In the late 1980s, Geoffrey Hinton was a few years into teaching at Carnegie Mellon University in Pittsburgh, Pa., when he became increasingly troubled about the state of the nation he had left his home country of England for a decade prior. Hinton took issue with Ronald Reagan’s foreign policy, particularly the mining of harbors in Nicaragua, and the fact that the A.I. research he was pursuing was largely funded by the U.S. Department of Defense. So when he was presented with an opportunity to head North, he jumped at the chance.

“My wife and I were very fed up with the U.S.,” Hinton told Observer, “and Canada seemed like a good place.” Enticed to Toronto by a strong social system and a generous offer to become a fellow at the Canadian Institute for Advanced Research (CIFAR), a global research organization, Hinton made his way to the country in 1987. He’s largely stayed put ever since, picking up a Nobel Prize for his contribution to A.I. research along the way.

Hinton wasn’t alone. Decades of sustained funding for curiosity-driven research has brought scores of pioneering A.I. researchers to Canada, where a series of breakthroughs laid the foundations for the A.I. products dominating today’s tech industry. Canada built upon this momentum in 2017 when it became the first country to implement a national A.I. strategy, one that congregated much of its innovative work in three A.I. hubs spread out across Toronto, Montreal and Edmonton.

Despite the country’s contributions towards the now-booming technology, many say Canada has failed to reap the rewards of its own innovations. It isn’t just ideas that have been exported to the U.S., but much of the nation’s talent. “It’s this historic Canadian challenge of being often the inventors and pioneers of new technology, but not necessarily seeing the commercial success here,” Cam Linke, head of the Alberta Machine Intelligence Institute (Amii), told Observer.

While attempts to establish competitive A.I. companies in Canada have been largely unsuccessful over the past few decades, a combination of enhanced government funding, bolstered research institutions and changing cultural attitudes is starting to make a gradual impact. The Toronto-based startup Cohere, for example, earlier this year raised $500 million—an unprecedented amount for a Canadian generative A.I. startup—from a mix of Canadian, American and international investors. While conceding that Canada’s A.I. “brain drain” is still an ongoing issue, Nick Frosst, a co-founder of Cohere, told Observer, “I feel the tide is turning.”

Attracting the best researchers in the game

Long before companies like OpenAI and Anthropic broke out into the scene, Canada was a beacon for those drawn to ambitious A.I. research. The country might have had less national funding than the U.S., but it was an oasis for those pursuing long-term and experimental projects. Due to its social system and funding for basic research, “there were three researchers who were very happy to live in Canada,” said Hinton. They were Rich Sutton, Yoshua Bengio and Hinton himself—the latter two of whom would go on to be donned “Godfathers of A.I.” after winning the 2018 Turing Prize alongside Yann LeCun, now Meta’s chief A.I. scientist.

After Hinton set up shop for himself at the University of Toronto in the late 1980s, Sutton, an American researcher known for his pivotal work in reinforcement learning, headed out west to the University of Alberta as he became disenchanted with U.S. politics. Deep learning pioneer Bengio, meanwhile, returned to his hometown of Montreal to work at the University of Montreal. The presence of the three talents, in combination with Canada’s more lenient immigration politics, drew in even more A.I. researchers, according to Amii’s Linke. “That created this cycle of great people wanting to work with those folks.”

While they may have been spread out across the country, Hinton, Sutton, and Bengio were aligned in their passion for a particular field of A.I. research—one that for decades wasn’t even linked to the term A.I. “There was something called A.I. and there was something called neural nets, and they were in opposition,” according to Hinton, who described the two as “warring camps.” Traditional A.I. emphasized symbolic reasoning, while the neural net worldview was based on mirroring the human brain.

Despite being regarded as a “crazy theory” at the time, according to Hinton, the neural net field was backed by CIFAR. In 2004, for example, it began “Neural Computation and Adaptive Perception,” a program directed by Hinton that Bengio and LeCun also took part in. “It was a while before [neural nets] had practical applications, and so you needed to fund people to work on them without being able to produce any spectacular applied uses of them and so on,” said Hinton. “In the U.S., it was much harder to get that money.”