00:00 – 00:13
John Wilson welcomes Daniel Wigdor to the CEO Global Network Podcast.
00:13 – 02:14
What’s the missing link in Canada’s ability to commercialize AI research, and how is AXL closing that gap?
Daniel Wigdor explains that while Canada produces world-class research (thanks in part to figures like Geoffrey Hinton), the country lacks the “applied research” layer that bridges academia and industry. He shares a formative moment from his PhD at U of T, when Apple presented multi-touch as an innovation without recognizing its Canadian origins. Unlike the U.S., which has DARPA and other applied research organizations, Canada’s missing “middle layer” often results in Canadian talent and ideas being pulled south. AXL is working to fill that gap.
02:24 – 03:27
What misconception holds back mid-sized businesses from adopting AI?
Daniel notes that many businesses equate AI with chatbots like ChatGPT or Claude. When these tools give wrong answers, companies dismiss AI altogether. In reality, chatbots are just thin demos. Real value comes when businesses bring AI in-house, integrate their own data, and eventually rethink their business models around it.
03:27 – 06:18
What does “unlocking human potential with AI” actually look like in practice?
Daniel stresses that AI shouldn’t replace people, but empower them. Drawing from Marshall McLuhan’s theory, he highlights how new media often imitates old ones before truly innovating. At AXL, they study industries deeply to create tools that enable people to do things they couldn’t before.
– Example: In education, instead of replacing teachers with AI tutors, they’ve designed systems where students teach AI “novices.” This approach leverages proven pedagogy—that teaching others is the best way to learn—while empowering both teachers and students.
06:18 – 08:26
How do academia, research at Meta, and entrepreneurship complement—or clash—when tackling big tech problems?
Daniel says the common thread across science, startups, and big tech is the lean methodology: experiment, measure, iterate. But academia, especially in Canada, often resists commercialization due to fears that money will corrupt research. While valid, this tension means global giants (Google, Meta) often capture the value from Canadian taxpayer-funded innovation. Daniel argues Canada needs to better support local commercialization opportunities.
08:26 – 10:17
If you had the ear of policymakers, what’s the one change that could accelerate Canadian AI innovation?
Daniel offers two:
- Reform the SR&ED tax credit program. Right now, startups get addicted to government funding and avoid raising capital, capping growth. Instead, Canada should subsidize big companies to adopt products from startups—letting the market pick winners.
- Stop funding data centers with taxpayer money. Instead, invest in AI applications and ventures that directly benefit Canadians.
10:17 – 12:30
Looking 10 years ahead, what human-computer interaction will feel as natural as touch screens do today?
Daniel explains that touchscreens only felt “natural” because Apple paired them with simplified mobile apps and pre-released tutorials disguised as commercials. In the future, we’ll be surrounded by sensors, and computers will use context (what you’re doing, where you are, what your team is working on) to tailor interactions seamlessly.
– Example: in a car, only voice interfaces make sense; on a plane, voice is disruptive. The future lies in context-aware systems that switch modes fluidly.
12:30 – 13:02
Closing thanks. John Wilson thanks Daniel for the discussion; Daniel expresses gratitude to John and the listeners.
