Front Burner – "Canada’s Bet on an AI Boom"

Date: October 14, 2025
Host: Jayme Poisson
Guest: Murad Hemmadi (Reporter, The Logic)

Episode Overview

In this episode, host Jayme Poisson explores Canada’s current position in the global artificial intelligence (AI) landscape as the government pushes to turn early research investments into concrete economic gains. With Evan Solomon as Canada’s first Minister of Artificial Intelligence spearheading a rapid “30-day sprint” to draft the country’s new AI strategy, the episode looks at risks, opportunities, infrastructure needs, and the hype cycles shaping the sector. Guest Murad Hemmadi, a reporter for The Logic, joins to break down the realities behind the headlines and the government’s ambitions for AI in Canada.

Key Discussion Points and Insights

1. Canada’s AI Landscape: Research-Powered, Commercially Lagging

[02:50]

• Canada is globally known for pioneering AI research, with regions like Toronto, Montreal, and Edmonton as centers for cutting-edge study.
• This foundational research, largely backed by government investment 20 years ago, hasn’t translated into an equivalent landscape of high-scale homegrown AI companies.
• Quote:
  “Most listeners will probably have heard people talk about how great Canadian AI is, and that's because of research... What that has not necessarily translated into at large scale is companies.” (Murad Hemmadi, 02:50)
• Major breakthroughs in neural networks by Canadian researchers were mostly bought up or whisked away by major US tech firms, partly due to more limited adoption and resources domestically during the crucial early 2010s.

2. AI Adoption and Productivity: Businesses and Consumers

[05:08]

Businesses

• Canadian businesses have historically lagged in adopting digital technologies and the same trend is now visible with AI.
• Adoption is most notable in highly regulated, data-rich sectors like financial services.
• “Canadian businesses aren’t doing it any faster than anywhere else.” (Hemmadi, 05:08)

Consumers

• Canadians use AI tools like ChatGPT at relatively high rates (less than the US, but more than many countries).
• Most consumer-facing AI tools operate on a “freemium” basis; only power users, often for work, pay steep subscription fees.
• Use cases include homework help, travel/bookings, and as a companion or for brainstorming—though dependency and trust issues are emerging.

3. The Future of AI and “Agents”

[09:06]

• The dream: Fully autonomous AI (“artificial general intelligence”) capable of surpassing human capability vs. today’s incremental, task-oriented automation.
• “Agents”—AIs that execute tasks, not just generate responses—are the new industry buzz.
• “That's what's called agents, which, roughly speaking, you can think of it as AI that can do things for you rather than just give you responses.” (Hemmadi, 10:18)

4. Data Centers: Infrastructure and Sovereignty

[11:31]

• Canada currently lacks the giant, “training” data centers powering AI development, but is home to many serving general digital needs.
• There’s a major push for new facilities dedicated to either training or inference (running models).
• Why build them in Canada? The government frames it around “digital sovereignty”—protecting Canadian data and ensuring reliance on domestic infrastructure.
• Quote:
  “Canadians want to know that their privacy and their data is protected from deep fakes, that their kids are protected. …This notion of digital sovereignty to make sure that our privacy and our data is protected.” (Evan Solomon, 13:13)
• Geopolitical risks are cited: If foreign-run data centers power key services, hostile actors could potentially “turn us off.”

5. The 30-Day Task Force Sprint

[15:59]

• Minister Evan Solomon is overseeing a diverse task force (26 members from private sector, academia, and major startups) to deliver concrete policy recommendations within a month.
• The government aims to announce a revised AI strategy by December, using input from this panel and public consultation.
• Areas of focus: commercialization, adoption, digital sovereignty, productivity, but, as Hemmadi notes, sustainability and job retraining are low priorities.

6. Regulation, Public Concerns, and Governance

[17:25]

• Public polling shows strong desire for AI regulation, particularly on privacy.
• Canada’s key privacy law is over 20 years old; a previous government attempt at an AI law died in Parliament, with only privacy updates potentially moving forward under Solomon.
• Pace is fast; government feels an “AI race” is underway globally.

