Canada’s bet on an AI boom

Summary

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

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

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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.

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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.

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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)

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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

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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.

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Transcript

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[Jamie Poisson]

Hey everybody, it's Jamie. I just wanted to take a minute to recognize some of the folks who've been writing into the show. A very special thanks to Braden, who had a few suggestions for Alberta related topics for us like the current teachers strike and the Alberta Prosperity Project. We appreciate this feedback so much. You can reach the show anytime at FrontBurnerCBC Cat to Tell us about the kinds of stories that you want to hear and make sure that you're following us on your podcasting app of choice. It is the best way to make sure you catch every episode. Okay, here's the show.

[Evan Solomon]

How do we make sure that Canadians are benefit from AI, that it is AI for everyone, reflecting our values, benefiting Canadian workers, but, you know, not everyone gets it.

[Jamie Poisson]

So. That is Evan Solomon speaking with David Common earlier this month on CBC's Power & Politics. Solomon is Canada's first ever minister of artificial intelligence, and right now he is spearheading what he is calling a 30 day sprint to nail down Canada's AI strategy, figuring out how the government should approach it and how to use it to best boost the Canadian economy. AI is, of course, probably the single biggest driver of economic hype in recent memory. Billions of dollars worldwide have been invested and seemingly everyone is trying to figure out how to cash in. So today we wanted to take stock of where the industry is in Canada right now and take a closer look at that strategy that Salomon and the Liberal government are working on. What could it all mean for our jobs, economy, society and environment? For that I am joined by Murad Hammadi. He's a reporter with the Logic where he covers the ins and outs of this industry. Murad, hey, it is so great to have you back onto frontbrner.

[Murad Hammadi]

Thanks for having me back.

[Jamie Poisson]

Always a pleasure. I think maybe the most useful place for us to start is to enumerate what we actually have right now. What does the Canadian AI industry industry look like right now? How does it compare to other countries?

[Murad Hammadi]

Yeah. So most listeners will probably have heard people talk about how great Canadian AI is, and that's because of research. A couple of decades ago, the federal government put a bunch of money into AI research at a time when not many people were doing that. And as a result, we have these clusters of research in Toronto, Montreal, Edmonton, other places as well, but primarily those three places where graduate students and doctoral students are working on the cutting edge of AI, or they have been for the last 20 or so years. What that has not necessarily translated into at large scale is companies. So there are AI companies, don't get me wrong, in Canada, some pretty significant ones, the likes of Cohere or Coveo or Ada. The minister in particular likes to name some of these names, but most of them are not at the scale or at the level of success yet of some of the companies, particularly in the United States.

[Jamie Poisson]

Why do you think that? Is that that research acumen hasn't necessarily translated.

[Murad Hammadi]

So there's two reasons. One is that some of the principles that made some of those breakthroughs in what are called neural nets and neural nets, the easiest way to think of them is that they're sort of AI models that work a little bit like we imagine the brain does. Those underpin a lot of what we talk about as AI today, the systems that power them. The people that made those breakthroughs often went to for the likes of Google or Microsoft, you know, other Silicon Valley companies. Now, there's some dispute about why that happened, but it's certainly true that in the era when these breakthroughs were happening, call it the early 2010s, not a lot of Canadian companies were using this technology. And the US companies saw something in these people and in this technology and hired them up.

[Jamie Poisson]

Got it.

[Murad Hammadi]

There are other factors about tech in Canada in general, involving money and the availability of capital, of talent, of customers. These are long, longstanding issues, and they apply to AI as well.

[Jamie Poisson]

And what about companies here that are just using AI, not necessarily creating AI? You know, we know one of the big sales pitches for generative AI is how it's going to create huge value for businesses. And do we know if a lot of companies here are adopting the tech?

