Four-Year Blueprint for America’s AI Growth, Skills, Infrastructure, and Global Leadership
As America stands at the dawn of a new political era, the convergence of policy, private sector innovation, and skilled workforces offers an unprecedented chance to reshape the nation’s economic trajectory through artificial intelligence. The opportunity has been described as a once-in-a-generation moment to harness transformative technology in a way that sustains long-term prosperity, competitiveness, and security. A leading technology company positions its view within this larger national narrative: AI is akin to electricity for the age, a general-purpose technology with the power to redefine productivity and opportunity across every industry. The path forward rests on a clear, three-part framework: building and maintaining world-leading AI technology and infrastructure, expanding AI skilling to empower a broad cross-section of Americans, and actively exporting American AI to allied nations in a way that benefits both the domestic economy and international partners. This approach, grounded in collaboration among government, business, academia, and non-profits, aims to lay a durable foundation for American economic success over the coming decades. The content that follows expands on these ideas, articulating a detailed, multi-faceted strategy for turning ambition into lasting impact.
Vision for American AI-Led Economic Growth
America’s future prosperity hinges on a deliberate, comprehensive, and well-coordinated national strategy for AI that leverages the nation’s strengths while addressing its challenges. The central premise is straightforward: technology leadership—particularly in artificial intelligence—can drive sustained economic growth, improve public services, and expand opportunity for workers at all skill levels. To translate this premise into tangible outcomes, the plan unfolds across three interlocking pillars. First, it calls for sustained advances and investments in world-class American AI technology and the infrastructure that makes it practical and scalable. Second, it emphasizes a robust, widespread skilling program designed to accelerate AI adoption and unlock career opportunities for millions of Americans across the economy. Third, it prioritizes exporting American AI to trusted allies and partners, ensuring that other countries benefit from AI breakthroughs while strengthening domestic economic resilience and strategic influence. Together, these pillars form a blueprint for a national AI strategy that seeks to replicate and extend the country’s historical pattern of innovation leading to broad-based economic gains.
This vision builds on foundational ideas in AI policy that were articulated during the early parts of the previous administration’s term and recognizes that policy momentum must extend into the coming years. It is not enough to boast about technological prowess in the abstract; the United States must translate its leadership into practical advantages for workers, businesses, and communities. Achieving this involves a partnership that unites public leadership with private enterprise, academia, and the nonprofit sector. Such a partnership is essential because AI’s impact is pervasive, affecting every sector—from manufacturing and logistics to healthcare, education, and government services. The private sector has long been the primary engine of American AI innovation, and the current ecosystem—ranging from dynamic startups to established companies—continues to produce breakthroughs that redefine what is possible. However, the government’s role remains critical in shaping the conditions under which this innovation can flourish, including investment in foundational research, workforce development, and export strategies that expand the market for American AI while maintaining national security and consumer trust.
A key element of this vision is to recognize AI as a universal productivity enhancer rather than a narrow technology confined to a handful of sectors. Just as earlier general-purpose technologies—steam power, electricity, machine tooling, computer chips, and software—redefined entire economies, AI stands poised to act as the latest broad-based catalyst for innovation. This recognition carries with it both opportunity and responsibility: opportunity because AI can unlock new economic value, create jobs, and raise living standards; responsibility because it requires careful governance, ethical considerations, and inclusive access to training and resources. The three-pillar framework helps ensure that opportunity translates into broad-based prosperity rather than selective gains. It also highlights the importance of a national AI talent strategy that enables Americans from diverse backgrounds to compete for, and benefit from, AI-enabled roles across industries.
Beyond the policy framework, there is a pragmatic imperative to build on enduring strengths in the private sector. The United States has historically depended on a strong, competitive private sector to drive innovation, attract investment, and scale technologies for public benefit. In AI, this dynamic is even more pronounced: American companies—ranging from nimble startups to global platforms—invest heavily, take on substantial risk, and deliver the AI tools and services that enable widespread adoption. A successful national AI strategy thus balances incentives for private investment with strategic public support in critical areas such as infrastructure, workforce development, and export readiness. It also requires a cooperative stance that invites collaboration across supply chains, academic institutions, and regional ecosystems to ensure that AI benefits are distributed broadly and equitably.
Crucially, the vision calls for a modernized, inclusive approach to AI education and training. Skilling must reach not only traditional technologists but also frontline workers, small business owners, educators, and communities that historically faced barriers to participation in high-growth digital economies. AI literacy—encompassing basic fluency with AI tools, data literacy, and the capacity to design or manage AI-enhanced processes—should become a foundational competency, much like reading, writing, and numeracy. In this context, a national AI talent strategy becomes a unifying objective that aligns educators, industry, and policymakers around shared goals: to prepare the workforce for AI-enabled roles, to reskill workers displaced by automation, and to create pathways toward entrepreneurship and higher-witness careers.