7. Economic Bet and the “Bubble”

[19:17]

• Canada’s small population is a challenge, but its large economy could help build AI export businesses if local governments and corporations support them.
• Canadian institutions rarely give startups initial contracts, unlike their US counterparts.
• AI hype caution:
  • Does placing national bets on AI make sense if much of the tech’s economic upside doesn’t materialize?
  • If the bubble pops, Canada’s exposure (limited by its smaller AI sector) might be less risky, with infrastructure (like data centers) eventually finding other uses.
  • “If companies in Canada build a bunch of data centers and the AI bubble pops, those companies might go bankrupt. But that data center capacity will probably end up getting used.” (Hemmadi, 24:20)

8. Environmental and Labour Impacts

[24:55]

Environment

• Data centers require huge water and energy resources; proposed Alberta sites may rely on new captive natural gas plants.
• The government frames the environmental gamble as economic necessity: “This is happening somewhere—may as well reap benefits”—but emissions aren’t solved by that logic.
• Some AI institutes (e.g., Montréal’s Mila) are working on ways to reduce data center energy use, but this isn’t high priority.

Jobs

• AI impact on jobs is ambiguous: It could eliminate, transform, or simply augment them.
• Notably, the majority of government AI investment goes to infrastructure, with minimal focus (only $50M of $2.4B) on workforce retraining or support.
• Retraining late-career workers is historically ineffective, and those forced out of jobs by automation often struggle to re-enter the workforce.

Notable Quotes and Moments

• On Canada’s research-to-commercialization gap:
  “Canada is a relatively small consumer market, so that's 40 million people, but we are actually quite a large economy. … It would help if Canadian institutions were willing to help them along their way.” (Murad Hemmadi, 19:17)

• On the need for trusted, sovereign infrastructure:
  “Unless they trust that, they won’t adopt this technology. … Not just AI for everyone, but this notion of digital sovereignty to make sure that our privacy and our data is protected.” (Evan Solomon, 13:13)

• On the AI hype cycle and pragmatic risk:
  “If companies in Canada build a bunch of data centers and the AI bubble pops, … that data center capacity will probably end up getting used.” (Murad Hemmadi, 24:20)

• On the potential disconnect between job loss rhetoric and reality:
  “It is notable that, that you hear companies whose job it is to sell AI to people talking about how devastating this is going to be for jobs. …Another way you could read that is: our technology is so good that you won't need workers anymore.” (Murad Hemmadi, 25:50)

Timestamps for Key Segments

• [02:50] – State of Canadian AI research and commercialization
• [04:38] – Why Canadian researchers left for US companies
• [05:08] – Business adoption of AI in Canada
• [06:36] – Consumer adoption and examples of daily AI use
• [08:57] – The future of AI: general intelligence vs. task automation (agents)
• [11:31] – Data centers: current state and proposals
• [13:13] – Solomon on digital sovereignty and privacy
• [15:59] – 30-day AI task force and its membership
• [17:25] – Public demand for regulation and stalled AI legislation
• [19:17] – Challenges of building a sovereign AI industry
• [22:28] – Risks of AI bubble and Canadian exposure
• [24:55] – Environmental and labour market implications

Conclusion

The episode frames Canada’s AI strategy as simultaneously ambitious and cautious. It acknowledges Canada’s world-beating AI research, but highlights the country’s struggle to foster commercial winners. The government’s new sprint aims for rapid policy and infrastructure gains, but questions linger about environmental costs and disruption for workers. Pragmatically, Canada’s limited domestic “AI bubble” may shield it if the tech sector deflates, but the episode ends with a note of realism: even without world-changing breakthroughs, AI’s current trajectory could still be economically meaningful—if governments and institutions choose to invest with eyes open to both promise and risk.