Goals of National AI Strategies

Although the particulars differ, most national AI strategies focus on a common set of objectives:

• Innovation and Growth: Promote R&D, support startups and private investment, and fund compute and research ecosystems.
• Safety and Governance: Establish regulations, standards, and testing protocols to mitigate risks, from bias to misuse.
• Talent and Skills: Enhance computer science education, reskill the workforce, and attract global talent.
• Compute and Data: Secure access to advanced chips, cloud computing, supercomputing resources, and high-quality datasets.
• Public Sector Use: Integrate AI responsibly within government services and procurement processes.
• International Cooperation: Foster collaboration on principles, safety, and trade to reduce fragmentation.

These objectives are reflected in frameworks such as the OECD AI Principles, embraced by numerous countries, and in national roadmaps from the United States, European Union, United Kingdom, India, Singapore, the UAE, China, Canada, and others.

Evaluating the Effectiveness of AI Strategies

No singular metric exists to measure effectiveness; however, a practical scorecard can assess key outcomes relevant to citizens and businesses:

• Innovation Indicators: Research outputs, commercialization efforts, startup activity, patent filings, and private investment share per country. Refer to the Stanford AI Index for consistent, cross-country trend data.
• Safety and Governance Maturity: Adoption of risk management frameworks (e.g., NIST AI RMF 1.0), regulatory clarity (such as the EU AI Act), and the establishment of evaluation and incident reporting systems.
• Talent and Skills: Availability of AI practitioners, immigration pathways for specialists, and large-scale reskilling efforts. Track trends through the AI Index and OECD skills reports.
• Compute Access: Investments in supercomputers, cloud credits, and shared research resources, such as the US NAIRR pilot or the EU’s AI Factories and EuroHPC.
• Adoption and Trust: Enterprise usage of AI alongside public sentiments regarding its risks and benefits. Explore insights in McKinsey’s State of AI and Pew Research.
• International Alignment: Engagement in shared standards and safety frameworks, such as the G7 Hiroshima AI Process, the Global Partnership on AI, and agreements from safety summits.

Regional Scorecard: Who Is Doing What, and How Is It Going?

United States: Innovation Flywheel with Variable Implementation

The US maintains its leadership with cutting-edge model development, substantial private AI investments, and top-tier research. According to the AI Index, the US dominates private AI funding and features prominently among the most cited AI publications.

Since late 2023, the US has introduced a series of governance measures, including the comprehensive AI Executive Order 14110, the NIST AI Risk Management Framework, and detailed guidance via OMB memo M-24-10 requiring federal agencies to designate Chief AI Officers, conduct impact assessments, and inventory AI systems. The government has also launched a pilot program for a National AI Research Resource (NAIRR) to enhance access for researchers.

Strengths: Robust capital markets, leading research labs, and vibrant open-source communities. Challenges: Regulatory inconsistencies across states, immigration barriers for AI talent, and disparities in compute access for startups and research organizations.

European Union: Leading with Comprehensive Regulations and Enhanced Compute

The EU’s groundbreaking AI Act came into effect in 2024 with phased obligations extending through 2025-2026. This law introduces a risk-based regulatory framework, demands transparency for general-purpose models, and sets rules for high-risk applications, such as hiring and critical infrastructure. An EU AI Office is responsible for coordinating implementation and establishing codes of practice.

On the infrastructure front, Europe is investing in high-performance computing through EuroHPC and launching AI Factories to provide startups and researchers with access to GPUs and datasets.

Strengths: Regulatory clarity, safety-oriented design, and robust privacy protections under GDPR. Challenges: Difficulty securing scale-up capital, fragmented market execution, and ensuring that regulations remain practical for SMEs and open-source initiatives. The Commission has indicated potential exemptions and proportionate obligations for open-source projects while maintaining safeguards against systemic risks.

United Kingdom: Leading in Safety with a Pro-Innovation Approach

The UK has adopted a regulator-led, pro-innovation approach, establishing the AI Safety Institute to assess advanced models. In 2023, it hosted the Bletchley Park AI Safety Summit and co-hosted a subsequent summit in Seoul in 2024, which produced voluntary safety commitments and aligned research priorities.

On the compute side, the UK is developing an AI Research Resource that includes new supercomputers such as Isambard-AI and Dawn, aimed at supporting researchers and startups (government announcement).

Strengths: Strong capacity for safety evaluations, flexible oversight models, and prestigious research institutions. Challenges: Limited domestic market scale and the necessity to translate safety research into widely accepted testing standards.

China: Ambitious Plans with Limited Compute Resources

China’s 2017 AI strategy aims for global leadership by 2030. The nation has introduced regulations for algorithms and provisional measures on generative AI to establish provider requirements and content governance (translated overview).