[Murad Hammadi]

Yeah, I like the line that it's one of the greatest drivers of economic hype, translating that into one of the greatest drivers of economic productivity that's still work in progress. I mean, it's true that every successive new digital technology, whether that's computers or the Internet, Canadian businesses have been slower to adopt than their peers around the world. It does not look like this cycle is Any different in that AI is also, they're also being slower to adopt AI. There are pockets of significant adoption and they're in places you might not imagine, places like financial services, you know, areas where there's a lot of data and a lot of tight regulation actually serve pretty well for AI. So it differs by industry to industry. But on the whole, businesses around the world are figuring out how to use this technology. And it's certainly true that Canadian businesses aren't doing it any faster than anywhere else.

[Jamie Poisson]

Let's talk about Canadian consumers now. So I imagine pretty much everyone listening will have probably either tried ChatGPT at this point or encountered like that AI summary that you get when you do a Google search. But I'm curious if you have a sense of the extent to which actual Canadians are going out of their way to incorporate this technology in their daily lives and in particular whether they're spending money on it.

[Murad Hammadi]

I think the first question is easier to answer than the second. There are the two companies that have a consumer facing chat interface that's widely used. So that's OpenAI and Anthropic. They both put out some research recently and they find that Canadians are in general less likely than people in the US to be using say ChatGPT, but more likely than people in a lot of other places. So they're using it at pretty high rates. The payment is an interesting question in part because some of these companies operate on what's called a prosumer model. So like they give away the main product for free ChatGPT you can use freely, but then their subscription rates are actually fairly high because they're designed for people that are using them essentially for their jobs. So if you're just like meal prep prepping or travel planning on ChatGPT, you're probably never going to hit the limit that requires you to pay $200 a month. But if you're using it for your work day or day every day, you'll probably pay that. And frankly you're probably expensing that. Right.

[Jamie Poisson]

And just give me some examples of what people are using it for. The meal prepping, the travel planning I get. But just beyond that, I kind of use it like Google in a way like a Google search. But I feel like I'm using it in a very rudimentary way compared to other people.

[Murad Hammadi]

I mean, I think a lot of people are using it in that way. You know, I've heard of cases where people are using it for say helping their kids with math homework. Right. Like, I mean it's been a long time since I did high school calculus. And imagine trying to help someone when either you didn't do that or, you know, you've certainly forgotten a lot of it. So that kind of thing. We are seeing more and more reports of people using it as a sort of social lever or like a companion. People using it just to kind of bounce thoughts off. Sometimes you don't want to call someone. Certainly if you are having that thought at whatever 2am, you're not going to call someone to run it through. ChatGPT is always on. There are more concerning instances of people becoming dependent on feedback from it, and there are questions about the feedback that it provides, but certainly people are using it as a sort of auxiliary to their human interactions.

[Jamie Poisson]

Where do people generally think this technology is headed next? What is it on the cusp of doing, potentially?

[Murad Hammadi]

Yeah, potential is an important word in this conversation because there's the AI God version of it, like what people call artificial general intelligence. And there's a lot of dispute even in the industry about what this term means. But generally speaking, you could think of it as AI that can match and then surpass human performance. So that's one direction that it could be going in, which is to say, the bigger these models get, the closer they're able to do the full scope of things that humans are capable of and then figure out things that we can't conceive of because our imaginations are limited by being human. The other version of it is maybe this technology eventually gets good enough that you can let it run certain tasks or certain parts of the economy. And it's worth emphasizing that we sort of do this already. Like, if you think about a package that you order online, some algorithm is routing it, like from the place where it's packed to your door automatically. And so we're already relying on AI to some extent for those kinds of things, but automating more parts of the economy or more tasks within jobs.

[Jamie Poisson]

So like, for example, it books my dentist appointments.

[Murad Hammadi]

Correct. And that's what's called agents. There's a lot of that is the sort of buzzword of the moment is agents, which, roughly speaking, you can think of it as AI that can do things for you rather than just give you responses.

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[Jamie Poisson]

Another thing that we hear a lot about is data centers, which are these facilities that house the computing infrastructure that generative AI tools run on. And what do we have here in the way of those types of facilities right now? And maybe more importantly, what is being proposed in terms of future projects.

