---
title: "Leveraging AI Infrastructure as Canada's Top Capital Priority"
author: "Richard St-Pierre"
date: 2025-09-11
category: Policy
tags: ["ai-infrastructure", "capital-allocation", "canada", "productivity", "sovereign-compute", "clean-energy", "multilateral-alliances", "ai-policy"]
summary: "Canada's economic productivity remains stagnant, and traditional growth initiatives are slow to yield results. This briefing argues that large-scale deployment of AI infrastructure and applications is the most capital-efficient and urgent use of capital to boost productivity. It surveys peer national initiatives, confronts Canada's scale disadvantage, and lays out a strategy of multilateral alliances, pooled investment, and clean-energy advantage to treat AI deployment as a nation-building mission."
url: https://richardstpierre.com/articles/leveraging-ai-infrastructure-as-canada-s-top-capital-priority
---

# Leveraging AI Infrastructure as Canada's Top Capital Priority

> **Key finding:** Augmenting roughly 5% of total work hours with AI could, through compounded effects by 2030, add value equivalent to creating an entirely new industry the size of Canada's finance sector — without new permanent government spending. Yet Canada's entire installed AI computing capacity is estimated at only 0.7% of global capacity.

Canada's economic productivity remains stagnant, and traditional growth initiatives are slow to yield results. This briefing argues that large-scale deployment of artificial intelligence (AI) infrastructure and applications is the most efficient and urgent use of capital to boost productivity. It builds on Canada's broader productivity strategy by outlining why AI infrastructure offers unparalleled near-term gains and how strategic alliances and investments can overcome our scale constraints. The goal is to inform an immediate policy shift to treat AI deployment as a nation-building mission for prosperity and sovereignty.

## AI as a Rapid Productivity Catalyst

AI technologies, particularly in the deployment of **AI inference** (the application of trained models in real-world operations), offer **rapid and scalable productivity gains** across the economy. Even modest adoption of AI — enhancing only 5–10% of work tasks — can have outsized impact. Recent analyses indicate that augmenting roughly 5% of total work hours with AI could, through compounded effects by 2030, add value equivalent to creating an entirely new industry of the size of Canada's finance sector. Crucially, this boost does not require new permanent government spending; it arises from efficiency in private and public workflows. In effect, AI acts as a **workforce amplifier**, quickly increasing output per worker without commensurate increases in labor or capital inputs.

By contrast, other major growth investments such as clean energy infrastructure, while essential for long-term sustainability, involve **long development cycles and heavy upfront costs**. Building power plants, grids, or transportation links often takes many years of planning and billions in capital before yielding economic returns. AI deployment is different. Software-based AI solutions can be rolled out within months on existing digital infrastructure. The capital requirements for scaling AI (primarily computing hardware and software development) are **relatively modest and continuously declining**, especially when leveraging cloud and specialized hardware. For example, **AI inference is far less capital-intensive** than training new foundational AI models, yet delivers immediate productivity benefits across industries. This means every dollar directed to AI enablement can generate productivity improvements faster than a dollar invested in most physical infrastructure projects. In the context of Canada's urgent need for growth, **capital efficiency** is paramount: AI offers a high return on investment in the near term, accelerating GDP growth more quickly than projects with decade-long horizons.

## Multiplier Effects Beyond Direct Output

Investing in AI infrastructure — particularly in **high-performance computing capacity for AI** — produces **multiplier effects** that traditional infrastructure projects cannot easily match. A new highway or pipeline primarily boosts growth through the direct use of that asset. In contrast, **AI infrastructure (especially large-scale data centers for AI inference)** becomes a platform upon which countless innovations and services can be built. One key example is the concept of AI **compute hubs**: clusters of advanced AI processors that companies, startups, researchers, and public agencies can all leverage to deploy AI solutions. The presence of such infrastructure tends to create a self-reinforcing ecosystem:

