🇺🇸 United States' National AI Strategy

Contents

This report was prepared by GINC in mid-2025 to provide a comprehensive analysis of the United States' national AI strategy, drawing on the latest policy developments, regulatory frameworks, and global positioning. The table of contents below outlines the structure of the report, organized into five thematic parts covering strategic vision, innovation systems, sectoral deployment, social impact and policy evolution over time.

Part I: National Vision and Strategic Foundations
Strategic Vision & Objectives
Governance Architecture
Policy Instruments & Incentives

Part II: Innovation System and Talent Development
R&D and Innovation Ecosystem
Talent, Education & Mobility
Data, Compute & Digital Infrastructure

Part III: Sectoral Integration and Global Influence
Industrial Deployment & Tech Diffusion
Regulatory, Ethical & Safety Frameworks
International Engagement & Standards
Defence, Security & Dual-Use Considerations

Part IV: Performance, Resilience and Social Impact
Performance Metrics & Monitoring
Strategic Foresight & Resilience
Foundational AI Capabilities
Public Trust, Inclusion & Social Equity

Part V: Evolution of US National AI Strategy (2015–2025)

Strategic Vision & Objectives

The United States’ 2025 AI Action Plan sets forth a bold and comprehensive strategy to secure American leadership in artificial intelligence as a pillar of 21st-century national power. Framed as both an economic and geopolitical imperative, the strategy positions AI as the next great frontier of global competition—on par with the space race in its transformative potential. The plan articulates a vision in which the U.S. must dominate AI development, deployment, and standard-setting, guided by three strategic pillars: accelerating innovation, building foundational infrastructure, and shaping international norms. At the heart of this vision lies a commitment to embedding American values—freedom of speech, objectivity, and democratic governance—into the core architecture of future AI systems. This section explores how the U.S. frames AI as a domain of strategic competition, lays out the foundational pillars of its approach, and seeks to project democratic values into the global AI order.

Framing AI as a Strategic Domain

The United States’ new AI strategy, outlined in the July 2025 America’s AI Action Plan, is unapologetically ambitious. It casts artificial intelligence as a defining arena of strategic competition, comparing it to a new frontier of innovation capable of reshaping the global balance of power. Much like the 20th-century space race, the AI race is described as one the United States must win—not only for economic gain, but as a matter of national security. At its core, the strategy asserts that “whoever builds the largest AI ecosystem will set global standards”, and it is the U.S.’s ambition to lead this race, in both scope and scale.

Anchoring Vision to Three Pillars

To operationalize its vision, the Action Plan introduces a tripartite architecture: innovation, infrastructure, and international alignment. First, the United States must lead in the development of AI systems and their transformative applications by fostering conditions in which private-sector-led breakthroughs can flourish. Second, it must develop the physical and digital infrastructure—particularly in energy, compute, and semiconductor capacity—needed to support AI deployment at scale. Third, the U.S. must shape the global AI ecosystem by exporting American-built systems, software, and values, thereby influencing international norms and standards to align with democratic principles and U.S. interests.

Embedding Values into AI Leadership

Crucially, the vision is grounded in an ideological stance: that AI systems must reflect American ideals such as free expression, objectivity, and constitutional freedoms. The plan calls for government-developed or procured AI to reject “top-down ideological bias” and prioritize factual accuracy. It explicitly contrasts this with authoritarian approaches, where AI systems may embed state control, censorship, or opaque objectives. Thus, the U.S. seeks to lead not just in technical metrics, but in shaping the ethical and normative contours of global AI governance—ensuring the foundational technologies of the 21st century reflect democratic values.

Governance Architecture

To deliver on its AI ambitions, the United States has constructed a comprehensive governance architecture designed to align executive authority, technical expertise, and interagency coordination. The 2025 AI Action Plan establishes centralized oversight through the Executive Office while distributing implementation responsibilities across key federal departments. It introduces formal mechanisms like the Chief Artificial Intelligence Officers Council (CAIOC) and an AI talent exchange to operationalize policy at scale and pace. Simultaneously, it elevates technical governance through the National Institute of Standards and Technology (NIST), empowering it to set evaluation protocols and redefine risk management frameworks with a sharper focus on transparency and system performance. This section explores how the U.S. is institutionalizing AI governance to ensure policy coherence, regulatory alignment, and value-driven deployment across both civil and defense applications.

Centralized Oversight, Distributed Implementation

The governance framework proposed by the U.S. AI Action Plan is a whole-of-government architecture anchored at the highest level of the Executive Office. The plan was jointly authored by the Assistant to the President for Science and Technology and the National Security Advisor, reflecting a convergence of scientific leadership and national security oversight. The White House Office of Science and Technology Policy (OSTP) is assigned a lead role in policy coordination, while the Office of Management and Budget (OMB) is charged with executing regulatory streamlining across departments—particularly in alignment with Executive Order 14192. This creates a centralized strategy hub, with distributed responsibility across agencies including Commerce, Defense, Energy, and Labor.

Institutional Mechanisms for AI Governance

A cornerstone of this architecture is the formalization of the Chief Artificial Intelligence Officers Council (CAIOC). This interagency body will drive AI adoption, harmonize strategies across departments, and enable rapid knowledge sharing. To complement this, the plan proposes a federal AI talent exchange program, allowing experts such as data scientists and AI engineers to rotate across agencies where their skills are most needed. Technical coordination is further supported by the National Science and Technology Council (NSTC) AI Subcommittee, tasked with setting minimum data quality standards and guiding R&D investments.

Embedding Safety and Standards Through NIST

The Department of Commerce’s National Institute of Standards and Technology (NIST) plays a pivotal role in the plan’s technical governance strategy. Its newly formed Center for AI Standards and Innovation (CAISI) is responsible for model evaluation, convening cross-sector stakeholders, and developing testbeds for AI risk management. In a controversial but telling move, the plan directs NIST to revise its AI Risk Management Framework to eliminate references to misinformation, DEI, and climate change, focusing instead on performance, security, and transparency. Procurement standards are also tightened: government contracts for LLMs will only go to vendors whose systems align with free speech principles and demonstrate objectivity—further cementing ideological alignment as a contract condition in public-sector AI.

