that quantum will go the way of Canadian AI, yet another Canadian technological gift to the world, still debated long after we lost the thread. Rafal and Paul have effectively the same message to share:

Rafal:

“Canada needs to decide, ‘do we want sovereign quantum computer capacity’? If the answer is yes, it needs to get very serious about what level of investment is required to actually deliver that.”

Paul:

“The irony of all of this is there are only four thousand people in the world or so who are Quantum experts. So those four thousand people are going to appear or reappear in the one, two, or three Quantum companies that are going to win this. Canada has a chance of having one, two, or three of those companies based in Canada, from a hardware perspective.”

[Interviewer’s note: Paul’s 4000 number apparently refers to people in “Canada's commercial and academic quantum talent pool” from this report, which as far as I can tell is an accounting of Canadians with PhDs in quantum physics. The global quantum workforce beyond PhDs is far larger, as he confirms in the interview.]

If we want to capitalize on 25 years of investment in quantum and a world-leading position on a technology that has been established in Canada, we need to get it done before DARPA Stage C.

As to what comes next, it is clear from all three interviews that the ball is firmly in the court of Canadian policymakers. As Paul puts it:

“Those companies, by the way, are fiercely Canadian. So this view that we are in it to sell to the highest bidder couldn't be further from the truth. We are companies, we do have cap tables, we do have investors, we are subject to capital flows. But speaking for Photonic, Photonic is two thirds owned by Canadians, and it's fiercely Canadian. We’re banging the table going, ‘I want to stay Canadian. Give me a chance to stay Canadian.’”

The will and the money are there, what remains is action. It is up to Canada to make a choice, and it is a very simple choice: Now, or never.

Interview with Paul Terry of Photonic

KB: Let's start with a bit of context. Give me a short intro to Photonic, what you're attempting to build, and what it's taken to get where you are today.

PT: Photonic was founded in 2016 by Stef Simmons, who's a physicist at SFU, a professor there. She had the idea of using colour centres that are photonically interconnected. I I mentored her in 2016-2019 as we calculated the technology necessary to actually sort of win this. So it took three or four years to do the research and to find the attributes of the foundational technology that's necessary to actually make a computer. So in 2019, we were looking for a CEO. Hard to find a CEO that has the experience of a startup physics degree and engineering degree etc. So I stepped in to be the CEO. We actually raised our first round in 2021.

That's where we are now. We're 150 people based in Vancouver, BC, in Coquitlam and we are building the company in Canada. About 30% of the company doesn’t work in the head office and they work wherever they are - across Canada and around the world. That's allowed us to hire the greatest and the best in the world just wherever they live and of course, if anybody wants to come to Canada, when immigration allows it, we bring them into Coquitlam.

KB: Canada has a leadership position in quantum tech currently. What is it about the Canadian ecosystem that made it possible for Canada to be a place where you can build this kind of company?

PT: Canada has long had a culture of funding research. It does not have a culture of funding commercialisation. I've been here for 40 years. This is my seventh company in Canada. I've had five acquisitions and one public company in the last 40 years, and I've been on almost every Prime Minister's committee over the years, on various technologies. I've also had the experience of spending 20 years on the Board of Michael Smith Foundation, plus Genome BC, plus Life Sciences in BC, plus a hospital in Vancouver.

So I spent a long time on the research side and a long time on the commercialisation side. Canada Funds Research Well. Researchers will say it's not enough. and that’s probably true, but then all researchers around the world will say it's not enough and that's probably true too.

But 20 years ago Canada funded quantum. Stef Simmons, the founder of this company, was one of the first graduates of a quantum programme at the University of Waterloo. So she is a direct product of Canada's investment in quantum.

Quantum is a complex subject. To actually work in it, you need a PhD. I think we have 70 PhDs in our company right now. And Canada has them. The UK has them. The US does not have them. So there are various peoples around the world, various countries that have bits of them, because they did not spend 20 years spending billions of dollars on the research. So Canada actually should be credited for doing it, because now you planted the seeds 20 years ago and now the trees are growing. We actually have the best quantum people in the world in Canada. There is really no argument about that at all. So we benefit from that, as evidenced by the fact that we could prove it.

