Part 2 of 8: A Deep Dive into the State of AI in Canada and the World

Nikki Matarazzo, AI Evangelist

Part 2 of 8: A Deep Dive into the State of AI in Canada and the World

The State of AI in Canada

Canada’s federal AI for All strategy marks a shift in emphasis: from being known primarily as a world-class AI research nation to becoming a country that can also build, deploy, power, host, and scale AI at home.

That shift matters because Canada is not starting from zero. The strategy describes a national AI ecosystem that is already economically meaningful, technically mature, and increasingly integrated across the AI value chain. Canada’s digital sector employs roughly 800,000 workers and contributes more than $140 billion to GDP, with about 150,000 jobs directly associated with AI. More than 3,500 Canadian firms are developing advanced AI models, tools, and applications, and together they have raised over CAD$37 billion in venture capital.

The most important question now is no longer whether Canada has AI potential. It is whether Canada can convert that potential into domestic capacity, domestic adoption, and domestic value capture.

The federal strategy gets one thing exactly right

For years, Canada’s AI story has been strongest at the top of the funnel: research talent, institutes, and early innovation. The country helped produce globally recognized AI leadership through CIFAR, Mila, Vector, and Amii, and the federal government now explicitly frames that foundation as something to commercialize and operationalize more aggressively.

The current strategy is built around that reality. It aims to:

• increase business AI adoption from roughly 12% to 60% by 2034

• support up to 250,000 new AI-related jobs by 2031

• expand sovereign compute, cloud, and infrastructure capacity inside Canada

• use public policy, procurement, and training to help Canadian firms scale rather than sell early or move abroad.

In other words, Ottawa is acknowledging a longstanding Canadian problem: we have been better at inventing AI than at industrializing it.

Canada’s AI Value Chain

One of the strongest parts of the federal strategy is that it looks at AI as a full-stack ecosystem, not just a software category. That matters. AI competitiveness depends on much more than researchers and applications. It depends on AI applications, models, tools and integrators, data centres, hardware infrastructure, energy and power and research and development. The remainder of this article will look at each of the layers of this AI ecosystem, analyze the federal strategy’s view of each layer, highlight key players and compare Canadian’s strength in each, to the rest of the world.

AI Applications, Embedded AI and Physical AI

The first layer represents the tangible front-end of AI—where foundational models are translated into industry-specific software, embedded applications, and physical robotics to solve practical enterprise problems. Canada boasts remarkable deep-tech innovators at this frontier: companies like Clio are revolutionizing vertical enterprise software in the legal tech space, Ada is scaling automated customer experience globally, and Sanctuary AI is pushing the boundaries of physical AI by developing human-like intelligence for general-purpose robots. Beyond these household names, a vibrant wave of specialized, Canadian-owned application firms is making a significant impact. Edmonton-based AltaML co-develops applied AI solutions with industrial partners to optimize operations in heavy sectors like agriculture and energy. Ottawa’s Solink has become a global leader in physical security by turning standard security footage into real-time operational data, while Montreal's DeepLite specializes in embedded AI, optimizing complex models to run efficiently on low-power edge hardware.

However, when compared to global giants like OpenAI, Palantir, or Tesla’s Optimus robotics division, Canada’s primary hurdle is not innovation, but domestic market adoption. While Canadian start-ups build world-class tools, traditional Canadian industries historically lag behind international peers in integrating them. For an enterprise to transition from a passive observer to an active adopter of Canadian AI software, three pillars must be established: quantifiable ROI through low-code integration, robust data governance frameworks that alleviate corporate legal anxieties over data privacy, and aggressive internal reskilling programs to bridge the gap between technical tools and non-technical business operators.

To remain globally competitive, the federal strategy must focus more heavily on driving a higher volume of local firms to adopt these technologies, ensuring our best applications find a fertile home market before seeking traction abroad. The public sector can accelerate this shift by acting as the "first customer," using federal procurement to validate homegrown startups. Furthermore, public policy must double down on risk-subsidy initiatives like the SCALE AI global innovation cluster and the newly launched AI Compute Access Fund, which covers up to two-thirds of eligible compute costs for domestic businesses. By driving down the massive overhead required to train and deploy tailored applications, public support can effectively bridge the commercialization chasm, turning Canadian research into an enduring economic engine at the application layer.