Policy Instruments & Incentives

The policy engine behind the U.S. AI strategy rests on a calculated combination of domestic deregulation, strategic investment, and international leverage. The 2025 AI Action Plan outlines a deregulatory framework designed to eliminate barriers to innovation, streamline federal oversight, and incentivize states to adopt growth-friendly AI environments. This is coupled with a surge in infrastructure investment—expanding data centers, energy systems, and semiconductor manufacturing—backed by executive-level coordination bodies and workforce development programs. Beyond national borders, the plan positions U.S. technology as a geopolitical asset, deploying trade policy and export controls to build an AI alliance and shape global norms. Together, these instruments form a comprehensive policy architecture aimed at consolidating American AI leadership through institutional agility, industrial capacity, and diplomatic reach.

Deregulatory Foundation to Spur Innovation

At the heart of the plan is a sweeping deregulatory effort aimed at unshackling AI development from bureaucratic friction. The administration began by rescinding Executive Order 14110, which had introduced more restrictive rules around AI governance. It replaced this with Executive Order 14192, mandating that each new regulation be offset by ten rule eliminations and requiring agencies to reduce total regulatory costs each year. The OMB is tasked with auditing every AI-related policy for potential repeal, while federal funding will be withheld from states that implement overly restrictive AI laws—creating a compliance-based incentive structure across the nation.

Infrastructure Investment and Buildout Acceleration

In tandem with deregulation, the strategy introduces robust investment instruments to scale physical and digital infrastructure. Permitting reform under the National Environmental Policy Act (NEPA) and the expansion of FAST-41 enable rapid buildout of data centers, energy systems, and chip fabs. The National Energy Dominance Council and U.S. Investment Accelerator serve as executive-level bodies to coordinate capital deployment. Meanwhile, workforce initiatives—driven by the Departments of Labor, Commerce, and NSF—focus on training trades critical to AI infrastructure, such as power grid technicians and advanced manufacturing specialists.

Global Leverage Through Trade, Alliances, and Export Controls

Internationally, the U.S. aims to cement its AI leadership through economic diplomacy and standards-setting. The Commerce Department is tasked with enabling full-stack AI exports—chips, models, software, and regulatory frameworks—to allied countries as part of a global “AI alliance.” This not only promotes American technology but also prevents allies from adopting adversary systems. At the same time, the plan strengthens enforcement of AI-related export controls, using tools like the Foreign Direct Product Rule to limit chip sales to high-risk states. Lastly, the U.S. seeks to dominate global AI governance by engaging in standard-setting bodies like the OECD, ISO, and UN working groups, advancing a pro-innovation, democratic model for AI safety and ethics. This dual-pronged approach—domestic deregulatory acceleration and global norm-shaping—defines the architecture of America’s AI strategic toolkit.

R&D and Innovation Ecosystem

The United States’ strategy to lead in AI innovation is grounded in a deliberate effort to unlock and amplify the potential of its research and development ecosystem. The AI Action Plan charts a policy framework that removes regulatory barriers, promotes open-access models, and directs targeted investment toward frontier applications and enabling infrastructure. By reducing administrative friction and aligning with Executive Order 14192, the federal government seeks to empower private-sector-led experimentation and commercialization. Simultaneously, the plan emphasizes the strategic importance of open-source AI and equitable access to compute—ensuring that American institutions retain global influence over technical and ethical AI standards. The approach is further reinforced by targeted investments in scientific discovery, robotics, and AI assurance, positioning the U.S. not only as an innovation hub but as a steward of responsible and scalable AI deployment across critical sectors.

Regulatory Enablement for Private Sector Innovation

The America’s AI Action Plan outlines an aggressive push to expand the nation’s AI research base as the foundation for technological and geopolitical leadership. Central to this is the belief that the U.S. must “have the most powerful AI systems in the world” and create the conditions for private-sector-led innovation to flourish. This begins with eliminating bureaucratic and regulatory friction. The plan mandates that federal agencies repeal or revise any rules that stifle AI development, a directive aligned with Executive Order 14192, which promotes “unleashing prosperity through deregulation.” The administration explicitly rejects precautionary approaches, warning that excessive regulation would paralyze one of the century’s most transformative technologies.

Strategic Value of Open Models and Research Access

Beyond deregulation, the Action Plan champions the promotion of open-source and open-weight AI models as a mechanism for distributed innovation. These models, openly available for modification and use, reduce dependence on proprietary platforms and allow academia, small businesses, and government to experiment freely. Their geostrategic importance is emphasized: if U.S.-built open models become the default, American values can shape the global AI ecosystem. To support access, the government is investing in scalable compute infrastructure via the National AI Research Resource (NAIRR) pilot and developing a market structure for compute that makes it affordable and tradable, akin to spot energy markets.

Frontier Applications and Scientific Infrastructure

The Plan targets transformative sectors where AI can accelerate discovery and deployment. This includes significant investment in AI-enabled science, through automated laboratories, long-term grants to Focused Research Organizations, and mandates for public dataset disclosure. Concurrently, the government will leverage CHIPS Act funds, the Defense Production Act, and SBIR programs to catalyze domestic robotics, autonomous systems, and drone manufacturing. Finally, it calls for breakthroughs in AI interpretability and robustness, and recommends establishing a nationwide AI evaluations ecosystem to benchmark AI reliability in real-world use cases.

Talent, Education & Mobility

To sustain long-term AI leadership, the United States is investing heavily in building a future-ready workforce—from the classroom to the job site. The AI Action Plan articulates a “worker-first AI agenda” that embeds AI literacy and technical skills across the entire education and labor ecosystem. Beginning with early integration of AI curricula in K–12 schools and extending through university, trade, and apprenticeship programs, the plan ensures that American students are equipped for an AI-driven economy. For mid-career workers, the strategy includes robust retraining and tax-incentivized upskilling pathways, enabling transitions from disrupted sectors into emerging AI-related roles. At the infrastructure level, it recognizes the need for tens of thousands of skilled technicians to build and operate the physical backbone of U.S. AI capacity. This comprehensive approach to human capital development positions the American workforce not just as a beneficiary of AI progress, but as a core enabler of national competitiveness in the decades ahead.