The final dimension of the vision is a pragmatic export strategy that leverages American AI strengths while expanding markets in a way that is secure, trusted, and aligned with democratic values. Export leadership does not simply mean selling more software; it means shaping how AI technologies are deployed globally, building international standards, safeguarding critical infrastructure, and ensuring that partnerships advance shared goals such as security, privacy, and human-centered innovation. An effective export strategy also recognizes the central role of private capital and commercial incentives in scaling AI globally. By combining thoughtful policy with market-led deployment, the United States can set the pace for responsible, widely adopted AI across the world while preserving domestic opportunities for American workers and firms.
As this vision unfolds, the nation will rely on a constructive, collaborative approach that integrates government, industry, education, and civil society. The aim is not to win a temporary race but to establish a durable, adaptive system that can respond to evolving technologies, global competition, and changing economic conditions. In this sense, AI is not merely a tool for incremental gains; it is a strategic lever that, if harnessed wisely, can help build a more prosperous, innovative, and resilient America for the long term. The coming years demand a unified effort to translate opportunity into outcomes—more reliable AI infrastructure, broader and deeper skills across the population, and stronger, smarter international partnerships that reinforce American leadership in the global AI economy.
World-Leading AI Technology and Infrastructure
Artificial intelligence is advancing toward becoming a global general-purpose technology with the potential to reshape productivity across every facet of the economy. The United States, leveraging its private sector strengths and the world’s most dynamic innovation ecosystems, is well-positioned to lead this wave if it sustains its investments in AI technology and the sophisticated infrastructure that supports large-scale AI deployment. This section details how the U.S. can maintain its leadership in AI technology and the essential infrastructure that underpins AI-enabled applications, from data centers and chip architectures to software platforms and open-source ecosystems. It also highlights the interdependence of public policy, industry collaboration, and skilled labor in delivering reliable AI solutions.
At the core of this leadership is the emergence of artificial intelligence as a transformative general-purpose technology, capable of spurring productivity gains across a broad spectrum of activities. Unlike single-purpose tools, AI systems can appeal to multiple industries by accelerating tasks, enabling better decision-making, and unlocking new capabilities that were previously impractical. The AI revolution requires an ecosystem that combines private investment, world-class hardware, advanced software, and scalable data infrastructure. In the United States, this ecosystem is characterized by a diverse mix of companies—from early-stage startups to global platform providers—that invest in AI research, model development, and the deployment of AI-enabled services. The role of private capital and competition remains central: it fuels rapid experimentation, drives cost reductions, and fosters the development of robust AI systems that can operate at scale. This is not a hypothetical scenario but a live, evolving landscape defined by ongoing investment, user adoption, and continuous improvements to AI models and tools.
A significant driver of American AI capability is the substantial capital allocated to building out datacenters and the underlying infrastructure necessary to train, test, and deploy AI models. For fiscal year 2025, a major technology company projects investment volumes approaching eighty billion dollars to construct and upgrade AI-enabled datacenters aimed at training models and delivering cloud-based AI and related services to a global customer base. Notably, more than half of this investment is earmarked for operations within the United States, underscoring a commitment to domestic economic vitality and to creating high-skill, high-wage jobs in American communities. These investments do more than increase capacity; they also provide essential resilience by enabling faster response times, improved reliability, and enhanced security for AI-driven applications. In an era where data and compute are the fuel of AI progress, such infrastructure expansions are foundational to maintaining leadership in both industry and government applications.
The AI infrastructure narrative is inseparable from the broader ecosystem that supports AI innovation. A thriving AI economy depends on a healthy, competitive set of players across multiple layers of the technology stack. Open-source software, semiconductor suppliers, AI software developers, system integrators, enterprise solution providers, and a broad community of skilled professionals all contribute to a feedback loop that accelerates progress. The United States has built a robust ecosystem in which these components interact to deliver scalable AI solutions. The openness of this ecosystem—embracing collaboration across competitors, suppliers, and partners—has been a key driver of rapid innovation. The widespread use of open-source development, complemented by proprietary technologies, supports a diverse set of AI capabilities and accelerates the dissemination of advancements across industries.
A strong, resilient AI economy is also anchored in a broad workforce pipeline that includes highly trained electricians, engineers, software developers, data scientists, and many other roles integral to the AI value chain. The construction of large datacenters, the deployment of energy-efficient cooling systems, and the development of sophisticated data pipelines demand skilled labor across multiple trades. Industry remains deeply interconnected with labor markets, with unions and skilled trades playing a meaningful role in delivering the physical infrastructure necessary for cloud and AI ecosystems. The collaboration among construction firms, steel manufacturers, electrical contractors, and other suppliers becomes part of a national technology backbone that supports AI adoption at scale. This interconnected workforce helps ensure the reliability and security of AI operations, while also contributing to economic growth and regional development.