KB: Careful not to let Lisa Lambert hear you say you need a PhD to work in quantum.

PT: Let me just qualify that. You need a PhD to do quantum science and engineering. Having said that, a quantum company is actually quite wide. You need some very strong engineers, you need some good marketing people. In our company we have people with degrees in English, all the way to PhDs in computing. We often have something like a thousand applications for every position in the company, give or take. It's a very competitive place to get into. You don't need a PhD to get in. It depends what job you're going to do.

KB: Photonic is one of four Canadian companies that are part of this DARPA QBI challenge, intending to deliver an enterprise-scale quantum computer by 2033. Tell me a little bit about the challenge that's been put before you and Photonic's part in it.

PT: Let's go back to DARPA. Previously to this I was a CTO of Cray Supercomputing. I've done many DARPA programs in the past. This is not a normal DARPA program. It's the first thing to know. DARPA normally is a defense type thing. They do things to aid the defense of the realm, as it were. This isn't that, which is odd, because DARPA, in this particular case, is solely focused on the commercialization of quantum. Not necessarily the defense aspects of quantum.

You may know that DARPA, of course, funded all the IP networking stuff back in the day. They did a lot of compiler design, a lot of supercomputing design, a lot of things going into space and so forth. These tend to be closed contracts, defense-oriented, with terms and conditions that almost prevent you from commercializing. This program is not like that. This program says, unless you can commercialize, we are not interested in talking to you. That's a very important and subtle difference, and a very big one.

DARPA had a trial run of this program two years ago. Two years ago it said, “before we announce this program, let's work out what the program is and do the trial run with Microsoft PsiQuantum and Atom Computing”. They limited the trial run to only American companies and they took them through the process as a pro-forma for the process they're in now.

DARPA is in three stages, A, B and C. Stage A was a million dollars. Stage B is up to $15 million. And Stage C, there's a promise, not a guarantee, subject to Congress, the Senate and many other political machinations of up to $300 million per company in matching funding for the production of, let's say, a prototype or something that looks like a prototype.

Two of those three original companies are in DARPA Stage C. They have now attracted, 18 additional companies into DARPA Stage A, which is almost a due diligence program before you get to DARPA Stage B.

Notice that DARPA is not funding an end-user computer. It's really about putting a stamp on Quantum, about creating a pivot point for Quantum. Quantum is happening now, so let's fund these companies and advise these companies.

If you're a company in DARPA that does this very well, but doesn't do that very well, DARPA is also interested in doing whatever it takes to make what they call a utility-lifter-scale computer. So as an example, if you had a supply chain issue, if you needed to build a fabrication facility or whatever, they could act as a matchmaker to make that happen. Their sole goal is to make the environment to make a utility-scale computer a reality in a world where people can't easily diligence quantum computing. So it's very much a reality-driven commercial arrow.

To do that, there's a core DARPA group based in Washington, DC, and then there's a Surround group, which is what they call their test and evaluation team. For that team, I think they've hired several hundred people across all the institutions, across institutions like National Labs and so forth, to be the testing evaluation team. Those testing evaluation teams are only for particular companies. T&E teams are not shared across companies. The DARPA main team can go across companies, but they are very, very, very sensitive to confidentiality. A lot of things that we share with them, things they share with us, they keep confidential, even between T&E teams.

They're interested in finding out the integral details, but not sharing it, and in fact, when we send them documents and so forth, those are all marked up with what's confidential, what's not, what they can share, and what they can't. The T&E teams meet with us anyway every week. If you've ever done a PhD and you have an external examiner going through every sentence, that's kind of what it's like. They are very detail-oriented, very keen to learn, and very keen to understand.

One of the things that is a misnomer, is that people think the DARPA team are quantum experts and they know all the answers and they don't. In my experience they are learning, they're absorbing, they're very smart people, very fast, and so there's an educational part to this, to explain to them why this is, why this matters, how this matters, what the ups and downs are. What they're not expecting is for teams to have everything dotted and crossed, what they are expecting is to have a plan, a strategy, a view, a risk profile, and a plan of action to obviate that particular issue.