Embedding AI in the Education System

The Action Plan strongly advocates for a “worker-first AI agenda” that ensures American citizens benefit from AI-driven prosperity. The educational component begins early: Executive Order 14277 mandates the integration of AI education into K-12 settings, while EO 14278 supports training programs for high-paying trade jobs. The plan directs the Departments of Education, Labor, Commerce, and NSF to embed AI literacy and technical skills in all relevant education and workforce funding streams, spanning from high schools to universities. This includes scaling up computer science, machine learning, and digital infrastructure instruction across public systems.

Workforce Retraining and Tax-Incentivized Upskilling

Beyond early education, the strategy focuses on mid-career workforce transformation. The Department of Labor will launch retraining programs aimed at helping workers transition from AI-disrupted industries into new roles in the AI economy. Simultaneously, the Department of the Treasury will clarify eligibility under the Internal Revenue Code to ensure employers can offer tax-free reimbursement for AI-related skill development. These dual efforts ensure not only individual adaptability but also incentivize private-sector participation in workforce development. The creation of an AI Workforce Research Hub will help forecast skill needs and assess displacement risks in near-real time.

Skilled Trades and Early Career Exposure

Recognizing that AI infrastructure buildout requires tens of thousands of skilled technicians, the plan highlights roles such as electricians, HVAC specialists, and IT engineers as priority occupations. It calls for a national initiative to define competencies, modernize technical curricula, and expand Registered Apprenticeship programs. Pre-apprenticeship and career exposure initiatives in middle and high schools will seed awareness of AI-driven opportunities. This whole-of-nation workforce plan ensures not only that the U.S. will have elite research capacity, but also the construction and operations workforce to power AI infrastructure at scale.

Data, Compute & Digital Infrastructure

The U.S. AI Action Plan recognizes that world-class AI capabilities are impossible without world-class infrastructure—spanning energy, semiconductors, cloud environments, and data systems. At its foundation, the plan confronts the unprecedented energy demands of AI by modernizing permitting, expanding dispatchable energy sources, and securing the national grid against foreign influence. It reasserts the strategic importance of semiconductor manufacturing by accelerating domestic chip production and establishing high-security data centers purpose-built for defense and intelligence applications. Just as importantly, it elevates data to the status of critical infrastructure, setting national standards for scientific datasets and expanding secure access through new platforms like the National Secure Data Service. Together, these initiatives form a cohesive strategy to equip the United States with the physical, digital, and informational infrastructure required to support AI leadership at national scale.

Powering AI at National Scale

The Action Plan identifies energy generation as a foundational challenge for AI leadership. It calls AI the first technology of its kind to require more energy than the U.S. grid currently delivers. To respond, the government has reformed permitting processes via NEPA exclusions, created the National Energy Dominance Council, and expanded use of FAST-41 to speed infrastructure deployment. A modernized grid, upgraded for AI load, will require dispatchable energy sources including nuclear and geothermal, while data centers must avoid adversarial inputs in their ICT stack.

Semiconductor Sovereignty and Cloud Security

A central feature of digital infrastructure is reclaiming U.S. leadership in semiconductor fabrication. The CHIPS Program Office is directed to maximize return on investment, streamline grant administration, and remove extraneous regulatory demands. The plan also mandates the development of high-security AI data centers for classified workloads, built with domestic hardware, compliant with federal security standards, and designed to withstand nation-state-level cyber threats. These secure facilities will support sensitive government AI deployments including intelligence analysis, defense modeling, and classified research.

Strategic Data as Infrastructure

Finally, the U.S. strategy treats data itself as critical infrastructure. It mandates the establishment of data quality standards for biological, materials, and physical datasets used to train models. It pushes for regulatory reform under CIPSEA to unlock more federal data for AI research, while preserving privacy and confidentiality. The creation of a National Secure Data Service (NSDS) will give approved researchers centralized, secure access to restricted government datasets. A flagship initiative proposes a whole-genome sequencing program for life on federal lands, generating one of the world’s most expansive training resources for biological foundation models.

Part V. Evolution of the U.S. National AI Strategy (2015–2025)

This section traces the development of the United States’ national AI strategy over the past decade, highlighting key milestones, policy shifts, and emerging themes year by year. From early awareness under the Obama administration to a flurry of initiatives in the late 2010s, followed by intensified focus on competition and safety in the mid-2020s, the U.S. approach to AI has evolved significantly. The timeline underscores how bipartisan consensus on AI’s importance drove continuity, even as each administration put its own stamp on priorities – whether it be industry deregulatory emphasis, or ethical and global leadership concerns. Major external events (the COVID-19 pandemic, geopolitical tech rivalry) and breakthrough AI advancements (like the advent of deep learning dominance and generative AI) each spurred adaptations in U.S. strategy. The cumulative trajectory shows an initial period of laying foundations, a middle period of scaling up investment and coordination, and a recent period of grappling with societal implications and international norms. By mid-2025, the U.S. AI strategy is more comprehensive and institutionalized than ever, though poised to further adjust in light of rapid technological change and the upcoming presidential term.