[Murad Hammadi]

So what we have right now is not actually the kind of data center that all the AI hype is around. We have data centers right now in Canada that serve software and other digital services to consumers and businesses. So, like you use software on your computer or you use the Internet, it's definitely run out of a data center somewhere. It may be running out of a data center in Canada. So like Microsoft or whatever company you use, the documents you store in the cloud, those are somewhere, they're kind of distributed. But some of those might be in Canada. The kinds of data centers that people are talking about, that people are proposing, there's two categories. One is training. So that's when you build the model that powers the application. Those are the gigantic data centers. Everyone's talking a lot about the gigawatts of power, billions and billions and billions of dollars. They're generally very large. Nobody actually does that kind of activity in Canada right now for a bunch of different reasons, not least of which is there isn't the capacity to. That is some of what's being talked about. Build a gigantic data center so that you can train the next generation of models and the next generation after that. Then there's what's called inference, which is basically when you ask ChatGPT a question, it doesn't operation get you an answer. Those data centers can be smaller, they can be less energy intensive, and there's also a lot of talk about building those kinds of things in Canada.

[Jamie Poisson]

I'm sorry, this is a dumb question, but why would we want them here?

[Murad Hammadi]

I mean, I think that is an excellent question and a question that the government is going to have to try and figure out a concrete answer to. One reason is the word sovereign gets thrown around a lot in this context. It's something that Minister Solomon talks about a lot.

[Evan Solomon]

Canadians want to know that their privacy and their data is protected from deep fakes, that their kids are protected. That's really critical because unless they trust that, they won't adopt this technology. So that's critical to our AI strategy. And I talked about this, not just AI for everyone, but this notion of digital sovereignty to make sure that Our privacy and our data is protected. So on that I know that was.

[Murad Hammadi]

A one part of the argument here is Canadians have data that we don't want in the hands of another entity or government. So should that data particularly say government data that's sensitive, should that be processed on Canadian soil? Should it be required to be held there already? To be clear, some requirements around this. There's that argument. The secondary argument is essentially geopolitics is messy and getting increasingly so. Say AI becomes what the most optimistic projections say it will be. Say it's running large parts of our economy. We want to be sure someone else can't turn that off. And if the processing and the running of that AI is happening in data centers outside our borders, we have less control over whether somebody else turns us off or not.

[Jamie Poisson]

Okay, so let's bring Evan Solomon into this conversation a little bit more. So you mentioned his thoughts around AI sovereignty, but just elaborate on what you're hearing from him right now and what we've heard from him in terms of his goals and what he thinks that this technology, and presumably the Prime Minister think that this technology can do for Canada.

[Murad Hammadi]

Yeah, so the sovereignty question is certainly a big part of it. The other part of this is the productivity question. Right. So Canada has a productivity crisis emergency, certainly a significant problem. We've heard various people in positions of authority say that over times productivity is essentially output for workers. So, you know, can we make more stuff with the same number of people? And over the years, Canada's answer has generally been no. AI is looked on as if there is a silver bullet to this problem. AI might be it in the sense that AI allows each worker to do more with the same number of hours in the day by taking away certain things that they have to do. If the goal of the government is to boost economic output in order to pay for the services that the government provides, you have to find a way to do that. The government has decided that increasing the population by immigration is not going to be the way we do that. And therefore we need something else. AI, Is that something else?

[Jamie Poisson]

He's recently struck this task force. He's calling this like a 30 day sprint now to create an AI strategy. And just tell me a little bit about who's involved in that and what we might expect to actually come out of it. A little bit more about that.

[Murad Hammadi]

Yeah, so it's 26 people. They are generally people who are involved in the AI industry. Quite a lot of them come from the private sector. So there are people like Joel Pinault who's a very well respected researcher now working at Cohere, a company that I mentioned earlier. People like Ajay Agrawal, who founded the Creative Destruction Lab, which is a major hub for startups in Canada. It's people like Taylor Owen, who's a professor at McGill, who's been studying some of these issues of, of technology and the economy and in society. These 26 people are broken up into groups. They have to go away, talk to their networks and come up with concrete recommendations. Over the course of this 30 day sprint, the government will take those recommendations as well as input from a public consultation. All of that's due by sort of the end of this month and they'll put that together into a revision of their AI strategy which they're going to announce sometime in December.

