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

Key Chapter Title List

1. Establishing a new economic and security relationship with the United States and strengthening cooperation with reliable global trade partners and allies
2. Building a unified Canadian economy by eliminating interprovincial trade barriers and identifying and accelerating national construction projects
3. Lowering the cost of living for Canadians and helping them succeed
4. Improving housing affordability by unleashing 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 top global talent to aid economic construction 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 G7 economy

Document Introduction

This policy white paper was co-authored by multiple researchers from leading 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 Mark Carney in May 2025 as a policy analysis framework, the report delves into the multidimensional interactions between AI development and the nation's seven strategic priorities. It seeks to provide a strategic blueprint for shaping a responsible, "Made-in-Canada" AI development path distinct from the Silicon Valley model.

The report's structure systematically analyzes the seven priority areas outlined in the mandate letter. On trade and security relations, the report notes that Canada currently plays a role primarily as 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." Consequently, it recommends planning data center growth corridors that balance clean energy and social impact, and explores the feasibility of establishing public cloud infrastructure via Crown corporations to enhance digital sovereignty. Regarding domestic economic integration and climate action, the report analyzes the opportunity of building a national low-carbon grid to support the energy demands of the AI industry, while cautioning against the hasty use of fast-track powers under Bill C-5 (Uniting the 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 could "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, as well as holistic transition support programs for workers displaced by automation, incorporating geographic, gender, and social factors. At the intersection 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 severe challenges. The report points out that Canada's defense sector plans to achieve AI enablement by 2030, but excessive reliance on international (especially U.S.) cloud and algorithm services will compromise data sovereignty and security. The U.S. CLOUD Act means data stored in Canada can still be accessed by the U.S. government. Furthermore, algorithmic bias in military AI applications can have lethal consequences, as evidenced by the warning from Israel's use of AI systems for target identification in Gaza leading to increased civilian casualties. The report suggests Canada focus its AI R&D on areas of comparative advantage and ethical legitimacy, such as emergency response and peacekeeping technologies, to position itself as a leader in humanitarian AI standards.

Regarding immigration and talent strategy, the report argues that the traditional "high-skilled" vs. "low-skilled" immigrant classification is becoming obsolete in the AI-driven labor market transformation. 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 both immigrants and "low-skilled" workers in transition retraining programs. Finally, on government efficiency, citing lessons from the U.S. "Department of Efficiency" where large-scale automation led to skilled worker layoffs and project chaos, the report warns that AI applications in the public sector must be narrow in scope, transparent, and subject to external oversight. The long-term capacity of government relies on expert staff, not expert systems.

The report's core argument is that Canada should not engage in a "winner-takes-all" race to develop the largest general-purpose AI models, disregarding 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 involves cultivating smaller, 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 AI deployment decision, ensuring technological development serves the public interest and long-term national interest.