Beyond hardware and physical infrastructure, software platforms and tools are essential to AI’s everyday utility. The U.S. AI technology leadership is reflected in the constant evolution of AI-enabled applications across the economy. From enterprise software to consumer tools, AI is being reimagined to deliver more personalized experiences, smarter automation, and more effective decision support. This transformation is underscored by the rapid transition from traditional hardware-focused pipelines toward AI accelerators and specialized architectures that optimize model training and inference. Across the United States, a new generation of AI firms is emerging, drawing on rapid advances in AI models and silicon technologies and moving toward higher efficiency and performance with AI accelerators that complement traditional GPUs. This shift—from GPUs to accelerators with tensor capabilities—reflects the ongoing refinement of AI hardware to maximize throughput, minimize cost, and enable scalable deployment in real-world environments.
A robust AI ecosystem is inseparable from open innovation and collaboration. While leading companies are delivering groundbreaking capabilities, success increasingly hinges on the health of partnerships across the broader technology community. This includes suppliers of chips and silicon, developers of AI software tools, system integrators that help businesses implement AI, and a vast network of service providers that deploy AI-based solutions for customers. The vitality of this ecosystem depends on an ongoing dialogue among competitors, customers, researchers, and policy makers to address shared challenges—ranging from interoperability and safety to data governance and privacy. In this way, American AI leadership is not the product of a single entity’s success but the outcome of a thriving, multi-faceted community capable of supporting diverse deployment scenarios at scale.
Historical context reinforces the significance of a strong AI infrastructure. The United States has long relied on two core ingredients to sustain technological leadership: sustained support for basic research and a robust, ongoing commitment to product development and commercialization. From the postwar era onward, this dual approach—universities and federal funding for foundational science paired with private-sector execution and scaling—has powered America’s most enduring innovations. In artificial intelligence, that legacy remains vital. Federal and university research, including programs funded by the National Science Foundation and related agencies, lays the groundwork for new AI breakthroughs. At the same time, private firms translate those discoveries into market-ready products and services through development, capital investment, and global deployment. The result is a virtuous cycle in which research fueling discovery and practical deployment reinforce each other, strengthening the nation’s competitive position and the public’s access to the benefits of AI.
In this context, it is essential to acknowledge the role of national policy in creating a conducive environment for innovation. A well-calibrated mix of open competition, security considerations, and collaboration across sectors helps ensure AI progresses in a way that benefits society at large. For example, public policy that prioritizes data accessibility for research, while simultaneously enforcing high standards for safety, privacy, and cybersecurity, can accelerate the translation of knowledge into practice. It can also help align incentives for both basic research and applied product development, enabling universities, startups, and established companies to contribute to AI progress. A forward-looking policy framework recognizes the importance of data resources and computing infrastructure as national assets and seeks to maximize their value for citizens while maintaining the highest levels of trust and accountability.
In sum, the United States’ AI leadership depends on a robust triad: top-tier technology and infrastructure, a thriving and inclusive ecosystem, and clear, forward-looking public policy that fosters innovation while safeguarding national interests. The convergence of private investment, specialized hardware and software, and a widely distributed data and compute foundation creates a powerful platform for AI-enabled growth. With these elements in place, the U.S. can sustain leadership in AI innovation, deliver tangible benefits to workers and businesses, and deepen ties with international partners who share values and aims for responsible AI development and deployment.
AI R&D, Basic Research, and Product Development
A healthy AI ecosystem rests on two enduring, closely linked ingredients: sustained, high-quality basic research and continuous, robust investment in product development by companies of all sizes. This combination has historically been a defining strength of American innovation, enabling discoveries born in universities to be transformed into practical technologies and widespread applications that empower the economy. The United States has long excelled at moving ideas from discovery to deployment, a dynamic that relies on public support for foundational science, private-sector capital and risk-taking, and an environment that encourages collaboration and cross-pollination among sectors. This section explores how those two pillars continue to shape AI progress and what content policy and investment patterns can sustain them in the coming years.
Basic research has historically driven long-term breakthroughs that may not have immediate commercial returns but generate profound and lasting knowledge with broad societal benefits. Universities and national laboratories are hubs of inquiry where researchers pursue fundamental questions about intelligence, learning, perception, data processing, and algorithmic principles that can eventually lead to transformative technologies. Across this landscape, a critical mechanism is sustained funding for basic research through federal agencies such as the National Science Foundation and related bodies, complemented by private philanthropy and institutional commitments from universities. In practice, this ecosystem of curiosity and exploration often yields discoveries that the commercial sector later exploits, sometimes in unexpected and unanticipated ways. The public nature of many foundational results—such as publications, open datasets, and shared insights—facilitates broad diffusion and accelerates wider adoption, enabling firms of all sizes to leverage early-stage knowledge to build new capabilities.