Models, Tools, and Integrators

The model layer serves as the intellectual engine room of the AI stack, housing the foundational Large Language Models (LLMs), developer frameworks, and enterprise software integrators that translate raw compute into cognitive capability. Canada holds a uniquely powerful position here, primarily anchored by Cohere, a premier independent foundation model company. This foundational strength is heavily augmented by established domestic giants like Coveo, a pioneer in AI-powered search, and OpenText, an international information management titan driving AI integration across vast corporate software networks. Together, these firms provide Canada with an enviable domestic ecosystem capable of delivering sophisticated enterprise intelligence.

However, viewing this layer through a global lens reveals that Canada is engaged in an asymmetric capital war with the United States. While Canada’s model layer is highly sophisticated, American tech behemoths like Microsoft, Google, Meta, and OpenAI operate on a scale that dwarfs domestic capital markets. US hyperscalers are spending tens of billions of dollars per quarter on model training and infrastructure, treating the foundation layer as a hyper-capitalized arms race.

Rather than attempting to match the raw, general-consumer capital of Silicon Valley, Canada has aggressively leaned into an alternative path: digital sovereignty, data privacy, and targeted enterprise utility. A definitive example of this strategy is Cohere’s recent, massive transatlantic merger with Germany’s Aleph Alpha. This landmark deal created a combined powerhouse valued at approximately $20 billion, establishing a critical geopolitical counterweight to the US-China AI duopoly. By anchoring its operations in both Canada and Germany, the combined entity provides G7 governments and highly regulated corporations with an essential alternative: secure, multilingual, and cloud-agnostic AI that guarantees absolute data sovereignty and freedom from foreign data legislation.

To solidify this defense of domestic IP, the Canadian government has stepped up to provide a crucial public-sector backstop. Under the federal Sovereign AI Compute Strategy, Ottawa finalized a massive $240 million investment in Cohere to anchor new domestic data centers and compute capacity. This has been paired with a major federal Memorandum of Understanding (MOU) to explore deploying Cohere's enterprise tools—like its custom agentic platform, North—directly within public service operations.

Ultimately, for Canada’s model layer to thrive against US dominance, public and private strategy must continue to move in lockstep. The path forward requires doubling down on secure, customizable "middle-power" alternatives that prioritize enterprise privacy over consumer novelty. By using government procurement to give homegrown champions a guaranteed runway, and by actively supporting open-source developer frameworks, Canada can ensure its local innovators can scale without being forced into financial or structural dependency on Big Tech's astronomical capital.

Data Centres

One of the most practical ways to assess AI readiness is to ask a blunt question: where will the compute actually live? Recent global counts place Canada at roughly 285 to 287 operational data centres, ranking about 6th globally by country. The leaders remain the United States by a huge margin, followed by countries such as Germany, the United Kingdom, China, and France.

That gives Canada an important but nuanced position:

• Canada is already a top-tier data-centre market globally by count.

• It is clearly relevant in global infrastructure terms, not peripheral.

• But it is still far behind the scale of the U.S., which has more than 4,000 data centres in major datasets and remains the dominant global compute hub.

This gap is not just about scale. It is about control, cost, latency, sovereignty, and commercial leverage.

Canada’s major hubs remain Toronto and Montreal, with Vancouver and Calgary also serving as important regional nodes. Montreal, in particular, benefits from hydroelectric power and a cooler climate that lowers cooling costs. That combination gives Canada a serious structural advantage for certain classes of AI infrastructure.

Canadian-owned vs foreign-owned colocation and data-centre players

If the policy goal is sovereign AI capacity, then ownership matters. A facility located in Canada is not automatically a Canadian-controlled asset.

