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Canada Is Spending Billions on AI. Why Are Companies Still Fleeing?

The country is a global talent factory, but many see the US as the only logical next step

At first glance, Canada has committed substantial resources to artificial intelligence. The Pan-Canadian AI Strategy has directed more than half a billion dollars into the ecosystem since 2017. The $2 billion Canadian Sovereign AI Compute Strategy followed in 2024. Budget 2025 added nearly $926 million over five years for sovereign compute infrastructure and $25 million over six years for Statistics Canada’s TechStat program to measure AI adoption. Organizations such as Scale AI fund industry-led integration projects. By any measure, this is serious public investment.

The reality, however, is different. Many Canadian AI founders continue to incorporate in Delaware, and venture capital continues to flow south. In January, Y Combinator announced it would no longer invest in Canadian-incorporated start-ups, requiring founders to reincorporate in the United States, Singapore, or the Cayman Islands. The accelerator quickly backtracked, but the fact remains that the Canadian companies that went through Y Combinator and became unicorns had all nearly already converted into Delaware corporations.

The talent pipeline tells a similar story. A study of STEM (science, technology, engineering, and math) graduates from the University of Toronto, the University of Waterloo, and the University of British Columbia found that one in four opted to work outside Canada, with the rate for software engineering graduates reaching 66 percent.

The question is not whether Canada is spending enough on AI, but whether that spending reflects the reality of building an AI company in this country.

Many founders have identified a structural gap at the centre of Canada’s approach: policy invests heavily at the research end of the pipeline and announces large figures at the infrastructure end. But the missing middle—where prototypes become products, where start-ups find their first customers and iteration speed determines survival—remains largely neglected.

Without going further, we should look at how research is set up. The conventional wisdom for a middle-power country is to select a few niche verticals and concentrate resources there, but the reality is that most entrepreneurs reject this logic. Innovation frequently emerges from non-obvious and general-purpose applications rather than from predetermined silos.

Consider that two of Canada’s most valuable AI companies, Cohere and Ada, both emerged from Toronto’s foundational AI research community rather than from any government-designated vertical. Cohere builds general-purpose language models for enterprise use; Ada automates customer interactions across industries. Neither would have been predicted by a vertical strategy, yet both are now globally competitive. A country that bets on a specific vertical research risks missing technological breakthroughs and finding itself locked into a domain with limited transferability.

On the contrary, the more productive approach is to fund foundational AI capabilities, such as open-source models, shared compute infrastructure, and talent development, that enable a broad range of applications. This is what American and Chinese universities do, and it is what Canadian institutions, such as Mila—Quebec Artificial Intelligence Institute, are positioned to do,........

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