While China has a vast and rapidly evolving ecosystem, dominated by leading firms and competitive models, access to top-tier chips is limited due to export controls from the US and allied nations (BIS guidance). This situation incentivizes the development of domestic hardware and drives efficiency improvements.

India: Enhancing Access and Skills

India has launched the IndiaAI Mission in 2024 to invest in compute infrastructure, datasets, and support for startups, with a focus on inclusive applications in healthcare, agriculture, and education. The country benefits from a large STEM talent pool and an active developer community, while also working to scale research compute and attract advanced R&D initiatives.

Singapore: Practical Governance and Testing

Singapore’s Model AI Governance Framework 2.0 and AI Verify testing program serve as practical resources that many multinational organizations reference when operationalizing AI risk management (IMDA). The city-state pairs clear guidance with regulatory sandboxes and targeted incentives to assist startups and enterprises in transitioning from pilot projects to full-scale production, all while meeting governance expectations.

United Arab Emirates: Open Models and Strategic Partnerships

The UAE is adopting a partnership-driven strategy, supporting open models through the Technology Innovation Institute’s Falcon family. The country has also secured strategic investments and cooperation in AI infrastructure, highlighted by a significant collaboration between Microsoft and G42 in 2024, underscoring alignment with US security frameworks (Microsoft announcement).

Canada: Promoting Responsible AI by Design

Canada was an early adopter with a voluntary code of conduct for generative AI and has introduced legislation to regulate high-impact systems. Its public research institutes and computing facilities support a robust academic landscape, while policymakers strive to maintain a balance between innovation and safety measures.

What Is Working

Several elements of national AI strategies across countries are showing tangible results.

1) Significant Investments in Compute and Access

AI advancement relies heavily on computing power, and strategies that fund shared resources are yielding positive outcomes. Initiatives such as the US NAIRR pilot, the EU’s AI Factories, and EuroHPC, along with the UK’s AI Research Resource, are breaking down barriers for universities, startups, and public-interest researchers. When coupled with cloud credits and standardized datasets, these programs broaden participation beyond the largest labs.

2) Established Safety Frameworks and Evaluations

Governments are progressing from principles to more detailed guidance and evaluative measures. The NIST AI RMF is becoming a standard benchmark for AI governance frameworks. The UK’s AI Safety Institute and partner laboratories are developing evaluations for model capabilities and associated risks. Furthermore, the EU AI Act complements this with regulations for high-risk systems, transparency for general-use models, and rigorous post-market monitoring.

3) Modernization of Public Sector with Safety Precautions

Public agencies are implementing AI under clearer guidelines. The US OMB M-24-10 memo mandates impact assessments, mandates human oversight, and inventories of public AI systems. Similar guidance is emerging across the G7 and EU. These initiatives build trust and promote responsible use in services such as benefits administration and fraud detection.

4) International Coordination

Joint efforts are minimizing fragmentation. The G7 Hiroshima AI Process, GPAI, and AI safety summits (such as Bletchley Park in 2023 and Seoul in 2024) have fostered shared commitments and research agendas. UNESCO’s Recommendation on the Ethics of AI serves as a global reference for values and rights.

Where Strategies Are Falling Short

While there has been progress, several shortcomings remain evident from data and interviews.

1) Regulatory Fragmentation and Divergence

Companies are navigating overlapping and inconsistent regulations across jurisdictions and even within nations. While the EU AI Act establishes a common baseline in Europe, the US continues to grapple with a mix of federal guidelines and state laws, complicating compliance for cross-border operations.

2) Inequality in Compute and Data Access

Access to cutting-edge GPUs remains a barrier for startups, academic institutions, and non-profits. While shared resources are beneficial, demand often exceeds availability. Additionally, high-quality, well-governed datasets are not uniformly accessible, hindering progress in essential sectors such as health, climate, and support for underrepresented language communities.

3) Talent Bottlenecks Persist

The demand for skilled AI engineers and researchers continues to surpass supply. Immigration barriers and slow reskilling initiatives hinder efforts to bridge these gaps, particularly for small and medium-sized enterprises (SMEs) and the public sector.

4) Evaluation and Incident Reporting Are Still Developing

While advancements in evaluating complex AI systems are being made, challenges persist. Shared databases like the AI Incident Database are valuable, yet reporting remains voluntary and often incomplete. Many organizations are lacking standardized procedures for red-teaming and post-market evaluations.

5) Public Trust Is Fragile

Surveys indicate that many individuals remain more apprehensive than enthusiastic about AI’s implications for jobs, privacy, and safety (Pew Research). Clear benefits and robust safeguards will be crucial for shifting public sentiment towards a more positive view of AI.