• 2015: Laying the Groundwork & Private Sector Boom – AI burst into mainstream awareness as deep learning achievements captured headlines (e.g., image recognition surpassing human levels). The Obama White House recognized AI’s potential and risks, launching an initial wave of fact-finding. The Executive Office of the President released a pivotal report, “Preparing for the Future of Artificial Intelligence,” in October 2016, but the work began in 2015 with OSTP organizing the first public workshops on AI’s impacts. These workshops – on topics like safety, regulation, and economic effects – indicated a proactive stance. Meanwhile, 2015 saw the founding of OpenAI as a nonprofit research lab in California, backed by tech luminaries, marking a novel model to drive AI forward safely outside of Big Tech. In industry, Google open-sourced its TensorFlow AI library (Nov 2015), accelerating global AI development on U.S. software. The federal government’s AI activity in 2015 was relatively nascent, but seeds were planted: DARPA ramped up funding for AI research, and the DoD’s “Third Offset Strategy” identified autonomy and AI as core to maintaining military superiority. This year is often seen as the inflection when U.S. AI moved from academic niche to strategic priority, catalyzed by private sector breakthroughs and quiet government conceptual work.
• 2016: First Strategic Plans and Public Engagement – The U.S. government released its first formal strategic documents on AI. In addition to the Preparing for the Future report, OSTP published the National AI R&D Strategic Plan (October 2016), outlining seven focus areas for research investments. Key priorities included long-term investments in AI, developing effective methods for human-AI collaboration, and understanding ethical, legal, and societal implications. These documents, though late in the Obama presidency, set a baseline. OSTP also established the Machine Learning and AI Subcommittee under the National Science and Technology Council (NSTC) to coordinate interagency efforts. Public engagement ramped up: OSTP held a series of well-attended workshops in academic venues (Seattle, Pittsburgh, etc.), directly involving over 1,000 stakeholders in discussions that year. The high-profile victory of DeepMind’s AlphaGo (a London-based Google subsidiary) over a human Go champion in March 2016 vividly illustrated AI’s advancing capabilities and underscored the competitive stakes – it spurred China to increase investment, which in turn got U.S. attention. On the legislative side, Congress passed the American Innovation and Competitiveness Act in late 2016, touching on computing and STEM but not yet specifically targeting AI – still, it signaled bipartisan support for science that would benefit AI. By year’s end, the U.S. had in place a vision and initial framework for AI policy, although it was largely advisory. The impending administration change left questions on how these plans would be implemented.
• 2017: Transition and Defense Acceleration – The incoming Trump Administration initially took a less visible approach to AI – OSTP’s staff was downsized and there was a pause in public AI initiatives in early 2017. However, momentum in defense and intelligence picked up: the Department of Defense launched Project Maven in April 2017, an initiative to deploy AI algorithms to analyze drone surveillance footage and relieve human analysts . Maven was the first large-scale combat-zone AI deployment and a wake-up call in Silicon Valley after Google’s involvement became public, leading to employee protests by 2018. Nonetheless, Maven achieved its Phase I goals by year’s end (identifying objects in Iraq/Syria drone video), showcasing AI’s military value. Recognizing adversaries’ strides, senior U.S. defense officials made speeches calling AI a “game-changer” and established the Algorithmic Warfare Cross-Functional Team to coordinate AI across the Pentagon. Meanwhile, Congress inserted language in the FY2018 NDAA (passed late 2017) creating the National Security Commission on AI (NSCAI), a temporary independent commission to study how the U.S. should foster AI for defense – a sign of legislative concern about strategic competition. In the civilian space, 2017 was relatively quiet federally, but private sector and academia kept advancing: tech giants expanded AI research labs (e.g., Google Brain, Facebook AI Research grew significantly) and new startups flourished in autonomous driving, fintech, and healthcare AI, supported by record VC funding. The lack of early Trump admin public engagement on AI ended by the close of 2017, when the White House OSTP co-hosted the AI for American Industry summit in May 2018 (planned in late 2017) – planning for that was underway in late 2017, indicating the administration would pursue AI through an economic competitiveness lens.
• 2018: Institutional Coordination and AI Goes Mainstream – This year saw the U.S. government formally organize for AI leadership. In May 2018, the White House held a Summit on AI with over 100 industry executives, academic leaders, and government officials, reaffirming a commitment to “maintain the United States’ leadership in AI” and soliciting input. Following the summit, the Trump administration created the Select Committee on Artificial Intelligence under NSTC to coordinate federal R&D and strategy. Led by OSTP’s Dr. Lynne Parker, it brought together senior R&D officials from across agencies – effectively rebooting and elevating the Obama-era subcommittee. In parallel, Defense Secretary Mattis wrote to President Trump urging an national AI strategy, citing China’s rising investments; this influenced the administration’s thinking and was later revealed publicly. DARPA announced its ambitious “AI Next” campaign in September 2018, committing $2 billion to a portfolio of new programs (e.g., in contextual reasoning, common sense AI) . On the legislative front, bipartisan support grew: Congress established the Joint AI Center (JAIC) in the DoD through the FY2019 NDAA (mid-2018) to accelerate AI adoption in defense, with initial $1.7 billion funding over a few years. By late 2018, agencies started releasing AI strategies (e.g., DoD’s Summary of 2018 AI Strategy emphasized rapid fielding and ethics; DHS published an AI strategic plan for using AI in border security and disaster response). The idea of AI as a national priority had fully taken hold. Culturally, 2018 was the year AI firmly entered public discourse – self-driving car tests were expanding (with high-profile incidents like an Uber autonomous car fatality in March leading to discussions about safety), and tools like Amazon’s Alexa and Apple’s Siri brought rudimentary AI into daily life. This normalization increased public expectations and anxieties, prompting policymakers to consider not just promoting AI, but also managing its societal implications.
• 2019: Launch of the American AI Initiative – The U.S. federal government’s first comprehensive AI strategy was unveiled via executive action. In February 2019, President Trump signed Executive Order 13859: Maintaining American Leadership in AI, officially creating the American AI Initiative. This EO articulated priority actions: (1) invest in AI R&D (doubling funding was a stated goal), (2) unleash federal data and resources for AI (e.g., making data available on Data.gov, providing AI compute on cloud), (3) set standards for AI safety and interoperability (tasking NIST to lead), (4) build AI workforce (through education grants, apprenticeships), and (5) engage internationally to promote a supportive environment. Importantly, it was an unfunded mandate but directed agencies to prioritize existing funds toward AI – which they did in subsequent budgets. Following the EO, agencies released AI plans: e.g., DOE launched an AI Technology Office, USDA used AI for crop monitoring. In April 2019, the U.S. joined the Global Partnership on AI (GPAI) as a founding member, working with G7/OECD allies. NIST quickly responded to the EO by publishing a Plan for Federal Engagement in AI Standards (August 2019) urging U.S. leadership in global standards bodies. Another major milestone: Congress passed the National AI Initiative Act as part of the FY2021 NDAA in December 2020 (technically 2020, but written in 2019), which legislated many aspects of the AI Initiative, including the formation of the National AI Initiative Office at OSTP, a National AI Advisory Committee, and NSF AI Institutes expansion . 2019 also saw public scrutiny: face recognition’s accuracy disparities were spotlighted by NIST’s study in December (finding false positive rates higher for African-Americans in some algorithms), leading lawmakers to propose bills to curb government use pending improvements. Overall, 2019 established the structural framework for U.S. AI policy and signaled to the world that the U.S. was serious about leadership, even as it grappled with ethical debates.
