The taxi driver who picked me up at Toronto’s MaRS Discovery District last week wanted to talk about artificial intelligence. “My daughter’s studying computer science at Waterloo,” he said. “She tells me Canada invented AI but America’s taking it all now.” He wasn’t entirely wrong.
Canada’s pioneering role in developing the deep learning techniques powering today’s AI revolution is well-documented. When Geoffrey Hinton, Yoshua Bengio, and Richard Sutton were laying the groundwork for neural networks at Canadian universities, most of Silicon Valley remained skeptical. Fast forward to 2023, and the trillion-dollar question emerges: can Canada maintain relevance in the technology we helped create?
The numbers paint a sobering picture. While Canadian AI startups raised a respectable $952 million in 2022 according to the CVCA, American AI companies pulled in over $40 billion. Meanwhile, China invested approximately $43 billion in its AI ecosystem last year through government initiatives and private capital. Canada’s early intellectual advantage hasn’t translated into commercial dominance.
“We’re great at creating AI technology but struggle with scaling it,” explains Erin Bury, CEO of Toronto-based estate planning platform Willful. “Canadian founders often hit a ceiling where they need to choose between maintaining independence with limited growth capital or selling to American giants.” This pattern repeats across our innovation landscape – promising AI startups get acquired before reaching their potential, with their intellectual property and talent relocating south.
The federal government’s Pan-Canadian Artificial Intelligence Strategy, first launched in 2017 with a $125 million commitment and renewed in 2021 with an additional $443 million, helped establish world-class research centers like the Vector Institute in Toronto, Mila in Montreal, and Amii in Edmonton. These institutions have kept Canada competitive in fundamental research, but the commercial translation gap widens each quarter.
What’s particularly concerning is the brain drain. Canadian universities produce exceptional AI talent, but retention remains problematic. A Statistics Canada report revealed that over 65% of software engineering graduates from top Canadian universities were working outside Canada within five years of graduation. For AI specialists with advanced degrees, American tech companies offer salaries two to three times higher than Canadian counterparts.
“I graduated from University of Toronto’s machine learning program last year,” a young researcher told me, requesting anonymity because she recently accepted a position at a major American tech company. “I wanted to stay in Canada, but the difference was $190,000 in California versus $85,000 in Toronto. With housing costs similar in both places, the decision made itself.”
The geopolitical stakes couldn’t be higher. AI isn’t just another industry – it’s rapidly becoming the foundation of national economic competitiveness and security. The recent scramble by American and Chinese governments to regulate and advance AI capabilities shows they view it as infrastructure as critical as electricity or telecommunications. The EU’s comprehensive AI Act demonstrates similar urgency.
Canada’s response has been more measured. The Artificial Intelligence and Data Act (AIDA), introduced as part of Bill C-27, represents our first significant regulatory framework. While it addresses important issues around AI governance, critics argue it lacks the urgency and ambition needed for a country fighting to maintain relevance in a technology we helped pioneer.
“We need a whole-of-government approach to AI competitiveness,” argues Dan Breznitz, Munk Chair of Innovation Studies at the University of Toronto and author of “Innovation in Real Places.” “This means coordinated industrial policy, immigration incentives for AI talent, procurement strategies that support domestic companies, and regulatory frameworks that protect Canadians while enabling innovation.”
The computing infrastructure gap presents another challenge. Modern AI development requires massive computational resources. While the Digital Research Alliance of Canada provides some high-performance computing for academic research, Canadian companies lack access to the computing infrastructure available to American competitors. The recent $40 million federal investment in a national quantum computing strategy acknowledges this gap, but implementation remains years away.
Meanwhile, Canadian AI companies face growing pressure from American venture capital. When US investors lead financing rounds, they often encourage promising startups to relocate their headquarters or operations stateside, citing proximity to customers and capital. This creates a paradoxical situation where Canadian taxpayers fund early research and talent development, only to see the economic benefits flow elsewhere.
Not all the news is discouraging. Several Canadian AI companies have achieved global recognition while maintaining significant operations here. Toronto-based Deep Genomics, which uses AI to develop genetic medicines, secured $180 million in Series C funding. Montreal’s Element AI (though later acquired by ServiceNow) demonstrated Canada’s capacity to build category-defining companies. Vancouver’s AbCellera leveraged AI to help develop COVID-19 treatments, showcasing how Canadian innovation can address global challenges.
Public-private partnerships show promise as well. The Scale AI supercluster, headquartered in Montreal, has invested in over 350 projects connecting AI researchers with industrial applications. The Vector Institute’s commercial partnerships help bridge the gap between academic research and business implementation. These models could be expanded and replicated with greater funding.
“Canadian AI needs patient capital and government procurement programs that allow startups to scale without leaving,” says Abdullah Snobar, Executive Director of the DMZ tech accelerator at Toronto Metropolitan University. “We should be creating an environment where our companies can reach $100 million in revenue while based here, not watching them leave when they hit $10 million.”
What would a more competitive Canadian AI strategy include? First, targeted immigration programs specifically for AI talent, offering pathways to permanent residency for international students who complete AI-related degrees. Second, expanded tax incentives for companies developing and implementing AI technologies domestically. Third, government procurement policies that prioritize Canadian AI solutions, creating reference customers for startups.
Most critically, Canada needs to address the commercialization gap. The federal government recently announced a $2.4 billion Canadian Innovation Corporation, aimed at helping companies scale technologies developed here. If implemented effectively, this could provide the missing link between our research excellence and commercial success.
The global AI race isn’t just about economic prosperity – it’s about sovereignty in a digital age. Countries that develop and control AI capabilities will shape the rules for everyone else. Canada’s position as a middle power with strong research traditions offers a unique opportunity to develop and advocate for responsible AI that reflects our values.
My taxi driver’s concern reflects what many Canadians intuitively understand – we helped create this technology but risk becoming mere consumers rather than producers. The window for establishing enduring Canadian AI leadership hasn’t closed entirely, but it narrows with each passing quarter. The decisions we make now will determine whether Canada remains a significant player in the AI century or becomes a cautionary tale of innovation without commercialization.
The future of Canadian AI competitiveness depends not just on maintaining our research excellence, but on building the commercial, regulatory, and investment ecosystem that transforms ideas into globally competitive companies. That’s a challenge worthy of the country that helped spark the AI revolution in the first place.
