The artificial intelligence boom isn’t distributed equally across America’s economic landscape. While tech giants concentrate their AI investments in familiar Silicon Valley strongholds, a new analysis reveals how this geographic clustering could reshape regional economies—and which cities might emerge as unexpected winners in the AI transformation.
The Brookings Institution, a Washington-based think tank, released comprehensive research mapping AI development across U.S. metropolitan areas, revealing stark disparities in how different regions are positioned for the AI economy. The findings matter because AI adoption isn’t just about technology—it’s about jobs, economic growth, and which communities will thrive as artificial intelligence reshapes entire industries.
Understanding these geographic patterns becomes crucial as businesses decide where to locate AI operations, workers consider career moves, and policymakers craft strategies to ensure their regions don’t fall behind in the AI revolution.
The research divides American cities into five distinct categories based on their “AI readiness”—a composite measure examining local talent pools, institutional innovation capacity, and corporate adoption potential. This framework helps identify which regions possess the infrastructure, workforce, and business environment necessary for AI companies to flourish.
At the apex sit the “AI Superstars,” a category reserved exclusively for parts of the San Francisco Bay Area. These regions are so dominant in AI development that they warrant their own classification, housing the headquarters of major AI companies like OpenAI, Anthropic, and countless startups pushing the boundaries of machine learning and artificial intelligence applications.
The next tier, dubbed “Star AI Hubs,” includes major metropolitan areas with established technology sectors: Boston, Seattle, New York City, and Miami. These cities benefit from strong universities, venture capital networks, and existing tech ecosystems that can support AI innovation. Boston, for instance, leverages its concentration of universities like MIT and Harvard, while Seattle builds on its foundation of major tech companies including Microsoft and Amazon.
This concentration creates what economists call agglomeration effects—when companies cluster together, they benefit from shared talent pools, supplier networks, and knowledge spillovers that accelerate innovation. For AI specifically, this means access to specialized engineers, research partnerships, and the rapid iteration cycles that drive breakthrough developments.
The data reveals just how concentrated AI talent and entrepreneurship have become. Nearly two-thirds of workers who advertise AI skills on professional platforms work in the Superstar and Star AI Hub regions. This includes everyone from machine learning engineers and data scientists to product managers specializing in AI applications.
The startup ecosystem shows even more dramatic concentration. Over 75% of AI-focused startups were founded in these same metropolitan areas, reflecting the powerful combination of available talent, venture capital access, and market proximity that makes these regions attractive for AI entrepreneurs.
Star AI Hubs like New York City, Seattle, and smaller cities such as Columbus, Ohio, and Boulder, Colorado, capture another significant portion of both workers and startups. This secondary tier demonstrates that AI development can succeed beyond the Bay Area, but still requires certain foundational elements like research universities, existing tech infrastructure, and access to capital.
This geographic clustering creates a self-reinforcing cycle. As more AI companies locate in these areas, they attract more skilled workers. As the talent pool deepens, it becomes even more attractive for new companies to establish operations there. However, this concentration also means that vast portions of the country remain underserved by AI development, potentially missing out on the economic benefits of this technological transformation.
Beyond the established tech hubs, the research identifies 14 regions demonstrating potential for AI development but struggling with particular challenges that limit their growth. These emerging centers often possess some foundational strengths—such as major universities or growing tech sectors—but lack the comprehensive ecosystem needed for AI leadership.
Consider Columbia, South Carolina, which serves as a representative example. Despite having a metropolitan population of approximately 860,000 and the University of South Carolina as an anchor institution, the region struggles with talent development. Relatively few students graduate with science and engineering degrees, and even fewer professionals in the area showcase AI-related skills in their job profiles. This creates a talent gap that makes it difficult for AI companies to establish significant operations there.
Tampa, Florida, faces a different challenge. While the metropolitan area has a substantial population and growing business sector, local universities haven’t achieved the research productivity levels necessary to drive AI innovation. Universities play a crucial role in AI development by conducting fundamental research, training specialized workers, and creating the knowledge base that commercial applications build upon.