Broadly, Canada’s colocation and data-centre landscape appears to break down into three categories (note: These listings are selective and intended for illustrative purposes rather than an exhaustive index):

Canadian-owned or Canadian-controlled players: eStruxture, Qu Data Centres, SPUR Data Centres, Core Data Centres, QScale, BUZZ HPC, UDCS, ThinkOn, Whipcord, Bell, Telus, Shaw, Nuday Networks, PureColo, and Coloware.

Foreign hyperscalers with Canadian regions: AWS, Microsoft Azure, Oracle, and Google Cloud.

Internationally owned or externally controlled infrastructure footprints: IREN, Equinix, Digital Realty, Cologix, Vantage Data Centers, STACK Infrastructure, Telehouse, and Compass Datacentres.

When comparing the space and power capacity owned within each category, the story becomes clear: foreign hyperscalers and international colocation providers own the vast majority of high-capacity data center footprints on Canadian soil; therefore, Canadian businesses and government bodies will need to evaluate how to balance leveraging Canadian-owned and operated capacity with capacity owned by foreign entities. There simply isn’t enough native, Canadian-owned mass capacity to support the modern, AI-driven economy that Canada is looking to establish.

Hardware Infrastructure

A critical evaluation of Canada’s national AI strategy reveals a fundamental paradox within its fourth layer: the desire for domestic technological sovereignty versus the harsh realities of global hardware manufacturing. The federal strategy frequently champions Canadian-owned IT infrastructure and hardware companies as the bedrock of our domestic AI ecosystem. However, a pragmatic look at the supply chain demonstrates that Canadian players cannot cross the finish line alone. To build and deliver GPU infrastructure at scale, Canadian-owned entities must structurally rely on, and partner with, global Tier 1 Original Equipment Manufacturers (OEMs).

The path to staying ahead in the global AI race does not lie in a protectionist pursuit of total isolation, but rather in a highly strategic symbiosis between nimble Canadian innovators and the industrial titans of global tech execution.

The strategy frequently puts forward three distinct Canadian entities, each representing a different facet of the hardware and deployment chain, yet each hitting the same global bottleneck.

Hypertec

Montreal-based Hypertec (specifically through its Ciara Technologies division) is a master of custom server architecture, modular data center construction, and cutting-edge liquid cooling. They excel at building the specialized "car" required to house highly demanding AI workloads. However, a car needs it’s engine. Hypertec’s value proposition shifted dramatically when they signed a milestone agreement this year to become an official NVIDIA OEM Partner for the NVIDIA RTX 6000 Pro. This move explicitly proves the point that to gain the credibility, design files, and engineering blueprints required to scale, premier Canadian players rely on direct, authorized relationships with the world-class suppliers.

Denvr Dataworks

Calgary's Denvr Dataworks provides high-density specialized AI cloud environments, utilizing innovative immersion-cooling technologies to deliver highly efficient compute. A glance at Denvr’s product architecture reveals an immediate reliance on foreign hardware as well, as all of their clusters are powered entirely by NVIDIA H100, H200, and GB200 architectures leveraging tier 1 OEM compute nodes. Denvr cannot scale its data center footprint or support massive foundation model training for Canadian enterprises without a steady, uninterrupted pipeline of hardware from global OEMs. Their sovereignty is operational, but their supply chain is entirely international.

Celestica

Toronto-headquartered Celestica has experienced an unprecedented economic boom, acting as a foundational engineering and manufacturing partner for global hyperscalers. They design elite storage controllers and 1.6T network fabrics essential for connecting thousands of GPUs. Yet, Celestica operates primarily as an Electronics Manufacturing Services (EMS) provider and co-designer. They excel at building high-availability architecture to prevent GPU data bottlenecks, but they do not own the core IP of the accelerators themselves. Celestica’s massive growth is a direct reflection of its integration into the global AI arms race, scaling with and for global hyperscalers rather than standing as an independent, sovereign hardware silo.

