Canada has a choice to make about its artificial intelligence future. The Carney administration is investing $2-billion over five years in its Sovereign AI Compute Strategy. Will any value generated by “sovereign AI” be captured in Canada, making a difference in the lives of Canadians, or is this just a passthrough to investment in American Big Tech?
Forcing the question is OpenAI, the company behind ChatGPT, which has been pushing an “OpenAI for Countries” initiative. It is not the only one eyeing its share of the $2-billion, but it appears to be the most aggressive. OpenAI’s top lobbyist in the region has met with Ottawa officials, including Artificial Intelligence Minister Evan Solomon.
All the while, OpenAI was less than open. The company had flagged the Tumbler Ridge, B.C., shooter’s ChatGPT interactions, which included gun-violence chats. Employees wanted to alert law enforcement but were rebuffed. Maybe there is a discussion to be had about users’ privacy. But even after the shooting, the OpenAI representative who met with the B.C. government said nothing.
When tech billionaires and corporations steer AI development, the resultant AI reflects their interests rather than those of the general public or ordinary consumers. Only after the meeting with the B.C. government did OpenAI alert law enforcement. Had it not been for the Wall Street Journal’s reporting, the public would not have known about this at all.
Moreover, OpenAI for Countries is explicitly described by the company as an initiative “in co-ordination with the U.S. government.” And it’s not just OpenAI: all the AI giants are for-profit American companies, operating in their private interests, and subject to United States law and increasingly bowing to U.S. President Donald Trump. Moving data centres into Canada under a proposal like OpenAI’s doesn’t change that. The current geopolitical reality means Canada should not be dependent on U.S. tech firms for essential services such as cloud computing and AI.
While there are Canadian AI companies, they remain for-profit enterprises, their interests not necessarily aligned with our collective good. The only real alternative is to be bold and invest in a wholly Canadian public AI: an AI model built and funded by Canada for Canadians, as public infrastructure. This would give Canadians access to the myriad of benefits from AI without having to depend on the U.S. or other countries. It would mean Canadian universities and public agencies building and operating AI models optimized not for global scale and corporate profit, but for practical use by Canadians.
Imagine AI embedded into health care, triaging radiology scans, flagging early cancer risks and assisting doctors with paperwork. Imagine an AI tutor trained on provincial curriculums, giving personalized coaching. Imagine systems that analyze job vacancies and sectoral and wage trends, then automatically match job seekers to government programs. Imagine using AI to optimize transit schedules, energy grids and zoning analysis. Imagine court processes, corporate decisions and customer service all sped up by AI.
We are already on our way to having AI become an inextricable part of society. To ensure stability and prosperity for this country, Canadian users and developers must be able to turn to AI models built, controlled, and operated publicly in Canada instead of building on corporate platforms, American or otherwise.
Switzerland has shown this to be possible. With funding from the federal government, a consortium of academic institutions—ETH Zurich, EPFL, and the Swiss National Supercomputing Centre—released the world’s most powerful and fully realized public AI model, Apertus, last September. Apertus leveraged renewable hydropower and existing Swiss scientific computing infrastructure. It also used no illegally pirated copyrighted material or poorly paid labour extracted from the Global South during training. The model’s performance stands at roughly a year or two behind the major corporate offerings, but that is more than adequate for the vast majority of applications. And it’s free for anyone to use and build on.
The significance of Apertus is more than technical. It demonstrates an alternative ownership structure for AI technology, one that allocates both decision-making authority and value to national public institutions rather than foreign corporations. This vision represents precisely the paradigm shift Canada should embrace: AI as public infrastructure, like systems for transportation, water, or electricity, rather than private commodity.
Apertus also demonstrates a far more sustainable economic framework for AI. Switzerland spent a tiny fraction of the billions of dollars that corporate AI labs invest annually, demonstrating that the frequent training runs with astronomical price tags pursued by tech companies are not actually necessary for practical AI development. They focused on making something broadly useful rather than bleeding edge—trying dubiously to create “superintelligence,” as with Silicon Valley—so they created a smaller model at much lower cost. Apertus’s training was at a scale (70 billion parameters) perhaps two orders of magnitude lower than the largest Big Tech offerings.
An ecosystem is now being developed on top of Apertus, using the model as a public good to power chatbots for free consumer use and to provide a development platform for companies prioritizing responsible AI use, and rigorous compliance with laws like the EU AI Act. Instead of routing queries from those users to Big Tech infrastructure, Apertus is deployed to data centres across national AI and computing initiatives of Switzerland, Australia, Germany, and Singapore and other partners.
The case for public AI rests on both democratic principles and practical benefits. Public AI systems can incorporate mechanisms for genuine public input and democratic oversight on critical ethical questions: how to handle copyrighted works in training data, how to mitigate bias, how to distribute access when demand outstrips capacity, and how to license use for sensitive applications like policing or medicine. Or how to handle a situation such as that of the Tumbler Ridge shooter. These decisions will profoundly shape society as AI becomes more pervasive, yet corporate AI makes them in secret.
By contrast, public AI developed by transparent, accountable agencies would allow democratic processes and political oversight to govern how these powerful systems function.
Canada already has many of the building blocks for public AI. The country has world-class AI research institutions, including the Vector Institute, Mila, and CIFAR, which pioneered much of the deep learning revolution. Canada’s $2-billion Sovereign AI Compute Strategy provides substantial funding.
What’s needed now is a reorientation away from viewing this as an opportunity to attract private capital, and toward a fully open public AI model.
This essay was written with Nathan E. Sanders, and originally appeared in The Globe and Mail.