On the other side of the equation lies product development and commercialization—where the private sector translates abstract ideas into usable tools, platforms, and services. The United States has a proven track record of efficiently moving ideas from the academic sphere into the market. This process rests on several interdependent elements: strong venture investment that takes calculated risks on nascent concepts; a mature ecosystem of suppliers, service providers, and developers capable of translating research into market-ready products; and a customer base eager to adopt new technologies that deliver measurable value. The net effect is an environment in which basic research feeds product development, and the resulting products generate revenue that funds new rounds of research and innovation. This virtuous cycle sustains a robust national innovation system that supports a broad arc of AI advancements—from core algorithms and models to practical tools and AI-powered services that touch daily life.
A pivotal moment in this interplay occurred when the government took deliberate steps to strengthen AI leadership through policy initiatives aimed at aligning research investments with national priorities. In 2019, the President approved an executive order designed to enhance America’s AI leadership by increasing federal investments in AI research and making federal data and computing resources more accessible to researchers and developers. This policy move recognized that public data assets and computing resources are essential accelerants for AI progress and that expanding access to these assets can spur breakthroughs by universities and research institutions. Five years later, the question becomes how to build on those efforts in a way that is both ambitious and sustainable. The answer lies in expanding support for basic research at the National Science Foundation and through universities, while simultaneously strengthening mechanisms that translate discoveries into scalable, competitive products and services backed by industry.
A practical, forward-looking approach to AI policy should seek to preserve the delicate balance between basic research and private sector development. Basic research remains intellectually driven and curiosity-led—pursuing questions that may not have immediate commercial goals but have the potential to reshape entire industries. It is often publicly funded, with results that are published and shared with the broader community. The other side of the equation—applied research and product development—transforms ideas into practical tools, often supported by private capital, market incentives, and customer feedback cycles that drive iterative improvements. The United States has historically been successful because it has managed to fuse these strands into a coherent system: universities and researchers generate breakthroughs; companies and investors scale them; and markets distribute the benefits across the economy. A future AI policy should reinforce this model, leveraging classroom-to-lab-to-market pathways that enable rapid translation from discovery to deployment while maintaining the openness that fosters further innovation.
The administration can strengthen these foundational elements by expanding funding for basic AI research while continuing to encourage swift translation of new ideas into practical applications. One of the core challenges is ensuring that basic research outputs are accessible and usable by researchers and developers across sectors, thereby reducing duplication and accelerating progress. This involves supporting open access to data, models, and benchmarks where appropriate, along with careful policy frameworks that protect privacy, security, and ethical considerations. Public funding should be complemented by industry partnerships that provide real-world testing grounds, user feedback loops, and avenues for commercialization. Such collaboration helps ensure that foundational research remains vibrant and relevant to the needs of businesses, workers, and public services.
The long-term success of AI policy depends on a shared commitment to both robust knowledge creation and practical deployment. In the short term, this means sustaining and expanding the existing framework of basic research funding while amplifying mechanisms for technology transfer from universities to the private sector. In the medium term, it involves creating incentives for cross-disciplinary work that brings together computer science, cognitive science, ethics, economics, and social science to address complex AI challenges holistically. In the long term, it requires a national strategy that harmonizes research priorities with workforce development, policy considerations, and international collaboration. The aim is to cultivate an environment in which ideas can flourish, be rigorously tested, and scaled with speed and responsibility.
A critical element of sustaining AI leadership is recognizing the value of both curiosity-driven exploration and mission-oriented research. The former fuels foundational breakthroughs that redefine what is possible, while the latter ensures that research aligns with national priorities and public needs. The right balance enables researchers to pursue bold questions without losing sight of real-world impact. In this framework, public policy can provide a foundation that supports both streams—encouraging curiosity and facilitating application—through mechanisms such as grant funding, collaborative research centers, and incentives for industry-university partnerships. The ultimate goal is to maintain a steady pipeline of ideas that can be refined into market-ready solutions, enabling the United States to sustain its competitive edge while delivering tangible benefits to citizens.
Finally, these R&D dynamics underscore the importance of strategic policy choices that enhance America’s AI ecosystem without stifling innovation. A key policy objective is to encourage broad participation across geographic regions and sectors, so that AI progress is not concentrated in a few urban centers but distributed across the country. This involves supporting regional innovation ecosystems, strengthening ties between academia and industry in diverse communities, and ensuring that workers in different contexts have access to the training and opportunities created by AI advances. The outcome should be a resilient, inclusive AI economy in which basic research informs applied development, and both converge to drive productivity, growth, and improved quality of life for people everywhere in the nation.
