In the late 1980s, Geoffrey Hinton found himself increasingly disillusioned with the political climate in the U.S. after moving from England a decade earlier. Teaching at Carnegie Mellon University in Pittsburgh, he was particularly concerned about Ronald Reagan’s foreign policy and the fact that much of his A.I. research was funded by the U.S. Department of Defense. When the chance arose to relocate to Canada, he eagerly accepted.
“My wife and I were really fed up with the U.S.,” Hinton recounted to the Observer. “Canada appeared to be a much better fit.” Drawn to Toronto by its robust social support systems and an enticing fellowship at the Canadian Institute for Advanced Research (CIFAR), he moved there in 1987 and has remained ever since, later earning a Nobel Prize for his groundbreaking work in A.I.
Hinton wasn’t alone in this transition. Years of consistent funding for innovative research attracted many leading A.I. scientists to Canada, where significant advancements laid the groundwork for the A.I. technologies that now dominate the tech landscape. The momentum continued in 2017 when Canada became the first nation to establish a national A.I. strategy, consolidating its innovative efforts in three main hubs: Toronto, Montreal, and Edmonton.
However, despite Canada’s significant contributions to the tech industry, many believe the country has not fully capitalized on its own innovations. It’s not just the ideas that have been exported to the U.S.; much of the talent has followed suit. “Canada has this long-standing issue of being the birthplace of new technologies but not necessarily enjoying the commercial success of these innovations,” Cam Linke, head of the Alberta Machine Intelligence Institute (Amii), explained to the Observer.
While establishing competitive A.I. companies in Canada has proven challenging, a combination of increased government investment, strengthened research institutions, and evolving cultural attitudes is beginning to make a difference. For instance, Toronto-based startup Cohere recently raised an impressive $500 million from a mix of Canadian and international investors—setting a record for Canadian generative A.I. startups. “Although Canada’s A.I. brain drain is still a pressing issue, I believe we’re seeing a shift,” noted Nick Frosst, co-founder of Cohere.
Attracting Top Talent in A.I.
Long before the emergence of giants like OpenAI and Anthropic, Canada was a magnet for ambitious A.I. researchers. While the country had less national funding than the U.S., it offered a nurturing environment for those engaged in long-term experimental projects. Hinton mentioned that “there were three researchers very content to stay in Canada”—himself, Rich Sutton, and Yoshua Bengio—who would later be recognized as the “Godfathers of A.I.” after receiving the 2018 Turing Prize alongside Yann LeCun, now the chief A.I. scientist at Meta.
After Hinton established himself at the University of Toronto in the late ’80s, Sutton moved to the University of Alberta, dissatisfied with the political climate in the U.S. Bengio, on the other hand, returned to Montreal to work at the University of Montreal. Their collective presence, along with Canada’s more welcoming immigration policies, attracted even more A.I. researchers, according to Amii’s Linke. “This created a cycle where top talent wanted to collaborate with them,” he noted.
Although they were spread across different provinces, Hinton, Sutton, and Bengio shared a deep commitment to a specific area of A.I. research that was often overlooked. “There were two camps: traditional A.I. focused on symbolic reasoning and another centered around neural networks,” Hinton explained, referring to the latter as “the crazy theory” at the time. Traditional A.I. and neural networks were often seen as opposing forces.
Despite the initial skepticism surrounding neural networks, CIFAR provided crucial support. In 2004, it launched the “Neural Computation and Adaptive Perception” program, with Hinton at the helm, alongside Bengio and LeCun. “It took time for neural networks to find practical applications, so funding was essential for researchers to explore without immediate results,” Hinton said. “In the U.S., securing that funding was much more difficult.”
Researchers participating in the CIFAR program came together annually to share their insights, as noted by Ruslan Salakhutdinov, a professor at Carnegie Mellon University. In 2005, he found himself at a crossroads, having drifted away from A.I. and working in banking. A chance encounter with Hinton on the street reignited his passion, leading him back to academia to pursue a Ph.D. under Hinton’s mentorship.