12-Month Outlook: What to Watch

• EU AI Act Implementation: Focus on codes of practice for general-purpose AI, conformity assessments for high-risk applications, and guidance from the EU AI Office.
• US Rulemaking and Safety Evaluations: Government agencies will implement the AI Executive Order, expand NIST testing workstreams, and provide further OMB updates for governmental use.
• UK Safety Evaluations and Benchmarks: Reports from the AI Safety Institute on model testing and red-teaming, along with international collaborations on evaluation methodologies.
• Compute Capacity and Access: New GPU clusters being launched in the US, EU, and UK, along with programs designed to allocate compute resources to researchers and startups.
• China’s Hardware Development: Progress in domestic accelerator technologies under export restrictions and efficiency optimization strategies.
• Enterprise Adoption Beyond Pilots: Indicators that governance, security, and ROI challenges are decreasing as organizations standardize internal frameworks and practices.
• Common Safety Signals: Advancements towards shared incident reporting, capability evaluations, and model cards that can be utilized across borders.

Practical Takeaways for Policymakers and Leaders

• Focus on Interoperable Standards: Develop programs around shared frameworks like the NIST AI RMF and align with the EU AI Act where appropriate to reduce friction for cross-border activities.
• Invest in Shared Compute and Open Tools: Continued funding for research compute, cloud credits, and open-source evaluation tools can amplify participation and enhance safety.
• Prioritize High-Quality Data Assets: Publish comprehensive public datasets with clear licenses, privacy protections, and thorough documentation to hasten safe innovation.
• Bridge the Talent Gap: Expand scholarships, apprenticeship programs, fast-track visa options for AI specialists, and reskilling initiatives for the wider workforce.
• Operationalize Safety Measures: Mandate algorithmic impact assessments, adversarial testing, model documentation, and post-deployment monitoring for systems with significant impact.
• Engage in International Cooperation: Participate in GPAI, G7 activities, and safety summits to align on evaluations, incident reporting, and best practices.

So, Are AI National Strategies Working?

The answer is mixed. Where strategies align clear objectives with funding, infrastructure, and practical guidelines, they yield positive outcomes. We observe increased compute accessibility, robust governance frameworks, and enhanced international collaboration. However, significant challenges remain: addressing compute and talent discrepancies, minimizing regulatory fragmentation, and fostering public confidence through visible safety measures and tangible benefits.

The nations poised to excel are those that combine ambitious investments with pragmatic regulations and shared standards. They also place emphasis on measuring outcomes rather than just publishing strategies. In the upcoming year, watch for signs of broader compute access, widespread adoption of evaluation frameworks, and a decrease in regulatory disparities across borders. These indicators will determine if these strategies transform from mere declarations into powerful catalysts for safe and effective AI implementation at scale.

FAQs

What is a national AI strategy?

A national AI strategy is a government’s plan to foster AI innovation while managing related risks. It typically encompasses areas such as research funding, safety and governance, talent development, compute infrastructure, public sector adoption, and international collaboration.

Which countries are leaders in AI?

The US excels in frontier models, private investments, and research outputs. The EU takes the lead in comprehensive regulations through the AI Act. The UK is pioneering model evaluations, while China demonstrates scale and speed but encounters hardware limitations. Singapore, the UAE, India, and Canada are also making notable strides through focused governance and investment.

What is the EU AI Act and its significance?

The EU AI Act represents the first comprehensive AI regulation, utilizing a risk-based approach. It outlines obligations for high-risk systems and introduces transparency requirements for general-purpose models. As many companies operate in Europe, the Act will significantly influence global product design and compliance efforts.

How can smaller companies ensure compliance?

By adopting interoperable frameworks like the NIST AI RMF, thoroughly documenting their systems, utilizing vendor tools for model evaluations, and engaging in regulatory sandboxes or codes of practice. Many regulators are creating proportionate rules and offering guidance specifically for SMEs.

Where can I track policy changes and metrics?

Useful resources include the OECD.AI Policy Observatory, the Stanford AI Index, and official government websites in the US, EU, and UK, which provide frequent updates along with links to primary documents.

Sources

1. OECD.AI Policy Observatory
2. Stanford AI Index Report
3. EU Artificial Intelligence Act (Official Journal)
4. European Commission AI Office
5. EU AI Factories Initiative
6. EuroHPC Joint Undertaking
7. NIST AI Risk Management Framework 1.0
8. US Executive Order 14110 on AI
9. OMB Memorandum M-24-10
10. National AI Research Resource (NAIRR) pilot
11. UK AI Safety Institute
12. UK AI Research Resource Supercomputers
13. China’s Provisional Measures on Generative AI (Translation)
14. US BIS Semiconductor Export Controls
15. IndiaAI Mission Announcement
16. Singapore Model AI Governance Framework 2.0
17. Microsoft-G42 AI Alliance (UAE)
18. Canada Voluntary Code of Conduct for Generative AI
19. G7 Hiroshima AI Process
20. Global Partnership on AI
21. UNESCO Recommendation on the Ethics of AI
22. AI Incident Database
23. McKinsey State of AI 2024
24. Pew Research on Public Attitudes Toward AI