- **Innovation Ecosystem:** AI infrastructure acts as a magnet for **tech startups and research initiatives**. Ready access to powerful computing enables companies to experiment and build AI-driven services that can be exported globally. Countries that have invested in national AI supercomputers and cloud clusters report thriving local AI ecosystems. **France**, for instance, has invested in sovereign AI computing capacity (e.g. the Jean Zay supercomputer) which now supports thousands of research and industrial projects. This has enabled public-sector pilots showing 20–30% energy savings and 50% reductions in downtime in utilities, with typical returns on investment in 3–4 years. These gains illustrate how AI infrastructure allows many actors to innovate in parallel, amplifying the economic impact far beyond the facility's direct output.
- **Exportable Services:** An AI infrastructure base allows Canada to **export "intelligence" as a service** much like we export natural resources. With sufficient computing capacity, Canadian firms can host and run AI solutions for international clients. Over time, this could position Canada as a global provider of AI-enabled services (from digital government solutions to advanced manufacturing tools), tapping external markets and generating high-value exports. Notably, AI services are weightless and do not face the trade barriers of physical goods — they can scale rapidly via the internet, further multiplying the economic benefits of the initial infrastructure.
- **Talent Attraction:** Building cutting-edge AI facilities and initiatives will attract **skilled talent and firms** to Canada. Entrepreneurs, data scientists, and AI engineers gravitate to locations where they can access world-class infrastructure and supportive ecosystems. The presence of an AI hub signals opportunity. Already, we see globally that when countries announce major AI investments, talent flows follow. Singapore's recent AI program (S$1 billion over five years) not only aims to triple its pool of AI practitioners to 15,000, but is also explicitly designed to **crowd in private sector participation and draw global AI companies to set up Centres of Excellence locally**. Canada can similarly leverage AI infrastructure investment to reverse brain drain and become a net importer of innovation talent.
- **Public Sector Modernization:** Unlike traditional infrastructure, which mainly supports private-sector productivity, AI infrastructure also directly empowers government transformation. With robust AI computing resources available domestically, public agencies can modernize service delivery (through automation, digital assistants, predictive analytics in areas like healthcare and social services) without relying on foreign cloud providers. For example, **France's national AI strategy** pairs investments in local compute capacity with funding for public-sector AI adoption, enabling government-owned firms to automate routine tasks and reduce costs while keeping data sovereign. Early results include improved public services and operational savings in energy, transport, and defense sectors. A Canadian AI infrastructure push would likewise allow federal and provincial services to deploy AI for better citizen outcomes (e.g. smarter infrastructure maintenance, personalized education tools) at lower cost, all on Canadian soil.

In summary, a dollar invested in AI capacity does more than build a single asset — it creates a **platform for continuous innovation**. The **multiplier effect** comes from enabling many users and use-cases on a shared foundation. Traditional projects (ports, pipelines) are vital but largely linear in impact; AI infrastructure is multiplicative, catalyzing an ecosystem that grows autonomously. This ecosystem effect attracts further capital and companies, reinforcing a virtuous cycle of innovation-led growth. The overall outcome is a broader boost to productivity, competitiveness, and resilience than most one-off infrastructure investments could achieve.

## Global Peers: National AI Infrastructure Initiatives

Canada is not alone in recognizing the strategic value of AI infrastructure. **Peer nations — excluding the superpowers of the U.S. and China — are moving swiftly to build national AI capacity** as a foundation for future growth. A brief survey of initiatives in the United Kingdom, France, Singapore, and the Netherlands illustrates the global momentum and the early benefits these countries are realizing:

- **United Kingdom:** The U.K. has elevated AI to a strategic priority, launching significant programs to enhance its computing backbone and industrial adoption. The government committed £900 million for a new exascale supercomputer and an **AI Research Resource (AIRR)** to ensure British researchers and companies have access to cutting-edge compute power. In parallel, the **BridgeAI program** (£100 million) was introduced to accelerate AI uptake in traditional sectors, bridging the gap between AI innovation and industry needs. These investments tie into a national vision of the U.K. as an "AI maker, not an AI taker" — i.e. a creator of AI solutions, not merely an importer. Early signs of impact include a flourishing AI startup scene in London and emerging **"AI Growth Zones"** regionally that co-locate compute facilities, research institutions, and industry innovation hubs. The U.K. expects a twenty-fold expansion of its secure AI compute capacity by 2030, which is anticipated to drive job creation in tech clusters and modernize sectors from healthcare to finance through AI adoption.
- **France:** France was one of the first countries in Europe to launch a comprehensive AI strategy, and it has backed it with substantial investment in infrastructure. It built the **Jean Zay supercomputer** (now at 125 petaflops) as a sovereign AI/HPC resource, part of an initial €1.5 billion AI plan. Under the **France 2030 plan**, a further €2.22 billion over ten years is committed to expand AI capacity and double the number of AI specialists by 2030. France pairs public grants with private co-investment to finance AI hubs and has secured long-term clean energy contracts (leveraging its nuclear fleet) to power these data centers. The strategic outcome is twofold: a growing ecosystem of AI startups and research (with multiple French institutions now ranking in the global top 50 for AI research excellence), and modernization of public services. French public enterprises — from national railways to utilities — are piloting AI for predictive maintenance, energy management and customer service, reporting significant efficiency gains. Notably, waste heat from the AI supercomputer now even warms nearby homes, underscoring the synergy between digital infrastructure and sustainable practices. France's example shows how a national AI infrastructure, combined with clear ethical and governance frameworks, can yield tangible improvements in both the economy and government operations.
- **Singapore:** A technologically forward city-state, Singapore has made AI infrastructure a pillar of its economic strategy. In its 2024 budget, Singapore announced over S$1 billion in new funding to **secure advanced AI chips and establish AI innovation centres** over the next five years. This effort is coupled with aggressive talent development goals — tripling the number of AI practitioners to 15,000 — and partnerships with global tech firms to base their AI Centres of Excellence in Singapore. Early benefits are visible in the form of a vibrant startup scene in AI applications (particularly in finance and smart city solutions) and the integration of AI into public initiatives like smart urban mobility and e-government services. Singapore's public agencies are early adopters of AI in areas such as port operations and municipal services, improving efficiency despite a tight labor market. By ensuring local access to the latest AI hardware (through chip procurement and data center collaborations), Singapore has **shortened the innovation cycle** for its companies — new AI solutions can be developed and deployed domestically without dependency delays. Moreover, the country's pro-innovation regulatory stance and focus on upskilling (e.g. **SkillsFuture AI training programs** for mid-career workers) have created a confident workforce ready to embrace AI, further amplifying the technology's impact on productivity.
- **Netherlands:** The Netherlands, though a smaller economy, is taking bold steps to build national AI computing capacity in partnership with its European allies. In October 2025, a consortium in the Netherlands secured €70 million from the EU (matched by national and regional funds for a total €200 million project) to establish an **"AI factory" in Groningen — a new national AI supercomputing and data facility**. This facility, operational by 2027, will provide vast computing power to startups, SMEs, researchers, and government, focusing on critical sectors like healthcare, mobility, and security. The impetus for this project is both economic and strategic: it will give Dutch businesses easy access to world-class AI infrastructure, **stimulating job creation and innovation especially among smaller firms** that could not afford such resources on their own. At the same time, it serves digital sovereignty goals by reducing dependence on foreign cloud providers. Early expected benefits include a stronger international position for the Netherlands in AI knowledge (attracting expert talent to the region) and new partnerships across Europe as this "AI factory" plugs into a larger EuroHPC network of AI supercomputers. The project's funding model itself is instructive: it pooled European, national, and local capital, recognizing that **without multilateral support the initiative would not have launched**. The Netherlands is also aligning its physical infrastructure (high-speed networks, energy grid) to support these AI clusters, and planning integrated policies on spatial planning and skills to ensure the success of its AI infrastructure strategy.

**Takeaway:** These peer examples underscore that **AI infrastructure is now central to economic strategy** in advanced nations. They report benefits such as new high-skilled jobs, formation of AI startups and research hubs, more efficient public services, and increased attractiveness for investment. Each country has tailored its approach (be it supercomputers, regional AI zones, talent programs, or international alliances), but the common thread is clear: those who invest early in AI capacity are positioning themselves for leadership in the next wave of growth, while those who lag risk permanent strategic disadvantage. Canada, unfortunately, has begun to lag by comparison — as peers accelerate, our share of global AI computing and investment is slipping — which makes urgent action all the more critical.