[Jamie Poisson]

Number yeah, and just either from Solomon herself or maybe anybody on this task force. Are we hearing any kind of concerns, any sort of feelings that we need to pump the brakes here, like there's regulation that needs to come with this?

[Murad Hammadi]

Certainly the polling suggests that the public is desirous of there being regulation in this area. The quick version of this is that in the last Parliament there was an AI law tabled as part of a privacy bill that that bill never passed through Parliament for a bunch of different reasons. And Minister Salman has said that he won't be reviving wholesale the AI part of that bill. But they do plan to do things on privacy, securing consumers privacy and data, which our private sector privacy law is 20 plus years old. And so they've talked about that. Certainly there are people who feel like this process is going too quickly. I think the government's position has been essentially that we're in an AI race. And it's not like there hasn't been a lot of talk about what Canada's position in AI should be. The issues are fairly well discussed. The recommendations were made as part of the last bill. And certainly on the industrial side, there are lots of people with lots of ideas about what we need to do next.

[Jamie Poisson]

Murad, we began this conversation with you telling us about how this country did invest in research and actually made quite a bit of headway only for American companies to then take our people and build their own companies. Right. So if the goal here is to try and create this thriving sovereign AI industry, how are we going to do that with a country whose entire population is about the same as the state of California? And with the track record that you talked about earlier.

[Murad Hammadi]

I'd be making a lot more money if I could answer that question. It's true that Canada is a relatively small consumer market, so that's 40 million people, but we are actually quite a large economy. And a lot of what you will hear AI startups say is it would be really nice if the Canadian government and big Canadian Canadian companies would buy Canadian made tech to do the things that they need to do. So if you are a startup building an AI product in financial services or mining or energy, that can make a company more efficient. It helps if you have a large Canadian company or the Canadian government or someone like that to say we will give you your first contract. It doesn't have to be a ton of money, but we'll help you prove that this is useful. And, and what you hear companies say a lot is that in the States big companies are willing to try out local companies technology and that helps them scale up. You've heard this Prime Minister saying Canada is an export economy. We make things here, whether they're physical things or digital things and we sell them to the world. AI is not going to be any different if it is to be a thriving economy. No one is building an AI company just for Canada. But it would help if Canadian institutions were willing to help them along their way.

[Jamie Poisson]

You know, we've talked about how, there's a lot of talk about the potential of this technology, about what it could be, what it might do. But are you speaking to people who are concerned that we're putting like too much emphasis on something that just really might not get much better than it already is?

[Murad Hammadi]

So there's the most utopian version of, of this, that AI helps us solve climate change, create more leisure time for everyone, gives us basically the world and the society and the economy that at least a lot of people would hope for. It doesn't have to do that to be economically significant. So we've talked a little bit about the productivity problem in Canada. If you could free up some time from workers, if we could produce things more efficiently, if we could move stuff across the country more efficiently, and I want to be clear, more efficiently can mean with fewer workers, but it can also mean with the same number of workers just doing things quicker. There is a benefit to the economy from that. There is a growth benefit, there's a sort of growth dividend that, that produces. I think the argument that sure, AI as it is right now is limited, but you are seeing real outcomes that are beneficial to individual companies and to individual people. Now is that worth the billions and billions of dollars that are going into data centers and into AI companies? So that's a difficult question to answer. Is the government making a bad bet by focusing on this? You'd have to assume that it doesn't get any better. It stops today. And we never use it for any more than we use it for today in order for it not to be worth the thing to put some effort into.

[Jamie Poisson]

And what about the argument that it's such a high bubble right now? I was looking at this Bloomberg piece the other day illustrating all these deals, I'm sure you've seen it, that these AI companies have with each other. And it's just like kind of the circular buying of debt from each other, just shocking amounts of money. What happens to the Canadian economy if and when this hype bubble pops, if we're very wrapped up in it especially.

