AI Places: How To Benchmark and Boost Your Region’s AI Competitiveness

While AI demand is rising across industries worldwide, the future of work isn’t happening somewhere in the cloud, but in real communities. Looking through a regional lens shows us where talent, training, and companies come together—and where the benefits of AI adoption will ultimately be realised.

For workforce development agencies and local policymakers, this means knowing how to prioritise AI-readiness interventions. This requires a clear view of regional strengths, an assessment of gaps, and the ability to connect the players that matter most. As highlighted in a recent Brookings Institution report (which was built using Lightcast data), some regions already have thriving ecosystems, while others are only beginning to emerge.

Knowing where you stand is the first step to becoming an AI-ready region. That’s why places are the focus of the third blog in our Building AI Capability: People, Pathways, Places series. Building on our earlier work on global trends and career pathways, we now turn to regional ecosystems—providing insights and outlining how local leaders can act today to be best positioned for tomorrow.

AI is on the rise everywhere, but the pace varies by region

AI demand and talent are growing fast across the US, but the distribution is uneven. Washington (4.6%) and Washington DC (4.3%) lead in share of workers with AI skills, followed by Massachusetts (3.6%). By volume, California has over 600,000 AI-skilled workers, New York more than 300,000, and Texas over 280,000. Washington DC also tops for job postings mentioning AI (5.7%), followed by Delaware (4.3%) and Washington State (4%).

This uneven geography means some regions are emerging as clear leaders while others lag behind. For policymakers and workforce agencies, benchmarking against peers is essential to understand your relative strengths and gaps—and to decide which levers to pull to boost local competitiveness.

Demand and supply usually move together—but not always

Further zooming in on metropolitan areas shows that AI talent and AI job demand tend to mostly cluster together. For example, Silicon Valley leads by far, with 15% of job postings mentioning AI and 9% of workers listing AI skills. Just to the north, San Francisco ranks second on both measures (7.5% for both profiles and postings), followed by Seattle (6% of profiles, 5% of postings). New York, Dallas and Boston also appear among the metropolitan areas with the most AI talent and demand.

Yet mismatches are also visible. In some areas, demand outpaces supply, while in others talent pools exist without equivalent local opportunities. For example, metro areas such as Rochester, NY and Gainesville, FL have an above average availability of AI talent, but below average demand for AI roles by businesses. Conversely, Jackson, MS and Montgomery, AL have an above average demand for AI roles and a below average share of workers with AI skills. 

This matters because it determines which strategies to prioritize. Identifying whether your challenge is demand (not enough employers hiring locally) or supply (not enough skilled workers) guides decisions ranging from education investments to employer outreach.

To be AI-ready, you need to know your key players

Each region’s AI ecosystem looks different. To illustrate this point, we looked at AI employment in three cities across the US: Seattle, Princeton, NJ, and Boulder, CO. In Princeton, the university dominates the local talent pool, accounting for 7% of AI workers, yet recruitment demand is led by private firms like Bristol Myers Squibb and Johnson & Johnson. In Boulder—highlighted in our Talent Attraction Scorecard as an up-and-coming hub for prime-age workers—the picture is similar, with the University of Colorado as a major employer of AI workers, while private companies like Medtronic and Google are leading the demand for AI roles. In contrast, In Seattle, the picture is reversed: Amazon dominates both sides of the equation, employing 18% of AI workers and driving nearly half (45%) of all AI job postings in the past year. 

These examples show that demand can be driven by universities, by the private sector, or by a mix of both. For workforce development agencies, this highlights the importance of knowing the key players in your region. AI competitiveness depends not only on the amount of talent in a region but on knowing who the anchor institutions are and how ecosystems are connected. Regions that can map their key players are better placed to foster partnerships, strengthen pipelines, and align employers and educators.

So how do you boost your region's competitiveness? 

AI talent isn’t evenly distributed, and neither are AI opportunities. Regions that succeed will be those that understand their position, identify their challenges, and connect the dots between talent, education, and employers.

That starts with data: to see where your region stands, whether the challenge is supply or demand, and who the key players are. And it continues with outreach: with Lightcast’s expanded profiles and company data, you can not only map your ecosystem but also connect directly with the institutions and employers shaping it.

From global trends to career pathways and now to regional ecosystems, this series has shown that building AI capability requires clarity at every level. For regions, the task is clear: benchmark your AI competitiveness, then take action to boost it.

To read more about trends affecting the global labor market including AI, labor shortages, and geopolitics, read our latest research Fault Lines.