## Canada's Capital Scale Disadvantage

Despite being a G7 economy with strong research talent, Canada faces a **relative disadvantage in sheer capital scale** when it comes to technological mega-projects. We lack the domestic market size and corporate giants that the U.S. and China deploy in funding AI development. Recent data illustrate this gap starkly: in 2024, U.S. private investment in AI reached around $109 billion — roughly **24 times the U.K.'s level and 15 times the total venture capital invested across *all* sectors in Canada**. Likewise, the United States accounted for the vast majority of notable new AI models produced globally, while Canada contributed only a small fraction. Canada's entire installed AI computing capacity is estimated at **only 0.7% of global capacity**, far behind not only the U.S. but also behind several European peers. In short, our current trajectory and scale of investment are not sufficient to keep pace in the AI race.

This capital constraint is not just in the private sector. Public resources are also limited. While Canada has made important investments (such as the Pan-Canadian AI Institutes and superclusters), these are measured in the low billions — orders of magnitude smaller than the sustained tens of billions that larger nations can marshal annually. The result is that **Canada cannot win an AI arms race through spending competition alone**. If we attempted to unilaterally finance AI infrastructure at the scale of global leaders, we would strain public finances and still likely fall short in absolute terms. Recognizing this reality is key: Canada must be **strategic and collaborative in how we deploy capital for AI**.

## Strategy: Alliances and Pooled Investment for AI Infrastructure

To overcome scale limitations, Canada should pursue a two-pronged strategy.

### 1. Multilateral Alliances for Shared AI Capacity

Forge partnerships with like-minded nations that face similar constraints (mid-sized advanced economies, excluding the U.S. and China) to **pool resources for AI infrastructure and negotiate better access to technology**. Just as smaller European countries have combined efforts through the EU's EuroHPC initiative to build world-class supercomputers, Canada can lead or join a coalition to establish shared AI computing resources and frameworks. Potential partners include G7 and OECD allies such as the U.K., France, Germany, Japan, South Korea, Singapore, the Netherlands, and others who value an open and democratic AI ecosystem. Collaboratively, these countries can:

- **Co-invest in large-scale compute facilities** that serve multiple jurisdictions, lowering per-country costs. For example, a joint AI cloud or a network of linked national supercomputers could be created, with equitable access rules. The Netherlands' "AI factory" model, supported by EU funding, shows the viability of such pooled investment. Canada could negotiate reciprocal use of EU or U.K. AI compute centers while offering partners access to any Canadian facilities, maximizing utilization.
- **Exercise collective bargaining power in procurement.** By combining demand for AI hardware (such as high-end GPUs or AI chips), an alliance of countries can negotiate more favorable terms with suppliers, ensure supply security, and potentially co-develop needed technologies. This approach mitigates the risk of any one country being outbid or deprioritized in the global semiconductor supply chain. A coordinated **"compute procurement alliance"** could secure dedicated capacity for members at lower cost per unit, which is critical given the global shortage in advanced AI chips.
- **Share governance frameworks and standards.** Allied countries can jointly develop standards for AI infrastructure governance — addressing issues like data sovereignty, security, and interoperability of AI systems. This not only spreads the cost of policy development but also creates a larger unified market that tech providers can cater to. Common standards and **interoperability** agreements would allow AI solutions or trained models developed in one partner country to be used seamlessly in another's systems. Such alignment amplifies the return on each country's AI investments and reinforces a bloc of "trusted AI" nations. (Notably, Canada has already demonstrated leadership in convening international AI cooperation through initiatives like the **Global Partnership on AI (GPAI)**; similar commitment can be extended to infrastructure sharing.)
- **Joint talent and research programs.** An alliance could include exchange programs for AI researchers and a shared pipeline for training specialists, effectively pooling human capital. This would help smaller countries collectively retain talent that might otherwise be drawn exclusively to the biggest tech hubs. For example, a Canadian AI PhD might split time between a facility in Canada and one in France under a joint program, accessing greater resources than Canada alone could offer while contributing to both countries' innovation output.