The majority of these emerging centers struggle most with adoption—measured primarily by local company engagement with AI-related tools like enterprise data platforms and cloud services. This suggests that while these regions might have some research capacity or talent, local businesses haven’t yet embraced AI technologies at the scale necessary to create a thriving ecosystem.
Academic institutions like the University of Wisconsin in Madison and Texas A&M University in College Station anchor some promising regions, while others like Pittsburgh, Detroit, and Nashville benefit from their roles as regional cultural and economic centers. However, each faces distinct obstacles that prevent them from reaching the next tier of AI development.
The research reveals an interesting split in how emerging AI centers attract investment and development resources. Rather than competing across all funding sources, many regions appear to specialize in either federal research contracts or private venture capital, but rarely both simultaneously.
Cities like Huntsville, Alabama, have emerged as centers for federal AI research and development contracts. Huntsville benefits from its proximity to Redstone Arsenal and NASA’s Marshall Space Flight Center, making it a natural hub for government-funded AI research in defense and aerospace applications. These federal contracts provide stable, long-term funding for AI research but often focus on specific applications rather than commercial development.
Conversely, regions like Sacramento, California, attract venture capital attention for AI startups. VC funding typically flows toward commercial applications with rapid scaling potential, creating a different type of AI ecosystem focused on market-ready products rather than fundamental research.
This specialization pattern suggests that emerging centers might need to choose their development path strategically. Federal contracts can provide the stable foundation for building AI research capabilities, while venture capital can accelerate commercial AI applications. However, the most successful AI hubs typically combine both funding sources, creating a comprehensive ecosystem that spans from basic research to commercial deployment.
Mark Muro, one of the report’s authors and a senior fellow at Brookings, emphasizes that “university presence is a tremendous influence on success here.” The relationship between academic institutions and industry becomes particularly crucial for regions seeking to develop AI capabilities, as universities provide both the research foundation and the skilled workforce that AI companies require.
These geographic patterns carry significant implications for businesses planning AI initiatives. Companies seeking to establish AI development centers must weigh the benefits of locating in established hubs against the potential advantages of emerging markets.
Established AI hubs offer immediate access to skilled workers, established supplier networks, and proximity to other AI companies that can provide partnerships and market insights. However, these advantages come with substantial costs—both in terms of real estate and compensation packages needed to attract talent in competitive markets.
Emerging centers present a different value proposition. While they may lack the deep talent pools of established hubs, they often offer lower operational costs, supportive local governments, and the opportunity to build relationships with regional universities. Companies willing to invest in local talent development might find these regions offer better long-term economics.
For businesses in regions not identified as AI leaders, the findings suggest several strategic approaches. Companies can establish remote development teams connected to talent in AI hubs, partner with universities to develop local AI capabilities, or focus on AI adoption rather than development—implementing existing AI tools to improve operations rather than creating new AI technologies.
The concentration of AI development also raises questions about economic equity and regional development. As AI transforms industries from healthcare to manufacturing, regions without strong AI capabilities may find themselves at a disadvantage in attracting investment and high-skilled jobs.
The current concentration of AI development reflects the technology’s early stage, when specialized knowledge and venture capital access were paramount. However, as AI tools become more standardized and accessible, the geographic distribution may shift toward regions with other competitive advantages.
Manufacturing-focused regions might emerge as AI implementation leaders as artificial intelligence transforms production processes. Financial centers could become hubs for AI applications in banking and insurance. Healthcare-focused metropolitan areas might develop specialized AI capabilities for medical applications.
The federal government’s role in AI development could also reshape geographic patterns. As national security considerations drive increased federal investment in AI research, regions with strong relationships to government agencies and defense contractors may gain advantages in attracting federal AI contracts.
For regional policymakers and business leaders, the key insight is that AI readiness requires coordinated investment across multiple dimensions: talent development through education and training programs, institutional support through university partnerships and research initiatives, and adoption facilitation through business support programs and infrastructure development.
The AI revolution is still in its early stages, and while current geographic patterns show significant concentration, the ultimate distribution of AI capabilities across American regions remains to be determined. Regions that act strategically to build comprehensive AI ecosystems may find opportunities to compete with established tech hubs, while those that wait may find themselves increasingly left behind in the AI-driven economy.
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