As an example, if you had a supply chain issue for a bent piece of metal that you need for your quantum computer, then they would say, “okay, so that's good. I recognize you don't have it today. What's your plan to have it, when will you have it, how important is it, what's the second source of it, what's the third source of it?” From a diligence perspective, it's probably the highest level of diligence I've ever seen in my 40 years of doing technology in the world. So welcome to my world.

We have teams of people, normally senior people, who are anticipating the meetings, providing information before the meetings, so that people going into the meeting have the information ready to go, so it's not a presentation every week. It's not a marketing thing, it's a deep technical thing. The best way I could describe it, if you’ve done a PhD, it's like a PhD supervisor going through and trying to uncover all of the details, and they're doing that with all of the 20 companies in DARPA, with different T&E teams.

They're learning a lot, actually. I can say from personal experience, things that they took to be true, they're learning are not necessarily true. It’s actually accelerating, not decelerating, the perception that quantum is going to be here sooner rather than later. Which actually is very good, because no number of marketing materials can beat showing you how QRE actually works, what resourcing means, what a qubit actually does, and then understanding that at a deep technical level. They understand it, even if you haven't done it yet, they understand it, and that actually sets a path for the full thing. The third-party aspect is especially important, because the marketing materials are coming from the quantum companies, and it's powerful having third-party validation.

The other side of that, of course, is that DARPA is a US government object, and you're subject to US government oversight, and nobody knows what they're going to do next, in a whole range of things, to state the obvious, and whether or not DARPA-C is actually real. Someone will say it is, because there are two companies, PsiQuantum and Microsoft, already in DARPA-C, so there are already participants in DARPA-C. So there's an argument to say DARPA-C is already in place, but how many companies will make it to DARPA-C is a question.

Governments are notorious at stating a budget and then cutting it. So will that actually limit the number of people going through? For the people that don't go through, is that a statement that their technology doesn't work? Is that a statement that, politically, they are people that are not meeting the US requirements? Those requirements could be wide-ranging. So for example, those requirements could have IP constraints and restrictions. They could have US domestic considerations. They could require you to move to the US. So this is basically, this is also a threat for all the DARPA-C companies, because once you're in it, you'll do your best, and believe all the Canadian companies will do very well technically, being among the strongest in the world technically. So two questions then to the Canadians: What do you do with Canadian companies that get into DARPA-C? And question two, what do you do with Canadian companies that don't get into DARPA-C?

It could be that the technology is not why they do or do not get into DARPA. If you speak to Rafal or Julian, they’ll tell you the exact same thing: that we worry about the signal of getting in, or not, to DARPA-C. Politically, you could take the view that people going to DARPA are pandering to the American stuff, or you could take the view that the technology wasn't worthy. It's a “Sophie's choice” from our perspective, we can't actually make the right decision. What we do is we make the decision that best showcases the technology that we have and take advantage of the fact that this actually is a two-way street. DARPA also talks to us and goes, “well, we'd like to see this better, or that better, et cetera.”

So it's a forcing function on the technology. Even with the best way in the world, you can't see what you can't see, and so having a third party independent say “you should do that, or think about this, or have a good statement for that” is a very healthy situation. So at the end of the day, the decision that I made, that we made, is that, it's better to be in it and have that forcing function than not be in it. The Canadians need to understand what the future looks like here. And frankly, the British and the Europeans as well, because this has had quite the geopolitical effect on all countries as the US is moving to close. And the US is very good at that, moving to close. It's not, “let's just keep this going and then make a decision in three years.” This thing [DARPA] will all be over in 18 months.

KB: As I understand it, the first two stages of this DARPA program will not be heavily IP-generating and that most of the breakthroughs, should they happen, will be in Stage C. Does that align with your understanding? Has there been a lot of IP transfer in the necessary part of this diligence process? What is the timeline that you expect before the IP-generating breakthroughs need to be delivered assuming that stage C continues and Photonic is part of it?