In pursuing alliances, it will be important to exclude the United States (and China) in initial arrangements not out of adversarial intent, but to ensure the focus remains on countries that cannot individually match those superpowers' scale. Once a multilateral foundation is in place, the alliance would still coordinate with the U.S. and other partners on research and norms, but **Canada's negotiating position and resilience will be stronger as part of a pooled initiative** of peer nations.

### 2. Mobilizing Sovereign and Institutional Capital

The second prong is to **leverage external capital sources — including sovereign wealth funds and global institutional investors — to finance Canadian AI infrastructure** without overburdening public budgets. Canada can offer an attractive value proposition to long-term investors (pension funds, global asset managers, and the sovereign wealth funds of allied nations) by structuring AI infrastructure as a stable, utility-like investment:

- **Public-Private Co-Investment Vehicles:** The Government should establish co-investment funds or special purpose vehicles for AI infrastructure projects (such as major data center campuses, cloud infrastructure expansions, or fiber-optic network upgrades to support AI connectivity). These vehicles would allow government seed funding to **crowd-in much larger private capital commitments**. For example, a federal contribution or guarantee on initial losses can de-risk projects sufficiently to attract pension funds looking for steady returns. Canada's large pension funds (like CPP Investments, CDPQ, etc.) could be invited to participate, alongside foreign partners, under a framework where public funds take a junior position or provide insurance, thus limiting the public's fiscal exposure while unlocking private dollars.
- **Sovereign Wealth Fund Partnerships:** Many nations with sovereign wealth funds (SWFs), such as Norway, Singapore, or Gulf states, seek diversified, future-oriented investments. Canadian AI infrastructure — underpinned by our stable policy environment and clean energy — can be pitched as an ideal SWF investment in the technology infrastructure category. We can negotiate partnership agreements where, for instance, a Middle Eastern SWF finances part of a Canadian AI data center in exchange for a share of the returns and possibly preferential access for their own governmental uses. This mirrors historical practices in other infrastructure domains (e.g., foreign investment in Canadian pipelines or real estate) but applies them to digital infrastructure.
- **Infrastructure Bank and Incentives:** Canada's Infrastructure Bank and export credit agencies can be directed to prioritize AI and digital infrastructure. By providing low-cost debt or loan guarantees, these bodies can significantly lower the cost of capital for projects. The key is to use public financing as **leverage rather than direct expenditure**. Mechanisms like accelerated depreciation for AI hardware, tax credits for AI infrastructure spending, or "compute capacity credits" for small businesses can stimulate private investment in equipment and facilities. All these approaches improve capital efficiency by drawing in private and institutional money to do the heavy lifting, with the government catalyzing but not shouldering the entire cost.

Overall, Canada's strategy should be to **act as an orchestrator and guarantor, not the sole funder**. By setting favorable conditions (clear long-term policies, risk-sharing instruments, streamlined approvals), we enable market actors to build the needed capacity. Notably, analysis suggests that AI data center deployments in Canada could be financed almost entirely by private capital if governments provide a credible enabling framework — such as pre-zoned land, allocated clean power capacity, and fast-track permits with requirements for open access and competition. We already see major cloud providers eager to invest in new regions given the right signals: for example, AWS recently announced a $18 billion CAD investment for new Canadian data centers over the next decade. Similar or greater investments can be attracted for AI-specific infrastructure if we demonstrate commitment and resolve bottlenecks (like electricity grid connections, which provinces can expedite with federal coordination).

By pursuing multilateral pooling and innovative financing, Canada can **compensate for its smaller capital base with ingenuity and partnership**. This approach ensures we remain a player in the global AI arena while protecting fiscal sustainability.

