AI and Canada: Pioneering Innovation, Searching for Homegrown Success

Deep Learning’s Canadian Roots and Revolution

It’s easy to forget, amid the hype around Silicon Valley’s AI giants, that many of the foundational breakthroughs of modern AI were born in Canada. In fact, two of the three “godfathers of AI” – Yoshua Bengio and Geoffrey Hinton – built their careers at Canadian universities (Université de Montréal and University of Toronto, respectively). The third, Yann LeCun, did seminal work at Bell Labs and also spent time at U of T. Decades ago, these pioneers were academic mavericks betting on neural networks when the field was out of fashion. British-born Hinton moved to Canada in 1987, drawn by its support for fundamental research (and a distaste for U.S. military funding). He joined the Canadian Institute for Advanced Research (CIFAR), and together with Bengio in Montreal and Richard Sutton in Alberta, kept the “deep learning” flame alive through the AI winters. This long-nurtured expertise paid off spectacularly in 2012, when Hinton and his students Alex Krizhevsky and Ilya Sutskever shook the tech world by training a neural network (AlexNet) that blew away the competition in a global image recognition contest. That breakthrough, achieved at the University of Toronto, is widely credited with kick-starting the modern AI revolution – it showed the power of deep neural networks and directly laid the groundwork for tools like today’s ChatGPT.

The achievements continued: Hinton and Bengio’s labs churned out ideas that now permeate AI systems everywhere. Backpropagation, the algorithm that allows neural nets to learn by correcting errors, was refined and popularized by Hinton and is now standard in virtually all deep learning models. In 2003, Bengio’s team introduced neural word embeddings, enabling AI to understand language in a more nuanced way – a concept that underpins how systems like ChatGPT understand context. A few years later, one of Bengio’s PhD students, Ian Goodfellow, invented Generative Adversarial Networks (GANs) – a technique that allows AI to imagine and create (from art to deepfakes) by pitting two neural nets against each other.

For these and other contributions, Hinton and Bengio (together with LeCun) received the 2018 Turing Award – often called the “Nobel Prize of computing” – with the citation noting that their work “is used by billions today,” essentially anyone with a smartphone. As Nick Frosst, a Canadian co-founder of the AI startup Cohere, remarked, “there will be huge sections dedicated to the people in Canada and what they were doing” when the history of AI is written. In short, Canada’s outsized impact on the science of AI is undeniable – its researchers gave the world the keys to deep learning’s potential.

Big Names from Canada: The Talent Exodus and Legacy

Not only did Canada cultivate pioneering ideas, it also trained many of the leaders driving AI’s latest chapter. Hinton’s former PhD student Ilya Sutskever went on to co-found OpenAI and is the chief scientist behind ChatGPT. Another Hinton protégé, Alex Krizhevsky, designed the original AlexNet model that demonstrated the power of GPU-fueled deep learning. From Bengio’s lab emerged Goodfellow (GANs) and others who joined Google Brain and Apple. Aidan Gomez, a University of Toronto graduate, co-authored the landmark 2017 paper on transformers (the architecture behind ChatGPT) and later co-founded Cohere in Toronto. Andrej Karpathy, who studied in Toronto early in his career, became Director of AI at Tesla and a key figure at OpenAI. Indeed, OpenAI might not even exist without Canada’s AI pioneers – “OpenAI is not a Canadian company, but perhaps it should have been,” quipped Global News, pointing out how much OpenAI’s work draws on Canadian-born research and researchers.

Yet, this success has a bittersweet edge. For years, Canada’s challenge has been holding on to the talent it produces. The lure of Silicon Valley’s salaries, resources, and tech giants often proved irresistible. It became almost a cliché that a star graduate from Waterloo or U of T would head straight to California or Seattle. (At one point, observers joked it was “California or bust” for Canada’s top students.) Geoffrey Hinton himself split his time between Toronto and Google for many years (and only recently left Google in 2023, citing concerns over AI’s rapid progress). Element AI, a startup co-founded in Montreal by Bengio to commercialize AI research, struggled to scale and was eventually acquired by U.S. software firm ServiceNow in 2020, with much of its talent moving under foreign ownership. And when Canadian startups did make world-class advances, they often found their way into American hands: for example, Microsoft acquired Maluuba (a Waterloo/Montreal-based NLP startup built by Bengio’s students) in 2017, and Google’s DeepMind chose Edmonton – home of Rich Sutton’s renowned reinforcement learning group – for its first international AI research office. These moves brought investment and jobs, but also meant that Canadian innovations frequently ended up enriching foreign tech giants’ product lines.

