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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteAI growth in the United States is likely to spread beyond the Bay Area, but not replace it. A Brookings Institution analysis published July 16, 2025, finds that San Francisco and San Jose remain uniquely strong while a wider set of metropolitan areas has the talent, research capacity, investment, infrastructure, or business demand to build meaningful AI activity.
The charts are best read as a map of regional readiness—not a prediction that companies will relocate to any particular city. They benchmark 195 metropolitan areas using 14 measures grouped into talent, innovation, and adoption. The underlying data comes from different years, mainly through 2024, so this is a 2025 baseline rather than a live 2026 ranking.
What the four charts actually measure
“AI readiness” is not a single observable statistic. Brookings combines proxies for three capabilities:
- Talent: computer science, engineering and mathematics graduates, PhD enrollment, AI-skilled job postings and the ability to develop and retain skilled workers.
- Innovation: AI publications and patents, federal research contracts, university and laboratory capacity, startups, venture capital and access to high-performance computing.
- Adoption: business use of AI, cloud and data readiness, exposure of local jobs to generative AI, and industries that can deploy AI in real operations.
Those measures can point in different directions. A university town may produce excellent researchers but lose graduates. An industrial region may have strong customers for automation but few AI startups. A metro with many job postings may be recruiting aggressively without filling all of those positions. The classifications therefore describe ecosystem configurations, not a definitive league table.
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Brookings groups the metros into six categories:
| Category | Metros | What it means |
|---|---|---|
| Superstars | 2 | San Francisco and San Jose; exceptional across talent, innovation and adoption. |
| Star Hubs | 28 | Broadly capable ecosystems with relatively balanced strengths. |
| Emerging Centers | 14 | Strong in two pillars but comparatively weaker in the third. |
| Focused Movers | 29 | Distinctive strength in one pillar or industry. |
| Nascent Adopters | 79 | Moderate performance across all three pillars, with room to build. |
| Others | 43 | Lagging across multiple measured capabilities. |
Source: Brookings, “Mapping the AI economy,” July 16, 2025.
Chart one: the Bay Area still sets the ceiling
San Francisco and San Jose are the only metros classified as Superstars. Their advantage is cumulative: universities and research institutions produce talent; established firms supply experienced workers, customers and managers; venture capital funds new companies; and successful companies attract more capital and workers.
Brookings’ analysis found that the Bay Area accounted for 13% of all U.S. job postings requiring AI skills. That figure is a recruitment signal, not a count of filled jobs, and it reflects the report’s underlying data vintage rather than a 2026 measurement.
This concentration matters because frontier AI depends on networks that are difficult to reproduce quickly. A lower-cost city can offer cheaper offices or labor, yet still lack senior researchers, specialized engineers, investors, early customers and the informal connections that speed hiring and commercialization. The likely outcome is incremental diffusion around an enduring core, not a wholesale replacement of Silicon Valley.
Chart two: Star Hubs provide the broadest alternatives
The 28 Star Hubs are the second tier of broadly capable AI ecosystems. Coverage of the Brookings charts identifies examples including Boston, Seattle, New York, Miami, Columbus, Ohio, and Boulder, Colorado. They should not be treated as interchangeable: some are research-heavy, some have major technology employers, some benefit from federal contracts, and others are strongest as enterprise-adoption markets.
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A Star Hub can support several kinds of expansion:
- research laboratories and university partnerships;
- engineering, sales or customer-success offices;
- AI vendors serving large local industries;
- startups spun out of universities or established companies; and
- government-funded or defense-related projects.
Being a Star Hub does not mean every company will move its headquarters there. It means the region has enough complementary assets that additional investment is more plausible than in a metro with only one isolated strength.
Chart three: Emerging Centers have a strength—and a missing piece
Brookings identifies 14 Emerging Centers. These metros perform strongly in two of the three pillars but have a comparatively underdeveloped third. Examples cited in coverage include Pittsburgh, Madison, Wisconsin, Detroit, Nashville and College Station, Texas.
Pittsburgh and Madison: research that must connect to markets
University-centered regions can offer computer-science graduates, faculty expertise, publications, patents and federal research. Their challenge is often retention and commercialization: keeping graduates locally, attracting experienced managers, raising later-stage capital and turning laboratory work into durable companies.
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A manufacturing region may have immediate use cases for computer vision, robotics, logistics and predictive maintenance. That creates customers and adoption potential, but the region still needs founders, specialized engineers, investors and technology-transfer channels to convert demand into a larger local AI industry.