• 2020: Pandemic, AI Applications in Crisis, and Continuing Momentum – The COVID-19 pandemic upended the world and demonstrated AI’s utility in a crisis. The U.S. government and companies leveraged AI for vaccine and drug discovery, epidemiological modeling, and telemedicine. In March 2020, the White House OSTP organized the COVID-19 Open Research Dataset (CORD-19), a public dataset of scholarly articles on coronaviruses, and challenged AI researchers to develop text-mining tools to glean insights – within weeks, AI systems were helping scientists parse tens of thousands of papers . The pandemic also accelerated adoption of AI in supply chain logistics and medical imaging (AI systems were authorized by FDA for detecting COVID pneumonia in lung scans). Meanwhile, policy didn’t slow: in January 2020, the White House issued OMB Memo M-20-36 providing Guidance on Regulation of AI applications, advising agencies to avoid over-regulation and adhere to 10 principles like fairness, transparency, and public participation in rulemaking. This memo essentially set a light-touch, innovation-friendly posture at the federal regulatory level. In August, the U.S. joined G7 science ministers in endorsing AI principles for pandemic response, marrying AI policy with emergency response. Geopolitically, U.S.-China tech tensions grew: the Trump administration expanded export controls (adding more Chinese AI firms to the Entity List) and in August 2020 announced the intention to ban TikTok/WeChat (citing data and influence risks) – not directly AI issues but part of tech decoupling that affects AI data flows and business. The NSCAI released interim reports through 2020 warning the U.S. was still not fully prepared to compete with China in AI. Partly in response, Congress overwhelmingly passed the William (Mac) Thornberry NDAA 2021 in Dec 2020, which included the full National AI Initiative Act (mentioned above) and the AI in Government Act to boost federal agency AI capacity. By the end of 2020, the U.S. had weathered a trial by fire with AI aiding in pandemic management, and positioned itself with new laws and an incoming administration likely to further elevate AI (as Biden’s campaign hinted at major tech and R&D investments).
• 2021: New Administration, Global Alignment, and Societal Concerns – The Biden Administration took office with AI as a priority within a broader science and tech agenda. Eric Lander, as OSTP Director (elevated to Cabinet rank), spoke of a “Bill of Rights for Automated Society” early on – signaling more attention to AI ethics. In June, the U.S. joined G7 leaders in launching the Global Partnership on AI (GPAI) formally (the Trump admin had joined late 2019, but Biden admin fully embraced it). Internationally, the U.S. re-engaged in multilateral AI efforts, co-sponsoring the first UN Security Council discussion on AI (December 2021) which focused on AI in warfare and stability. Domestically, in October 2021 OSTP initiated the process for the AI Bill of Rights by issuing an RFI for public input. On the R&D front, the Infrastructure Investment and Jobs Act (Nov 2021) authorized large investments in broadband and electrification that indirectly support AI deployment; and the administration proposed the CHIPS and Science Act (though it wouldn’t pass until 2022) with tens of billions for AI-related R&D. The Pentagon in June 2021 created the Chief Digital and AI Officer (CDAO) position (filled in early 2022) to unify its data, analytics, and AI functions, showing continuity and expansion of JAIC’s mission. There was also a significant public-private milestone: in May, Google’s DeepMind (though UK-based, closely tied to Google US) solved a 50-year grand challenge, protein folding, with its AlphaFold AI – demonstrating AI’s scientific prowess and leading OSTP to host discussions on how AI can accelerate biomedical research. However, 2021 was also marked by high-profile AI incidents fueling public concern: Facebook’s whistleblower Frances Haugen testified to Congress in October that its algorithms harmed teens and sowed discord, reinforcing bipartisan calls to rein in AI-driven social media harms. This fed into FTC and Congress exploring regulations on recommendation algorithms and transparency. Thus, 2021 was a year of integrating AI into broader policy (tech competition with China, Big Tech accountability, infrastructure) and setting the stage for ethical guardrails.
• 2022: AI Legislation and First Steps on Governance – This year saw landmark funding and initial regulatory frameworks. In August, after long negotiation, Congress passed the CHIPS and Science Act of 2022, which authorized roughly $200 billion for science R&D over 5 years and $52 billion for semiconductor manufacturing, explicitly highlighting AI as a major beneficiary of the science funds. It included expansion of NSF AI Institutes, new DOE AI research programs, and STEM workforce programs – effectively supercharging the National AI Initiative with resources. On governance, the White House unveiled the Blueprint for an AI Bill of Rights in October. Though not binding, it was a White House policy document defining principles like Safe and Effective AI and Algorithmic Discrimination Protections , intended to guide federal agencies and perhaps inspire future regulation. Earlier, in January, the NIST AI Risk Management Framework project released its draft; throughout 2022 NIST worked with industry and civil society to shape this voluntary but influential framework, signaling a move to operationalize ethics and risk reduction in AI development. Agencies also exercised existing authorities: the EEOC launched investigations into AI hiring tools and the Consumer Financial Protection Bureau warned lenders that using biased AI underwriting could violate fair lending laws. The year was also big for the generative AI revolution: OpenAI released DALLE-2 (image generation) in April and ChatGPT in Nov 2022, captivating the public and policymakers alike with AI’s creative and conversational abilities. This led OSTP and others to start grappling with generative AI’s implications (misinformation, IP, education cheating, etc.), foreshadowing the flurry of 2023 actions. Internationally, the U.S. in late 2022 opposed the EU’s push to advance negotiations on a global AI treaty at the UN, preferring its multi-stakeholder approach; instead, it focused on bilateral ties like launching the U.S.-UK Atlantic Declaration in December which included AI cooperation on research and standards. By the end of 2022, with CHIPS Act passed and early AI governance frameworks introduced, the U.S. had made significant strides in both strengthening AI capabilities and addressing its risks.
