Artificial intelligence is moving from the margins of innovation policy to the core of economic and geopolitical strategy. As major economies accelerate investment in AI infrastructure, talent, and industrial capacity, the competitive landscape is shifting rapidly. While Canada has built strong foundations in AI research, the next phase of competition will be determined less by discovery and more by the ability to scale, deploy, and govern AI at speed.
This shift was captured succinctly by Mark Schaan, Deputy Minister of Artificial Infrastructure and Digital Innovation at Innovation, Science and Economic Development Canada (ISED), who described AI as “a full factor of production” during the 2024 AI Symposium hosted by the Lawrence National Centre for Policy and Management at Ivey Business School. His remark set the tone for a wide-ranging discussion on Canada’s position in an increasingly competitive and strategically charged global AI environment.
Across two interconnected discussions, the symposium examined how Canada can align its infrastructure, talent, and policy systems to remain competitive. Salim Teja of Radical Ventures and Dev Saxena of Eurasia Group and OpenAI explored the United States’ newly launched AI Action Plan and its global implications, while Schaan later joined Kara Beckles, Chief Data Officer at the Treasury Board Secretariat, for a fireside conversation on the federal government’s role in strengthening AI infrastructure and adoption. The discussion was moderated by Mahmood Nanji, LNC’s Power Corporation of Canada Policy Fellow.
AI and the New Era of Techno-Geopolitics
Global AI policy is increasingly shaped by national security considerations and fierce global competition. Teja outlined the U.S. AI Action Plan — spanning accelerated innovation, expanded infrastructure, and a new AI export stack — as part of a broader restructuring of global technology markets. Saxena underscored the scale of this shift: “Three pillars globally have emerged: a U.S. tech stack, a China tech stack, and an emerging European intention to build its own.”
This evolving architecture has immediate implications for Canada. The U.S. AI export stack may tie access to advanced chips (e.g., NVIDIA chips) to corresponding American software, models, or applications. Teja warned that such bundling could sideline Canadian AI firms not because they lack capability, but because the geopolitical environment increasingly privileges integrated national ecosystems over open competition.
At the same time, rapid global adoption raises questions about the durability of responsible AI frameworks (e.g., ethics, privacy, safety) and whether already strained energy grids can support surging compute demand. As Saxena summarized, the challenge for Canada is “how to create value inside these increasingly closed systems” while protecting domestic capacity to innovate and govern responsibly.
From Research Excellence to Scaled Deployment
The fireside conversation shifted from the discussion from geopolitics to domestic readiness. Nanji noted that public debates about AI and government often focus heavily on rules and safeguards, even though the current federal mandate places equal weight on building adoption capacity and strengthening the wider ecosystem. The creation of a new Minister of Artificial Infrastructure and Digital Innovation—and a platform commitment to “deploy AI at scale”—signals this broader shift.
Schaan traced this evolution through the Pan-Canadian AI Strategy. The first phase, launched in 2017, strengthened domestic research institutions such as CIFAR, Mila, Vector, and Amii. The second phase aims to industrialize these strengths by proving out use cases across sectors ranging from proteins to oceans and life sciences.
Yet as Beckles emphasized, infrastructure alone is not enough. The public sector faces long-standing challenges around data quality, interoperability, and legacy systems that impede cross-departmental collaboration. While federal departments have applied AI for decades — from weather forecasting to satellite-based crop modelling — scaling these capabilities requires upgrading the systems that safeguard Canadians’ rights, benefits, and daily interactions with the state. “The technology is the easy part,” Beckles noted. “The foundations are what determine success.”
Literacy as Canada’s Structural Bottleneck
Despite recent investments, Schaan argued that the most pressing barrier is not compute or cost, but literacy. Many firms remain hesitant to experiment because they lack clarity on how to evaluate solutions, scope pilots, or identify viable use cases. Misaligned pilots and vendor overpromising have reinforced this reluctance.
This challenge is especially acute in foundational sectors like forestry, oceans, agriculture, natural resources, where firms often don’t know what AI adoption should look like. “What does AI for forestry look like? What does AI for the ocean sector look like?” Schaan asked. It is at this intersection that literacy becomes inseparable from sovereignty.
Earlier, Teja emphasized that Canada’s ability to participate meaningfully in global AI markets depends on developing both the technical infrastructure and the organizational capacity required to adopt AI confidently. Sovereign compute capacity, robust data governance, and responsible model ecosystems are essential, but they cannot succeed without the literacy required to make use of them.
In his view, Canada must cultivate the conditions for firms to experiment, understand viable use cases, and engage with AI from a position of strength rather than dependency. At the same time, the country must help its most promising AI companies scale into global markets, ensuring that domestic champions, such as Cohere are able to innovate and compete internationally. Teja expressed cautious optimism that the pieces are beginning to align, observing that “momentum and urgency are building… [in Canada] there is a framework emerging.”
Across the discussion, one message stood out: AI is reshaping the foundations of economic advantage and state capacity. It is not only a general-purpose technology but also a strategic layer that now underpins national competitiveness and the modernization of public institutions.
Canada has begun assembling the essential components of AI readiness: sovereign compute, strong data governance, and responsible model ecosystems. However, a sizeable gap remains between technical capability and scaled adoption, which will require: (a) sustained investment in human capital and institutional capacity building, (b) bold and inspiring leadership from the partners; and (c) strong and integrated policy coordination across governments anchored on a clearly defined industrial strategy that enables firms to adopt and scale AI responsibly.