## Canada's Advantage: Clean Energy + Stable Policy = AI Hub Potential

Despite challenges, Canada holds a **unique comparative advantage** that it can leverage in the AI infrastructure space: an abundance of **clean, reliable energy under a stable political and regulatory regime**. In the emerging landscape of AI, these factors are decisive for where global AI infrastructure is established:

- **Clean and Affordable Energy:** Modern AI data centers, especially those running large inference workloads, are extremely energy-intensive. Operating costs and carbon footprint are major considerations. Canada, with its large reserves of hydroelectric, nuclear, and other non-emitting power (over 80% of our electricity is GHG-free), offers some of the lowest-carbon, most stable electricity in the world. In many provinces, industrial power rates are competitive (on the order of $0.05–0.12 per kWh) and can be contracted long-term. Few countries can supply the **gigawatt-scale power** that AI campuses require while meeting stringent sustainability goals — Canada can. Furthermore, our cool climate in much of the country yields natural advantages for data center cooling and efficiency. This clean energy edge means AI firms can scale here without accruing massive carbon liabilities, a selling point as global companies commit to net-zero operations. It also aligns with our national climate objectives, turning a potential strain (increased electricity demand for AI) into a driver for further clean energy investment and grid expansion, thereby reinforcing energy security.
- **Policy and Political Stability:** Canada's robust legal system, intellectual property protections, and transparent regulatory environment make it a **trusted location for hosting sensitive AI infrastructure**. Companies and governments around the world are increasingly conscious of where their data and AI models reside. With rising geopolitical tensions, many prefer jurisdictions that are geopolitically neutral, respect rule of law, and have strong privacy and data governance standards. Canada scores high on these counts. Unlike some markets, there is low risk of sudden expropriation or instability here. Additionally, Canada's commitment to multilateral norms and ethics in AI (e.g., through involvement in GPAI and OECD AI principles) makes it a palatable host for international AI projects. We are seen as a **bridge builder and a safe steward** of technology. This reputation can be leveraged to invite allies to place some of their AI computing workloads in Canada, confident that their digital assets will remain secure and under a fair jurisdiction.
- **Geographic and Strategic Position:** Our geography — notably our proximity to the United States market while being a distinct jurisdiction — can be an asset. Canada can serve as an **alternative North American AI hub** for allies and companies that want diversification away from a single country's infrastructure. We can offer time-zone advantages (covering the Americas and overlapping with Europe), and vast land availability for building data center campuses. Moreover, Canada's cultural and immigration openness means we can welcome international experts to live and work here at scale, something that some other potential host countries struggle with. If properly promoted, Canada could become the preferred location in North America for setting up large inference clusters that serve global users, especially given recent U.S. moves to impose export controls and foreign investment scrutiny — Canada provides a friendlier yet proximate base.

By capitalizing on these strengths, **Canada can position itself as an ideal host for AI inference hubs** — the physical locations where advanced AI models are run to deliver services worldwide. In effect, we have the ingredients to become a **global exporter of AI computation**. Just as previous eras saw Canada export resources and manufactured goods, the coming era could see Canada export AI-driven productivity (running other nations' AI applications on Canadian soil, for a fee). This is not speculative; already hyperscale cloud providers are locking in sites for future AI data centers, and they are drawn to places with cheap clean power and political reliability. Canada must seize this moment by actively courting such investments and smoothing the path for them. Every major AI data center built here not only creates construction and operations jobs, but also cements Canada's role in the AI value chain for years to come, generating ongoing revenue and innovation spillovers locally.

However, this opportunity window will not remain open indefinitely. Global companies are making multi-decade localization decisions **right now (2024–2027)**, committing capital to regions where conditions are favorable. If Canada does not move decisively — for example, by rapidly resolving interconnection delays to bring new renewable power online and by offering pre-approved sites for data centers — investors will take their billions elsewhere. Our clean energy advantage exists in principle, but in practice it must be matched with policy certainty and speed of execution. In short, **Canada's advantages must be operationalized through responsive governance**. By doing so, we can secure a significant share of the AI infrastructure build-out that is underway globally, anchoring a new industry on our shores.