• 2023: Generative AI Hype and First Comprehensive Executive Order – The emergence of generative AI as a mass-market phenomenon (ChatGPT reached 100 million users by January, the fastest ever) spurred urgent policy discussions. In May 2023, OpenAI CEO Sam Altman testified in Congress, acknowledging AI’s risks and surprisingly endorsing regulation , which added momentum to legislative efforts. While Congress held multiple hearings and Sen. Schumer convened AI insight forums (with tech CEOs, experts) to shape legislation, the Biden Administration acted with existing tools. In July, the White House negotiated Voluntary Commitments with 7 leading AI firms (Amazon, Anthropic, Google, Inflection, Meta, Microsoft, OpenAI) on safety, security, and transparency measures for AI models – a stopgap governance measure. Then on October 30, 2023, President Biden issued a sweeping Executive Order on AI – the first of its kind globally. This EO instituted new requirements: companies must notify and share test results with the government when training models above a certain power threshold, federal agencies were tasked to set standards for watermarking AI-generated content, and a plethora of actions around privacy, job impact, and innovation were ordered across government. Essentially, it operationalized many ideas from the AI Bill of Rights and NSCAI within the executive branch. It also addressed national security, e.g., directing Commerce to curb exports of AI compute to foreign adversaries and DHS to manage risks of AI in critical infrastructure. On the Hill, while comprehensive AI legislation wasn’t passed in 2023, momentum was building; some narrow bills did advance (like the National AI Commission Act to create a new study commission, and aspects of generative AI in political ads being tackled in election legislation). Internationally, the U.S. participated in the UK’s Global AI Safety Summit (Nov 2023) and took leadership in launching the Code of Conduct for Advanced AI with the EU at the G7, aiming for voluntary global standards ahead of formal law. By end of 2023, AI was a visible part of everyday discourse, and the U.S. had moved from planning and capacity-building to active governance and norm-setting, without losing sight of competitiveness (the EO also talked about attracting talent and promoting innovation, not just regulation).
• 2024: Implementation, Global Leadership, and Election Spotlight – In 2024, the focus turned to implementing 2023’s policies and shaping global governance, all under the watch of a presidential election where AI itself became a subject. The Commerce Department’s Bureau of Industry and Security finalized updated export controls on AI chips (expanding the 2022 rules to cover newer GPUs and other countries mirroring them) , reinforcing hardware resilience. Federal agencies began complying with the Executive Order: NIST launched a pilot AI safety test center (as mandated), OMB issued guidelines for agencies to evaluate AI in procurement and use (building on the EO’s principles), and DHS started a “red team” unit to probe AI models for misuse potential. President Biden, in a March 2024 international conference, proposed a Global Advisory Body on AI under the UN framework, attempting to coordinate AI governance among nations – an idea that gained traction especially among G7 and G20 partners. Meanwhile, Congress continued bipartisan meetings on AI legislation, with draft bills circulating that would create a federal AI licensing regime for advanced models and establish liability for AI-caused harms. AI became a campaign topic: “deepfake” campaign ads appeared (the RNC released an AI-generated hypothetical ad against Biden in April), prompting several candidates of both parties to pledge not to use deepfakes and increasing calls for an election deepfake ban (California had passed one at state level). The FEC by mid-2024 was considering rules on AI in political ads. The campaign also elevated discussions on automation’s impact on jobs, with candidates pressured to articulate plans for managing AI-driven changes in the workforce. On the innovation front, U.S. companies rolled out even more powerful models (rumors of OpenAI’s GPT-5 training, Google’s Gemini model combining language and vision, etc.), keeping the U.S. at the cutting edge as competitors emerge (a notable open-source model from UAE’s Tech Institute made waves, reflecting globalization of capability – which U.S. strategists noted as a reason to double-down on talent and research funding). The OECD in 2024 launched an AI policy observatory, heavily influenced by U.S. data and case studies, showing the U.S. as a reference model for others. Summits like the “Summit for Democracy” included sessions on AI governance, led by the U.S., linking AI ethics to democratic values globally. By late 2024, the U.S. national AI effort was firing on all cylinders: industry still leading in tech, government guiding with a heavier yet collaborative hand, and societal discourse actively shaping both opportunities and guardrails for AI’s future.
• 2025: Consolidation and Strategic Self-Sufficiency – By mid-2025, the United States’ AI strategy emphasized consolidating gains and pursuing self-sufficiency amid global competition. Early in 2025, the National AI Advisory Committee delivered its Year 2 report, recommending the establishment of a dedicated AI Safety and Standards Agency to continually oversee high-risk AI – this is under consideration by the administration. The change of (or continuity in) administration after the January inauguration could slightly shift emphasis: a second Biden term would likely continue strong regulation and alliances, whereas a new administration might recalibrate towards deregulation and pure innovation – indeed, January 2025 saw a hypothetical Executive Order draft titled “Removing Barriers to American AI Leadership,” suggesting a rollback of some prior guidelines. Regardless, the core investment strategy persists: the CHIPS Act fabs are coming online (TSMC Arizona producing 4nm chips by 2025, Intel’s new fabs starting trial runs), moving the U.S. toward secure hardware supply. The U.S. is also pushing the frontier: a national lab-led project on quantum-enabled AI launched in 2025 with multi-agency funding to explore AI algorithms on quantum computers, reflecting foresight into post-Moore’s Law technologies. Globally, the U.S. convened the first-ever Global Summit on Generative AI in June 2025, inviting not just allies but also China (which attended warily) to discuss norms for responsible development and incident sharing – a diplomatic win that kept the U.S. at the center of rule-making. On the domestic front, early 2025 brought some legislative action: Congress, noting public pressure, passed a bipartisan bill banning AI-generated deceptive content in political ads (with criminal penalties), narrowly in time for the 2026 midterms. Implementation of federal AI use saw tangible results: a GAO audit in April found that 85% of federal agencies now have an inventory of AI applications and 68% have conducted at least one algorithmic impact assessment, up from near zero in 2020 – indicating institutionalization of thoughtful AI deployment. Economically, AI contributions were evident as inflation remained moderate partly due to AI-driven productivity gains in logistics and manufacturing, a point touted in administration economic reports. However, concerns remain: tech layoffs in late 2024, attributed to generative AI automating certain white-collar tasks, led to renewed calls for retraining programs – which the Department of Labor scaled up with a $500 million AI Resilience Workforce Fund in early 2025, supported by some of the Big Tech firms as an initiative to maintain public goodwill. Thus, in 2025 the U.S. AI strategy is characterized by bolstering resilience (secure chips, diversified talent), fine-tuning governance (from voluntary to more mandatory where needed), and doubling down on innovation to ensure the U.S. stays not just a leader but the leader in the transformative AI era, shaping it in accordance with American interests and ideals.

