Embracing Risk

Successful incubation of emerging technologies is a numbers game, but we keep trying to pick winners

Many of my recent posts here have been themed around the idea of risk tolerance. This post attempts to put some mathematical weight behind the assertion that embracing risk is a prerequisite for value creation through innovation, and makes the case that public sector leadership is required if Canada is to ever secure socioeconomic benefit from publicly funded research.

If you are involved at all in the startup or VC space you have no doubt heard all about power laws. In a the context of emerging technologies, a power law distribution of technology value means that the vast majority of the value arises from a small minority of technology portfolios. Unfortunately, it is very difficult to tell the difference upfront, and the earlier you seek to get involved, the harder it gets.

In this post, I elaborate on the implications of this value distribution for public-sector risk tolerance for innovation funding, demonstrating how Canada’s strategy of attempting to pick winners results in exactly the opposite. I discuss the origins of systemic risk-intolerance that has prevented public embrace of the power law as a means to benefit from Canadian emerging technologies, and highlight ways other ecosystems are thinking about creating value from emerging technologies.

Together, it all suggests that a shift in the way we think about the value and role of public sector spending is a necessary precursor to addressing Canada’s misnamed Productivity Paradox.

The VC model has been around for a while

Venture capitalists (VCs) learned how to navigate power law dynamics a long time ago, and the basic structure of the approach persists to this day. The phrase has its origins in early 19th century whaling expeditions. Agents (analogous to modern VCs) would raise money from corporations and high net worth individuals (LPs) to fund ship’s captains (founders) to go whale hunting (ventures). Even the distribution of payout (the 2 and 20 rule) arose during this time.

As with modern investments in emerging technologies, the vast majority of whaling expeditions ended in failure, while the top 2% returned so much value that they more than paid for the rest. Without any real way to predict which ones would return profits when they set sail, the only winning move was to fund many expeditions, enough to ensure (statistically speaking) that at least one of the investments would be among the 2%.

The process has changed since then, but the underlying model is still effectively the same.

Power law math

Simulation modelling of power law dynamics has been done well by a number of people: Matt Lerner over at Medium developed a very simple yet enlightening toy model that simulates random investment with power-law distributed returns, while Mike Arpaia from Moonfire developed a more sophisticated model as a means to test variations on early-stage investment theses. Rather than reproduce their analysis, I will simply summarize their findings here. I encourage you to skim these articles before reading further if you are not already familiar with power law dynamics.

In a nutshell, Matt’s Blind Squirrel model does a Monte Carlo simulation of portfolio values over random investments made in prospects with a power law distribution of returns. Mike Arpaia builds a more sophisticated framework that allows comparison of different fund structures and allocations, with the obvious goal of optimizing payout from early-stage investments, but the core of the model is the same: an underlying distribution of investment outcomes is sampled according to a set of rules that collectively define an investment thesis, and the hypothetical returns are compared.

In both cases, it quickly becomes clear that even if all you do is throw darts you’re all but guaranteed a positive return as long as you build a sufficiently large portfolio to have a reasonable certainty of hitting a few top performers. The Blind Squirrel approach achieves this simply by making a lot of bets and letting the law of averages sort it out, while the Moonshot model advocates for deploying most of your investments in the first stage, with minimal reserve for follow on rounds, for the simple reason that this allows more bets to be made.

While the Blind Squirrel approach underperforms average VC performance, it is still net positive. The implication is that you do not need to be able to pick winners to have reasonable certainty of return when playing a power law, as long as you make enough bets to cut through the noise.

The numbers-game nature of early-stage investment comes across most clearly when considering the impact of the top performer in a given portfolio. In the Blind Squirrel model, if you remove the top company from each portfolio, the returns drop dramatically, with the top company providing as much as 50% of the profit for small portfolios. This dependence on the top performer gets less and less important as the number of investments made by your fund increases.

In other words, if you are running a small fund and you pass on the one big opportunity, it’s the difference between wild success and complete failure. As a Blind Squirrel, the only winning move is to invest in a much larger pot of companies, and let statistics take care of picking winners. The Moonfire model arrives at broadly the same conclusion.