PT: The IP considerations for phase one and possibly phase two, I haven’t seen the phase two ones yet, are actually very friendly. So they're not at all problematic. We have had experiences in the past with the a government where the IP terms were so outrageous that we just turned them down despite a very significant revenue opportunity. So I agree that stage one and two IP terms look good. DARPA is very keen, and they were very receptive, by the way, to changing IP terms, which we did. On behalf of everybody, nobody signs the same IP term to my knowledge. I don't know if anybody has different IP terms.

The decision to go to DARPA C is a two-way decision. They can offer it and we can decline it. And so if there is only one, two, or three companies that go to stage C, then that will be a negotiated IP thing compared to stage one and two, which was a more general IP thing.

Now, the breakthroughs happened before DARPA started. So the IP terms for matching funding are going to be interesting. I don't really anticipate, and knowing that companies have gone to stage three, that their IP terms have been necessarily problematic. But those are American companies, not Canadian companies.

So we have choices. We do have choices. Stage one and two, good. Stage three, we have choices. And whilst it's up to 300 million per company, it's not 300 million per company you can choose. It's matching funding, so you have to have enough investment in the company to match. Why would a government give you monies without IP terms?

Now, there is a conversation that says that DARPA C wouldn't actually be funding R&D. It would be funding pre-sales, and I don't think DARPA knows yet, frankly, what form it will take. I don't think they have a good view on it. They recognise that they can do it in different forms. They also recognise that it's going to get their money from Congress. So there's a political battle to be had. I don't actually know that anybody knows what the answer is.

KB: Has the current geopolitical chaos substantively changed Photonic’s IP strategy in general?

PT: The growth of Quantum and the understanding of what Quantum is going to do to nations has created quite an export control problem for everybody. There are several agreements. The Wassenaar agreement, there's NATO, AUKUS, Five Eyes, national agreements, trade agreements, all of which have a bearing on what you can do and when you can do it. Can you move a Quantum computer from Canada to England? There are at least two or three agreements that will dictate what you have to do and how you have to do it. So, the government of Canada really has to kind of figure out what it’s going to do with that.

That's if you're selling hardware, lasers, semiconductors and stuff between countries. What if you're not selling that and you're selling Quantum services? You host a computer in a country, could be Canada, and then people access that via the Internet, which is probably the form it will take. The regulations for that today are only privacy type regulations. So, we have GDPR and stuff like that as a regulatory body. That one is going to be interesting. What are the regulations about you using a database piece of software on Amazon Cloud that's hosted in Germany?

I imagine that the Canadians or governments will have something to say about releasing Quantum services of this power to anybody in the world, because people could use those Quantum services to decrypt things, to break passwords, to do stuff. That regulation has not started yet because we don't have Quantum services yet. So there's a problem with hardware today. It's a mess, but that problem will get dwarfed by the provision of Quantum services and whether you have to host a piece of hardware in a country to offer services in that country. That kind of happens today in Microsoft or Amazon, which will actually have different data centres in different countries specifically to stay on the side of the regulatory bodies in those countries. That may happen. So, that's a brave new world. Welcome to my world. That's going to be an interesting one.

KB: It sounds like there is an 18 month timeline for most of the key elements to resolve with respect to the DARPA QBI. What policies or changes does the Canadian government need to make within that timeframe in order to capitalise on the lead that Canada has right now?

PT: The first one is recognition, of course. I would say that having kind of banged on the door of the Canadian government for the last four years, nothing has happened until this change of government and then as we saw this morning they have a Minister for AI and Quantum now. So, this is good. The recognition is good. This event is good. Having the G7 state that Quantum is here and real is good. We met the Prime Minister of the UK on the weekend. The leaders and the political motivations are good. I think we are having the leaders recognise what this is. There are only now three or four companies in Canada that are world leaders in Quantum. I recognise my colleagues in other companies here. There will be a consolidation at some point in the next four years, I would think, of Quantum companies in the world. Recognising that lots of Quantum companies will fail, lots of small, four, ten, twenty person Quantum companies that are late to the game will fail. There's two hundred and something Quantum companies in the world. In three years there may be four. Every industry goes through this, every industry goes through a consolidation stage. The irony of all of this is there are only four thousand people in the world or so who are Quantum experts. So those four thousand people are going to appear or reappear in the one, two, or three Quantum companies that are going to win this. Canada has a chance of having one, two, or three of those companies based in Canada, from a hardware perspective. The Canadian government should take an opportunity here to say, ‘this is a pivotal point in the history of computer science. This is a once in a lifetime change in computer science. This will basically set the state for the next hundred years of computer science” This really isn't in any debate at all.

