AI Can Strengthen Canada’s Economy — If We Let It

Artificial intelligence is dominating headlines, but for Canada, the real story isn’t hype ­— it’s hesitation.

In Episode 10 of Canada’s Economy, Explained, economist Avi Goldfarb shares why Canada — despite world-class AI research and academic talent — is losing ground in the global AI race.

Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare at the University of Toronto and co-author of Prediction Machines and Power and Prediction. His work focuses on how AI reshapes decision-making, productivity and competition.

He starts with a critical distinction: AI isn’t magic, it’s prediction and that prediction lowers the cost of decision-making across sectors.

AI won’t replace humans — it will reshape judgment.
Who controls that shift will define the future.

“It’s not about replacing people. It’s about improving how we make decisions,” says Goldfarb.

While the media often frames AI as a threat to jobs, Goldfarb suggests that the deeper disruption is institutional. The sectors that stand to benefit most — healthcare, education and government — also face the most resistance. Why? Risk aversion, bureaucracy and uncertainty around regulation.

Goldfarb cites Bill C-27 as an example. It proposes guardrails for AI use in Canada. While he supports the principle “humans must remain accountable for AI outputs” he warns that burdensome red tape could kill progress in the very sectors we want to modernize.

“The very investments we’re making in AI for public services could become too complicated to implement,” he cautions.

He draws a parallel to the electrification of factories. Even after electricity was invented, it took 40 years to redesign factories to use it effectively. We’re now in that same “in-between” period with AI. The technology is here but our systems, workflows and institutions haven’t caught up.

“We’re in the 1890s, not the 2020s. Organizations aren’t yet designed for this technology.”

Goldfarb highlights how the U.S. has handled AI regulation differently. Rather than setting up regulatory walls before adoption, they focus on enforcing existing laws and encouraging competition. It’s a lesson Canada might consider if it wants to stay nimble.

He’s especially hopeful about AI’s potential in healthcare — better diagnosis, scheduling and patient management — but fears our risk aversion will keep us locked in mediocrity.

“We could make healthcare better. But fear of getting worse might stop us from even trying.”

The talent is already here. Canadian universities continue to train top-tier AI graduates. What’s missing is the leadership.

Canada doesn’t have a technology gap — it has an adoption gap.
That’s a choice, not a constraint.

“Companies tell us, ‘We believe AI matters, but we don’t know what to do with it.’ That delay is costly. Once someone else figures it out, they’ll scale fast and take your customers.”

Looking ahead, Goldfarb urges a shift in how Canada views AI. It’s not just a tech issue — it’s an economic one. The country’s productivity problem won’t be solved by waiting for someone else to take the first step.

“If we lead now, we can shape how AI works for Canadians. If we wait, others will decide for us.”

Key takeaways

• Canada excels in AI research, but business adoption and public-sector implementation remain slow.
• AI’s true economic power lies in prediction not automation.
• Smart, light-touch regulation is needed to avoid stifling innovation.