# US AI Strategy

The US AI Strategy encompasses federal policies, executive orders, and initiatives to promote artificial intelligence development, manage risks, and maintain global leadership in AI technology.

The United States AI Strategy refers to the coordinated set of federal policies, executive actions, and strategic initiatives aimed at advancing [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) research, development, and deployment while addressing national security, economic competitiveness, and societal risks. The strategy has evolved through multiple presidential administrations, reflecting shifting priorities from research investment to regulation and international cooperation.

## Early Foundations

Federal involvement in AI research predates the formal strategy. The Defense Advanced Research Projects Agency funded early [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) and [neural-network](https://www.wikiprompt.org/wiki/neural-network) work in the 1960s and 1970s, including the [perceptron](https://www.wikiprompt.org/wiki/perceptron) and [backpropagation](https://www.wikiprompt.org/wiki/backpropagation) research at institutions like [mit-csail](https://www.wikiprompt.org/wiki/mit-csail) and [stanford-ai-lab](https://www.wikiprompt.org/wiki/stanford-ai-lab). The National Science Foundation supported foundational work in [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) through the 1980s and 1990s, including grants to researchers such as [geoffrey-hinton](https://www.wikiprompt.org/wiki/geoffrey-hinton) at the [university-of-toronto](https://www.wikiprompt.org/wiki/university-of-toronto) and [yann-lecun](https://www.wikiprompt.org/wiki/yann-lecun) at bell-labs.

In 2016, the Obama administration released the first comprehensive federal AI report, "Preparing for the Future of Artificial Intelligence," which outlined opportunities and risks across sectors. This was followed by the National Strategic Computing Plan and the establishment of the National Science and Technology Council's Select Committee on AI.

## The American AI Initiative

In February 2019, President Donald Trump signed Executive Order 13859, titled "Maintaining American Leadership in Artificial Intelligence," which launched the American AI Initiative. This order directed federal agencies to prioritize AI research and development, expand access to federal data and computing resources, and establish AI standards. The initiative allocated $1.2 billion in non-defense AI research funding for fiscal year 2020, a 14% increase from the previous year.

The White House Office of Science and Technology Policy (OSTP) and the National Science Foundation (NSF) were tasked with implementation. The NSF established the National AI Research Institutes program, awarding $20 million each to seven universities in 2020, including [carnegie-mellon-university](https://www.wikiprompt.org/wiki/carnegie-mellon-university), [mit-csail](https://www.wikiprompt.org/wiki/mit-csail), and [stanford-ai-lab](https://www.wikiprompt.org/wiki/stanford-ai-lab). The initiative also created the American AI Initiative's AI.gov portal to coordinate federal AI activities.

## Executive Order on Safe, Secure, and Trustworthy AI

In October 2023, President Joe Biden issued Executive Order 14110, "Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence." This order represented a shift toward risk management and regulatory oversight. It required developers of large AI models to share safety test results with the federal government before public release, particularly for models that could pose risks to national security, public health, or economic security.

The order directed the National Institute of Standards and Technology (NIST) to develop the AI Risk Management Framework, which was released in January 2023. It also established the U.S. AI Safety Institute within NIST in February 2024, with a budget of $10 million for its first year. The institute was tasked with developing testing protocols for [large-language-model](https://www.wikiprompt.org/wiki/large-language-model)s and other advanced systems.

The order also mandated that federal agencies assess AI-related risks in critical infrastructure, including energy grids, water systems, and healthcare networks. It required the Department of Homeland Security to issue guidelines for AI use in critical sectors by late 2024.

## National Security and Defense Applications

The Department of Defense (DoD) has been a major driver of the US AI Strategy. In 2018, the DoD released its AI Strategy, which emphasized accelerating AI adoption across military operations. The Joint Artificial Intelligence Center (JAIC) was established in 2018 with an initial budget of $1.7 billion over five years. In 2022, the JAIC was reorganized into the Chief Digital and AI Office (CDAO), which consolidated AI, data, and digital transformation efforts.

The DoD's AI investments have focused on areas such as [computer-vision](https://www.wikiprompt.org/wiki/computer-vision) for surveillance, [natural-language-processing](https://www.wikiprompt.org/wiki/natural-language-processing) for intelligence analysis, and autonomous-vehicles for logistics. The Defense Innovation Unit has partnered with companies like [openai](https://www.wikiprompt.org/wiki/openai) and [anthropic](https://www.wikiprompt.org/wiki/anthropic) on safety research, though specific contracts remain classified.

In 2023, the DoD released its Data, Analytics, and AI Adoption Strategy, which set goals for scaling AI across the department by 2025. The strategy called for a "data-centric" approach, emphasizing the need for high-quality training data over algorithmic improvements.

## International Cooperation and Competition

The US AI Strategy includes significant international dimensions. In 2021, the US and the European Union launched the Trade and Technology Council (TTC), which established working groups on AI standards and risk assessment. The US also joined the Global Partnership on AI (GPAI), an international initiative launched in 2020 with 15 founding members.