We shouldn't allow the foreign country to take it away from us. We have it here, as we have with lots of other things. We should double down on supporting those companies. Even if those companies eventually become two companies or one company, it doesn't really matter. It's a fact that people live here and like to work here and so forth. That's important.

The second thing that I think they don't fully understand is that a quantum computer is not useful unless it has quantum applications running on it. Quantum applications are typically written by Master’s, PhD type people. Ten people can write an application to do some quantum chemistry that could change the nature of climate change, for example.

Small numbers of people with this amount of power can change the whole world. We have an opportunity here to create what happened in Silicon Valley 30 years ago in Canada. I'm actually more excited about the surround, the quantum companies in the application space, around the quantum computers that we have here, more than that than the quantum computers themselves. Quantum computers themselves are, to a large extent, a solved problem. We will solve that problem. It is going fast. You need these applications people to change medicine, material science and so forth. We kind of ignore that knock-on effect. For example, I often get politicians saying that, “in Photonic you're creating 300 jobs. 300 jobs isn't interesting.” Well, it's interesting to me because these are super PhDs. These are the best in the world, people. They create five jobs for every job I put in here, but more importantly, the application surround around this is going to create thousands of jobs and change the GDP of a nation, which is not necessarily a function of the number of jobs that you have.

Especially with AI, people are understanding that actually fewer jobs can lead to higher GDP growth. So if your focus is on GDP growth, it's a false logic to believe that actually equals jobs. It did in the 18th century, but it's not going to do it in the 21st century. Let's get a grip here, people.

KB: Is there anything that I should have asked you that I didn't ask you?

PT: Canada is historically risk averse and has a history of losing commercialization south of the border.

To some extent, we've made that easier for them because there are three hardware companies, and this is not 300 now, four if you include Anyon, and so the winners have kind of picked themselves. They've fought in the trenches here to get to this, and the Americans see it. So fund them.If you want to, pick any two out of four, three out of four, one out of four, and then fund it. So as far as I'm concerned, as a Canadian, you're going to have to fund it. And the benefit is huge to Canada.

Those companies, by the way, are fiercely Canadian. So this view that we are in it to sell to the highest bidder couldn't be further from the truth. We are companies, we do have cap tables, we do have investors, we are subject to capital flows. But speaking for Photonic, Photonic is two thirds owned by Canadians, and it's fiercely Canadian. We’re banging the table going, “I want to stay Canadian. Give me a chance to stay Canadian.”

It's not a given that you form a company and just sell it. That is not a given at all. If you can form a company and have sufficient revenues, and have revenues with high gross margins, like Johnson & Johnson, which is a private company, you don't want 1) to sell it or 2) go public. There's no need to raise capital once you get past this kind of stage that you're in. To be a great company operating is actually a value thing that people do not understand. Often companies run into cash issues, so they are forced to take capital from other places. And of course the capital the Americans can bring to bear is high. The irony of all of this is the capital for quantum is a fraction of the capital for AI. A fraction. If you're an AI company and my PhD was in AI, so I can speak with a certain amount of knowledge, your cost base to run your LLMs is enormous. Absolutely enormous. So in quantum, the cost base is actually small. So it is a very strong high gross margin business, which is good. So you want high gross margin businesses that are actually creating opportunities for other businesses, which will also be high gross margin businesses to enter GDP. That's basically what you want to do as a society.

Interview with Allison Schwartz of D-Wave

KB: Tell me a little bit about yourself, about D-Wave, and about what you're trying to achieve.

AS: I’m Allison Schwartz, I'm the head of global government relations and public affairs at D-Wave. I've been with the company now for almost five years. D-Wave is a full-stack quantum computing company, which means we have the computer, hardware, real-time cloud access, support services, professional services, and all. We have right now a quantum annealer, which is in the cloud today, solving real-world problems. We are also building a gate model system, and we are using it through superconducting chip fabrication.

