Advancing AI Innovation in Canada

I've heard so much noise lately about AI adoption in Canadian businesses. I've heard that entrepreneurs aren't ambitious enough. I've heard that we're laggards. I've heard that small businesses are risk-averse. I read a particularly offensive line that "many Canadian businesses never miss an opportunity to miss an opportunity." Um, I don't think so. The small and medium-sized enterprises (SMEs) I work with every day aren't risk-averse. They don't lack ambition. They've taken some serious punches in the past few years and are still kickin'. And when it comes to their AI adoption, they're not missing an opportunity. The SMEs I know are eager to adopt AI, they just need some guidance on where to begin, and advice on strategy. As Kirsten Koppang Telford of The Forum, Sarah Stockdale of Growclass, and I say in our op ed in today's edition of The Hill Times: All Canadians need access to AI training, but business owners need more than just a course. They need support systems that work in practice. That’s what turns training into lasting organizational change. This isn’t the time for passive optimism. AI alone won’t solve our productivity problem but people will, if we give them the resources they need. As co-leads of the AI Skills Lab Canada pilot, with co-investment by DIGITAL, Canada’s Global Innovation Cluster for digital technologies, we launched the country’s first program to help women and non-binary entrepreneurs adopt AI. Since April, the AI Skills Lab Canada pilot has trained 103 women and non-binary entrepreneurs and business leaders using a wayfinding approach with expert-led instruction, small peer-learning cohorts, practical AI integration roadmaps, and support from AI coaches. And it’s working. Participants’ ability to set up AI systems and processes grew by 90 per cent, and confidence in selecting AI tools increased by 89 per cent. Their understanding of ethical and regulatory considerations rose by 119 per cent. When AI training is timely, practical, and supported by a trusted peer network, people apply what they learned. That’s not just a win for inclusion. It’s a win for the economy. When entrepreneurs have the tools and training to adopt AI in a way that is values-aligned, more businesses can grow, hire, and innovate. Equity and productivity move together. Now is the moment for Canada to be as ambitious about equitable AI adoption as we are about AI innovation. Let's not waste it. (link to the op ed in The Hill Times is in the comments below)

This is truly building the rails for real-world health AI 👊🏾 We talk a lot about new healthcare algorithms, but far less about the infrastructure that actually gets AI into clinics. A new brief communication in npj Health Systems showcases the Mayo Clinic Platform (MCP): a secure, de-identified, multi-institutional environment with standardized EHR data from over 15M patients and integrated tools for cohort building, analytics, and AI development. Key takeaways for health AI builders: 👉🏾 Invest in platforms, not one-off models—reusable pipelines for RCT emulation and prediction unlock far more value than siloed projects. 👉🏾 Standardized, de-identified, multi-institutional data is a strategic asset for trustworthy real-world evidence and generalizable AI. 👉🏾 Bridging no-code interfaces for clinicians with pro-code environments for data scientists materially lowers the barrier to high-quality translational research. For Canadian universities and hospitals, this paper reads like a blueprint. A pan-Canadian, MCP-like infrastructure—built on privacy-preserving, standardized data and shared tooling—could supercharge our translational pipeline: faster RWE studies, more robust AI validation across provinces, and a more attractive environment for industry and global collaborations, all while keeping patient trust at the centre. It's great to see TIAP - Toronto Innovation Acceleration Partners member institutes like University Health Network part of the larger consortium. If you’re working on Canadian health data platforms, AI, or translational research, this is well worth a read. Link in comments.