This “brain drain” has long been a point of introspection. The historic Canadian challenge is being the pioneers in various aspects of emerging tech (and I wrote about many of them), but not being able to achieve commercial success at home. In other words, Canada has often been the brains of the operation – now it wants a bigger share of the business. The numbers tell the story: It’s clear Canada has some of the best AI minds in the world, and yet we lag behind in commercializing our greatest research achievements. Yoshua Bengio, who is currently the most-cited computer scientist in the world, has expressed concern that if Canada doesn’t improve at scaling up its AI innovations, “we may squander our current advantage”. He notes that Canadian venture capitalists have traditionally been more cautious – unwilling to bet big on risky ideas – which has forced many AI entrepreneurs to seek American money. “The culture of innovation and risk-taking isn’t nearly as developed here as in the U.S.,” Bengio observed, meaning even when startups stay in Canada, they’re often “selling part of their ownership to American investors.” This dynamic has been improving (at least now companies can raise funds while remaining in Canada, whereas in the past they had to relocate entirely), but the core challenge remains: how to convert Canada’s brainpower into homegrown industrial success.

First in the World: Canada’s National AI Strategy

One thing Canada has not lacked is foresight. It was, notably, the first country in the world to launch a national AI strategy, back in 2017. The federal government allocated C$125 million to this Pan-Canadian AI Strategy’s first phase, aiming to cement the country’s status as an AI leader by investing in talent and research hubs. This led to the creation or expansion of three AI institutes that anchor the nation’s AI ecosystem: Mila (Montreal), the Vector Institute (Toronto), and Amii (Edmonton). Each institute leveraged local strengths – Montreal under Bengio became a deep learning and ethics powerhouse, Toronto co-founded by Hinton focused on machine learning for health, finance and more, and Edmonton built on the University of Alberta’s world-leading reinforcement learning pedigree (Sutton’s group). The strategy’s emphasis was on attracting and retaining top minds: Canada offered generous AI Research Chair positions and fellowships. In fact, since 2017 over 100 top researchers have been recruited as Canada CIFAR AI Chairs – roughly half of them lured from abroad by Canada’s funding and vision. These star scientists, in turn, train legions of graduate students and postdocs (over 1,500 so far), seeding the next generation of talent.

By many measures, the strategy paid off in its early years. Canada quickly became known as a global hotbed for AI research, and officials touted that Canada ranked 5th in the world on the Stanford AI Vibrancy Index (and third among G7 nations). Cities like Montreal earned nicknames such as “Silicon Valley North” for AI, as giants like Google, Facebook (Meta), Microsoft, and Samsung set up AI labs there to tap into local expertise. Toronto now boasts the highest concentration of AI startups in the world, by some counts, and together with the Waterloo region forms a bustling corridor of AI activity. In 2022, the Canadian government launched Phase 2 of the Pan-Canadian AI Strategy with C$443 million in new funding, explicitly shifting focus from pure research to “commercialization and adoption” of AI innovations. This included support for industry-academic collaboration, startup incubation, and even efforts to shape global AI standards in line with Canadian values of inclusion and fairness. Canada’s Global Innovation Clusters program also received funding to help AI startups scale and to attract private investment. The goal: ensure that Canadian ideas and knowledge are mobilized at home, not just overseas.

These moves underscore a broader vision: Canada doesn’t want to only be the AI lab of the world; it wants to be a thriving market and producer too. As Minister François-Philippe Champagne put it, the plan is to “solidify Canada as a global leader in AI… while accelerating trustworthy technology development” that benefits Canadians. In practical terms, that means helping Canadian companies adopt AI and grow, so that the country sees economic dividends from its AI talent. It’s a bold plan for a nation of only ~40 million people, essentially punching above its weight on the global stage.

