Commercializing Canadian Deep Tech
A contribution to the Canadian Council of Academies report on "The State of Science, Technology, and Innovation in Canada 2025"
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
Canada’s quantum advantage provides a blueprint for deep tech dominance that can be replicated,
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
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.