🤝 Meet Daniel Wigdor. He is a computer science professor and researcher at the University of Toronto, the former director of Meta’s Reality Labs in Toronto, and the CEO of AXL, a new Canadian venture studio. We sat down with Daniel to talk about the future of Canadian entrepreneurship, why academics have a hard time building businesses, and his frustration with Canada not benefiting from its own innovation.
Can you give our readers a brief overview of yourself and AXL?
I'm a computer science professor at the University of Toronto, but before that, I was the architect of the Microsoft Surface devices. When I came back to Canada, I started founding companies and sold several of them. Most recently, I sold one to Facebook. That company became its local research centre, so I was the founding director there for several years.
Since then, I have been genuinely frustrated by the fact that Canada keeps inventing all of these foundational technologies and not benefiting from them, and having myself commercialized tech before, I wanted to see what I could do about it. So that's why we created AXL. It's a venture studio, so not an incubator, not an accelerator, not a venture fund. Unlike all of those things, we're not looking for other companies; we are creating companies ourselves. We want to flip the script on Canadian innovation, where we can actually be the great bridge between academia and applying to solve real-world problems.
You describe AXL as a human-centric AI venture studio. What does that mean?
Fundamentally, it is using AI as a tool to solve a real problem and empower people. Human-centric means we look at problems that exist for which there were no solutions, but now that we have AI, we could potentially solve them. At the end of the day, we'll see a net massive increase in the number of humans doing great work as opposed to a decrease.
You know, people forget this, but the word computer was named that because there used to be a human who had a job called a computer, and then the computer replaced them. That's where the word comes from. So jobs are definitely gonna disappear, but at the same time, the employment rate has not gone down over time between before the computer and after, even though that particular job doesn't exist anymore.
What is the biggest challenge of turning promising academic research into commercial products in Canada?
I think the biggest challenge is finding the real application for the technology. I think that too often academics look at industry and say, oh, if I worry about industry problems, then I'm thinking too short term, or my pure research is going to get corrupted by money. Those are all real concerns, but people think they can just build a better mousetrap, but you only need a better mousetrap if the old ones aren't working.
There's this lack of understanding of real potential and how to build a business. It's an overt hostility that exists in academic culture, and it's worse in Canada than in other places. Overcoming that and finding real human problems to solve with your technology is the challenge.
We hear a lot about the funding gap between Canada and the U.S. in terms of being able to take a really good idea and turn it into something profitable. Are there other differences beyond that gap that you've seen in your work?
Absolutely. The funding gap is real, but ultimately, there is still money there. To me, the difference is that academics here invent this shiny new thing and believe that the world is going to reorganize itself around that shiny new thing.
And that’s what we’re doing differently at AXL. We invest hundreds of thousands of dollars every year doing deep investigations of our corporate partners and their customers, trying to, you know, uncover opportunities. It's all about asking what is the problem and how we're gonna solve it. It’s start-up 101 stuff that in San Francisco they get, and we just don't get it here.
What did working for some of the biggest tech companies in the world shape your work now?
I've worked with Meta, I've worked with Microsoft Research, and I've also worked at Mitsubishi's research lab. What I've seen is these big companies invest real money in creating research labs so that they can, they can themselves invent the products that are gonna replace their own.
You know, Mark Zuckerberg famously invested almost a billion dollars in buying Oculus because he almost blew Facebook’s lead during the transition to mobile. They almost lost the company because they didn't make the transition to mobile fast enough. So this time, they believe the future's gonna be virtual reality, so they’re going to invent it themselves and be ahead of everyone.
The business mindset enters the research side so much earlier than we would allow it to happen here in Canada in the academic realm. I think that's been the most important lesson for me.
What is the coolest part of being a professor that people might not realize?
I think a lot of people just view professors as a bunch of teachers. And of course, we are teachers, and that's the most important thing that we do. But we're also responsible for billions of dollars a year of funding to invent the future. As a professor, you get to look 15, 20, sometimes 30 years into the future and take these wild rides of discovery. So, I think the coolest thing is being able to take those sorts of entrepreneurial style risks and go on deep journeys of discovery, and having the freedom to do it. It's all about that freedom.
What do you make of the recent market selloff affecting SaaS companies?
I think there are different aspects of what endangers these companies. Existing SaaS companies, a lot of them are built around business models that become shaky with new technology. If you point at any individual software company and ask if its product is future-proof, I think for a lot of them, you'd say no. And that's kind of where the panic's setting in a bit in the markets.
What do you think is the opportunity for some of these smaller Canadian companies to carve out a niche in AI?
The companies at AXL are smaller in the sense that we don't need to make the investments in training language models. That's what's so expensive. But at the same time, if you were to talk to Mark Zuckerberg in 2003 and say this Facebook thing is smaller than IBM, that would be true. It's smaller in the sense that it's cheaper for me to start because I'm building on top of other people's platforms. AXL’s mission is that 10 years from now, instead of nine of the top 10 largest companies in the world being on the West Coast of the United States, we want nine of the top 10 to be in Canada. We are building companies of real scale.
What's happening right now is people can't invest in the applications that are actually gonna be worth anything, because they haven't been released yet. They haven't been created yet. Everyone's stuck investing in the infrastructure, which will inevitably be in a race to zero. That's where we're at with AI. So, we're smaller scale, and it is cheaper for us to launch them, but we're not smaller scale in the sense that our companies are gonna be small in the future. Our companies can be the biggest companies in the world.
Do you think that these AI companies’ valuations are overblown as a result of them being the only ones tapped into the infrastructure right now?
Their valuations are being set largely by an exuberance of the potential overall value for AI, which I think is just like it was with dot com. I think they’re actually undervalued. I think it's worth hundreds of trillions of dollars.
Which AXL company are you the most excited about?
We started an AI education company called Learn Aid. Education is a great example of how people aren't understanding the AI use case. How can I solve education with AI? The obvious thing to do is to have an AI tutor, but it's just a thin layer on top of ChatGPT teaching me things. If you actually look at first principles, the best way to learn something is to teach someone else. Everyone remembers having reading buddies in school. When you were in grade 5, you had to teach a kindergartener. The big secret is that it wasn't to help the kindergartener — it was to help you.
Learn Aid gives every person who's learning something a reading buddy, but it's an AI that they have to teach. The AI can ask them questions and ask them to explain concepts. With that formula, we’ve found that the learning outcomes are massively better. Everyone knows what it's like when you've learned something, but then you go to explain it to someone, and you realize you didn't really understand it as well as you thought. That's why our PhD students are teaching assistants, by the way. It's not so that the undergrads can learn, it's so the grad students can get better at the material by teaching it.
How should founders these days think about building moats right now with AI advancing so quickly?
At the end of the day, it's all about human connection. Human connection will start to be worth a premium. Founders should be looking at how to provide a better human experience with their company. The future of how humans use AIs has not been written yet. Everyone's looking at chatbots and thinking this is how I'll use AI. To me, that’s the equivalent of looking at an old flip phone and thinking this is the future of how I'm going to use a phone.
At AXL, we've got companies with first customers lined up, problems ready to go, and we've got a tech team that builds it. What we're lacking are founders with business experience. And I don't even need them to be able to spell AI. We don't need tech founders. We need people who understand people and problems.