The excitement within Canada’s neural network community surged in the early 2010s as breakthroughs like improved speech recognition began to surface. In 2012, Hinton, along with his students Alex Krizhevsky and Ilya Sutskever, gained international attention by winning an object recognition competition using neural networks. This success paved the way for DNNresearch, a startup later acquired by Google for $44 million.
As neural networks gained recognition, prominent researchers such as Hinton, Sutton, Bengio, and LeCun received enticing offers from tech giants like Google, DeepMind, and Meta. Many young Canadian researchers also ventured south for better prospects, lured by higher salaries.
To combat the ongoing brain drain in the A.I. sector, the Canadian government initiated the Pan-Canadian A.I. Strategy in 2017, investing billions into A.I. research. This strategy solidified three key hubs for A.I. development, with leading researchers like Bengio, Sutton, and Hinton at the forefront.
Despite the advancements in A.I. research, Canada’s tech industry has been slow to integrate these innovations. Companies like BlackBerry and Element AI have struggled to leverage the potential of neural networks, hindered by conservative approaches and financial limitations. The University of Toronto also faced challenges in fostering entrepreneurial endeavors within its academic community. Students transforming their research into startups often had to relinquish larger stakes in their companies compared to their peers at prestigious U.S. institutions, such as Stanford and Carnegie Mellon, as noted by Salakhutdinov. The University of Toronto stated that equity agreements are typically negotiated on a case-by-case basis, with the university’s share varying from single to low double digits.
Moreover, Canada faces significant challenges in compute infrastructure compared to the U.S., which Hinton identified as a major drawback for aspiring researchers. He recounted an instance where a former student, Jimmy Ba, struggled to access the necessary graphics processing units (GPUs) for training large language models while at the Vector Institute. Ba ultimately joined Elon Musk’s A.I. startup, xAI. Hinton expressed concerns that, despite its foundational research success, Canada may find it difficult to achieve global leadership in A.I. due to resource constraints.
While many talented individuals have left Canada, some researchers have opted to stay. International companies have also set up research facilities in Canada, creating job opportunities for local graduates. Lacoste-Julien, who heads a Samsung lab at Mila, noted that the presence of global offices has positively influenced talent retention in Canada after graduation. He acknowledged that while the brain drain issue isn’t entirely resolved, there has been noticeable progress.
The values inherent in Canadian culture, which initially attracted pioneers like Hinton and Sutton, may pose challenges in competing with foreign companies in the A.I. sector. In provinces like Quebec, there exists a strong emphasis on quality of life and equality rather than the traditional metrics of success tied to company size. Nevertheless, some innovative startups are beginning to disrupt the status quo. For instance, Artificial Agency, founded by former Google DeepMind researchers, recently secured $16 million in funding for its groundbreaking approach to enhancing gaming through generative A.I.
The Canadian startup scene is experiencing a resurgence, particularly in urban centers like Toronto. In 2022, the Canadian A.I. sector attracted $8.6 billion in venture capital, establishing the country as a prime destination for A.I. investment. Companies like Waabi, focused on autonomous vehicles, and Cohere, a rising star in A.I., have garnered significant funding and attention. The increasing support from venture capital firms like Radical Ventures, based in Toronto, demonstrates the growing potential of Canada’s A.I. ecosystem.
Collaboration between businesses and research institutions such as Vector and Amii has further stimulated startup growth across Canada. The retention of talent, especially in Ontario, reflects a shift in perspective among local companies, with an increasing number focusing on building and expanding their operations within Canada. The experience of Artificial Agency underscores the evolving partnership between startups and academia, emphasizing the importance of attracting and retaining top graduate talent.
As Canada’s A.I. ecosystem continues to advance, a new generation of successful startups is paving the way for future researchers and entrepreneurs. This shift towards fostering a vibrant local A.I. industry holds promise for a sustainable and innovative future within Canada’s technological landscape.