In 2023, the US and the UK signed the Atlantic Declaration, which included commitments to collaborate on AI safety research. The US also participated in the Bletchley Declaration at the AI Safety Summit in November 2023, which was signed by 28 countries and called for international cooperation on frontier AI risks.

Competition with China has been a central theme. The US has imposed export controls on advanced semiconductor chips, including [nvidia](https://www.wikiprompt.org/wiki/nvidia) A100 and H100 processors, to China since October 2022. These controls were expanded in October 2023 to include more advanced chips and manufacturing equipment. The strategy also includes the CHIPS and Science Act of 2022, which allocated $52 billion in subsidies for domestic semiconductor manufacturing, including [tsmc](https://www.wikiprompt.org/wiki/tsmc) and [intel](https://www.wikiprompt.org/wiki/intel) facilities in Arizona and Ohio.

## Federal Research and Development Programs

The National AI Research Institutes program has expanded significantly. As of 2024, NSF has funded 25 institutes across the country, with total federal investment exceeding $500 million. Each institute focuses on specific areas such as AI-for-science, AI-for-education, or AI-for-agriculture. The institutes are required to partner with industry and non-profits, with matching funds required from non-federal sources.

The Department of Energy (DOE) has also invested heavily in AI for scientific discovery. In 2023, the DOE announced $100 million in funding for AI research at its national laboratories, focusing on [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) for materials science and climate modeling. The DOE's Argonne National Laboratory and Oak Ridge National Laboratory have deployed some of the world's most powerful supercomputers for AI workloads, including the Aurora system at Argonne, which achieved 1.02 exaflops in June 2024.

The National Institutes of Health (NIH) has funded AI research in medical-imaging and drug-discovery. In 2023, the NIH announced $130 million in grants for AI-based health research, including projects using [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) to predict disease outcomes from electronic health records.

## Regulatory and Ethical Considerations

The US has taken a sectoral approach to AI regulation, rather than a comprehensive federal law. The Federal Trade Commission (FTC) has used existing consumer protection laws to address AI-related harms, such as algorithmic bias in credit decisions. The Equal Employment Opportunity Commission (EEOC) issued guidance in 2022 on AI use in hiring, warning that automated systems could violate anti-discrimination laws.

The White House released the Blueprint for an AI Bill of Rights in October 2022, which outlined five principles: safe and effective systems, algorithmic discrimination protections, data privacy, notice and explanation, and human alternatives. While not legally binding, the Blueprint has influenced federal procurement and agency guidance.

The Office of Management and Budget (OMB) issued a draft memorandum in November 2023 requiring federal agencies to designate AI governance officials and conduct impact assessments for AI systems. The final memorandum was expected in 2024, with implementation deadlines extending through 2025.

## State and Local Initiatives

While the federal strategy is central, many states have developed their own AI policies. California, New York, and Texas have passed laws requiring transparency in AI-driven hiring and credit decisions. In 2023, California enacted SB 313, which requires companies to disclose when they use AI to make consequential decisions. New York City's Local Law 144, effective January 2023, mandates annual bias audits for automated employment decision tools.

These state-level efforts sometimes conflict with federal guidance, creating a patchwork of regulations. The National Conference of State Legislatures reported that 45 states introduced AI-related legislation in 2023, with 17 states enacting laws.

## Future Directions and Challenges

The US AI Strategy continues to evolve. In late 2024, the Biden administration proposed a national AI research cloud to provide researchers with access to advanced computing resources. The proposal, which would cost an estimated $10 billion over five years, aims to democratize access to [GPU](https://www.wikiprompt.org/wiki/gpu) clusters and large-scale [training-data](https://www.wikiprompt.org/wiki/training-data).

Key challenges include the rapid pace of [generative-ai](https://www.wikiprompt.org/wiki/generative-ai) development, which often outpaces regulatory frameworks. The emergence of open-source models has complicated export controls, as models can be copied and distributed without physical restrictions. Additionally, the federal government faces a shortage of AI talent, with the Office of Personnel Management reporting that only 0.1% of federal employees have AI-related skills as of 2024.

The strategy also faces political uncertainty. The 2024 presidential election could lead to significant changes in AI policy, with some candidates proposing moratoriums on certain AI development and others advocating for deregulation. As of late 2024, no comprehensive federal AI law has passed, leaving the strategy largely dependent on executive orders and agency action.

## Conclusion

The US AI Strategy represents a complex, multi-agency effort to balance innovation, security, and societal protection. From early research investments to recent executive orders, the strategy has shifted from a focus on competitiveness to include robust risk management. The effectiveness of these policies will depend on continued funding, international cooperation, and the ability to adapt to rapidly evolving AI capabilities.

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Source: https://www.wikiprompt.org/wiki/us-ai-strategy
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-10-23T02:23:36.586378+00:00