AI Skilling and Workforce Transformation
A cornerstone of the national AI strategy is a concerted commitment to skilling that equips Americans with the abilities needed to thrive in an AI-driven economy. The premise is straightforward: AI technologies will transform the nature of work, automate routine tasks, and create opportunities for higher-value roles that rely on human creativity, problem-solving, and collaboration with intelligent systems. Preparing workers to adapt to these changes requires a well-structured, scalable skilling framework that spans education, training, and lifelong learning. The objective is to ensure a broad segment of the population—not just a technical elite—has the knowledge and confidence to leverage AI tools, contribute to innovation, and pursue meaningful careers in an evolving labor market.
Skilling has historically been a central priority for national AI policy. In 2019, a presidential AI executive order highlighted AI skilling as a core objective and called for integrating AI into existing educational programs, expanding fellowships, and developing apprenticeship and STEM-focused training to create a robust pipeline of AI-enabled professionals. The strategy recognized that AI skills are transferable across many industries and functions, enabling workers to apply AI technologies in diverse contexts and to adapt to changes in job requirements as automation and augmentation technologies mature. The foresight of that policy has proven prescient: five years later, AI skilling has become indispensable for the nation’s workforce strategy as AI reshapes the world of work, job design, and productivity.
A central aim of AI skilling is to lower barriers to entry for workers and to broaden participation. AI technologies have the potential to democratize access to better opportunities, enabling small businesses and individuals to compete in new ways with larger enterprises. This could help mitigate economic inequality by providing pathways for high school graduates and others with less post-secondary education to access higher-paying roles and new entrepreneurial opportunities. In this sense, AI skilling is not only about producing a new generation of software engineers; it is about equipping people across all sectors with AI fluency—an essential capability for modern work. AI literacy will enable workers to understand how AI supports decision-making, how to interpret AI outputs responsibly, and how to integrate AI tools with existing processes to boost productivity and innovation.
To achieve broad-based AI literacy, the national strategy must implement a wide range of training modalities. On-the-job training, online platforms, and formal education programs will all play a role. For many workers, the path will begin with on-the-job learning augmented by online modules, continuing through to formal education through community colleges and universities. For others, it may start with short, accessible online courses or micro-credentials that recognize competencies in AI-driven analysis, data interpretation, and the ethical use of AI. For advanced training, programs at community colleges and four-year institutions can evolve into AI engineering tracks that prepare individuals to design, build, and manage AI systems within organizations across the private, public, and non-profit sectors. This layered approach ensures there are multiple routes for people with different backgrounds and goals to acquire AI skills.
In this context, Microsoft’s efforts illustrate a broad and practical approach to AI skilling. The company has committed to training millions of Americans—in 2025 alone, it aims to prepare roughly 2.5 million students, workers, and community members for AI-enabled roles. The goal is to empower individuals to land new jobs, pursue new careers, and even create new businesses rooted in AI capabilities. This scale is necessary to address the breadth of AI adoption that will unfold across sectors, industries, and geographies. It also reflects an understanding that the benefits of AI will be amplified when a large portion of the workforce can participate in and contribute to AI-enabled transformation.
A focal area of AI skilling is the partnership with community colleges, which play a critical role in workforce development. Community colleges provide accessible, affordable, and flexible education that can be aligned with local industry needs. The approach includes aligning AI curricula with industry requirements, expanding faculty training through targeted programs, and developing curricula that prepare students and workers to apply AI tools in practical settings. An example of this strategy is Microsoft’s collaboration with the National AI Consortium for Community Colleges, which aims to deliver industry-aligned AI curricula that reflect real-world needs and opportunities. This approach ensures that education remains relevant, timely, and responsive to workforce demands in different regions.
Another key element is the development of AI training programs for teachers, recognizing that educators are critical multipliers for AI adoption. By equipping teachers with AI knowledge and effective instructional strategies, schools can create a cascade of learning that reaches students across age groups and disciplines. Partnerships with workforce agencies aim to enhance AI skills and career guidance through tools like a Microsoft Copilot for Career Navigators, designed to help communities navigate the AI-driven economy and identify pathways to employment in AI-related fields. These tools are intended to support guidance counselors, teachers, and advisors who assist learners in aligning their aspirations with demand in the AI economy.
National outreach efforts target every corner of the country, including rural communities where access to high-quality AI training can be limited. Initiatives such as the National 4-H AI Skills Partnership use Minecraft Education to introduce AI concepts to 1.4 million youth, broadening exposure to AI in a practical, engaging format. The Future Farmers of America’s FarmBeats for Students program applies AI to precision agriculture, helping students and farmers understand how AI can optimize crop management, yield prediction, resource use, and sustainability practices. These programs illustrate how AI skilling can connect with local industries and community priorities, translating technology literacy into tangible career and business opportunities.
One of the most compelling takeaways from these skilling initiatives is the recognition that AI has the potential to extend economic opportunity beyond traditional pipelines. The aim is to reach individuals who may not have pursued a post-secondary degree or who might face barriers to entry in high-skill tech roles. By providing accessible learning avenues, empowering educators, and connecting training to real-world applications, AI skilling can lower the barriers to entry and broaden the distribution of opportunity. The overarching message is that AI skilling represents a national priority, a “north star” guiding efforts to ensure every American has the chance to participate in the AI-enabled economy.
