Canada has committed at least 2 billion to artificial intelligence infrastructure. On paper, this is the sovereign backbone the country has long needed: data centres on Canadian soil, a new national public supercomputer, and an access fund to bring small and medium-sized firms into the ecosystem.
The intent is right, strengthening sovereignty and competitiveness. But execution risks drifting from the original AI strategy. Infrastructure is only the beginning. Without tying access to deployments, Canadian custody, and strong safety rules, the result could be racks of expensive hardware rather than the national capabilities Canadians actually need.
This is a digital reference for the ALL IN AI Conference and the roundtable “Securing Canada AI Sovereignty in an Uncertain World” by CIFAR, which I’m participating in tomorrow in Montréal, sharing here in case it’s of broader interest. Thank you, subscribers, for sticking with me.
What Canada’s AI Strategy Set Out to Do
When Canada launched the Pan-Canadian Artificial Intelligence Strategy in 2017 (renewed in 2022), the goal was not to win an infrastructure race. The strategy was designed as a capability engine. It built world-class research through the Canadian Institute for Advanced Research (CIFAR), translated that into talent through the Canada CIFAR AI Chairs program, and moved quickly into deployment through Solution Networks and Catalyst Grants.
Anchored by the three national AI institutes - Alberta Machine Intelligence Institute (Amii) in Edmonton, Mila – Quebec AI Institute (Mila) in Montréal, and the Vector Institute in Toronto, the strategy’s strength was translation speed. Its scoreboard was practical and visible: talent trained, startups launched, partnerships formed, and systems deployed in health, energy, logistics, and public services.
What Ottawa Recently Funded
The 2024–25 budget pivot focuses squarely on sovereign compute:
An AI Compute Challenge to seed domestic AI data centres.
A national public supercomputer operated with the Digital Research Alliance of Canada.
An AI Compute Access Fund to subsidize compute for small and medium-sized enterprises (SMEs).
Acceleration of the Pan-Canadian AI Compute Environment (PAICE): three GPU clusters—TamIA at Université Laval, Vulcan at the University of Alberta with Amii, and Vector’s cluster at the University of Toronto.
A safety layer through the Canadian Artificial Intelligence Safety Institute (CAISI), paired with the National Research Council (NRC) to advance detection, privacy, and human-oversight methods.
It’s the first time Canada has invested at this scale to bring compute under national control.
Why It Matters
Infrastructure is necessary but not sufficient. The Pan-Canadian Strategy was built around outcomes: working AI systems that improve health services, make grids more reliable, or speed up logistics. Infrastructure strengthens that edge only if it accelerates translation. If the investment stops at utilization dashboards, Canada risks owning expensive infrastructure that fails to deliver public outcomes or global leverage.
The Good
The backbone is finally funded. Canada now has a credible path to domestic compute that reduces reliance on foreign hyperscalers and keeps sensitive data and intellectual property under Canadian jurisdiction.
The PAICE clusters provide a national runway: researchers and firms can train, evaluate, and deploy at scale without leaving the country. Combined with the institutes and CIFAR’s network, the system now has a complete chain from research to deployment.
The AI Compute Access Fund is a smart bridge. It ensures that smaller firms, the engines of Canada’s innovation economy can participate while national capacity ramps up.
The Drift
Execution has tilted toward infrastructure. Where the Strategy’s DNA was capability-first, measured by deployed systems and visible impact, the new posture is capacity-first, measured by racks, GPUs, and access tickets.
That inversion risks infrastructure capture: clusters running at high utilization while hospitals, utilities, and ports see little change. SMEs could also get trapped in a cycle of pilots and proofs of concept, never crossing into production.
Equally important, sovereignty is not automatic. Unless compute awards require Canadian custody of model weights, data residency, third-party audits, and incident reporting, Canada will own the buildings but not the leverage.
The Risks
Cost without outcome. Sovereign compute becomes a line item rather than a national asset if not tied to deployments.
Pilot loops. SMEs could burn subsidies on demos that never move into regulated sectors like health or energy.
Clock-speed mismatch. Infrastructure builds are multi-year; without near-term deployment cohorts, political timelines will run out before public benefits show up.
What Canada Should Do Now
Canada doesn’t need a new plan. It needs to wire the new spend through the rails that already work.
First, tie access to deployments. Every compute award should name an operator, a ministry partner, and a delivery date. The national metric should be unambiguous: GPU hours to capability in use.
Second, make custody and safety default terms. Every project on sovereign infrastructure should declare where its model weights are held, where its data lives, how often it will be audited, and how incidents will be reported. The Canadian Artificial Intelligence Safety Institute already has patterns that can be applied across the board.
Third, accelerate approvals through evaluation commons. Open test benches on the PAICE clusters for natural language processing, computer vision, reinforcement learning, plus domain-specific suites would let projects earn a regulator-recognized “trusted to deploy” stamp. That stamp can shorten approvals at home and boost Canadian exports abroad.
What “Good” Looks Like in 12 Months
A public dashboard should show that at least half of new compute awards are tied to named deployments with accountable operators and dates. Outcomes per GPU hour should be visible, shorter wait times in clinics, fewer grid outages, faster port throughput. And every funded model should publish custody and oversight details so Canadians know where their data and models live, how they are tested, and how problems will be fixed.
The Stakes
Canada has the people, the institutes, and the hardware. The question is whether it will measure what matters. If Ottawa ties compute access to deployments, Canadian custody, and safety, then the new backbone becomes a national capability. If not, Canada will own the racks while the real value flows elsewhere.