Strategy Evolution

2025. Safety, standards & compute alliances
White House finalizes Executive Order on AI Safety; a National AI Research Resource pilot begins; U.S. deepens AI alignment with G7 and Quad partners while tightening chip export controls to constrain adversaries.

2024. Risk frameworks & AI diplomacy
NIST’s AI Risk Management Framework goes global; U.S. co-leads Hiroshima AI Process on generative AI standards; NSF expands AI Institutes to 27 across 40 states; voluntary safety pledges signed by leading U.S. labs.

2023. Generative AI governance & commercial dominance
ChatGPT, Bard, and Claude reshape public perception; OSTP’s Blueprint for an AI Bill of Rights gains traction; executive roundtables accelerate commitments to watermarking, red-teaming, and open safety R&D.

2022. CHIPS Act & ethical guardrails
CHIPS and Science Act unlocks $280 bn for tech R&D; AI Bill of Rights introduces five principles; White House task force proposes National AI Research Resource to democratize compute access.

2021. Institutionalization & whole-of-nation strategy
National AI Initiative Office opens at OSTP; AI Advisory Committee convenes; DOD elevates JAIC into Chief Digital and AI Office (CDAO) to accelerate military integration and data readiness.

2020. Strategic alignment & regulatory scaffolding
OMB releases principles for AI regulation—transparency, fairness, non-discrimination; Congress passes the National AI Initiative Act with bipartisan support, formalizing national coordination.

2019. American AI Initiative & JAIC scaling
Executive Order 13859 lays out five pillars for AI leadership; DOD funds early AI programs including Project Maven and predictive maintenance; NIST tasked with leading global AI standards work.

2018. Defense-first pivot
Pentagon issues first AI Strategy; Joint AI Center (JAIC) is established; DARPA launches $2 bn “AI Next” campaign targeting explainability, resilience, and adversarial robustness.

2016. Foundations & foresight
OSTP releases first National AI R&D Strategic Plan; “Preparing for the Future of AI” report frames AI as general-purpose and cross-sectoral, urging ethical foresight and workforce adaptation.

Pre-2015. Basic research & quiet incubation
DARPA and NSF bankroll foundational work in NLP, robotics, and vision; AI powers early intelligence systems post-9/11; strategy remains fragmented but rooted in Cold War R&D traditions.

2025

On January 23, 2025, President Donald Trump signed an executive order titled “Removing Barriers to American Leadership in Artificial Intelligence.” This directive aims to reinforce the United States’ position as a global leader in AI by promoting innovation free from ideological bias. The order revokes previous AI policies deemed obstructive to progress and mandates the development of an action plan within 180 days to enhance AI’s role in promoting human flourishing, economic competitiveness, and national security. Additionally, it instructs relevant agencies to review and amend existing directives to align with the new policy objectives.

2024: AI Risk Management and National Security Enhancement

• Event: In 2024, the U.S. government focuses on enhancing AI governance and addressing the risks posed by advanced AI systems. The federal government emphasizes public-private partnerships to strengthen AI safety and security in critical infrastructure, defense, and financial systems. AI standards for ethical use and transparency become more robust, with continued efforts to ensure AI applications align with U.S. democratic values.
• Document/Link: Pending release.

2023: AI Bill of Rights Released

• Event: The White House publishes an AI Bill of Rights, which outlines protections and ethical guidelines for the development and use of AI systems. It emphasizes principles such as privacy, fairness, and transparency, aiming to ensure that AI is developed in ways that protect civil liberties and do not exacerbate societal biases.
• Document/Link: AI Bill of Rights (2023).

2022: National AI Research Resource (NAIRR) Implementation

• Event: The U.S. government officially launches the National AI Research Resource (NAIRR), a shared platform designed to democratize access to AI resources for researchers across academic institutions, industries, and government agencies. NAIRR aims to accelerate AI research, innovation, and collaboration, particularly in underserved regions and sectors.
• Document/Link: NAIRR Roadmap (2022).

2021: National Artificial Intelligence Initiative Act

• Event: The National Artificial Intelligence Initiative Act becomes law in January 2021. This landmark legislation establishes a coordinated federal strategy for AI development across government agencies, research institutions, and private companies. The initiative focuses on advancing U.S. leadership in AI, promoting AI education and workforce development, and ensuring ethical and responsible AI use.
• Document/Link: National AI Initiative Act (2021).

2020: Executive Order on Promoting the Use of Trustworthy AI in Government

• Event: The Trump administration issues an Executive Order on Promoting the Use of Trustworthy AI in Government, which mandates that federal agencies adopt AI systems that are ethical, accountable, and aligned with American values. This order highlights the government's commitment to AI innovation while ensuring that public trust is maintained.
• Document/Link: Executive Order on Trustworthy AI (2020).

2019: American AI Initiative

• Event: President Trump signs the American AI Initiative through an executive order. The initiative sets out a strategy for federal agencies to prioritize AI research and development, ethical AI use, workforce development, and international collaboration. The initiative is the first comprehensive national strategy on AI, and it includes a call to action for federal agencies to improve access to AI resources and data for researchers.
• Document/Link: American AI Initiative (2019).

2018: Department of Defense AI Strategy

• Event: The U.S. Department of Defense (DoD) releases its first AI Strategy, recognizing AI as a critical tool for future military capabilities. The strategy focuses on accelerating the adoption of AI within defense systems, ensuring the ethical use of AI in military operations, and collaborating with allied nations to develop AI for defense applications.
• Document/Link: DoD AI Strategy (2018).

Summary of the 2018 Department of Defense Artificial Intelligence Strategy

Harnessing AI to Advance Our Security and Prosperity

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2017: Report on Preparing for the Future of AI

• Event: The White House releases a comprehensive report titled Preparing for the Future of Artificial Intelligence, which outlines the potential benefits and challenges of AI for the U.S. economy, national security, and society at large. The report also provides recommendations for government action in AI research, education, and policy to ensure that AI is used for public good.
• Document/Link: Preparing for the Future of AI (2017).

2016: National AI and Robotics Roadmap

• Event: The National AI and Robotics Roadmap is released as part of the U.S. government’s broader strategy to promote AI and automation technologies. This roadmap provides a detailed vision for AI and robotics development in sectors such as manufacturing, healthcare, and national security, with a focus on workforce readiness and ethical AI use.
• Document/Link: National AI and Robotics Roadmap (2016).

