Canada’s AI Infrastructure: The Economic Case for Building at Home

For centuries, human progress has depended on specialization. Surgeons master surgery, engineers design bridges, lawyers interpret tax law. Knowledge moves slowly between these domains through universities, books, conferences, and decades of practice. The diffusion of expertise is uneven, costly, and often delayed.

Artificial intelligence disrupts this model, and countries that fail to build the energy, compute, and institutions required for AI will become dependent on those that do.

AI systems are still imperfect. They hallucinate, make strange mistakes, and remain brittle. But their trajectory is unmistakable. Even in their early form, they are transforming how knowledge is generated, transmitted, and applied.

The Industrial Revolution automated mechanical labour. The intelligence revolution will automate large portions of knowledge work. That shift will force societies to rethink their economies, institutions, and labour markets. For countries hoping to thrive in the next century, adapting to this transition is not optional. The winners will be nations that invest in and trust their youngest citizens to lead this transition, unburdened as they are by the old world and native to the new one. But before economies change, cultures must. 

Build the Foundations of Canada’s AI Infrastructure: Energy and Compute

Modern economies and societies ultimately run on energy: hydro, solar, nuclear, oil, and gas. Energy powers compute. Compute, scaled by GPUs and combined with data, becomes AI. Countries that control energy and compute will control the production of knowledge. AI deployed across a society accelerates the creation of goods, discoveries, and wealth. Canada must be deliberate about building domestic wealth, particularly at the outset of this revolution.

Not doing so runs the risk of ceding economic sovereignty and, with the influence over populations that unencumbered AI-driven platforms wield, democratic norms. Canada is culturally shy about ambition, but this modesty, perhaps once a virtue, has become a liability in an era where nations that embrace wealth creation will pull further ahead.

The AI race is already an infrastructure race. American hyperscalers spent $650 billion in 2025. Training large models and operating global AI infrastructure requires enormous computing power. The largest AI training clusters now use 200,000 GPUs and require roughly 300 MW of electricity, comparable to the power needs of a quarter-million homes.

Canada’s Natural Advantages in Building AI Infrastructure

Canada should be uniquely positioned. It possesses vast hydroelectric capacity, nuclear expertise, abundant land, and stable institutions. Few jurisdictions in the world are better suited to host large-scale compute infrastructure.

If Canada wants to participate meaningfully in the AI economy, it must aggressively expand both energy generation and compute capacity. The goal should be straightforward: policy changes that make Canada one of the most attractive places to build and operate AI infrastructure. This could include fast-track permitting for large-scale data centers in regions with cheap power and public utility companies to build and maintain compute infrastructure and open-source AI that is accessible to anyone in Canada. 

AI as a Multiplier of Human Capability

We should use AI to solve real problems. 

For any problem where a human can vouch for the answer, AI can help generate ideas by synthesizing knowledge across disciplines, surfacing precedents, running calculations, drafting, checking, and iterating. The bottleneck in most forms of knowledge work then shifts from access to knowledge to the quality of the question being asked.

This changes how societies produce knowledge and create value. A single person equipped with the right AI tools can operate with meaningful competence across knowledge silos, a task that previously required entire teams. Not perfectly, but well enough to move faster, see further, and build things that were previously unreachable. We should accept this fact and use its consequences to our advantage.

Sector Opportunities Powered by Canada’s AI Infrastructure

Where do our advantages lie?

First, we should build to solve problems where local data and context matter. Consider healthcare: diagnostic AI trained on Canadian patient data will outperform models trained on other populations. Our multicultural society, genetics, environment, and healthcare practices are distinct.

Second, agricultural AI tuned to Canadian soil, climate, and growing seasons will be more valuable than generic models. Financial crime detection systems trained on Canadian banking patterns will be more effective than offshore alternatives. These are not niche problems. They are domains where Canadian-built AI will be superior precisely because it was built here, for here.

Imagine AI systems available in every public library across Canada; tools any citizen can use to reason through problems, learn new fields, and build ideas. This is how nations compete in an AI economy: not by copying what others do, but by solving their own problems first, then selling their outcomes globally. 

Culture Matters: Canada Must Embrace Ambition

Canada cannot afford tall poppy syndrome. Innovation requires environments where ambitious ideas can move quickly from concept to product. It requires capital willing to tolerate risk and institutions willing to support experimentation. It also requires cultural acceptance that efforts will fail.

This calls for reform to engender a culture within the country where poppies are incentivized to grow fast and tall, and to use what they find to uplift other poppies. This shift is necessary, and it will be uncomfortable.

But we must embrace this discomfort because hesitation cannot delay the march of progress. And there are worse things than our own discomfort.

Who Controls Canada’s AI Infrastructure Controls Economic Power

AI systems are here. Knowledge work will be restructured around them. But it matters whose AI we build with.