To achieve the capacity required for national initiatives—such as the federal government's goal of establishing a world-leading public supercomputer—local businesses must aggressively leverage the massive scale of global Tier 1 OEMs. Titans like Dell Technologies, HPE, and Lenovo dominate the Canadian enterprise market for a reason: they possess a history of large-scale execution, robust supply chains, and established capital that Canadian boutique firms simply cannot replicate.

Rather than viewing these global giants as existential threats to "Sovereign AI," Canadian-owned entities can weaponize global scale to their own advantage:

• Supply Chain Resilience: During severe GPU shortages, global Tier 1 OEMs hold massive purchasing power and priority allocations. Local integrators who form tight alliances with these giants can secure component availability that independent local firms could never access.

• The "Last Mile" Advantage: Global OEMs possess unmatched manufacturing velocity, but they often lack the localized agility to deploy complex, custom liquid-cooling grids or navigate provincial utility constraints. Canadian players can position themselves as the elite "last-mile" integrators—taking global reference architectures and customizing them for Canada's green, hydro-powered data centers.

• Co-Engineering and Access: As seen with Hypertec’s NVIDIA OEM status, partnering with top-tier global players grants local engineers pre-release product access and direct collaboration with Silicon Valley and Taiwanese engineering teams. This allows Canadian tech to be built in lockstep with future hardware roadmaps.

The ultimate takeaway for Canada's AI strategy is that isolationist sovereignty is an illusion; strategic interdependence is the reality. Canada commands world-class intellectual property in research and enviable structural advantages in green energy, cooling, and network fabric design. However, we remain inherently dependent on foreign fabrication and Tier 1 OEM hardware to achieve physical scale.

If Canada is to stay ahead in the global AI race, the national strategy must pivot from trying to build a self-contained domestic stack to fostering deep, formalized partnerships between Canadian-owned entities and global OEMs. By blending the monumental execution capacity of global titans with the sustainable infrastructure and agility of domestic players, Canada can secure the massive compute power it needs to fuel its economy without waiting decades to build a hardware supply chain from scratch.

Energy and Power

Compared with the United States and much of the world, Canada’s energy position is unusually strong for AI infrastructure. The nation benefits from a rare convergence of substantial hydroelectric capacity and a relatively cleaner grid mix in key provinces, making it a natural haven for sustainability-minded tech operators. This power advantage is further amplified by Canada's distinct geography; cold-weather advantages allow for extensive ambient cooling in several regions, directly cutting down on the massive energy overhead typically required to keep server racks from overheating. Looking further out, Canada possesses the long-term potential to pair new compute growth with cleaner electricity expansion, rather than relying on fossil-fuel stopgaps. This combination represents a genuine competitive edge, especially as AI workloads drive surging global demand for large-scale, power-intensive infrastructure.

However, a competitive edge is not a guarantee of dominance. The Canadian federal strategy is right to stress that the electricity system is also constrained today and will require major expansion over time—and it is precisely here where a comparison with the United States becomes sobering. The U.S. possesses advantages that Canada cannot easily duplicate: dramatically larger existing hyperscale capacity, significantly deeper pools of private infrastructure capital, and a faster clustering of frontier model labs and GPU demand. Furthermore, U.S.-based facilities enjoy more immediate access to a massive concentration of corporate buyers.

To compete effectively, Canada must lean hard into its own distinct strengths: a cleaner power potential, a top-tier research base, a credible (though smaller) infrastructure footprint, and a strong political salience around data sovereignty and residency. While the U.S. wins on pure velocity and volume, Canada offers a highly secure, structurally clean alternative.

Looking beyond North America, the rest of the world offers a mixed bag of comparisons. The Nordics and certain European markets compete strongly on clean energy and cold cooling conditions, matching Canada’s environmental pitch. Meanwhile, France and Germany command a larger or comparable infrastructure concentration within Europe, acting as deep-pocketed regional heavyweights. At the apex of absolute volume sit China and the U.S., both operating at vast, centralized industrial scales that Canada simply cannot match in the near term.