An AI Startup Ecosystem (Finally) Takes Off

For years, the venture capital and startup scene in Canadian AI lagged the country’s research renown. This was the frustrating paradox: brilliant ideas would emerge in Toronto or Montreal, but the big financing deals and product roll-outs happened in California. That tide is now beginning to turn. In 2022, Canadian AI companies attracted C$8.6 billion in venture funding, which on a per-capita basis trailed only the US and UK in the G7. The country now hosts 600+ AI-focused startups (Deloitte counted about 670 in 2023) spanning sectors from enterprise software and fintech to biotech and agriculture. Toronto alone is home to roughly one-third to almost half of these companies, depending on definitions, and has been reported to have over 450 AI startups – the densest cluster anywhere. Importantly, big money is flowing into some of these ventures, signaling global confidence in Canadian AI talent. Recent headline-grabbing examples include:

• Cohere (Toronto) – a startup building large language models akin to OpenAI’s, founded by ex-Google Brain researchers. It raised USD $270 million in June 2023. Cohere’s models, trained in Canada, aim to power chatbots and business applications globally, making it a direct competitor to the Silicon Valley giants.
• Waabi (Toronto) – founded by renowned UofT professor Raquel Urtasun (former head of Uber’s self-driving R&D), Waabi is developing AI for autonomous trucking. The company raised USD $83.5 million Series A in 2021, a huge haul for a Canadian tech venture. Waabi’s approach leverages simulation and AI to teach self-driving trucks, and its ambition and funding show that Canadian founders are willing to “think big” in a field historically dominated by U.S. firms.
• Tenstorrent (Toronto) – an AI hardware startup led by legendary chip architect Jim Keller, designing next-generation AI chips. Tenstorrent has raised over USD $1.1 billion to date, including a massive $234 million round in 2023. While it maintains R&D in Toronto, it has expanded to Silicon Valley and Texas as well, reflecting a strategy of keeping roots in Canada while accessing global markets. Tenstorrent’s rise hints that Canada can do more than import AI hardware – it can help invent it.

There are plenty of other notable players: Enterprise AI firms like Kinaxis (Ottawa, supply-chain optimization) and Element AI (before its acquisition) applied AI to traditional industries. Borealis AI, the R&D arm of the Royal Bank of Canada, runs labs in multiple cities to develop AI for finance, showing that even Canadian banks are directly investing in AI innovation. In healthcare, startups like Deep Genomics and Imagia (Montreal) have used AI for drug discovery and medical imaging. This flurry of activity is backed by a shifting investor landscape: Canadian VCs have launched new funds focused on AI and global investors from Silicon Valley, Asia, and Europe are increasingly coming to Canada to find the next AI winners.

Crucially, this means promising AI companies can now scale in Canada without immediately relocating. Success stories like Cohere staying in Toronto and attracting global capital would have seemed improbable a decade ago. And while many Canadian startups still raise follow-on rounds led by American firms (simply because the U.S. has more mega-funds), it’s a far better outcome to get foreign investment without losing the company to foreign soil. Attracting foreign investors to Canada is preferable to having our most promising startups leave, as Global News noted – even if it raises questions about who ultimately benefits from the windfall. In an ideal scenario, the ownership mix might be global, but the jobs, expertise and operations remain Canadian. And encouragingly, Frosst and other insiders say today’s young AI graduates are more inclined to stay in Canada than in years past, seeing ample opportunity at home and a tech scene that’s “getting better” with higher wages and more companies to join. The old “California or bust” allure has faded just a bit as “Canada is getting more of its own big players.”

Why Has Canada Struggled to Scale Homegrown AI?

Despite all the optimism, Canada’s self-critique is far from over. The country’s perennial question is: Why do we keep inventing things – but let others reap larger rewards? This isn’t unique to AI; it’s almost a historic Canadian tale. A classic example: in the 1870s, Alexander Graham Bell invented the telephone in Canada (conceiving it in Brantford, Ontario), but when his father tried to find Canadian backers for the patent, no one would pay up. He ended up selling the rights to a Boston company, which later formed Bell Telephone Company – essentially, Americans commercialized the telephone and then sold the service back to Canadians.