In the past, corporations that controlled user data controlled what people saw and wanted. The AI era amplifies this risk catastrophically. If a handful of private companies monopolize the datasets that train global AI, our health, research, financial, and geospatial data, they control not just information flow, but the generation of knowledge itself. In such a world, intellectual property becomes fiction, and power concentrates entirely around those who own the infrastructure.

As of now, what remains distinctly human is judgment, direction, and verification: knowing what to build, recognizing when the answer is wrong, and bearing responsibility for the outcome so that value can be reliably attributed to it.

If we are to ensure our long-term survival, we must empower people to create newer, better, faster, and more efficient AI companies, disseminate the use of AI through society, use it to create new knowledge, and direct that knowledge toward the problems that matter most to people: building shelter, preserving health, ensuring security, and advancing science.

Competition, Not Hand-Picked Winners

Economic dynamism depends on competition. Yet Canada often defaults to policies that attempt to identify and support national champions in advance. This approach rarely works well in fast-moving fields.

When governments hand-select winners, the result is typically higher costs for customers, weaker incentives to innovate, and reduced market dynamism. A healthier model is to create conditions where competition is constant and determines the winners.

The firms that succeed domestically will be far better positioned to compete globally and export value abroad. In an AI economy, value flows to those who build products used around the world. Countries that succeed will export technology, not simply consume it.

Financing Canada’s AI Infrastructure for Long-Term Growth

Transformations of this scale require capital.

When oil was discovered beneath the North Sea, Norway made a generational decision: it would treat resource wealth as national capital rather than temporary revenue. The result is a sovereign wealth fund worth roughly $350,000 per Norwegian citizen.

Canada largely made a different choice. Wealth created collectively from resource extraction should be used as an investment to seed competitive local markets which produce goods that are exported worldwide. From critical minerals to health records, geospatial datasets, multilingual text, agricultural telemetry, and financial transactions.

 

Canada has a chance to get it right a second time. And there may not be a third. 

Reversing this dynamic requires acknowledgement that the data we hold has value, and strategic investment to think about how it can best be used. Governments can encourage BDC and other wealth collectives tied to natural resources to spur public investment in computing, mobility infrastructure to enable building computing close to energy production, tax credits for reinvesting capital gains into new ventures, and incentives for domestic companies to reinvest profits into research and development. 

As a concrete example, our country’s health system generates 40 million patient records annually. With public benefit corporations with a mandate to use this data to improve care delivery and to export AI solutions outside the country, the resulting proceeds could flow into a healthcare fund to reinvest in biology and medicine. Rather than exporting the raw materials for the AI age, we can build a competitive advantage domestically with an eye to exporting the products outside. 

Canada’s AI ambitions will be challenged not only by foreign competition but also by domestic leakage and the slow bleed of value through systems with opaque processes. Equally important is transparency. Canadians already pay high taxes. Maintaining public trust requires ensuring that public funds at the federal and provincial levels are directed toward productive investments rather than dissipating within opaque processes.

Protecting Citizens During the AI Transition

Technological transformation inevitably disrupts labour markets. AI will be no exception. Governments must ensure that citizens remain healthy, educated, and capable of adapting. 

These are critical provincial mandates where governments must step up.

To ensure we use AI to its full extent, we need investment in education, including reskilling programs, and institutions that support lifelong learning. At a time when the use of AI runs the risk of cognitive offloading, we need ways to ensure that cognitive effort and a deep understanding of foundational concepts across the sciences remain a key component of the educational curriculum.

Indeed, polymaths can connect the dots across scientific disciplines with the help of AI. Canada produces exceptional graduates in physics, materials science, chemistry, biology, and computer engineering. Too often, these individuals leave to pursue opportunities elsewhere or work on problems defined outside the country. Retaining and attracting talent will require the creation of environments where ambitious projects can be built while learning and where the best minds are empowered to pursue them.

Regulation has a role, but it should not come prematurely. Free-form exploration and experimentation must precede rigid regulatory frameworks. Overregulation at the earliest stages risks slowing innovation before its benefits can fully emerge.

Without action, the AI that runs our healthcare, our courts, our financial system, and our children’s education will be built elsewhere, trained on others’ values, optimized for others’ priorities, and governed by foreign institutions. We will lose our best founders because we failed to make it worth their while to stay. We will watch our data train models elsewhere, which we then license back, at a margin, forever.

Choose Discomfort Now, So We Can Enjoy Comfort Later

Canada has clean, abundant energy, world-class researchers, a multicultural population that is itself a training asset, trusted institutions, and a global reputation for openness. If we’re able to incentivize building here, particularly through the uncomfortable phases of AI, perhaps we will have a chance to change the equilibrium of how AI dissipates through society. If we sit back and allow this change to happen to us, we will lose something important: the ability to shape the alignment of the AI, its veracity, fairness, and value for Canada. 

The path forward is not mysterious. Nations that dominate the intelligence economy will build energy and compute, create systems that encourage competitive ambition, protect human capital, and retain the intellectual property they generate.