This is not a particularly controversial or surprising conclusion. We are basically just restating the hypothesis that portfolio diversification is important. Anyone investing in a broad market ETF is effectively a Blind Squirrel investor, albeit using a different asset class.

When considering emerging technologies, the power law gets even more extreme, the timelines get extended, and it gets even harder to pick winners given that there is an entirely new class of risk involved. Not only do we need a team that can deliver and a willing market to buy, in many cases we also need the laws of physics to cooperate with the research efforts. With the added filter, it takes a lot more sampling to cut through the noise. If the top fraction f of companies or IP portfolios are responsible for most of the value creation, a Blind Squirrel needs to build a portfolio of 5/f companies to ensure a 99% chance of hitting at least one big winner (that’s 250 investments, assuming 2% of companies are the profit-creating outliers). The earlier an investment is made, the smaller f becomes.

The conclusion is simple: successful incubation of emerging technologies requires a support structure that is both willing and able to make a very large number of bets, understanding and accepting that the majority of them will fail. All that matters from a profit perspective is aggregate performance, which is dictated almost entirely by a small minority of investments. In many ways optimal investment strategy resembles a midwit meme, with VC as the midwit picking winners while both very early and very late stage investors are best served by just buying (almost) everything.

Drivers of public sector risk tolerance

There is a fascinating piece of research that gets into the dynamics of the unavoidable tradeoff between false negatives and false positives in public spending at a cultural level, which you can read here. The authors explore cultural differences in consideration of “second-best fairness”, the idea that there is “a trade-off between giving some individuals more than they deserve, false positives, and others less than they deserve, false negatives.”

While they focus on welfare payments, the idea generalizes well to anything that involves spending of public funds. If we optimize our systems to reduce the possibility of making a payment in error, we will end up refusing payments in error at least some of the time (false negative), which can have severe consequences for the individuals involved, or be the reason a startup moves south. On the other hand, if we optimize to ensure that payments are made to those who need them, we will make some payments in error, whether honest mistake or the result of fraud (false positive). It is not possible to build a system that completely eliminates both of these, and the balance selected is an active policy choice. (

Santi Ruiz

did a great Statecraft interview recently that touches on these issues).

Canadian tax code is a good example of a system optimized to avoid false negatives. CRA assumes your return to be accurate, a few basic checks aside. There is some degree of errors and fraud occurring at any given time as a result, but it is only worth correcting if the amount recovered by doing so exceeds the cost recovery, which leaves some false positives as acceptable outcomes.

Where innovation policy is concerned, Canada optimizes heavily to avoid false positives. If you have engaged with almost any public funding for innovation you find that these policy frameworks require that a success narrative be told for every project funded, irrespective of their aggregate impact.

Tom Goldsmith

did a good job summarizing the core of the issue in this recent post, in which he suggests that many policy decisions are made not to ensure maximal benefit, but to avoid blame for failures. No politician or public servant wants their name attached to a project that will not bear fruit in their term, since it becomes an easy target for opponents.

Picking winners

When the goal is to avoid the possibility of blame for failure at the level of individual investments, we lose sight of the broader context in which that investment occurs, a problem that is exacerbated by the lack of unifying mission that characterizes Canadian innovation policy space generally. Our innovation support programs are incentivized to avoid individual failures by only supporting low-risk projects, which in turn ensures low reward.

We try to pick winners, and we are not very good at it.

Many programs have minimum revenues or headcounts to be eligible, for example, the implicit assumption being that revenues (or jobs created, or T4s issued, or any numbers of other short-term measures of economic impact) represent de-risking of an idea. This is a problematic assumption when considering emerging technologies, which are often years of research away from any revenue and are often best advanced by small, agile teams. Where emerging technologies are concerned, the signals that our innovation support systems use to pick winners are uncorrelated to long-term impact until far too late in the game.

Power law math tells us that Blind Squirrels cannot afford risk aversion. The power law that dictates the value of emerging technology portfolios, combined with Canadian systemic public risk aversion, means that much of Canadian innovation policy is constructed to miss out on value creation arising from research. Canada’s Productivity Paradox is anything but paradoxical: it falls naturally out of even the simplest toy model we can conceive.