One could argue the same pattern repeated a century later with the smartphone revolution – Canada’s BlackBerry (Research In Motion) pioneered mobile email and dominated early smartphones, but it lost out to Apple and Google’s ecosystem and today is a shell of its former self. In AI, the concern is déjà vu: we pioneered the tech and trained the talent, but the Googles, Apples, and OpenAIs are capturing much of the value. As one report noted starkly, “Canada has been the worst-performing advanced economy in the OECD for decades” in terms of growth, largely because our commercialization of innovations has historically been low. In other words, brilliant research doesn’t always translate into GDP or global Canadian brands, and this “isn’t an AI-specific problem. That’s Canada in general,” says Goldfarb.

Several factors contribute to this commercialization conundrum. Market size is one: Canada’s domestic market is one-tenth the U.S., which makes it harder for startups to achieve scale quickly at home. Venture capital culture has also been conservative – until recently, Canada had few investors willing to pour $100+ million into moonshot ideas. (Bengio has pointed out that Canadian VCs haven’t been as bold, which led Canadian entrepreneurs to court U.S. investors who are willing to take big bets.) There’s also a corporate conservatism: Canadian industry historically under-invested in R&D compared to other countries, meaning local companies were slower to adopt cutting-edge AI, which in turn gave startups fewer local customers to sell to. Government funding heavily tilts toward research and early startups, but support for scaling companies (e.g. through procurement or growth capital) has been limited, so firms often hit a wall and sell or move. The result of these factors was that, for many years, if a Canadian AI venture showed promise, it almost inevitably either relocated or got acquired by a U.S. tech giant. This dynamic prompted worries that Canada would become merely an “AI farm” – cultivating ideas and talent that are then harvested by others.

The good news is that awareness of this issue is higher than ever, and steps are being taken. The massive second-phase AI strategy funding explicitly targets the research-to-commercialization gap, putting millions into helping startups and industry projects bridge the valley of death. New programs encourage Canadian businesses to adopt homegrown AI solutions, so startups find customers without leaving. Venture funding is improving, as mentioned, with more domestic funds and government-backed VCs (like BDC Capital) co-investing to keep companies here. Immigration and quality of life also play a role: Canada’s open immigration policies (Global Talent Stream visa) and public healthcare and safety net are attractive to many entrepreneurs and researchers. Hinton often cited Canada’s social systems and openness as reasons he chose to build his career in Toronto. This means Canada can attract global talent (and indeed has a brain gain from countries where visas are harder or society less stable). The key, however, is retention and growth: bringing a PhD student to Montreal is one thing; having enough opportunities so they don’t leave for California after graduation is another. As Bengio puts it, “we need to do a better job convincing Canadian industry to take this seriously [invest in AI]… otherwise, our industries will lag and we’ll lose market share”. It’s a call to action for Canada’s business leaders: embrace innovation or risk irrelevance. The federal government is also looking at regulatory incentives – for instance, requiring foreign tech firms to invest in Canadian R&D or talent programs as a condition of market access.

There’s a sense that the next few years are critical. AI is a huge opportunity to energize Canada’s stagnating economy, and the country “has a lot of the ingredients to build a robust industry” – world-class talent, strong research, a stable society, and now increasing capital. If Canada can combine its brainpower with business savvy, it could finally break the pattern of missed opportunities. The hope is that in the coming decade, Canada will produce not just ideas that feed Big Tech’s next product, but perhaps the next global AI company headquartered in Toronto or Montreal, proudly Canadian.

Values and Vision: Canada’s Ethical AI Edge

One aspect of Canada’s AI story that often flies under the radar is its emphasis on ethics, inclusivity, and societal impact. While the U.S. and China race ahead on pure investment, Canada has carved out a reputation for “AI done responsibly.” For example, Canadian researchers spearheaded the Montréal Declaration for Responsible AI in 2018, outlining principles to ensure AI development respects human rights and well-being. Bengio and other Canadian luminaries have been vocal about issues like AI safety and bias. This ethos is backed by policy: Canada is working on a new law, the Artificial Intelligence and Data Act (AIDA), that would set rules for high-impact AI systems to ensure they are developed and deployed safely. If passed, AIDA would require organizations to assess and mitigate AI harms related to health, safety, and bias, embedding a precautionary approach into law. The country also has strong privacy regulations – notably, in 2021 Canada’s Privacy Commissioner ruled that police use of Clearview AI’s facial recognition (which scraped social media photos) was illegal under federal privacy law. This sent a clear message that certain applications of AI cross the line in Canada, even as they might continue elsewhere.