[Murad Hammadi]

The counterpoint to what we were talking about at the beginning with Canada not having a ton of major companies in this space right now is that we're not actually as implicated in the bubble as we might otherwise be. Yes, it might actually help us to be kind of small here.

[Jamie Poisson]

Could this be like 2008 for us? We just sort of skirt by.

[Murad Hammadi]

Yeah, we get to skip the bubble. I think the more data centers that go in, the more reliability we have on this, for sure. During the dot com bubble, there were companies that jumped up out of nowhere. They had.com in the name, they raised a bunch of money, they went kaput. There's also companies like Amazon that were founded in that era that survived and have turned into payments. But a thing that a lot of people aren't aware of is this thing called dark fiber, which is that telecom companies built a lot of infrastructure in anticipation that the Internet was going to require a lot more bandwidth. Let's build out all these fiber networks because this thing is coming, it's for sure. And so we're going to need this. And a lot of those companies went bankrupt because it didn't happen as quickly as you as people expected. But all of that dark fiber is being used today and we're having to build more at a huge pace. The Internet expands to fill the available room. 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.

[Jamie Poisson]

I do just want to know if people are talking about the environmental concerns here. Meta seems to be planning to help to build a data center near Edmonton which would be entirely powered by its own natural gas power plant. And also, what about Canadian jobs? A lot of businesses talk about this tech being able to Automate stuff. Right. So if businesses adopt this on scale, what could it mean for Canadian workers? And is anybody concerned about that in the government right now? Do they have a plan?

[Murad Hammadi]

Yeah, so let's take those in turn. The environmental concerns are real. They are worth discussing. They're generally down to water and power. So data centers use water to cool. There have been advances in how that's happening, so it's not a problem. But there is work happening on that. There's also work happening at places like Mila, the AI Institute in Montreal, to make the use of what's called compute, which is the output of data centers. You can think of it as like the processing power to make compute more efficient. But certainly the new big data center proposals, particularly in Alberta, the idea is to have captive natural gas plants. So a natural gas plant that just serves that facility and they might be connected to the grid for backup power. The best argument that the government can make there is that that is going to happen somewhere, so we might as well capture the economic gains. But that's not a solution to the problem of the emissions that that produces. In this task force, there is some talk about sustainability, but it's certainly not issue number one. Issue number one is like adoption and commercialization. On the jobs front, there is a gen dispute right now about whether the effect of AI on jobs will be to remove jobs or to change the nature of those jobs. So it may be that AI does not automate an entire job, I. E. All of the work that a person does, but automates a lot of tasks within that job. Therefore, either that person has to do a lot more to justify their continued employment, or they have to figure out new tasks that will fit in. I think 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. And another way you could read that is our technology is so good that you won't need workers anymore. Just to illustrate the point of the government paying attention to it in last year's budget. So under Prime Minister Justin Trudeau, there was $2.4 billion put into AI. Two billion of that went into compute. The thing I was just talking about, processing power. I believe 50 million went to skills and training. That doesn't need to be damning. There are other parts of the government that focus on these things, but that's just a sense of scale. I do think there's a really difficult question that people need to answer about late career workers. There's actually not a ton of evidence that suggests that full scale retraining of workers, that is a worker who used to do something before and we're moving them completely to do something else that that is particularly effective. A lot of workers particularly mid and late career who are displaced from jobs by layoffs have a very hard time getting back into the workforce regardless of what training is offered to them and what government supports are offered to them. That's a problem that the government has to be alive to if it's going to go out there and say we need to use AI to make the economy more productive.

[Jamie Poisson]

Yeah, Murad, I could keep going but I think that that's a good place for us to leave it today though. I hope that you'll come back soon.

[Murad Hammadi]

Anytime.

[Jamie Poisson]

All right. That's all for today. I'm Jamie Poisson. Thanks so much for listening. Talk to you tomorrow.

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