For Canada, the conclusion is clear: it should not try to win a losing war on absolute scale. Instead, the path forward lies in specialization. Canada must position itself to win on trusted, efficient, sovereign, and clean-enough capacity—tailor-made for domestic needs and high-value allied use cases.

Research and Development

While Canada boasts an enviable energy profile, its signature strength remains its intellectual capital—yet a critical gap persists. Historically, Canada’s AI brand has been built first and foremost on research excellence. This is not a hollow cliché; it is a profound, foundational asset. The collective powerhouse of CIFAR, Mila, the Vector Institute, and Amii continues to command global credibility in foundational AI, talent formation, and pioneering safety-related work. Furthermore, Canada's national strategy rightly emphasizes AI literacy, workforce development, and applied training, solidifying the role of these national institutes in broader skills formation. This dense academic concentration matters for two reasons: first, frontier capability still begins with raw research density; second, this research leadership gives Canada the legitimate authority needed to shape the global conversation around AI safety, governance, and public-interest deployment.

But research alone is no longer a sufficient metric for victory. Canada’s long-term economic and technological advantage will not come from simply publishing papers; it will come from structurally connecting this research density to domestic compute, widespread sector adoption, practical industrial deployment, federal procurement, robust IP capture, and the scaling of exportable firms. The next chapter of Canadian AI must be less about academic incubation and more about turning temporary research strength into permanent institutional staying power.

We are finally seeing the physical blueprints of this transition come online. The federal government's newly launched Sovereign Compute Infrastructure Program (SCIP) is actively deploying a multi-billion-dollar framework to build a dedicated public AI supercomputer. This public backbone is designed to give domestic researchers and innovative startups direct access to secure, sovereign compute right at home, eliminating their dependency on foreign cloud providers.

This push for domestic sovereignty is materializing in powerful, localized industrial hubs where research and raw infrastructure converge. A prime example is the expanding collaboration between Mila, Hypertec, and QScale in Quebec.

Hypertec has positioned itself as Canada's first domestic NVIDIA OEM partner, manufacturing advanced liquid-cooled GPU infrastructure locally to ensure a resilient, secure hardware supply chain. Meanwhile, infrastructure providers like QScale are breaking ground on massive, high-density campuses—such as their Q01 facility—which pairs 100% clean hydroelectric power and sub-arctic "free cooling" with the extreme liquid-cooling requirements of next-generation chips.

By tying Mila’s world-class academic ecosystem directly to Hypertec’s domestic manufacturing and QScale’s ultra-dense, green data architecture, Canada is moving past the abstract. It is constructing a concrete, vertically integrated sandbox where sovereign compute and top-tier research live on the same grid. This type of groundbreaking collaboration is exactly what we need to maintain our strength in R&D.

A practical bridge plan for Canada

Ultimately, analyzing Canada’s AI value chain layer by layer reveals a nation at a critical crossroads. At the front-end application and model layers, Canadian innovation is world-class, yet it risks starving without rapid domestic market adoption and a distinct "middle-power" strategy to survive America's asymmetric capital war. Deep in the physical layers of data centers and hardware infrastructure, the illusion of isolationist sovereignty crumbles; Canada boasts vital structural nodes, but remains inherently reliant on international capital, foreign-owned colocation footprints, and strategic interdependencies with global Tier 1 OEMs to achieve meaningful scale. Finally, at the foundational layers of clean energy and R&D, Canada holds its truest competitive advantages—leveraging premium hydro grids, sub-arctic cooling, and globally revered research institutes like Mila, Vector, and Amii to build a secure, vertically integrated sandbox for deep compute.

So what else needs to be considered in a "bridge plan" that connects our present-day innovation strengths to tomorrow's industrial capacity?

1. Remove Development Barriers

AI infrastructure projects in Canada will rapidly stall if permitting, utility interconnection, land-use, and environmental review processes remain slow, fragmented, or unpredictable. To prevent multi-year backlogs, Canada needs a fast-tracked, unified national and provincial pathway for strategic AI infrastructure projects—especially where they align with regional grid planning and local economic development goals.