A critical question is how to measure progress and ensure impact. Progress can be tracked through metrics such as the number of individuals trained in AI-related competencies, the share of AI training delivered through community colleges and workforce partners, the number of teachers and counselors equipped with AI tools, and the rate at which AI fluency translates into new job opportunities or revenue growth for businesses. Monitoring these indicators helps ensure that skilling efforts are aligned with labor market needs, regional economic development goals, and broader social objectives. At the policy level, success will be measured by the ability to connect training to measurable outcomes—employment, earnings growth, climbing job quality, and increased opportunity for workers in diverse communities.
Ultimately, AI skilling is not a one-off initiative but a sustained, long-term effort that must evolve with the technology. It requires coordination among federal agencies, state and local governments, educational institutions, private sector partners, and non-profit organizations to create a coherent, scalable pipeline of AI talent. The objective is to create a world-class, inclusive AI workforce that spans every region and sector of the economy, ensuring that AI progress translates into broad-based growth, improved public services, and enhanced competitiveness in a rapidly changing global landscape. This is the core objective behind the skilling pillar of America’s AI strategy: to equip all Americans with the knowledge and capabilities needed to navigate and shape an AI-enabled future.
AI Exports, International Strategy, and Global Competitiveness
A crucial element of maintaining American leadership in AI is a proactive, strategic approach to exporting AI capabilities and fostering international adoption in a way that reinforces national security, promotes trusted technology partnerships, and expands the global footprint of American AI. Export strategy is not merely about selling products abroad; it is about shaping how AI technologies are deployed across diverse markets, influencing global standards, and ensuring that American innovations contribute to a rules-based, secure digital economy. The United States faces intense international competition, most notably from China, where the rapid advancement of AI has accelerated in tandem with targeted government support and strategic investments. The dynamics of this competition—whether in hardware, software, data capability, or AI services—shape how American AI can be adopted around the world. The best response for the United States is to move quickly, decisively, and effectively to promote American AI as a superior, trusted option, while working with allies and partners to build a coalition that can advance common interests in the global AI economy.
The export strategy must account for the realities of global markets and the network effects that shape technology adoption. In the technology sector, early movers can establish standards and secure a dominant position that is difficult to dislodge later. This means that the United States should pursue a comprehensive approach that combines safeguards for sensitive AI components with active international collaboration to accelerate the diffusion of American AI advantages. Export control regimes play a role in protecting critical technologies, but policy must be calibrated to avoid stifling innovation or constraining the private sector’s ability to scale and compete internationally. A pragmatic export policy should balance security protections for AI assets in trusted data centers with the need for U.S. companies to supply reliable, secure AI services to allies and partners around the world.
The competitive landscape also includes the actions of other major AI players, notably China. The Chinese government’s approach—supportive subsidies, development of domestic AI platforms, and efforts to expand AI data center capacity—has effectively positioned it to accelerate adoption in many developing markets. This has real implications for how global AI platforms are deployed, particularly in regions where cost considerations, local partnerships, and data governance rules shape technology choices. The United States must respond by leveraging the strengths of its private sector, including the reliability and trust associated with American hardware, software, and services, as well as the security guarantees that come with U.S. governance frameworks and standards.
The United States brings several tangible advantages to this strategic competition. First, American companies own and control some of the most advanced AI technology, from sophisticated AI models to secure chips and robust software ecosystems. Second, many U.S. firms have invested heavily in building AI that prioritizes cybersecurity, privacy, and responsible use, ensuring that AI applications adhere to high standards for user trust and data protection. Third, U.S. organizations have established datacenters and cloud services that meet stringent cyber and physical security requirements, enabling reliable, compliant AI deployment on a global scale. These advantages provide a solid foundation for exporting AI capabilities to international markets in a way that is both responsible and competitive.
Moreover, there is growing international regulatory collaboration among democracies across North America, Europe, and the Asia-Pacific region. If the United States can translate its most effective AI strategies into international diplomacy—leveraging platforms such as G7 and broader diplomatic channels—these efforts can help harmonize standards, foster secure cross-border data flows, and facilitate the adoption of American AI solutions in a manner consonant with shared values. A strong, globally coordinated approach to AI governance can reduce fragmentation and create predictable environments in which American AI exporters can operate confidently, while maintaining alignment with privacy, security, and human-rights considerations.
The private sector also plays a central role in expanding AI globally. Companies across the tech sector, including giants like Google, Amazon, and others, are committing substantial resources to scale AI capabilities and bring them to more markets. These efforts are often complemented by private capital that funds AI infrastructure and research initiatives across multiple geographies. The private sector’s capacity to mobilize capital, build AI platforms, and establish large-scale data centers is a critical advantage in expanding AI adoption abroad, particularly in the Global South where infrastructure gaps and digital inclusion challenges persist. The combination of private investment and international partnerships accelerates the diffusion of AI innovations and fosters the creation of local AI ecosystems with global connections.
