AI Procurement Compact would support scaling GTA’s AI ecosystem, report finds

The Toronto region is positioned to become an artificial intelligence cluster capable of generating, retaining and scaling globally competitive firms, finds a new report that draws on almost 50 interviews with investors, researchers and entrepreneurs in the community.

The report, published by the Innovation Policy Lab at the Munk School of Global Affairs and Public Policy, analyzes the artificial intelligence ecosystem in Toronto and concludes the region’s future prosperity and scale require greater co-ordination and foresight and a commitment to buy made-in-Canada technology.

Drawing on interviews with founders, venture capitalists, researchers, institutional leaders and government officials, as well as data on investment, patents, labour markets and firm formation, the report finds a community working at the cutting edge of tech adoption to advance the public good.

“Canada’s immigration policies and Toronto’s reputation as diverse and welcoming are key talent attractors,” the report states.

The report finds that:

• Toronto has double the national average demand for AI talent;
• the highest percentage of patents related to AI investment in Canada;
• is HQ for the largest number of AI companies in Canada – attracting nearly $2 billion in investment in 2024;
• but also shows a slowing rate of firm formation and capital inflows in recent years.
  
  

The ecosystem is especially strong in areas where research from the University of Toronto and its hospital network connects with sectors that are critical for Ontario’s economy, such as life sciences and financial services.  In health and life sciences, proximity to Toronto’s research hospitals, Canada’s universal health-care system and the region’s diverse population create significant potential for AI-driven innovation.

The region’s diversity has long been an asset for clinical trials, said David Wolfe, professor emeritus of political science and co-director of the Innovation Policy Lab, and lead author of the study.  Katya Bhada, an M.A. graduate of the Master of Urban Innovation at UTM’s Institute for Management & Innovation and Tara Vinodrai, Professor and Director, Master of Urban Innovation Program, Institute for Management & Innovation, co-authored the work.

 “The fact that more than 50 per cent of people living in the GTA were not born in Canada, let alone in Toronto, is an enormous advantage for health research and pharmaceutical companies,” Wolfe said at a report workshop at the Munk School this spring.  

Last month, VITAL, a hospital data-sharing platform founded by researchers at Unity Health Toronto and the University of Toronto received $100 -million in new federal investment to expand across Canada.

At a launch event for VITAL, Evan Solomon, Canada’s minister of artificial intelligence and digital innovation and minister responsible for the Federal Economic Development Agency for Southern Ontario, said the investment demonstrated the purpose of the national AI strategy “to serve Canadians.”

Financial services are another key area of economic potential, the report finds. Major institutions including RBC and TD have built substantial in-house AI capabilities. The report notes that RBC Borealis has a 550+-person AI team, while TD grew Layer 6 from 15 to more than 200 employees after it acquired the company.

The region’s AI advantage rests on a dense and long-standing community of institutions, including the Vector Institute, CIFAR, the Toronto Academic Health Sciences Network, the Creative Destruction Lab, MaRS, government partners and a growing base of startups and scale-ups.

In July, a new life sciences venture capital fund was launched by the University of Toronto, McMaster University, Venture Ontario and veteran investor Genesys Capital, to translate promising university-based commercial ideas into viable companies that address critical health challenges, improve patient care and create cutting-edge Canadian jobs.

To date, the Genesys University Seed Fund has received support from several backers including U of T, McMaster, the Temerty Group, Venture Ontario and RBC, and it has held a first close representing over $30 million of commitments.

Other areas of strength include materials science, through the Acceleration Consortium’s work on “self-driving labs,” AI safety and ethics, through the Schwartz Reisman Institute, and the ecosystem of startups located at the Schwartz Reisman Innovation Campus. The report also points to companies such as Cohere, Waabi, Blue J Legal, Xatoms and Ideogram as evidence of a maturing entrepreneurial base connected to Toronto’s research environment.

Together, U of T and Vector form what the report calls a joint engine for research and talent. U of T provides foundational research capacity, while Vector accelerates applied research, talent development and industry engagement. That relationship has helped drive knowledge circulation, attract firms and build a pipeline of researchers and engineers.

The report also tells a rich history about Toronto’s AI ecosystem and its growth from decades of investment in fundamental research, catalyzed by U of T, CIFAR and NSERC, and shaped by Geoffrey Hinton’s pioneering work in deep learning and neural networks.

CIFAR’s support for neural networks, including the Neural Computation and Adaptive Perception program launched in 2005 trained more than 2,700 alumni globally. Hinton’s work at U of T, and the students and collaborators who went on to found or lead major AI companies, established Toronto’s reputation as a birthplace of modern AI research.

A  shared table where the key actors can set priorities would further grow the region’s success, Wolfe said at the workshop.

“There is no coordinated cluster management organization for Toronto’s AI cluster,” Wolfe said, noting that organizations such as CDL, the Toronto Region Board of Trade, Toronto Global, MaRS, Vector and U of T all play important roles. “There is no table where all the key actors in the Toronto cluster come together and develop a coordinated strategy.”

Another important constraint to converting research and talent to anchor companies, the authors argue, is weak domestic demand. Large Canadian buyers are often risk-averse, and many startups must seek their first customers outside Canada, making it harder to build credibility at home before expanding internationally.

The report recommends four steps to strengthen the ecosystem:

• A Toronto AI Procurement Compact, bringing together financial institutions, hospitals, governments and large employers to procure and pilot Canadian-built AI;
• A dedicated cluster management organization that provides foresight connecting  talent development, procurement, governance expertise, professional services and data access;
• A regional data access strategy, especially for regulated health data, with privacy protections and responsible governance;
• A talent circulation and retention framework that anchors the next generation of researchers and innovators to Toronto.

The region’s AI advantage is real, but not guaranteed, the report suggests.

Toronto has the research base, talent density and institutional credibility to compete globally. The next phase will depend on whether those assets can be organized into a more coordinated system that helps Canadian-built AI companies find customers, access data and compute, retain senior talent and scale from Toronto.

Full Report: