What Canada's New AI Strategy Gets Right, and What Startups Still Need

On June 4, 2026, the federal government launched AI for All, its national AI strategy. The headline goals are large: nearly 200 billion dollars in new economic growth, 250,000 jobs, and business AI adoption rising from 12 percent to 60 percent by 2034. For a Canadian AI startup, the strategy matters less for its slogans than for one question. Does it make it easier to build and sell AI here?

Here is a grounded read.

What the strategy gets right

It funds adoption, not just research. Canada has never lacked AI research. We have the Vector Institute, Mila, and Amii, along with a deep talent bench. The gap has always been adoption and commercialization. AI for All puts money against that gap with a larger Compute Access Fund for sovereign GPU, regional AI funding through the development agencies, and programs aimed at getting small and mid-sized businesses to actually use AI. For a company selling to Canadian SMEs, a government actively pushing those buyers to adopt is a tailwind.

The tax credits are genuinely strong. This is the most useful and least discussed part. The SR&ED program now returns 35 percent of eligible R&D spending as a cash refund for small Canadian companies, with the limit raised to 6 million dollars under Bill C-15, and no revenue requirement. A two-person startup doing real engineering gets cash back on it. Paired with IRAP grants, this is one of the better early-stage funding environments in the world, and it predates the new strategy.

Sovereignty is a real edge. The strategy leans hard on Canadian compute, Canadian data residency, and Canadian law. That can sound like politics, but for startups selling into healthcare, legal, and government, data residency is a hard requirement that US competitors struggle to meet. The strategy turns a compliance burden into a selling point.

What still holds startups back

Most of the new money is not open yet. On launch day, the flagship programs had no application process. Announcements are not cheques, and "later in 2026" does not pay salaries.

The best programs are gated by revenue. The larger instruments, including BDC's LIFT financing, the regional scale-up funds, and the compute subsidy, generally require revenue or a Series A. That leaves the earliest companies, the ones who need help most, leaning on SR&ED and IRAP while everything else stays out of reach until they have traction.

Talent leaves, and so do the companies. Canada trains world-class AI talent and then watches a large share of its AI startups end up headquartered elsewhere, usually because growth capital and large customers are south of the border. The strategy gestures at this with a tech growth fund and talent programs, but capital depth and a real domestic enterprise customer base are the actual fix, and those take years.

What would actually help

If the goal is more Canadian AI startups, a few changes would move the needle more than another announcement.

1. Open the programs faster, with light applications. A founder should be able to apply for compute or adoption funding in an afternoon, not assemble a consortium over six months.
2. Make the government a real first customer. Procurement is the lever no grant can match. If Ottawa bought from Canadian AI startups at scale, it would do more than any subsidy.
3. Lower the revenue gates for the earliest companies. The pre-revenue stage is where Canadian founders decide whether to stay or leave. Non-dilutive support that does not require traction would keep more of them here.
4. Tie adoption incentives to Canadian vendors. LIFT already offers a better rate when a business picks a Canadian AI solution. Extending that logic across the strategy would ensure the adoption wave builds Canadian companies rather than importing tools from elsewhere.

The bottom line

AI for All is a real step. The tax and grant base is strong, the adoption push is the right target, and sovereignty is a genuine edge for Canadian builders. The risk is that the money arrives slowly and lands on companies that already have momentum, while the earliest founders keep facing the same walls. Encouraging startups is less about big numbers and more about removing friction at the bottom, and that is where the next chapter should focus.