A concrete example of America’s export strategy in action is the commitment to invest in a broad, multinational AI infrastructure that supports secure cloud and data center deployments. In a notable illustration, a major U.S.-based company announced plans to invest more than $35 billion over three years to establish trusted and secure AI and cloud data center infrastructure across 14 countries. This initiative expands an overarching global infrastructure that now reaches 40 countries, including regions that have historically been underserved by AI investment. These efforts are complemented by strategic partnerships designed to broaden access to AI infrastructure and supply chains. For instance, collaboration with regional and international partners like the United Arab Emirates’ sovereign AI company to extend AI infrastructure to other countries, or joint initiatives with leading investment firms to mobilize up to $100 billion for AI infrastructure and supply chains, demonstrate how public-private collaboration can scale AI deployment globally.
Competition with China remains a central dynamic in international AI strategy. While China’s government can fund national-scale AI deployments and subsidize market entry for AI platforms, the United States has a strong edge in private-sector leadership, global capital markets, and a track record of building trusted AI technology that emphasizes cybersecurity, privacy, and responsible use. The aim is not merely to counter subsidies but to outpace them by delivering AI technology that is perceived as more trustworthy, secure, and beneficial across diverse regulatory environments. By combining top-tier American AI models, secure data infrastructure, and a robust ecosystem of developers and enterprises, the United States can present a compelling value proposition to partners and customers around the world.
International collaboration is further enhanced by leveraging existing diplomatic and economic partnerships. The United States can build on established alliances and frameworks to advance shared interests in AI governance, regulatory cooperation, and the management of cross-border data flows. The Abraham Accords, originally established to deepen political and economic ties in the Middle East, offer a blueprint for broader technology and economic collaborations that extend to AI. By expanding technology and investment ties with key nations and sovereign investors, the United States can create a more integrated and resilient global AI ecosystem. This approach complements private-sector leadership by creating a favorable regulatory and economic climate in which American AI solutions can be adopted more widely, safely, and efficiently.
In this broader context, Microsoft’s role provides a concrete example of how American leadership can materialize in practice. The company recently announced a plan to invest over thirty-five billion dollars in AI infrastructure across multiple countries over a three-year period, aiming to deliver trusted and secure AI capabilities to a diverse set of markets. This initiative reflects a broader trend in which major technology firms are expanding their international footprints, forming strategic alliances, and contributing to the development of AI ecosystems in both developed and developing regions. By aligning with national goals for AI leadership, these corporate efforts help advance a shared digital future characterized by reliability, security, and broad-based participation.
At the same time, private-sector initiatives are supported by a broader coalition of financiers and institutions. Partnerships with financial entities and investment funds are designed to mobilize capital for AI infrastructure, with the goal of expanding data-center capacity, cloud services, and AI supply chains across the globe. This collaboration is essential for ensuring that AI systems can be deployed at scale to meet the growing demand from public and private sector users. In practice, the deployment of AI infrastructure and services across multiple markets requires careful coordination among technology providers, regulators, and local communities to ensure that projects align with local needs, address security considerations, and adhere to privacy and data protection standards. The outcome is a globally connected AI economy in which American technologies and business models drive adoption in a way that benefits both domestic and international stakeholders.
A central policy implication of this international strategy is the need for a balanced export-control framework that supports innovation without compromising national security. The United States must protect sensitive AI capabilities and critical data from misuse or adversarial exploitation while enabling private companies to expand rapidly and responsibly into international markets. A pragmatic approach to export controls acknowledges the realities of global supply chains, cross-border data handling, and international collaboration, and it seeks to minimize friction for compliant, legitimate AI deployments. By maintaining rigorous security standards and promoting openness and interoperability where feasible, the United States can sustain its competitive edge while facilitating responsible AI adoption around the world.
China’s ongoing efforts to subsidize technology adoption and expand its AI footprint reinforce the importance of a proactive, forward-looking American strategy. If the private sector can continue to outpace subsidies with innovation, reliability, and trust, American AI platforms are more likely to achieve global reach. The focus should be on prioritizing secure, privacy-preserving, and user-centric AI solutions that meet diverse local requirements and regulatory contexts. This approach fosters confidence among international customers and policymakers, encouraging broader, safer adoption of American AI technologies while preserving the integrity of global digital infrastructure.
In sum, AI exports and international strategy represent a critical pillar of national competitiveness. The United States can translate its technological advantages into global market leadership by combining aggressive private-sector deployment with a strategic policy framework that protects security and privacy, encourages international collaboration, and builds trusted AI ecosystems across regions. By doing so, America can extend the reach of its AI innovations, foster global economic growth, and reinforce the country’s position as a leader in responsible, innovative artificial intelligence.