Tech giants like Google, Amazon, and Salesforce are flooding into Toronto for 1 reason the U.S. refuses to address. When I first compared North American tech hubs, Toronto wasn't even on my radar. Now it's outpacing Seattle, Boston, and Austin. The reason is simple. One simple policy difference, i.e., immigration. Canada can bring tech talent to Toronto in just 30 days, while the US visa process often takes years with no guarantee. Global companies noticed this advantage. Google opened its Waterloo AI lab in 2016, Uber launched its Advanced Technologies Group office in 2016, Amazon announced major expansion plans in 2017, and Netflix established its content hub the same year. But it's not just immigration. Toronto's success came from three strategic moves: 1️⃣ Early investment in AI research (led by Geoffrey Hinton, the "godfather of AI") 2️⃣ Strong university-business partnerships that commercialize innovation, like the University of Toronto's partnerships with Google's Vector Institute 3️⃣ Consistent government support for tech education and entrepreneurship, including the $125 million Pan-Canadian Artificial Intelligence Strategy As a corporate commercial lawyer who's worked across borders, I've seen firsthand how policy shapes business landscapes. Sometimes the biggest competitive advantage isn't technology itself, but who can access the talent to build it. Is your business considering Canada for your next tech expansion?

Today the Government of Canada signed an MOU with Cohere, a Canadian AI leader, with the intention to explore AI deployment in public services. The non-binding agreement builds on Ottawa's existing $240M investment in the Toronto-based company (via the AI Compute fund) This comes alongside significant recent AI funding and acquisition activity in Canada. Some deals that stand out: • Cohere: $500M Series D at $6.8B valuation (Aug 2025) • Blue J: $122M for AI tax research (Aug 2025) • GeologicAI: $44M for AI for resources and mining (July 2025) • Clio: $1B acquisition of VLex + $900M Series F (2025 + 2024) • Waabi: Hiring Uber Freight CFO + $200M autonomous trucking round (June 2024) The Cohere MOU signals potential procurement opportunities for Canadian AI companies, though specifics remain undefined. More interesting is the timing - this follows similar agreements Cohere signed with the UK government and positions Canada to test sovereign AI capabilities ahead of defined regulatory frameworks. Taking an experimentation and learning approach before going further down the regulatory path could enable governments to build more AI expertise internally prior to developing more rigorous rules. The broader trend: Some Canadian AI companies are raising significant capital and making big moves, despite some downturn in broader VC funding for Canadian firms. This "flight to quality" given broader economic tradewinds and uncertainty is not surprising. Although it's worth noting that some AI companies, particularly in hardware and chips, (Untether, CentML, Tenstorrent) are getting acquired early or redomiciling). It remains a major gap for Canada to have these capacities at scale domestically, owned by Canadians. Others including Ranovus and Talaas continue the pursuit. Ultimately, Canadian governments are exploring how to develop and deploy AI and digital technologies domestically rather than consistently relying on foreign solutions, which is positive for our economic sovereignty. However, for Canada's AI ecosystem, the implications depend on execution. If the case of Cohere translates to meaningful government contracts, it could accelerate domestic AI adoption and open the door further for other companies to solve for particular verticals and aspects of public service delivery. If these kind of initiatives remain exploratory, it's primarily symbolic value. #AI #Canada #GovTech

Invented in Canada, Owned Abroad: Fixing the Innovation Deficit Today I testified before the Standing Committee on Science and Research Canada’s digital economy is booming. AI startups, fintech, data centres, and cloud services now drive over $100 billion a year, larger than agriculture or forestry. But here’s the paradox. We don’t own it. Only 12% of patents filed in Canada come from Canadians. Over 87% of our innovation is foreign-owned. And only 5% of university patents ever get licensed, meaning 95% of publicly funded research dies in the “valley of death.” That’s a $75 billion GDP loss every year. We are literally funding the future for someone else. In 2025, U.S. startups raised $91.5 billion USD in venture funding. Canada raised $920 million, and 80% of that was U.S.-controlled. Meanwhile, 80% of Canadian digital data sits on U.S. servers, subject to the U.S. CLOUD Act. If we don’t change course, Canada will enter every future trade negotiation, especially around AI and data, as a price taker, not a partner. It doesn’t have to be this way. We can double business R&D by 2030 if we act decisively. 1️⃣ Enact a Canadian Innovation and Data Sovereignty Act Protect Canadian IP and data the way we protect critical minerals and energy. Require Canadian commercialization plans for publicly funded research. 2️⃣ Establish a $100B Sovereign Innovation Fund Co-invest with private venture capital, take small equity stakes in IP-heavy firms, and keep Canadian ideas Canadian-owned. 3️⃣ Modernize Competition Policy Break up monopolies, ban killer acquisitions, and make innovation a core test of market dominance. As Jim Balsillie said, “You can’t commercialize what you don’t own, and Canada doesn’t own much.” It’s time to fix that. To make sure what’s invented in Canada is also owned in Canada. #Innovation #Research #Canada #IP #Competitiveness #Trade #DigitalEconomy #VentureCapital #RD #Policy