2. Build Citizen Buy-In Early

Data centers and AI infrastructure are not politically neutral utilities. As clusters expand, local communities will naturally ask hard questions about intensive energy consumption, land allocation, water usage, noise pollution, and who truly benefits from their presence. Governments and operators must establish a transparent public compact built around local job creation, grid transparency, explicit sustainability commitments, tangible community benefits, and a clear narrative on how these facilities reinforce national resilience. If the public hears only that "AI is draining the power grid," resistance will grow. If they hear that this infrastructure supports Canadian capacity, modernizes public services, drives local investment, and secures data sovereignty, the conversation fundamentally changes.

3. Prioritize Sovereign Compute for Startups and SMEs

If Canadian startups and small-and-medium enterprises (SMEs) are forced to default to foreign cloud providers due to prohibitive local costs, the domestic sovereignty agenda will remain mostly rhetorical. The most urgent bridge is providing affordable, localized access to high-performance compute. This is where programs like the newly launched AI Sovereign Compute Infrastructure Program (SCIP) and the expanded Compute Access Fund are vital—serving as a bridge to ensure homegrown scale-ups and researchers can train and run their models within a secure, Canadian-governed digital ecosystem.

4. Use Procurement as Industrial Policy

Canada must stop viewing public procurement as a mere bureaucratic function and start treating it as a potent tool for industrial policy. By aggressively buying from domestic AI vendors, public institutions can validate Canadian AI firms, create invaluable reference customers, and materially improve public services while reducing commercialization friction. The AI for All strategy explicitly points in this direction, but long-term success will rely entirely on disciplined execution rather than aspirational mandates.

5. Tie AI Growth to Power Planning

AI policy and electricity policy can no longer be developed in parallel silos. Canada needs institutionalized, joint planning tables that bring together utilities, provincial regulators, telecom operators, data center developers, cloud providers, and industrial policy teams. The winning jurisdictions will not be those that simply declare they welcome AI investment; they will be the ones that can present a synchronized roadmap showing exactly how power allocation, land zoning, permitting speed, fiber connectivity, and community alignment line up.

6. Keep Research Anchored, But Commercialize Harder

Canada must protect its historic advantage by continuously funding frontier research and safety work through its world-class institutes. However, this academic funding must be paired with clearer, high-velocity pathways into commercial spinouts, rapid pilot deployments, deep sector partnerships, and the scaling of export-oriented firms. Intellectual leadership must pave the way for industrial execution.

7. Focus on Sectors Where Canada Can Differentiate

Canada is unlikely to match the United States or China on raw, venture-backed scale across every single AI vertical. Instead, the country must ruthlessly focus on segments where its existing industrial base and policy context give it a natural edge. This means dominating high-value, regulated, and complex domains:

• Energy and Natural Resources: Optimizing grid efficiencies and resource extraction.

• Health and Life Sciences: Deploying targeted clinical applications and diagnostics.

• Government & Public-Interest Services: Building trusted, transparent, and bilingual citizen-facing systems.

• Industrial and Physical AI: Merging advanced software with advanced manufacturing and robotics.

• Regulated Enterprise AI: Providing high-compliance solutions for finance, insurance, and legal sectors.

Conclusion

Canada does not need to mimic the United States to succeed in the machine learning age. In fact, trying to compete on sheer volume is a losing proposition. The real opportunity lies in becoming something far more distinct: a nation that seamlessly combines world-class research, trusted public institutions, a clean energy potential, serious domestic infrastructure, and practical commercialization support into a coherent national advantage.

This is the true promise behind the AI for All doctrine. It is not merely about hosting an increased volume of AI activity within Canadian borders; it is about establishing true Canadian agency over how AI is built, deployed, secured, and governed. The challenge has been correctly diagnosed, and the pieces—the research reputation, the vibrant startup base, and a growing infrastructure footprint—are on the board. The next phase of Canada’s AI story will not be decided by the elegance of its policy papers, but by whether it can successfully execute the transition from an academic sandbox to an industrialized, sovereign powerhouse.

Until next time …