The Path Forward: Balance, Security, and Global Adoption
The path forward for American AI leadership must be navigated with care, ambition, and disciplined balance between innovation, security, and inclusion. A pragmatic approach to policy is essential to preserve the private sector’s wind at its back while ensuring that AI development and deployment adhere to robust safety, privacy, and ethical standards. The United States has a unique opportunity to coordinate a global AI diplomacy effort that harmonizes export controls with international collaboration, enabling American AI to scale rapidly while maintaining the trust of users, partners, and governments around the world. The aim is not to freeze progress with excessive regulation but to deploy a balanced policy that recognizes the needs of a fast-moving industry and the importance of safeguarding critical technologies and public welfare.
One of the central policy questions is how to manage export controls in a way that protects sensitive components and national security without unduly constraining the private sector’s ability to innovate and compete internationally. The best path combines targeted protections for truly critical AI elements with a broader framework that enables legitimate AI products, services, and platforms to reach markets efficiently. A flexible, risk-based approach can adapt to evolving capabilities while maintaining guardrails that safeguard privacy, cybersecurity, and ethical guidelines. In practice, this means calibrating licensing regimes, strengthening export controls where necessary, and simplifying procedures for compliant, beneficial AI exports. By doing so, policymakers can ensure that the United States remains a trusted partner for international customers and allies while pooling the private sector’s capacity to drive AI progress globally.
Another important consideration is the need to align export policy with security and trust. The rapid expansion of AI capabilities requires that developers and operators implement robust security measures in AI platforms, protect user data, and uphold privacy standards. This is essential for enabling broad adoption of American AI technologies across different regulatory contexts and cultural environments. The U.S. government can support this by promoting common standards for AI security, data governance, and privacy practices that can be applied consistently across markets. Such standards would help reduce friction for international users and institutions seeking to deploy AI responsibly, while also making it easier for policymakers to monitor and manage risk.
International collaboration is a critical enabler of AI adoption and prosperity. In practice, this means strengthening diplomatic and economic ties with partners who share values of democracy, human rights, and open markets. Cooperation can take many forms, including joint research initiatives, shared standards development, and coordinated investments in AI infrastructure that support development goals while protecting data sovereignty and privacy. The G7 and other international platforms are natural venues for dialogue on AI governance, safety, and ethical use. By leveraging these forums, the United States can contribute to a common set of norms that guide AI deployment globally, create predictable markets for American AI, and encourage responsible innovation across borders.
For American AI leadership to translate into broad-based national prosperity, it must connect technology with people. This means continuing to invest in AI skilling, emphasizing lifelong learning, and ensuring that workers in communities across the country have access to training that aligns with market demand. The private sector has a pivotal role in this effort by designing programs that translate AI knowledge into concrete career opportunities and by collaborating with educational institutions to ensure curricula remain relevant to evolving industry needs. The public sector, in turn, should provide the policy framework, funding, and public services that enable broad access to AI-enabled opportunities, including digital literacy programs, workforce development initiatives, and targeted support for rural communities and underserved populations. Together, these elements create a virtuous cycle in which AI progress strengthens the economy, expands opportunity, and enhances the capacity of government to deliver services more efficiently.
The ultimate measure of progress is a future in which AI advances are widely shared and beneficial. From raising productivity to creating new forms of entrepreneurship, AI can help the United States address some of the most pressing economic and social challenges of our time. By combining a resilient private sector, a strong public policy backbone, and inclusive skilling programs, America can navigate the global AI transition with confidence. The path forward requires steady, principled leadership that prioritizes security, privacy, and human-centered design while embracing the transformative potential of AI. If the United States can maintain an adept balance between innovation, security, and inclusion, the outcome will be a more prosperous, competitive, and resilient nation.
Conclusion
In the four years ahead, the United States has a rare chance to convert AI’s vast potential into broad-based economic growth and social progress. The key to realizing this golden opportunity lies in a cohesive, three-part strategy: relentless investments in world-leading AI technology and infrastructure; ambitious, scalable skilling to empower Americans from all walks of life; and proactive, principled AI exports that extend American leadership while strengthening international partnerships. This approach—built on the recognition of AI as the electricity of our era and anchored by a deep commitment to research, education, and collaboration—offers a practical pathway to a prosperous future. It requires not only innovation but also disciplined governance and a shared national purpose that spans government, business, academia, and civil society. The United States has historically demonstrated that coordinated public-private action can translate bold ideas into enduring prosperity; the coming years must reinforce that legacy by turning AI’s promise into tangible benefits for every American. By embracing a balanced, inclusive, and forward-looking AI strategy, the nation can secure its leadership, expand opportunity for workers and communities, and help shape a global AI environment that reflects the best of American values and capabilities.