Toronto’s AI Startup Case: Funding, Talent and Lower Operating Costs

Toronto’s case as an AI hub rests on a combination of research talent, new funding, established technology companies and comparatively lower operating costs. Willson Cross, co-founder and CEO of Borderless AI, argues that these forces could draw more founders to the city and build on one another over time. His forecast is ambitious: within five years, he expects Toronto to be widely recognized as a source of AI companies. The evidence he points to describes a growing ecosystem, though it doesn’t establish that Toronto will replace Silicon Valley.

That distinction matters. Silicon Valley has long attracted technology companies, investors and ambitious founders. AI startups now have a wider choice of locations, and Toronto is among the cities seeking to turn research strength into commercial activity. Cross’s view is that the city’s prospects come from the way its advantages reinforce each other: funding can attract companies, companies can deepen the talent pool, and experienced workers can go on to start ventures of their own.

Toronto already has a visible AI research community. Geoffrey Hinton, a prominent AI researcher who previously worked at Google, is among the figures associated with the city. Google and Uber have also established AI research labs there. Cross sees these arrivals as part of a wider shift in attention toward Toronto, alongside Canadian investment in AI research and development and immigration policies that welcome skilled workers. Together, those factors give founders reasons to consider the city as a base.

Funding and investor attention

Cross points to a Canadian commitment of more than $2 billion for AI innovation as a signal that the country intends to support the sector with substantial funding. He sees this as a factor that may encourage founders to establish AI companies in Toronto. The amount describes a national commitment, however; it doesn’t say how much will reach individual startups or how quickly that support will translate into commercial results.

Investor interest is another part of his argument. Cross says venture capital firms that have traditionally focused on Silicon Valley are paying more attention to Toronto’s AI scene. He cites Cohere’s $450 million fundraise, with the Canadian Pension Plan as its anchor investor, as an example. A large financing round shows that a company in the region can attract substantial capital. By itself, it doesn’t establish that funding is broadly available to early-stage companies.

For startups weighing locations, the combination of research talent and investor interest may be more consequential than any single funding announcement. More capital can give companies room to hire and build; more visible companies can, in turn, make a city more attractive to founders and investors. That cycle takes time, and its strength depends on whether promising companies can turn research and funding into products with durable demand.

Large labs create both competition and new founders

Google and Uber’s presence has two effects, in Cross’s account. Their labs compete with startups for a limited pool of AI and technology workers. That can make hiring harder for young companies that cannot match the resources or name recognition of large firms.

Cross believes the longer-term benefit may outweigh that pressure. Workers who gain experience at large technology companies may later join early-stage ventures or launch their own. That movement can transfer practical knowledge into smaller companies and create experienced founding teams. He also expects a larger pool of AI talent and more visible startup activity to attract additional venture capital and encourage further product development.

This is a plausible mechanism, not an automatic outcome. The talent pipeline helps startups only if experienced workers choose to make that move and if new companies can offer compelling problems to solve. For founders, the large labs therefore bring both a hiring challenge and a potential source of future employees, collaborators and competitors.

Trust has to be earned

Cross expects a period in which AI companies will need to show that they can handle ethical practices and compliance responsibly. He says guidelines for companies building AI infrastructure are already in place, but argues that written standards alone won’t earn user confidence. His comparison is with early online commerce, when customers were wary of sharing payment details over the internet.

The analogy points to a practical test for AI products: users need reasons to trust how a system works and how a company applies it. Cross expects some platforms to gain users by showing that they take ethics seriously, while others may lose ground if they don’t. For teams building AI products, his point is that trust needs to be part of development from the start, rather than a response to problems after launch.

The source doesn’t specify particular technical standards, evaluation methods or enforcement mechanisms. That leaves open how companies will demonstrate responsible practice and how users will compare their claims. As Toronto’s startup community grows, those questions will shape whether new products can earn sustained adoption.

Lower costs and a defined business problem

Borderless AI applies AI to global Employer of Record services and payroll. Its agent, Alberni, supports onboarding, managing and paying international team members. Cross describes the category as a potential $10 billion market opportunity by 2028, and argues that applying AI to an established business need can encourage wider adoption.

His advice reflects a broader challenge for AI startups: a technically capable system still needs a business model and a real customer problem. Cross cautions against building AI simply because it looks appealing or because a team wants to add the technology. Startups, he says, should talk with customers and users to understand their needs before investing in an AI system.

Location also affects Borderless AI’s own costs. Cross says the company can build its product in Toronto for about 70 cents for every dollar it would spend in the United States, a claimed saving of roughly 30%. He attributes the advantage to lower operating costs alongside access to the city’s AI talent and innovation. That figure is his company’s comparison; the article provides no breakdown of the expenses behind it, so other teams would need to assess their own costs and hiring needs.

What founders should prioritize

Cross’s recommendations for AI startups center on three practices. First, put trust and safety into product development early and follow sound practices from the beginning. Second, move quickly: successful products tend to attract larger rivals and younger competitors, so an early lead can matter. Third, build for a real use case. Customer conversations should identify concrete needs and pain points before a team commits to building an AI system.

These priorities are connected. Speed can help a startup reach customers before competitors, but rapid development without attention to trust can make adoption harder. Likewise, adding AI to a product doesn’t create value unless it addresses a problem customers recognize. The practical challenge is to develop quickly while learning from users and making responsible design choices.

A hub built through repeated company formation

Cross predicts that Toronto could become known globally as an AI hub within five years. He expects a compounding cycle: companies grow, some reach major outcomes, and experienced executives use what they’ve learned to start or support other ventures. In his view, that repeated creation of companies would give the city a distinct identity, much as Los Angeles is associated with film and New York with finance.

He doesn’t identify a specific AI field in which Toronto is certain to lead. Instead, he sees the city’s potential in the breadth of companies drawing on its existing talent and startup ecosystem. That makes the central question less about a single breakthrough than about whether Toronto can keep producing companies that attract workers, customers and capital. Funding, research labs and lower costs provide a foundation; lasting recognition will depend on what founders build and whether users trust it.