Canada’s multicultural and inclusive values also influence its AI initiatives. The national strategy explicitly ties into goals of fostering diversity in STEM and including underrepresented groups in the AI workforce. Programs like AI4Good labs and deep learning summer schools in Canada aim to train not just the next generation of coders, but to do so with gender balance and global inclusivity in mind. There’s also a focus on AI for social good: for instance, Canadian AI startups and researchers are known for work in healthcare (e.g. using AI to predict disease outbreaks or assist in diagnosis) and climate modeling, aligning with national priorities like universal healthcare and environmental stewardship. A striking example came at the onset of COVID-19, when a Toronto company BlueDot’s AI system detected the outbreak in Wuhan by analyzing news and travel data – giving an early warning of the pandemic’s spread. It was a reminder that Canadian AI expertise can contribute hugely to global challenges when directed with a public-good mindset.

This “ethical AI” branding might even become a competitive advantage. As AI technologies advance toward potentially transformative (even risky) capabilities, questions of safety and human-centric design are paramount. Canada, by virtue of its early attention to AI governance, could lead in areas like AI safety research, fairness, and policy frameworks. The federal government has partnered with the Standards Council of Canada to help develop global AI standards reflecting democratic values. All this suggests that Canada’s vision of AI leadership isn’t just about dollar figures and number of startups – it’s about shaping the kind of AI the world gets. That vision is one where AI is aligned with societal needs, where progress is measured not only by profit but by benefit to citizens. It’s a vision very much in line with Canada’s broader identity on the world stage as a middle power that champions cooperation, human rights, and inclusive growth.

Conclusion

Canada’s journey in AI is a fascinating study in innovation, ambition, and the trials of turning clever ideas into lasting prosperity. On one hand, it’s a story of visionary researchers who bucked trends and nurtured a technology that now shapes the world – without Canada’s bets on Hinton and Bengio, we might not have the AI capabilities we take for granted today. On the other hand, it’s a tale of a country reckoning with its own limitations, determined not to repeat the fate of past inventions that slipped away. As things stand in the mid-2020s, Canada has firmly established itself as a global AI powerhouse in research. The question “Can it translate that head start into lasting leadership in the AI era?” remains open.

There are encouraging signs: a vibrant startup scene, record investment, and a concerted strategy to bridge the gap between lab and market. There are also persistent hurdles: capital is still smaller than in the U.S., and competition for talent is fierce – Canada cannot match the raw spending of the US or China, as observers note. This means Canada will need to be smart in choosing its battles, perhaps focusing on niches (like AI in healthcare, or AI safety) where it can lead, rather than trying to do everything at scale.

One certainty is that Canada’s contributions to AI are now widely recognized and respected. The country has gone from a quiet incubator of AI ideas to a central player in the global AI conversation. Canadians are at the helm of some of the most exciting AI ventures, whether at home or abroad, and Canadian universities continue to attract brilliant minds from around the world. If Canada can keep those minds engaged locally – through opportunities to build world-class companies and technologies on Canadian soil – the payoff could be immense. Imagine a future where the next Amazon or Google of AI is headquartered in Montreal or Toronto, driving growth and inspiring a generation of innovators without the need to leave home. It’s not a far-fetched dream: Toronto already has the world’s highest density of AI startups, and Canada produces more AI patents per capita than any G7 country (and even more than China). The ingredients for success are here.

As someone who has lived and worked in Canada’s tech ecosystem, I’ve seen firsthand how passionately Canadians care about their innovations benefiting their society. There’s a genuine desire to ensure that AI technologies help create jobs, improve lives, and uphold democratic values in Canada, not just stock prices abroad. The story of AI and Canada is still unfolding, but it carries a hopeful message: that a country known for politeness and principles might just carve out a unique leadership role in one of the most important technological shifts of our time.