The models and discussion above make a key assumption: that our investment strategy does not alter the underlying distribution of investment returns. In reality, any program that invested in literally everything would immediately be subject to fraud that would drive the value of the underlying distribution to zero. Some degree of selectivity is required, but the math suggests that, at the earliest stage of technology development, the selection should not be much more stringent then filtering out obvious fraud, making sure the founder is both credible and coachable, and ensuring that the amount requested is aligned with the actual need.

Value is not synonymous with profit

While the track record shows that on average VCs do outperform the Blind Squirrel investor, the effort required limits a typical fund to 20-50 investments, and having profit as the primary driver limits the timespan over which those investments can be held. Both of these limitations make traditional VC poorly suited to supporting emerging technologies, where timescales are extended and picking winners is all but impossible. It’s also a vicious cycle: the need to pick winners increases due diligence requirements, which in turn further limits the number of investments that can be made, further increasing the required success rate. It works for B2B SaaS and biotech for which playbooks have been developed to assist in the process, but it translates poorly to other sectors.

There are two sources of investment that have the ability to truly play the numbers game while being tolerant to long development timelines: venture philanthropy, and the public sector.

To make early investment strategies compatible with securing socioeconomic impact from emerging technologies, we need to expand our definition of value creation to recognize that value creation is not synonymous with profit. If instead of pure profit-seeking we expand our definition of value creation to include economic development, security, and independence, retention of talent and IP, education of entrepreneurs, and progress toward ambitious societal goals like climate change targets, the Blind Squirrel approach becomes much more attractive. While such positive spillovers are of no use to a for-profit VC’s balance sheet, they are of clear value to the Canadian public and to mission-driven investors.

Unlike traditional VC that is limited to just a few investments over a fixed timespan and all that matters is (some metric of) profit, public or philanthropic investment in emerging technologies can afford to place many bets with patient capital, all but guaranteeing that opportunities are not missed. In a model that seeks economic development and social impact over profit it suffices to be evergreen, which is an easy target to hit given a sufficiently large portfolio.

Even companies that a for-profit VC firm would write off as failed bets have value in this model. A failed entrepreneur is now someone with invaluable entrepreneurial experience who is better equipped to navigate the process on round two and is incentivized to stay in Canada to try again, knowing they will be supported. Companies that return 2-5X in the long run, while complete failures on a VCs balance sheet, are all contributors to a strong and resilient domestic economy based on SMEs. A breakout success can easily be the basis for a cascade of positive spillovers as entrepreneurs become mentors and investors in the next generation (the story of the transformative impact of the Skype acquisition on the Estonian ecosystem is a great example of this).

A new approach to venture support

There is a desperate need in Canada to embrace the idea that a high failure rate in supporting emerging technologies is perfectly acceptable, so long as the aggregate, long-term impact of the whole portfolio is net positive, and to expand the definition of “impact” to include positive spillovers beyond direct return on investment.

Other ecosystems are way ahead of us.

DARPA has an 85-90% failure rate over its lifetime and the SBIR (“America’s seed fund”) has failure rates that exceed 90% in some cases. Far from being embarrassments, these programs have existed for decades and are widely recognized as cornerstones of American technological dominance. Early-stage risk-taking is a common feature of many other ecosystems that innovate effectively, as well.

A more directly comparable example to Canada is France, where Les Deep Tech has set an ambitious goal of “500 startups created every year, 10 deeptech unicorns, 50 new industrial sites per annum” by 2030, and has $3B in public funding committed to achieving this. Some quick math shows that they are comfortable with targeting a 2% success rate (10 unicorns out of 500 startups per year). They are taking the Blind Squirrel approach of making thousands of small, low-overhead bets, understanding that positive spillovers will ensure net positive value creation.

This is the kind of commitment Canada needs to move the needle in deep tech and emerging technologies, but to make this possible, we first need to embrace risk and recognize that aggregate value creation is more important than individual project success or profit. To do so the Canadian public sector must accept that a high failure rate is an essential and intentional feature of an effective innovation ecosystem and adopt a mission-driven, whole-of-government approach to enacting this in its innovation strategy.