Why Canada Will Never Build a World-Shocking LLM—And Why That's the Point

Why Canada Will Never Build a World-Shocking LLM—And Why That's the Point

Geoffrey Hinton won the Nobel Prize in Physics in

• Yoshua Bengio became the first living scientist to reach one million Google Scholar citations in October
• Both built their careers in Canada. Both are called "Godfathers of AI."

Ask which country leads the large language model race. Canada doesn't make the list.

OpenAI sits in San Francisco. Anthropic operates from the same city. DeepMind belongs to London. The Chinese giants—Baidu, Alibaba, ByteDance—dominate their domestic market. Canada? Silent.

This silence isn't failure. It's a clue.

The Country That Invented Modern AI

Deep learning began in Toronto. In 2012, Geoffrey Hinton's team submitted AlexNet to the ImageNet competition. The model obliterated everything else, cutting error rates by over 40%. That moment ended the AI winter.

Hinton had spent decades at the University of Toronto, working on neural networks when the field was considered a dead end. His collaborator Yann LeCun trained in his Toronto lab. Yoshua Bengio built Mila in Montreal—now the world's largest academic AI research center with over 140 affiliated professors.

The three won the Turing Award together in 2018. In 2025, they shared the Queen Elizabeth Prize for Engineering. Canada didn't just contribute to AI. Canada built the foundation that made ChatGPT, Claude, and Midjourney possible.

So why can't Canada produce a GPT-5 competitor?

The Math Doesn't Care About History

Training a frontier LLM costs somewhere between $100 million and $1 billion. Running the inference infrastructure costs more. OpenAI's valuation hovers around $500 billion. Anthropic approaches $183 billion. Microsoft, Google, and Amazon pour tens of billions into AI infrastructure every year.

Canada's entire federal AI compute investment? $2 billion over five years. A drop in a swimming pool.

Cohere, Canada's only homegrown foundational LLM company, raised $1.6 billion total and reached a $7 billion valuation. Impressive for Canadian tech. A rounding error compared to the American titans.

LLM economics favor concentration like gravity favors falling. More compute means better models. Better models attract more users. More users generate more data and revenue. That revenue funds even more compute. This flywheel spins fastest where capital pools deepest—Silicon Valley, not Toronto.

Cohere's general counsel Kosta Starostin said it plainly in 2025: while many companies build with AI, "few foundational LLM companies exist—Cohere stands alone in Canada."

Alone is a tough position.

The Brain Drain That Never Stops

Canada produces world-class AI researchers. Then watches them leave.

The University of Toronto and Mila train exceptional talent. When graduation approaches, the offers from OpenAI, Google DeepMind, and Anthropic arrive. A senior AI researcher in San Francisco might earn $400,000 to $900,000 annually. Canadian compensation typically falls 40-60% below.

The math is simple. The result is predictable.

Canada has become the farm team for Silicon Valley's AI majors. It scouts talent, trains talent, then loses talent to richer clubs. Studies confirm the pattern—young tech workers increasingly head south. Researchers call it a "localized brain drain." Big tech opens Toronto and Montreal offices. Hires local. Gradually pulls the best people into US-based projects.

Montreal has partially resisted. Around 90% of Mila's professional master's graduates stay in Quebec. Lower living costs help—running an AI company there costs roughly 33% less than the US average. Resisting isn't winning. But it's not nothing.

Cohere's Calculated Bet

Cohere hasn't tried to build ChatGPT. The company focuses on enterprise AI: document summarization, search engines, question-answering systems for business. Its flagship product North targets banking and professional services.

This reflects cold calculation. Consumer AI demands enormous scale. Enterprise AI demands trust, security, customization. Cohere can't outspend OpenAI on training compute. It can offer Canadian data residency, privacy compliance, models fine-tuned for specific industries.

In June 2025, Cohere partnered with Bell to provide AI solutions "while maintaining security, privacy, and data residency in Canada." The federal government committed $240 million toward Cohere's new data center. These moves won't produce viral products. They produce steady revenue.

The company's annualized revenue crossed $100 million in mid-2024. Projections for end of 2025 exceed $200 million. Not ChatGPT numbers. But not zero either.

A Cohere IPO might happen in 2026. It won't challenge OpenAI's dominance. It might become the trusted AI provider for banks, governments, and enterprises that need reliability over novelty.

What Canada Actually Has

Canada's AI advantage isn't scale. It's something harder to measure: moral authority on AI safety.

Yoshua Bengio spent 2025 warning about AI risks. He launched LawZero, a nonprofit building "honest" AI systems. He co-authored the International AI Safety Report published in January 2025. His warnings about AI deception and reward hacking carry weight because he helped create the technology being criticized.

Geoffrey Hinton left Google in 2023 specifically to speak freely about AI dangers. By 2025, he described AI as potentially existential: "These kind of digital beings we're creating are just a better form of intelligence than people. We'd no longer be needed."

When the godfathers of AI warn about AI, the world listens. That credibility belongs to Canada.

The government responded. The Canadian AI Safety Institute launched with $50 million. CIFAR leads global conversations on AI governance. The Pan-Canadian AI Strategy, launched in 2017, was the world's first national AI strategy. Other countries copied the model.

Reframing the Question

"When will Canada produce a world-shocking LLM?" assumes Canada should play the same game as OpenAI and Anthropic.

Games have different rules depending on who's playing.

Canada will never match American capital. It will never match Chinese market size. Competing on those terms guarantees losing on those terms.

What Canada can do: train the researchers who build frontier models elsewhere. Set the ethical standards those models must follow. Provide a regulatory environment that forces AI companies to prioritize safety. Build enterprise tools that value trust over hype.

That's not a consolation prize. That's a different race entirely.

The Honest Answer

Will Canada ever build a large language model that shocks the world?

Probably not.

Canada already shocked the world once—by inventing the deep learning techniques that made modern AI possible. The next Canadian contribution won't be another model. It might be the safety framework that prevents those models from causing harm. It might be the enterprise tools that make AI genuinely useful for business. It might simply be the continued production of researchers who build whatever comes after GPT.

Countries don't need to win every race. They need to choose races where they can actually place.

Canada's race isn't building the biggest LLM. It's building the most trustworthy AI ecosystem.

That race remains wide open.