I mentioned the two different systems, not to get into the science behind them, but to highlight the fact that quantum computing is not monolithic. There are different modalities or different qubit architectures. Each of these are advancing on a different timeline, and each of these have different strengths and weaknesses and capabilities.

Annealers are best for optimization problems, which are ubiquitous across industry. That's what's commercially available to now, and we're in our R&D phase for the gate model systems.

KB: Is a quantum annealer a hybrid system, or is it fully quantum-based?

AS: It's 100% quantum. We do have hybrid solvers that you can submit your problem in, and you can ask it to go to the QPU only and have the quantum annealer take care of all of it. But a lot of the problems actually don't need quantum to solve all of it. A hybrid solver might actually be a better solution. If there is the part of the problem that HPC is really good for: a hybrid solver will break the problem down, and the end user doesn't need to do anything. They submit it into the problem, and then our hybrid solver will break the problem down, send part of it to an HPC, send part of it to a QPU, and then give you back the answer, and most often sub-second response times.

KB: It's pretty clear that Canada is a leader in quantum and that we have an incumbent advantage in the space. What is Canada doing well, and where are the gaps?

AS: Canada had a “leader” position in the academic space, especially on the hardware side. D-Wave came out of UBC, and so a lot of our technology came out from there. A lot of our employees are from Canadian academic institutions, and we still have our research center of excellence out of the Vancouver area in Burnaby.

What Canada is doing right is investing in the hardware and seeing the value of quantum. Where Canada can grow is on the application layer of seeing where these actually can do useful things. We need both. We need both the investments in the hardware advancements as well as the software, and we need investments in all the different modalities that are out there. You're seeing investments in, let's say, the EU and the UK that don't have as much of the homegrown technology. So they are investing in Canadian companies, coming over and building those applications. It's about the end use for the technology. How do I solve my problem, whether it be transportation or personalized medicine or emergency response. Where Canada is also doing really well, and I know it's going to surprise people: immigration. It's actually easier, if we have a talent that we need to bring into our Canadian entity, to bring them there versus our US entity. In the US it’s harder because of the immigration laws to bring somebody in there who isn't already a US citizen.

KB: With respect to quantum strategies and comparing Canada, the US, and the UK, how are others doing it differently?

AS: The first difference between the UK and the US is in taking an explicitly inclusive approach on quantum, including quantum annealers as well as gate model systems. This was actually the UK approach to their SPARQ program. They talked about having to train on annealers as well as gate model systems. That inclusivity has also moved into the EU quantum programs. It's also in the Canadian quantum strategy. It's not in the US one that was first passed, but it is in the legislation since to reauthorize the national quantum strategy. That inclusivity language has been folded into it. The problem and the gaps happen in the implementation side. So you have these national quantum strategies, but you have quantum programs that are implementing them, and there's a gap between what the strategy says and what these programs implement. There is not a good oversight or course correction when those gaps occur.

The UK actually has instituted programs to be inclusive of all technologies. The EU just did a call for air traffic management that looked at quantum sensing and quantum computing and explicitly included quantum annealing technology as high yield, low risk, short term technology. The Canadian programs, unfortunately, if you look at the last two or three that relate to compute are for gate model systems only.

D-Wave is the third largest quantum patent owner in the world behind Google and IBM, the largest in Canada, and we are explicitly excluded from their calls. So that is a concern. So, how are we going to course correct that given that you have a program implementation that doesn't actually align with the roadmap that just came out of ISED or the national quantum strategy that's come out of ISED?

KB: D-Wave was explicitly excluded from the DARPA QBI program as a result of the focus on gate model systems, and you mentioned being the third largest owner of patents in the world after IBM and Google, which is of obvious relevance to the ongoing policy debate around IP ownership and sovereignty. Tell me a little bit about the process of trying to actually get the funding you need to build this company, balancing that against IP strategy and retention.

AS: Let’s start with the QBI program and then I can get into the investment side of things. Originally, the DARPA program was called Utility Scale. It was just about getting a quantum computer to Utility Scale and D-Wave did submit an application. They had identified four companies that they were interested in, all of them gate model only, and two of them made it onto phase two: PsiQuantum and Microsoft. Then they came in with the Quantum Benchmarking Initiative, the QBI, which was an even smaller subset and that one they actually designated it for gate model only.

