Challenges and Opportunities: The Development Path of Localized Artificial Intelligence in Canada Policy White Paper

Key Chapter Title List

1. Establishing New Economic and Security Relations with the United States and Strengthening Cooperation with Global Reliable Trade Partners and Allies
2. Building a Unified Canadian Economy by Eliminating Interprovincial Trade Barriers and Identifying and Accelerating National Construction Projects
3. Reducing the Cost of Living for Canadians and Helping Them Succeed
4. Improving Housing Affordability by Unleashing the Power of Public-Private Partnerships, Catalyzing a Modern Housing Industry, and Creating New Careers in the Skilled Trades
5. Protecting Canadian Sovereignty and National Security by Strengthening the Canadian Armed Forces, Securing Borders, and Enhancing Law Enforcement
6. Attracting Global Top Talent to Boost Economic Development, While Returning Overall Immigration Rates to Sustainable Levels
7. Reducing Government Operating Expenditures So Canadians Can Invest More in the People and Businesses That Will Build the Strongest Economy in the G7

Document Introduction

This policy white paper is co-authored by several researchers from top Canadian universities under the AI+ Society Initiative at the University of Ottawa. It aims to address the complex challenges arising from the widespread penetration of artificial intelligence technology into all areas of society. Using the mandate letter issued by Canadian Prime Minister Mark Carney in May 2025 as a policy analysis framework, the report delves into the multidimensional interaction between AI development and the country's seven strategic priorities. It seeks to provide a strategic blueprint for shaping a responsible, Canadian-made AI development path distinct from the Silicon Valley model.

The report's structure systematically analyzes the seven priority areas outlined in the mandate letter. Regarding trade and security relations, the report points out that Canada currently primarily plays the role of a supplier of raw materials (such as critical minerals, land, electricity) and a host for data centers within the AI value chain, facing the risk of becoming an appendage to a digital extractive economy. To counter this, the report recommends planning data center growth corridors that balance clean energy and social impact, and explores the feasibility of establishing public cloud infrastructure through Crown corporations to enhance digital sovereignty. In terms of domestic economic integration and climate action, the report analyzes the opportunities of building a national low-carbon grid to support the energy demands of the AI industry. It also warns of the need to exercise caution when using the fast-track powers under Bill C-5 (the Unified Canadian Economy Act) to avoid undermining commitments to Indigenous reconciliation and social trust.

The potential impact of AI on the labor market is one of the report's core concerns. The research indicates that AI may hollow out middle-income jobs, exacerbate wage stagnation and unemployment risks, and reshape labor pricing through new mechanisms like algorithmic wage discrimination. The report emphasizes that traditional strategies of skills upgrading and micro-credentials are insufficient. It calls for increased investment in higher education (particularly liberal arts programs emphasizing metacognition and critical thinking) and skilled trades apprenticeships. It also advocates for holistic transition support programs for workers displaced by automation, incorporating geographical, gender, and social factors. In the intersecting areas of housing and climate, the report evaluates the role of smart devices in energy conservation and emissions reduction, alongside their risks concerning privacy, affordability, and repairability. It urges the establishment of clear rules to prevent algorithms from causing discrimination and price inflation in the rental market.

The intersectional analysis of national security and AI reveals serious challenges. The report notes that the Canadian defense sector plans to achieve AI enablement by 2030, but over-reliance on international (especially U.S.) cloud and algorithmic services will compromise data sovereignty and security. The U.S. CLOUD Act means data stored in Canada may still be accessible to the U.S. government. Furthermore, algorithmic bias in military AI applications could lead to fatal consequences, as evidenced by the increased civilian casualties resulting from Israel's use of AI systems for target identification in Gaza, serving as a warning. The report suggests Canada focus its AI R&D on areas of comparative advantage and ethical legitimacy, such as emergency response and peacekeeping technology, to position itself as a leader in setting humanitarian AI standards.

Regarding immigration and talent strategy, the report argues that the traditional classification of high-skilled and low-skilled immigrants is becoming obsolete in the face of AI-driven labor market shifts. Canada can leverage uncertainty in U.S. immigration policy to attract global talent but needs to reform its immigration system to recognize skills more broadly and include immigrants and low-skilled workers in transition retraining programs. Finally, on government efficiency, the report cites lessons from the U.S. Department of Efficiency's large-scale automation, which led to skilled worker layoffs and project chaos. It warns that AI applications in the public sector must be narrowly scoped, transparent, and subject to external oversight, emphasizing that the government's long-term capacity relies on expert staff, not expert systems.

The core argument of this report is that Canada should not engage in a winner-takes-all race to develop the largest, most general AI models regardless of social and ecological costs. Instead, it should carve out a differentiated development path guided by Canadian values (pluralism, deliberative democracy, human rights) within a pluralistic global trade system. This can be achieved by fostering smaller-scale, customized AI models that prioritize ethical integrity, environmental responsibility, and contextual responsiveness. This requires policymakers to carefully weigh the tensions between sovereignty, reconciliation, environmental action, and affordability in every decision to deploy AI, ensuring technological development serves the public interest and long-term national interest.