College Station and other growing metros: capacity has to catch up
Fast-growing regions may attract firms and graduates yet lack enough experienced workers, high-performance computing, infrastructure or venture financing. Their opportunity is real, but growth depends on filling the missing pillar rather than relying on population growth alone.
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Chart four: population is not destiny
Large population does not automatically produce AI leadership. A big metro can have a substantial general labor force but relatively few AI specialists, major companies that use AI without developing it, or universities without a leading AI research position. Venture-capital networks, startup formation and specialized computing may also be limited.
That helps explain why coverage of the charts points to some underperformance by very large cities such as Los Angeles and Chicago, while smaller or mid-sized metros in Colorado, Texas and elsewhere show stronger results on selected measures. This is not a “small cities beat big cities” rule. A large city can still be a valuable enterprise market, corporate office location or specialized hub even if it is not a leading research-and-startup center.
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What “AI companies going there” can mean
Geographic expansion is broader than a headquarters move. A region may gain AI activity through:
- satellite engineering or research offices;
- data centers and other computing facilities;
- university collaborations and federally funded projects;
- startups founded by local researchers or industry workers;
- remote hiring by companies headquartered elsewhere; or
- local firms adopting AI in healthcare, manufacturing, defense, agriculture, energy, finance, logistics or government.
Brookings measures this broad AI economy, not only companies training frontier foundation models. A city can therefore benefit substantially as a customer, specialist supplier or applied-AI center without becoming the next Silicon Valley.
What each type of region needs next
Superstars
San Francisco and San Jose need to keep supporting startup formation, technical education and immigrant talent while managing housing, infrastructure and cost pressures.
Star Hubs and Emerging Centers
These regions can deepen clusters, improve university-industry links, expand affordable high-speed computing and strengthen graduate retention. Brookings specifically emphasizes broader access to computing resources for these metros.
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Regions with one signature advantage should connect it to commercialization. A defense, health, energy or manufacturing base becomes more valuable when local companies can build, finance and scale products around it.
Nascent Adopters and Others
The practical starting point is AI literacy, cloud and data foundations, workforce programs and help for existing businesses adopting proven tools. Adoption can improve productivity even before a region develops a large startup sector.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why decentralization will be difficult
Experienced talent
Graduates are easier to attract than senior researchers, engineering managers, founders and infrastructure specialists. Talent production without retention does not create a durable cluster.
Capital and commercialization
An incubator or one large funding round is not a complete ecosystem. Startups need seed and later-stage financing, experienced investors, paying customers and credible paths to acquisition or public markets.
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Compute, power and water
AI growth can require cloud access, high-performance computing, data-center capacity, reliable electricity, cooling and water. Brookings has separately examined the relationship between AI data centers, water and regional development in its water analysis.
Policy and operating conditions
Incentives can attract a facility or office, but they do not by themselves create researchers, investors, customers or startup networks. Housing, transport, broadband, energy prices and quality of life affect whether workers and firms stay.
How to judge a prospective AI hub
- Measure talent depth: graduates, PhD supply, experienced workers and retention.
- Check research capacity: publications, patents, federal contracts, laboratories and computing.
- Test commercialization: startups, venture deals, technology transfer and company survival.
- Look for real customers: firms already digitized and industries with clear AI use cases.
- Audit physical capacity: power, broadband, data centers, water, cooling, laboratories and offices.
- Assess coordination and livability: university-industry partnerships, workforce programs, housing and transportation.
What the charts cannot predict
- They do not show that companies have announced relocations.
- They do not forecast a city’s job gains, layoffs or investment returns.
- Exposure to generative AI measures potential task impact, not automation or job loss.
- Job postings measure recruiting activity, not filled positions.
- Venture capital may reflect a temporary boom or a few companies rather than durable depth.
- The 195-metro analysis can underrepresent meaningful activity in smaller metros.
- The metrics are collected on different schedules—sources include ACS 2023, HERD 2023, Lightcast 2024, USPTO 2023, USA Spending 2024 and PitchBook data from 2023–2024—so they are not one synchronized snapshot.
Earlier Brookings studies used different metro counts and labels, so their “early adopter” categories should not be combined with the 2025 Star Hub framework without checking the date and methodology. The relevant historical comparison is Brookings’ “The geography of AI”.
The likely shape of the next AI map
The evidence supports partial diffusion alongside persistent concentration. The Bay Area remains the deepest all-purpose ecosystem, while Star Hubs, Emerging Centers and specialized regional economies can capture particular kinds of AI work. The strongest candidates will connect skilled people, research, capital, infrastructure and local industry demand. Cheap real estate or a large population alone is not enough.
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