I am pleased to see that Innovation, Science and Economic Development Canada is seeking comment on AI strategy. I encourage all to participate. The first question asks: “How does Canada retain and grow its AI research edge? What are the promising areas that Canada should lean in on, where it can lead the world?”. Here’s what I wrote: The definition of AI under which most are operating is wrong, for the current phase of AI development. Though Canada has been instrumental in foundational AI research, we can’t possibly compete in a race against the US or China fueled by capital. The good news is that we don’t have to: we have the single most important edge, we just aren’t yet using it well. The success of new technology comes down to its applications. Presently, the world is looking at thin chat interfaces atop language models and, due to a lack of imagination and experience, believe that is what AI will look like when applied. That’s a little like thinking the whole value of the Internet will be in email. It’s important, but a tiny fraction of the potential. Doubling down on “AI Research” as we have been defining it would be like making email more efficient, or transmit faster, or support more fonts, or whatever. It might help to make GMail, but it misses the far bigger opportunity Canada has to own the future. The current Silicon Valley giants, like Google, Facebook, Apple, Netflix, etc., mostly are *application* companies, not infrastructure companies. BUT, they are now investing in infrastructure for AI, transforming themselves into the foundations others will build atop. We look at companies like OpenAI and think they will win the AI race. That’s like thinking Cisco or AT&T would be the ultimate winners as the Internet matured. Infrastructure enables, but it doesn’t capture a whole lot of the value of the ecosystem. Fortunately, Canada is #1 in the world in the discipline of *applied computing*; that is, deeply studying organizations, people, individuals, and building applications that employ cutting-edge technologies. The problem with government policy on AI presently is that our world leaders in this discipline are being shut out of the process, because they aren’t AI *infrastructure* researchers. But that’s the whole point: we won’t win on infrastructure, we will win on applications. If Canada wants to press its advantage, and realize its potential as the world leader in AI, we must immediately activate our hundreds of applied computing researchers with additional support, opportunities for collaboration, and tools such as startup studios (like AXL: Human Potential, AI Superpowered) Canada should immediately shift AI strategy to focus on enabling applied folks to build atop AI. By doing this, we can leap years into the future, and beat Silicon Valley at its own game. But we have to work fast! Please consider adding these thoughts to their survey (like below).

Eight years ago, we set out to build an AI ecosystem that could compete globally. New Vector Institute research prepared by Deloitte Canada proves that vision is reality—and the numbers are remarkable. AI-related jobs have contributed between $82 billion and $100 billion to Canada's economy over the past five years, with Ontario accounting for nearly half of this total. But as Deloitte Canada's Chief Economist Dawn Desjardins notes: "The impact of AI is already apparent; we're seeing this firsthand through the establishment of numerous AI labs and the economic impact of AI-related jobs. Our analysis shows that continued adoption of AI across the Canadian economy has the potential to drive significant economic growth, enhance labour productivity, and generate net new jobs—growth that would not be achievable without the integration of AI technologies." Overall, the research reveals compelling evidence of AI’s national and provincial impact: ➡️ Federal investment of $1.1 billion nationwide attracted $10.64 in private sector investment for every public dollar ➡️ Ontario attracted $446 million in federal AI investment, generating $9.53 in private investment for every federal dollar ➡️ Over 17,000 new AI jobs created in Ontario this past year ➡️ Canada projected to achieve $298 billion in AI-driven economic growth over the next decade The foundation is built. The question now is whether we can maintain the momentum to scale. Canada leads the G7 in AI talent growth, but talent follows opportunity. This research proves that strategic public investment catalyzes private sector commitment, creating an ecosystem where breakthrough research translates into companies, jobs, and economic growth. The global competition for AI leadership is intensifying. Our proven ecosystem gives us a distinct advantage—but only if government, industry, and research institutions continue working together. Thank you, Dawn, and the team at Deloitte Canada, including Audrey Ancion, and Anthony Viel, for collaborating with our Vector team members including Craig Stewart, Bob (YiAn) Zhou, to gather this comprehensive analysis, and to the federal and provincial governments whose strategic support continues delivering results.