## Recommendations and Conclusion

Canada stands at a crossroads in economic policy. We have the choice to **invest boldly in the AI revolution** — with all the capital efficiency, strategic positioning, and ecosystem gains it promises — or to proceed with business as usual and risk falling permanently behind. Based on the analysis above, it is recommended that the Privy Council Office champion the following actions as part of Canada's productivity strategy extension:

- **Adopt AI Infrastructure as a National Priority:** Formally recognize large-scale AI deployment (especially inference infrastructure and adoption across sectors) as a top-tier national project, on par with historical nation-building endeavors. This framing will help align federal departments and provincial partners towards common targets, such as raising business AI adoption from the current ~12% to over 35% by 2025, and commissioning significant new AI data center capacity from our 15 GW of pending projects in the next 2–3 years.
- **Coordinate a Multilateral AI Infrastructure Alliance:** Initiate talks with peer nations (e.g. G7 minus US, EU partners, Indo-Pacific allies) to develop a joint AI infrastructure plan. This could be pursued through an international task force or by leveraging existing forums (for instance, proposing an "AI Infrastructure" track within the Global Partnership on AI). Aim for agreements on shared investments and hardware procurement by the next major multilateral meetings. Canada can take a convening role, which also bolsters our international leadership credentials.
- **Enable Private Financing at Scale:** Direct the Department of Finance, Innovation, and the Infrastructure Bank to design investment vehicles that bring pension funds and global investors into AI infrastructure deals. This includes exploring **capacity auctions or offtake agreements** where government agencies commit to purchase a certain amount of AI computing capability from a privately-built facility (similar to power purchase agreements). Such commitments can guarantee baseline revenues and encourage private consortia to finance construction and operation. Likewise, consider an **"AI adoption tax credit"** or accelerated depreciation for businesses investing in AI hardware and software, to spur immediate uptake.
- **Fast-Track Regulatory Approvals and Standards:** The federal government should work with provinces to eliminate bottlenecks for AI infrastructure projects. This may involve setting up one-stop "AI infrastructure secretariats" to expedite environmental assessments, power hookups, and zoning for data centers. In parallel, develop **open standards for interoperability and data governance** in AI systems procured by government. By mandating interoperability and avoiding vendor lock-in, public investments will foster a broader competitive ecosystem of AI suppliers in Canada, increasing overall innovation and cost-effectiveness in the long run.
- **Leverage Clean Energy for Compute Deals:** Launch an initiative linking energy investments with AI goals — for example, an "Energy-for-Compute" program where new clean power capacity (wind, hydro, solar) is allocated partly to AI data centers under long-term contracts. This approach can ensure that as we expand our grid, a portion is strategically reserved to fuel AI growth. It provides certainty to both power developers and AI investors. Provinces with surplus renewable energy (e.g. Quebec, Manitoba) could be ideal hosts for such AI campuses, and federal facilitation of interprovincial grid projects would enhance this potential.

In conclusion, prioritizing AI infrastructure now is **the most capital-efficient decision to boost Canada's productivity and secure our economic future**. It offers a timely acceleration of growth — achieving in a few years what other measures might take a decade — and does so by harnessing mostly private and allied capital. Just as importantly, it positions Canada at the forefront of the next global economy, one defined by intelligent systems and digital services. The benefits radiate across all regions and sectors: from higher-value jobs and new companies at home, to improved public services, and a stronger voice in setting global tech norms. Conversely, inaction or incremental steps risk Canada entrenching a productivity gap that we may never close.

This briefing underscores that AI infrastructure is not merely a tech initiative; it is **a strategic national asset** — akin to railways or electricity in past eras — that will determine economic winners and losers in the coming years. With prudent policy and bold vision, Canada can marshal its strengths (clean energy, talent, stability) and partner wisely to become a leader in this domain. The recommendation is to proceed with urgency and unity of purpose. The opportunity to reshape our growth trajectory is here now; seizing it will deliver a lasting legacy of prosperity and innovation for Canadians.

> *Sources: Government of Canada internal economic strategy documents ("Framing an Industry Productivity Flashpoint"); international comparisons and AI investment data; national AI initiatives in the UK, France, Singapore, and the Netherlands (Nucamp, MARKETECH APAC, Techzine, Dutch Data Center Association, The Guardian, Computing); and policy analysis on AI adoption and capital allocation, as cited throughout.*