When Marvell, a global semiconductor and data-infrastructure company, announced a $238-million expansion in Ontario with hundreds of new engineering roles, it looked sudden on paper. One press release, three cities, and a promise to build custom AI silicon and next-generation data-centre hardware for the world’s biggest cloud providers. In reality, it was more than a decade in the making.
Moves like this are a window into how the AI economy is actually being built. Behind every chatbot or model launch is a stack of invisible infrastructure: specialized chips, high-bandwidth connectivity, dark fibre, data centres, and the teams who design and run them. The places that win on AI over the next decade will be home to more than clever algorithms. They’ll be regions that can design the hardware, move the data, and supply the talent that keeps global systems running.
The first conversations with Marvell began years before the recent commitment. What started as an early discussion around talent and market entry evolved through site visits, technical assessments, and workforce planning — only to be derailed by the pandemic and a global semiconductor supply-chain shock that temporarily shut Canadian operations altogether. When Marvell re-entered Canada through acquisitions in 2022, the opportunity reset at an entirely different scale.
As Liana Hovakimyan, Toronto Global’s Director of Investment Attraction, explains, the turning point wasn’t a single incentive or facility. It was the realization that Ontario could consistently supply, and move, the kind of specialized hardware and AI engineering talent Marvell requires to compete globally. Large semiconductor investments, she notes, don’t hinge on any one factor; they unlock only when talent, mobility, research depth, and long-term business confidence align at the same time.
Universities were central to that alignment. Longstanding relationships with institutions like the University of Toronto, Waterloo, and Ottawa-area research centres gave Marvell confidence that it could sustain growth well beyond its first teams. The ability to embed within Canada’s semiconductor and AI research ecosystem — rather than operate beside it — became decisive.
Once that strategic fit was clear, provincial support helped transform what might have been a modest expansion into a multi-site, multi-year platform for advanced design and AI-enabled connectivity. “These investments don’t happen overnight. Sometimes you work with a company for years before conditions finally line up for something transformational,” Hovakimyan reflects.
These investments don’t happen overnight. Sometimes you work with a company for years before conditions finally line up for something transformational.— Liana Hovakimyan, Director of Investment Attraction, Toronto Global
Today, Marvell’s new design and development centre in Ottawa, its scaling operations in York Region, and its growing Toronto footprint form a single integrated Canadian engineering system. The work underway — custom AI silicon, optical interconnects, and next-generation data-centre infrastructure — feeds directly into global cloud and hyperscale computing networks.
“The world’s hyperscalers, leading data centre operators and OEMs rely on our technology to power their most demanding AI workloads,” says Sandeep Bharathi, president of the Data Center Group at Marvell. “Ontario offers a vast pool of professionals with expertise in the latest semiconductor and AI technologies, and we are delighted to bring new talent into the company to help extend our leadership position in next-gen, advanced AI data centre infrastructure solutions.”
Marvell’s trajectory in Canada illustrates something essential about AI infrastructure investment: global firms don’t just drop in for a lease and a tax credit. They grow into ecosystems. And once they are embedded, their presence reshapes the talent base, research capacity, and industrial strength around them.
The Toronto Region’s growing AI gravity
Artificial intelligence has become the 21st century’s great land rush. Governments and companies around the world are scrambling to secure an edge in the technology that promises to redefine everything from finance to drug discovery. Billions in subsidies, research credits, and sovereign wealth investments are pouring into national AI strategies. Within this international competition, a small number of regions are emerging as structural anchors for the AI economy, and Toronto is increasingly one of them.
Over the last decade, the city has built a reputation for nurturing what the AI economy demands most: talent, research depth, and execution capacity. That formula has drawn some of the world’s most influential technology companies to expand their engineering and data operations here.
NVIDIA, which powers much of the world’s AI infrastructure, has deepened its Canadian research collaborations through the Vector Institute and the University of Toronto. AMD, whose largest Canadian engineering campus sits just north of Toronto in Markham, continues to expand its advanced processor and chip-design work—technology at the heart of AI training and inference. And Samsung, one of the world’s largest technology companies, operates a global AI research lab in downtown Toronto, developing machine-learning models across mobile computing, vision, and robotics.
“Toronto competes globally now,” says Anna Matta, Director of Industry Partnerships at the Vector Institute. Once dismissed as a polite satellite to Silicon Valley, the city now converts three advantages into real economic gravity: raw talent, unmatched diversity, and research firepower. Those strengths are no longer abstract selling points; they show up in where global firms place critical R&D mandates and the kinds of problems they choose to solve here.
Consider the fundamentals. The region produces 30,000 STEM graduates each year. It supports one of the world’s most multicultural workforces, which is a major asset for training multilingual, globally deployable AI systems. And it sits atop a research ecosystem that helped give birth to deep learning itself. It’s a combination few cities can match. The world is racing to define the next decade of technology. Increasingly, Toronto is where that race is being operationalized.
Where Snowflake builds the future of enterprise AI
Look at where the world’s biggest companies are planting their AI flag, and, increasingly, you’ll see Toronto. For Snowflake, the $50-billion data cloud company, the region has become a base for some of its most ambitious engineering mandates.
When Snowflake began mapping its global engineering footprint, the checklist was clear: time-zone proximity, deep technical expertise, and a workforce capable of scaling next-generation AI systems. Toronto met every requirement and offered something more: a dense cluster of enterprises that would ultimately rely on Snowflake’s platform to make AI usable inside large organizations.