While we are building both annlears and gate-model systems, we did not submit our gate model program and the reason is the Quantum Benchmarking initiative had a six-year timeline and we believed that our gate model program would get to Utility Scale, but we weren't sure if we could get it in the six-year time frame. We knew we could get there with our annealer, even though it was technically out of scope. Under the Utility Scale the annealer should have been in scope, but DARPA had determined they only wanted gate models, so when they shortened it, they focused it there. There are 18 or 19 companies that got through the QBI program Phase I. DARPA is assuming a majority of them won’t make it to the next phase. So they do have a bit of weeding out and people who have great ideas but can't implement them in the timeline they need or have problems scaling.

As for the investment piece, because D-Wave has most of our commercial success is in the annealer and we are building the gate model system, but it is in the R&D phase. We're not building a NISQ-era system. We're going for fully fault tolerant from the beginning. We know that there's a longer runway for that. We are not beholden to government dollars. We actually have more commercial customers, so our commercial success is because we have real-world applications in production today. We're optimizing the telecommunications network in Japan with NTT DOCOMO. We're optimizing automobile manufacturing of Ford Otasan in Turkey. We're optimizing some operations in Canada for Pattison food groups, the Save On Foods in Western Canada. They're looking at a few different grocery optimizations including e-commerce delivery staff scheduling and others. So we have real-world successes pinging our systems in real time. We went public in SPAC in 2022. You're right, a lot of the investment did come from VC investors, but we did have PSP as one of our major investors in Canada. They have since sold their shares, which was announced publicly, but they were bought in fully into supporting D-Wave and our technology advancements and moving forward. But when we went public, we went public through our SPAC. Our parent company is a US entity, although we still have the Quantum Center of Excellence in Canada in Burnaby where a majority of our employees are. And that's where our products and services are being developed.

KB: Recently we have seen the G7 and the PMO mention Quantum. This conference and recent NATO recognition of quantum as a key emerging defense technology all show intention to push on quantum tech. What concrete steps need to happen now that the intention has been expressed?

AS: There are a few concrete steps. One is course-correcting the implementation of current programs, making sure they're inclusive of all the technologies, and that really has to be done almost immediately. There's been a second call now that has come out that has not been inclusive and there aren't that many calls. If you don't course correct quickly, you're just going to widen that gap.

There's been a conversation for many years now about creating a quantum sandbox. It's specifically focused on near term applications, identifying a need, identifying applications, whether they're quantum or quantum hybrid, and solving a problem. Supply chain, for example. Can we optimize ports? Can we optimize railways? Can we optimize airports? How do we move things faster? How do we move things better? There was an application in Tokyo which optimized waste collection and it reduced the amount of CO2 emissions by 60% because they could collect waste better using fewer trucks on the road.

The government and Prime Minister Carney have mentioned housing being a critical priority. Well, part of that is a manufacturing problem. How do you manufacture things? Optimization of manufacturing is right in the wheelhouse of D-Wave. We’re already optimizing manufacturing in a variety of different industries. We could do it for the housing industry too.

Supplies have to come to the ports. I mentioned port logistics, that's another area. So there's ways in which you can identify those policy initiatives, put it into a quantum sandbox and build those applications. The UK has done this and they put a timeline on those of 18 months or less. You can do that here in Canada. We can make utilizing today's technology an actual priority, and not just studying it and thinking about what it could be one day in the future. That is a concrete step that could be done through Innovation Solutions Canada through partnerships with digital or the quantum algorithms institute. We're actually working with Staque and Verge through a digital funded project on autonomy for farming equipment in Canada. That autonomy is not just for farms. There is a national defense area. There is a variety of transport that's looking at it, too. So utilizing current programs or current funding structures and creating a quantum sandbox program that is dedicated to near term applications.

KB: It sounds like this is something that you've pitched before. What's the response when you get into this with the government in particular?

AS: “Wonderful idea. We'll get to it.” I think it's time. I've been with the company now for almost five years. I think it's time we got to it.