Canada must stop funding the world’s commercialization pipeline—and start building our own. Yesterday I shared my submission to Canada’s AI Strategy Task Force proposing a vision for creating 10ˣ Canadian global champions. Today, I’m sharing my second submission—this time focused on commercialization and the critical link between research and scaling Canadian AI companies. The takeaway: To reach our 10ˣ goal over the next decade, Canada needs to quadruple the number of funded seed-stage companies (1,200–1,600 annually) and quadruple Series A investments (450–650 annually). This is what it will take to compete globally. If you’re interested in what actionable policy can look like, read the recommendations here → https://lnkd.in/gxmHfcXT cc Creative Destruction Lab, Vector Institute, Perimeter Institute

Some more big news for Canadian AI 🍁 The Government of Canada has signed an MOU with Cohere to explore how AI could be applied in public services. This comes on the heels of Ottawa’s earlier $240M investment. Why does this investment matter, and what does it signal?  • Shows the government’s willingness to actually test Canadian-built AI in real use cases  • It could open procurement doors that accelerate adoption (we know this is desperately needed in Canada)  • Puts Canada in the mix with peers like the UK that are already testing sovereign AI approaches. Beyond this, Canadian AI is having a real moment— Blue J, GeologicAI, Clio, and Waabi have all seen big funding or acquisitions recently. The challenge? Making sure Canada doesn’t just grow AI companies for someone else to buy, but keeps capacity here at home. If this MOU turns into real contracts, it could mark a tipping point for Canadian AI sovereignty and adoption. If it doesn’t..... well, let’s just say it’ll look great in a press release. Either way, it’s a sign Canada is serious about building with Canadian AI. #AI #Canada #GovTech

Canada remains one of the most underrated forces in global AI! I recently had the pleasure of joining Ryan Purvis on the Valuu Maker podcast to discuss the state of AI in Canada, the role of SCALE AI | Canada's AI Global Innovation Cluster, and strategies for accelerating AI commercialization and adoption. We discussed a range of topics ⬇️ 🇨🇦 Canadian AI Ecosystem: Canada remains a global AI leader with strong research roots and emerging commercialization efforts, driven by innovation clusters like Montreal, Toronto-Waterloo, and Alberta. 🤖 Role of Scale AI: As a government-backed nonprofit, Scale AI bridges the gap between research and commercialization, offering funding and strategic support to de-risk AI investments for Canadian companies. 💼 Commercialization Focus: Emphasizing real market demand, Scale AI supports projects with practical applications, helping startups and industries translate research into tangible business outcomes. 🌍 Global Reach with Local Impact: While fostering Canadian innovation, Scale AI also encourages successful companies to expand internationally, contributing to both local and global economic growth. 🔄 Challenges and Opportunities: Balancing AI expertise retention while promoting international success is key to ensuring long-term benefits for Canada's AI ecosystem. Most countries (maybe apart from US & Israel) are facing similar tech transfer challenges. I would encourage policy makers around the world to look at this model for inspiration. 🎧 Listen to the full episode here: https://lnkd.in/ezwgDadn