“We came here because we wanted to look for that specialized talent,” says Qaiser Habib, Head of Canada Engineering at Snowflake. “It’s been working out super well.”
Since opening in 2022, Snowflake’s Toronto site has grown from a single engineer to more than 90, with aggressive hiring still underway. “In the last three months alone, I’ve hired about 25 people,” Habib says. The office now stands among Snowflake’s six global engineering hubs and serves as its Canadian headquarters — a shift that signals Toronto’s move from experiment to essential node in the company’s network.
What’s built here matters globally. The Snowflake Native Apps Framework, which allows developers to securely build and distribute AI-powered enterprise applications, was invented in Toronto.
“Without Toronto, Snowflake would not have a way to distribute AI into the enterprise,” explains Habib. “The Native Apps framework is like the iPhone for the enterprise world, and it was built entirely here.” For Toronto, that means core product IP, not just peripheral support work, is being designed and owned in-region.
Toronto’s advantage, he adds isn’t just technical but structural. “The quality of talent here is on par with the Bay Area, and the retention is dramatically better. In the first three years, I lost zero people. That’s unheard of.”
The quality of talent here is on par with the Bay Area, and the retention is dramatically better.— Qaiser Habib, Head of Canada Engineering at Snowflake
Habib also sees international investment as a catalyst rather than a threat to domestic innovation.
“We should have a mix of homegrown and international companies. U.S.-based firms bring a skill set many Canadian companies don’t yet have. They help up-level the market and grow the talent base. And having these companies here keeps our best engineers from moving to the U.S.”
Snowflake’s story captures a broader truth: Toronto is no longer an offshore outpost doing low-risk work. It is a core innovation node where global companies build mission-critical systems that determine how AI reaches the enterprise at scale.
“Ten years ago, companies would give their offshore teams easy projects,” Habib reflected. “That’s completely shifted. Now, Toronto gets the critical work.”
Unilever turns data into global design
When Unilever set out to launch its first and only global AI lab, the shortlist spanned more than 15 cities, from London and Paris to Bangalore and Silicon Valley. After months of analysis, the Fortune 500 company with 400 brands and 3.4 billion daily users chose Toronto.
“We’re shifting from data science to true AI innovation, building systems that don’t just analyze data, but generate new ideas, predictions, and products at a global scale,” explains Gary Bogdani, Head of Horizon3 AI Innovation Labs at Unilever. For a company of its size, that shift demanded a location that could support not only experimentation, but global deployment.
Toronto offered what few cities could at once: depth, diversity, and research power. Access to the third-largest tech talent pool in North America, a robust pipeline of STEM graduates, and an AI ecosystem anchored by the Vector Institute, Creative Destruction Lab, and more than 270 existing AI firms proved decisive.
“We are working to solve real-life problems through innovation,” says Andy Hill, Unilever’s Chief Data Officer. “Setting up this lab in Toronto allows us to access vibrant tech talent and some of the best partners in the business to bring solutions to life.”
The results are already material. Unilever’s AI agents are reshaping global procurement and forecasting, generating €100–150 million in annual efficiency gains. From Toronto, Horizon3 Labs teams are also tackling advanced forecasting challenges for global commodities. “It’s a frontier problem,” Bogdani notes, “because there’s no true historical data to rely on.” What begins as a research question in Toronto quickly becomes operational capability across Unilever’s worldwide supply chain.
Toronto’s diversity was another decisive factor. With more than half the population born outside Canada, the region offers multicultural datasets and perspectives that help reduce bias and improve global AI deployment. As Bogdani puts it, Toronto now sits at the heart of “one of the fastest-growing AI superclusters in the world.”
Toronto now sits at the heart of one of the fastest-growing AI superclusters in the world.— Gary Bogdani, Head of Horizon3 AI Innovation Labs, Unilever
From its base in Toronto, Unilever is building the systems and infrastructure that will define how AI integrates into every layer of global commerce — from how raw materials are sourced to how products reach store shelves and households around the world.
Global companies, compounding local strength
From semiconductors and optical systems at Marvell, to enterprise AI platforms at Snowflake, to global consumer innovation at Unilever, Toronto’s role in the global AI economy no longer exists on the periphery. These are not branch offices quietly servicing another market; they are global mandates that plug directly into the core of how AI is designed, deployed, and governed worldwide.
For Toronto, the real story isn’t just job counts, as important as they are. It’s what international investment brings into the ecosystem: mature product roadmaps, global customer relationships, operating know-how, and technical playbooks that local firms and researchers can learn from and build upon. Each new mandate deepens the bench of experienced engineers, product leaders, and operators in the region. Talent circulates between multinationals, scale-ups, and start-ups, seeding new companies and accelerating the ones already here.
These investments converge around a single reality: advanced artificial intelligence depends on more than software alone. It requires custom silicon, deep engineering talent, research-grade institutions, policy stability, and the ability to scale across borders. When global firms bring that full stack of capabilities and integrate it with Toronto’s existing strengths in research, diversity, and applied AI, the result is a cluster that becomes more valuable, influential, and impactful with every new arrival.
If the 20th century was defined by where companies built factories, the 21st is being defined by where they build AI infrastructure and make AI decisions. Each major mandate that lands in the Toronto Region adds to that gravitational pull as it solidifies its position as an AI supercity.
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