I testified before Parliament almost four years ago. I pitched the idea of a quantum sandbox and everybody thought it was a great idea. It was actually a recommendation from Parliament to the administration and we're still waiting for it to be implemented.

What it does is actually from an investment perspective is that by building these applications and building the demos, it actually de-risks the technology, which brings more investment in. The quantum sandbox program, it's not just about solving a problem. It's also de-risking the investment in this emerging technology and demonstrating that it actually can do something useful. Through that, we can know a company is one we can invest in, or that a startup is one that we want to navigate with. These technologies can work with companies that are coming out of Creative Destruction Lab as well as the hardware and software that come in. Which Canada has a lot of. Right.

KB: We’re in the midst of geopolitical chaos currently, which is part of the impetus behind a lot of the conversation around technological sovereignty. Has that shift in policy impacted D-Wave’s IP strategy?

AS: It has not. The way we look at this is the IP we need is what we need to advance and scale our systems. We just released our sixth generation system. It has 4400+ qubits. It's more performant than our fifth generation system, which had 5000 qubits. It's not about the number of qubits. It's actually about how well they work together. What is the IP we need to continue to make that move forward? That's what we're looking at. Where is the talent that we need in order to help advance that? We're already looking and identifying ways to build Advantage 3, which is going to be yet another exponential growth in our hardware, but also the software side. So it's not an IP strategy. It's more about talent. It's what we need from building it. We're going to need the right people, and if those people are in Canada, fantastic, but if those people happen to be in the UK, in the US and other places, we're going to have to go where the talent is.

KB: Can you give me a sense of scale for the optimization problems that you're looking at? Can you compare what it would take to solve that using a classical method to what you're able to do with your annealing systems?

AS: So it's hard to answer it from that perspective because there are some optimization problem sets that actually still are better on classical. We worked with the Australian army on autonomous vehicles for emergency response. When you looked at five trucks going from two depots to seven places, a classical computer could solve that. The minute it got to scale of an actual problem, you had seven depots, 300 trucks, etc., by the time you got the solution from a classical solver, it was too late for an emergency response, or it never got to that answer. Our hybrid solver could get there.

It's time to find the solution that you're looking at. If you are trying to optimize wildfire remediation and response, ERDC, which is an army research lab down in Biloxi, Mississippi, identified an optimized fuel break line. How do you actually do the fuel breaks to prevent those cascading wildfires?

It's not that you couldn't do fuel breaks before, it's not that classical can't do it, but we were able to optimize it so that they could tear down 60% less trees. They're also looking at cascading grid failures, which was important with Spain and Portugal recently. As you're looking at the wildfire response in BC that happens every year, it's not like we have an application that we can plug and play. You can't call us during next year’s fire season. The problem is that we have to build those applications now, so we can test it out next year. So it's less about the scale of what a classical do versus a quantum, it’s more about time to solution, and we do a lot of benchmarking in the beginning to identify whether or not we even need quantum for the solution that you're looking at..

KB: What should I have asked you but didn't?

AS: I guess the question you should ask is why people aren't inclusive, why the government programs are not inclusive of all the different technologies. A lot of that is that because quantum is scary, because quantum is so niche, they have a lot of folks that have 40 years worth of academic background that is forcing them to one viewpoint and they're not asking, “what are we not thinking about?” They kind of have that preconceived bias that's going in. There are some governments around the globe that have what's called a 13th man perspective. If 12 people agree in a room, the 13th man has to play devil's advocate. Unfortunately in the quantum programs, whether it's in the US, Canada or others, they don't ask “what if we're wrong.” China, on the other hand, has an all-inclusive approach of just going after everything. When I continue to talk to governments around the globe about what they need to engage and where they can look, when I say, “but what if you're wrong?”, they don't understand the question. So they really have to have a “what if we're wrong” mentality and do GAP analysis. If these are all the technologies that are out there and we've implemented programs, where are the gaps for talent? Where do we need to invest in talent in those areas? Is it applications? Is it annealing? Is it hardware? Is it sensing? Is it networking? There's a variety of these different niches, but we're not doing gap analysis and implementing programs from that.