K.S • March 11, 2026 7:19 AM
Nationalized means run by the government. In turn, this means expensive, inefficient, and full of political patronage jobs (just look at CBC). At this stage of rapid development, nationalized AI won’t work.
Facts Only
Canada’s Carney administration is investing $2 billion over five years in the Sovereign AI Compute Strategy.
OpenAI, the company behind ChatGPT, is lobbying Ottawa for its "OpenAI for Countries" initiative, described as coordinated with the U.S. government.
OpenAI’s top lobbyist met with Canadian officials, including Artificial Intelligence Minister Evan Solomon.
OpenAI employees flagged a B.C. shooter’s ChatGPT interactions involving gun violence but were initially rebuffed from alerting law enforcement.
OpenAI only notified law enforcement after meeting with the B.C. government, with details later revealed by the Wall Street Journal.
Switzerland developed Apertus, a public AI model, using federal funding and academic institutions (ETH Zurich, EPFL, Swiss National Supercomputing Centre).
Apertus was trained without pirated copyrighted material or exploited labor, using renewable hydropower.
Apertus is free to use and has a performance lag of about one to two years behind corporate AI models.
Canada has world-class AI research institutions, including the Vector Institute, Mila, and CIFAR.
The $2-billion Sovereign AI Compute Strategy is currently framed as an opportunity to attract private capital.
A commenter argued that nationalized AI would be expensive, inefficient, and prone to political patronage, citing the CBC as an example.
The article was co-written with Nathan E. Sanders and originally appeared in The Globe and Mail.
Executive Summary
Canada is at a crossroads in shaping its artificial intelligence future, with the Carney administration investing $2 billion over five years in its Sovereign AI Compute Strategy. The debate centers on whether this investment will benefit Canadians directly or primarily serve foreign tech giants like OpenAI, which has aggressively lobbied Ottawa for partnerships. OpenAI’s "OpenAI for Countries" initiative, described as coordinated with the U.S. government, raises concerns about data sovereignty and alignment with Canadian interests. Recent controversies, such as OpenAI’s delayed reporting of a shooter’s ChatGPT interactions to law enforcement, underscore risks of relying on private, foreign-controlled AI systems.
Proponents argue for a nationalized, public AI model—built and operated by Canadian institutions—to ensure alignment with public needs, democratic oversight, and economic sustainability. Switzerland’s Apertus model, developed by academic institutions with public funding, serves as a precedent, demonstrating that practical, ethical AI can be achieved without corporate-scale resources. Critics, however, warn that government-run AI could become inefficient and politicized, citing examples like the CBC. The discussion highlights tensions between public good, corporate profit, and geopolitical dependence, with Canada’s existing AI research infrastructure and funding presenting an opportunity to chart an independent path.
Full Take
The strongest version of this narrative is that Canada faces a critical choice: either cede its AI future to foreign corporate interests or assert sovereignty by building public AI infrastructure. The argument gains traction by highlighting OpenAI’s opaque practices—such as delaying law enforcement alerts—and its explicit ties to U.S. government coordination, framing corporate AI as inherently misaligned with Canadian values. Switzerland’s Apertus model is presented as proof that public AI can be ethical, cost-effective, and competitive, avoiding the pitfalls of profit-driven development. The call for democratic oversight and public input on AI ethics is compelling, especially as corporate models make these decisions behind closed doors.
However, the narrative employs a subtle **ARC-0024 Ambiguity** pattern by conflating "sovereign AI" with "public AI," implying that only a government-run model can achieve sovereignty, while dismissing the possibility of regulated private or hybrid models. The critique of OpenAI’s behavior is valid, but the leap to nationalization as the sole solution risks **ARC-0043 Motte-and-Bailey**, where the motte ("Canada needs AI sovereignty") is defensible, but the bailey ("only nationalized AI can achieve this") is overreach. The counterargument—that nationalized systems risk inefficiency—is acknowledged but framed as a strawman (e.g., comparing AI to the CBC without addressing potential safeguards).
Root cause: The paradigm assumes that corporate AI is inherently extractive and that public ownership is the only antidote. This echoes historical debates over nationalizing utilities or healthcare, where the tension between market efficiency and public good is unresolved. The unstated assumption is that private AI cannot be adequately regulated or incentivized to serve Canadian interests—a claim that merits deeper scrutiny.
Implications: If Canada pursues public AI, it could set a global precedent for democratic control over emerging technologies, but risks bureaucratic inertia and underinvestment. Conversely, reliance on foreign corporations may erode data sovereignty and amplify U.S. influence. Second-order effects include potential brain drain if private AI firms dominate, or conversely, a flourishing of homegrown talent if public infrastructure fosters innovation.
Bridge questions: What hybrid models (e.g., public-private partnerships with strict oversight) could balance sovereignty and efficiency? How might Canada’s existing AI research institutions adapt to a nationalized framework without stifling innovation? What evidence would convince you that private AI could be aligned with public interests through regulation alone?
Counterstrike scan: A coordinated influence campaign pushing this narrative would amplify fears of U.S. tech dominance, frame nationalization as the only patriotic option, and dismiss alternatives as naive or corrupt. The actual content aligns partially—it critiques foreign influence and advocates for public AI—but stops short of demonizing all private solutions, instead focusing on structural alternatives. No clear manipulation pattern is detected beyond standard advocacy framing.
Sentinel — Human
The article shows strong signs of human authorship, including a distinct voice, argumentative depth, and specific, verifiable references. No significant indicators of AI generation or synthetic coordination were detected.