It’s no surprise to see the Boston metro—home to MIT, Harvard, and a world-class entrepreneurial scene—named as a standout on AI readiness. But it is unexpected to see Mississippi mentioned in the same breath.

And, indeed, the two regions are on opposite ends of the spectrum when it comes to research, capital, and existing AI talent. What they have in common is smart, location-specific planning—with a heavy focus on ensuring that a wide range of workers benefit. 

In Mississippi’s case, the state has put money and muscle behind general AI literacy and also has zeroed in on manufacturing and defense as industries where adoption and skills should go much deeper. And Boston has focused on identifying and addressing gaps—including in who has access to computing power and training—amid what are otherwise obvious strengths.

The Big Idea: That kind of planning is essential but still lacking in many cities and states, even as AI diffuses beyond the coasts, according to new research by Brookings Metro.

“We came away from our scans somewhat alarmed that there hasn’t been more clarity about this,” says Mark Muro, a report author and senior fellow at Brookings. “We have more time than the AGI-or-bust folks say, but we don’t have a whole lot of time given how difficult it is to reorient systems, to get state and local policies into position, and to effect change.”

The Brookings report is part wake-up call, and part rough outline for regional plans.

The Details: The researchers looked at how AI creation and application is unfolding across 387 U.S. metropolitan areas, examining their relative strengths across 14 indicators. From that, three key groupings of AI readiness emerged:

  • Talent, measuring the production and flow of AI-capable workers,
  • Innovation, capturing research and innovation strengths, and
  • Adoption, charting industry uptake.

San Francisco and San Jose were clear standouts across all three, in a “superstar” class all their own. 

Another set of 28 cities, dubbed “star hubs,” came close to rivaling those two on adoption and especially talent, while being weaker but still strong on innovation. They include established tech leaders like New York and Seattle, state capitals with some of the country’s most productive research universities like Austin, Boston, and Columbus, and other large metros like Washington, D.C. and Chicago.

Together, the superstars and star hubs account for 67% of the nation’s total AI employment, as measured by absolute numbers of AI-related job postings in 2025.

Still, the researchers see real promise in the next two categories, ”emerging centers” and “focused movers,” which respectively have strengths in two areas or are real standouts in one. Detroit, for example, is growing local talent and seeing increased investment in AI—and cutting-edge tech more broadly—nurturing both start-ups and the next phase for behemoths like Ford. 

The places Muro and his colleague Shriya Methkupally worry about the most are places that don’t really have AI on their radar. They might avoid the short-term disruption likely to hit cities with a lot of highly-exposed white-collar jobs, but they also risk missing any upside.

“A place should not think that the absence of AI is necessarily a relief, and some may,” Muro says. “They should be worried about falling further behind.”

Finding Your Lane: Left to their own devices, high-tech innovations like AI tend to have a “winner-takes-most” dynamic, further concentrating capital, know-how, and opportunity in places that are already out ahead. That’s why every metro that isn’t San Jose or San Francisco needs to be mapping areas of opportunity and developing a plan now.

“We found that a lot of the initiatives were pre-2022. They’re attempts at what most people think will be the most basic requirement,” says Methkupally, a report author and senior research assistant at Brookings. “Deep thinking about what their strengths are and where they should be capitalizing was not really there.”

Mississippi, she says, is a good example of thinking creatively. Despite not having obvious hubs of AI activity, it launched that nation’s first statewide AI initiative and just inked a deal with Nvidia to expand the work. It’s targeting industries like manufacturing and defense that are existing strengths for the state. And more than 2,800 K-12 educators have gone through specialized training to raise overall AI skills.

“They see a huge opportunity in having this sort of training for attracting more employers,” Methkupally says.

Similarly, Fargo, N.D., is building itself into a hub for AI in agriculture, with leadership from the Grand Farm collaborative and North Dakota State University. The work gets federal support from the National Science Foundation, through the NSF Regional Innovation Engines program created by the CHIPS and Science Act.

In many of the metros Brookings studied, universities were a linchpin in both developing local talent and drawing in research dollars. That’s at risk as the Trump administration and Congress threaten research dollars and university funding more broadly. Especially concerning, the report authors say, is dwindling support for NSF-led National AI Research Institutes, which launched under the first Trump administration.

“Another missed opportunity is the thinness of AI research and computing flows into high quality but farther-flung universities,” the report says.

Regardless of federal policy, Muro hopes the new report can be a jumping off point for regions to inventory where they stand today and then go from there. It should be a sprint, not a multi-year effort. And it should start with the assumption that AI disruption, opportunity, and skills won’t look the same everywhere across the country.

“Skills and talent initiatives are some of the most important things to be taken on, but they have to be tuned to local industry,” Muro says.