Lisa Strug, Director of the Data Sciences Institute, is a Professor in the Departments of Statistical Sciences, Computer Science and the Division of Biostatistics, and the Director of CANSSI Ontario, at the University of Toronto. She is also the Associate Director of The Centre for Applied Genomics and a Senior Scientist at The Hospital for Sick Children.
The Canadian federal government’s Sovereign AI Compute Strategy commits billions to building and maintaining high-performance computing (HPC) domestically and ushers us towards a pivotal moment in our approach to artificial intelligence.
On the one hand, investing in HPC is overdue and welcome: our capacity for compute is a critical component of modern data-driven research, innovation in industry, and global competitiveness with G7 peers. On the other hand, compute alone cannot sustain Canada’s pre-eminence in AI innovation. Expanding our GPU racks may bring more HPC under Canadian control, but it does not solve equally pressing problems: stagnation in productivity, low AI adoption among firms, outdated public-sector digital infrastructure, uneven and unequitable access, and a lack of high-quality, well-linked, trustworthy data.
A myopic focus on hardware that insists the bottleneck for Canadian initiative is merely compute risks obfuscating the reality that multiple international bodies like the Organization for Economic Co-operation and Development (OECD), International Monetary Fund (IMF), and UNESCO have emphasized: AI performance and national competitiveness depend far more on human expertise in the data sciences and data quality than on raw compute. Canada has this human capital, and at the Data Sciences Institute (DSI) at the University of Toronto, we wonder why the strategy does not do more to capitalize on Canada’s existing strengths.
AI is a data science, but also it cannot exist without the data sciences, which provides the prerequisite know-how to collect, prepare and link the data that trains AI models and the skills to measure uncertainty and bias in the complex, unstructured datasets that AI is used to parse. Canada’s true strength is in its extraordinary reservoir of this kind of data science expertise across multiple fields: experts who know how to collect, clean, link, govern, interpret, and generate derived and missing variables from data.
These experts work in health, public policy, environment, social science, energy, Indigenous governance, academia, and industry. They build (synthetic-)data pipelines, design privacy-preserving systems, operationalize equity frameworks, extract insights, and shape the governance of data flows. At the DSI, we believe that this expertise is paramount to effective AI adoption and that individuals with data sense and skills need to be positioned across sectors for AI uptake to matter. We support and deliver training programs for both graduate students and prerequisite-agnostic reskilling programs for the public at large precisely because we need to increase data science literacy to make AI useful.
Across international benchmarks, Canada ranks near the top in AI-related human capital. According to Deloitte, Canada has the highest AI talent concentration in the G7 and that its AI workforce has grown faster than that of peer countries. The Canadian Institute for Advanced Research (CIFAR) asserts that the Pan-Canadian AI Strategy has helped build “one of the fastest-growing and most skilled AI communities worldwide.”
Canada’s public sector also hosts world-leading data-linkage capacity (e.g., ICES, Statistics Canada linkage environments, and Health Data Research Network Canada). This ecosystem is broader than “AI engineers”: it includes data engineers, statisticians, stewards, auditors, evaluators, and domain experts who make AI usable, trustworthy, and socially valuable.
Given these facts, we find it unhelpful that data scientists are often elided within the compute-first hyperscaler agenda that defines Canada’s strategy. While “data scientists” are mentioned, this strategy mostly seems to advance a much narrower definition of “talent”: deep-learning researchers, model engineers, and infrastructure specialists who build AI models. This group constitutes a tiny slice of the actual AI workforce. The Sovereign AI Compute Strategy only faintly gestures towards “data scientists,” despite OECD research that demonstrates that the highest productivity gains come from data-rich sectors and require cross-disciplinary data expertise.
To be clear: both kinds of expertise are critical, but Canada already has a deep backbench of data scientists, including its statistical sciences community, who should be at the forefront of these conversations, and not reductively relegated to the last item in a comma-separated list of “talent.” Compute investment is necessary, no question, but Canada must reassert leadership in the places where it has a genuine global edge: training, stewarding, and empowering data-science talent, and building the data ecosystems that make compute useful. Our policies should focus not only on large-scale model training but on the breadth of data-work that underpins real-world applications of AI that have the potential to benefit Canada’s leadership in AI development.
Narratives that insist that compute is the bottleneck matters for policy design. If talent is defined narrowly, investment follows narrowly, and compute infrastructure risks becoming under-used, unevenly accessed, or dominated by actors already positioned to train large models. The IMF’s 2024 review of AI readiness reinforces this point: the strongest national differentiators are human capital and data governance, not compute alone. In Canada, data constraints remain acute: many firms lack staff to prepare data for AI; public-sector data remains siloed and difficult to access; Indigenous data governance is under-funded despite commitments to sovereignty; and synthetic-data pipelines remain underdeveloped.
The federal government’s 2025 AI strategy correctly identifies research and talent and education and skills as central pillars alongside enabling infrastructure. The compute strategy is a necessary foundation, but it is not, by itself, a competitiveness strategy. The durable solution is expanding Canada’s data-and-AI workforce and institutions: education and upskilling, data readiness, governance capacity, and the applied expertise needed to deploy AI across hospitals, classrooms, municipalities, organizations, and environmental systems.
Compute matters, but it is talent and skills that determine whether that compute can enable broad-based productivity and international competitiveness.