2015: Broadening AI Research and Development

• Event: The U.S. government increases its focus on AI research, with a particular emphasis on funding AI initiatives through agencies such as the National Science Foundation (NSF) and DARPA. Several AI research programs are expanded, and public-private partnerships are encouraged to accelerate AI innovation. AI applications in healthcare, defense, and education begin to take root in government priorities.
• Document/Link: NSF AI Research Initiatives (2015).

2014: DARPA’s AI and Machine Learning Investment Surge

• Event: The Defense Advanced Research Projects Agency (DARPA) significantly increases its investments in AI and machine learning, focusing on next-generation AI technologies for defense applications. DARPA’s programs, such as the AI Next Campaign, target advances in natural language processing, autonomous systems, and AI for cybersecurity, laying the groundwork for future U.S. dominance in AI research.
• Document/Link: DARPA AI Next Campaign (2014).

https://thediplomat.com/2024/07/chinas-national-power-and-artificial-intelligence/

2023

NATIONAL ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT STRATEGIC PLAN 2023 UPDATE

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2023: Launch of the National AI Cybersecurity Strategy

In April 2023, the Biden administration introduced the National AI Cybersecurity Strategy, which emphasized the need for strong cybersecurity measures to protect AI technologies used in critical infrastructure sectors such as energy, healthcare, and transportation. This strategy aimed to ensure that AI systems are safe from cyber threats as they become increasingly integrated into the fabric of U.S. national infrastructure.

• Event: National AI Cybersecurity Strategy launch.
• Date: April 2023
• Details: The strategy promoted collaboration between government agencies, private companies, and international partners to strengthen the security of AI systems in critical industries.
• Link: National AI Cybersecurity Strategy

2022: Blueprint for an AI Bill of Rights

In October 2022, the White House’s Office of Science and Technology Policy (OSTP) introduced the Blueprint for an AI Bill of Rights. This non-binding framework was designed to protect citizens from the potential risks posed by AI technologies, particularly around issues of privacy, bias, and fairness. It outlined five key principles to ensure that AI systems operate in a way that respects human rights.

• Event: Release of the Blueprint for an AI Bill of Rights.
• Date: October 2022
• Details: The blueprint established protections against biased decision-making, a right to privacy and transparency, and recommendations for the responsible use of AI systems.
• Link: Blueprint for an AI Bill of Rights

2021: Updated National AI R&D Strategic Plan

The National Science and Technology Council (NSTC) updated the National AI Research and Development Strategic Plan in June 2021. This revision expanded on the 2016 version by adding new objectives, including the promotion of trustworthy AI systems and the need for diversity and inclusion in AI research. It reaffirmed the role of AI in national security and economic competition.

• Event: 2021 Update to the National AI R&D Strategic Plan.
• Date: June 2021
• Details: The updated plan focused on building ethical, trustworthy AI systems and emphasized equitable participation in AI research and development.
• Link: 2021 National AI R&D Strategic Plan

2020: National AI Initiative Act of 2020

In December 2020, the National AI Initiative Act was passed by Congress and signed into law in early 2021. This legislation established a formal framework for AI development in the U.S., promoting coordination between federal agencies, supporting AI research, and focusing on AI ethics and standards. The Act also encouraged international collaboration on AI governance.

• Event: National AI Initiative Act signed into law.
• Date: December 2020
• Details: The Act called for a federal strategy to ensure the U.S. remains a leader in AI, fostering R&D, workforce development, and standards.
• Link: National AI Initiative Act of 2020

2019: Executive Order on Maintaining American Leadership in AI

In February 2019, President Donald Trump issued an Executive Order on Maintaining American Leadership in Artificial Intelligence, marking the launch of the American AI Initiative. This was a pivotal moment in the U.S. AI strategy, directing federal agencies to increase investments in AI research and development, support AI education, and foster public-private collaboration.

• Event: Executive Order on AI leadership.
• Date: February 11, 2019
• Details: The Executive Order outlined the key pillars for maintaining U.S. leadership in AI, including R&D, governance, and workforce development.
• Link: Executive Order on AI (2019)

2018: Establishment of the Joint Artificial Intelligence Center (JAIC)

In June 2018, the U.S. Department of Defense created the Joint Artificial Intelligence Center (JAIC) to accelerate AI integration into military systems. The center was tasked with managing AI initiatives across the DoD, highlighting the growing importance of AI in national security and defense modernization.

• Event: Establishment of the Joint AI Center.
• Date: June 2018
• Details: The JAIC aimed to enhance U.S. defense capabilities through AI technologies, focusing on applications such as autonomous systems and cyber defense.
• Link: Joint AI Center Announcement

2017: AI and the National Security Strategy

In December 2017, the Trump administration’s National Security Strategy explicitly recognized AI as a critical component of national defense. This marked a shift toward integrating AI into U.S. military and national security policy, signaling the technology’s growing importance for defense and intelligence operations.

• Event: AI included in the National Security Strategy.
• Date: December 2017
• Details: The National Security Strategy highlighted AI’s potential to modernize U.S. defense capabilities and maintain strategic competitiveness.
• Link: 2017 National Security Strategy

2016: National AI Research and Development Strategic Plan

In October 2016, the U.S. government published the National Artificial Intelligence Research and Development Strategic Plan. This was the first comprehensive federal effort to coordinate AI research across government agencies, with a focus on areas like human-AI collaboration, long-term AI investments, and ethical AI development. The plan served as a roadmap for future U.S. AI initiatives.

• Event: Release of the National AI R&D Strategic Plan.
• Date: October 2016
• Details: The strategic plan outlined seven key priorities for AI development, including advancing human-AI collaboration and ethical AI research.
• Link: National AI R&D Strategic Plan (2016)

2013: Initiation of AI Discussions in Federal R&D

As early as 2013, the U.S. government began emphasizing the potential of AI in its broader science and technology agendas. The National Science Foundation (NSF) began prioritizing AI-related research projects, laying the foundation for future AI developments.

• Event: AI research highlighted by NSF.
• Date: 2013
• Details: The NSF started directing increased funding towards AI-related initiatives, acknowledging the importance of AI in maintaining U.S. global competitiveness.
• Link: NSF AI Research Focus

2016

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