This is the first in a three-part series exploring AI and the future of entrepreneurial opportunity.

For decades, geography has helped determine entrepreneurial opportunity. Where entrepreneurs lived influenced their access to capital, talent, customers, universities, business networks and infrastructure. Certain regions became synonymous with particular industries because the resources required to compete clustered there.

Artificial intelligence has the potential to change that equation. An entrepreneur no longer needs a large staff to access sophisticated research, marketing, analytics or operational capabilities. A small manufacturer can use AI to improve processes, a professional-services firm can automate administrative work, a retailer can analyze customer behavior and a founder can use tools once available primarily to much larger enterprises.

Yet the emergence of AI does not mean geography no longer matters. It means the geography of entrepreneurial opportunity is changing.

Access To AI Is Expanding Faster Than Adoption

One of the great promises of generative AI is that powerful technology can be placed directly into the hands of entrepreneurs. But access and adoption are two different things.

According to the U.S. Census Bureau’s Business Trends and Outlook Survey analysis , overall business AI use hovered between 17% and 20% from December 2025 through May 2026. The difference by company size is notable: 37% of businesses with at least 250 employees reported using AI, while fewer than 20% of firms with four or fewer employees did.

That adoption gap has important competitive implications. Large companies can hire consultants, create dedicated AI teams and invest substantially in technology and training, while smaller firms rarely have those same resources. For entrepreneurs, therefore, the next phase of the AI economy should not simply be about gaining access to tools. It should be about developing the capacity to use them strategically.

This makes the ecosystems surrounding entrepreneurs increasingly important. Community colleges, universities, financial institutions, business-support organizations and corporations can help businesses move from experimenting with AI to applying it in ways that improve productivity and competitiveness. Regions that build those bridges may create an entirely new kind of entrepreneurial advantage.

Recent research from Mark Muro, Senior Fellow, and Shriya Methkupally, Senior Research Assistant, at Brookings Metro provides an important lens into this changing geography. Their research, Mapping the AI Economy: Which Regions Are Ready for the Next Technology Leap? , examines 195 U.S. metropolitan areas using 14 measures organized around three pillars of AI readiness: talent, innovation and adoption.

One finding is particularly striking: The Bay Area alone accounts for 13% of all AI-related job postings. Yet Muro and Methkupally also find evidence that generative AI and agentic systems are beginning to expand AI activity into a broader collection of metropolitan areas.

This presents an opportunity for regions to think differently about competitiveness. The objective should not be for every region to replicate Silicon Valley. Instead, leaders should determine how AI can amplify the economic assets their regions already possess. A manufacturing region may become a leader in AI-enabled manufacturing. A community anchored by research universities may have opportunities around commercialization. Another region may create substantial economic value by helping thousands of existing businesses become AI-enabled.

This is where entrepreneurial strategy and regional economic strategy increasingly intersect.

The Cloud Still Has A ZIP Code

There is a paradox at the center of the AI economy: The technology feels increasingly decentralized, while the infrastructure supporting it is anything but. Every AI query ultimately depends on physical computing capacity—data centers, electricity, fiber networks, land and cooling systems—and the scale of that infrastructure is growing quickly.

A June 2026 report from Lawrence Berkeley National Laboratory estimates that data centers could account for 11.8% of total U.S. electricity consumption by 2030 in its reference case, with modeled scenarios ranging from 9.5% to 15.3%.

For cities and regions, that creates a different economic-development question. Data centers can produce construction activity, tax revenue and permanent employment, but the size of the investment does not necessarily translate into similarly large numbers of long-term jobs. Research from Brookings’ Dany Bahar and Greg Wright analyzed approximately 1,500 U.S. data center facilities, along with 52 announced-but-canceled projects used in their comparison. Their analysis estimates that a typical county receiving its first large data center sees roughly 100 to 200 additional jobs, depending on facility type.

If communities are providing land, energy and infrastructure for the AI economy, the business question should therefore extend beyond how many data centers they can attract to how those investments can create broader economic capacity. That could mean stronger local supplier networks, workforce programs, research partnerships, entrepreneurial development or connections between technology infrastructure and regional industries. The data center should not necessarily be the economic-development strategy; it can be an asset within one.

From AI Readiness To Entrepreneurial Readiness

Muro and Methkupally identify talent, innovation and adoption as three pillars of regional AI readiness. For business leaders, I would add another lens: entrepreneurial readiness.

That means asking whether local businesses can access the technology, whether entrepreneurs can find workers capable of using it, whether smaller companies can translate AI into productivity, whether universities and research institutions can connect innovation to commercialization, and whether major technology investments can create opportunities beyond the companies making those investments.

These questions matter because the economic impact of AI will not be determined solely by the companies that build it. It will also be determined by the millions of businesses that use it.

For regional leaders, the strategic mistake would be assuming there is one formula for competing in the AI economy. A more useful exercise is identifying a region’s existing economic strengths and asking how AI can multiply them.

For business leaders and entrepreneurs, the same principle applies. The competitive question is moving from s hould we use AI? to w here can AI materially change the economics of our business? For institutions that support entrepreneurs, the challenge is ensuring that technological access becomes productive capacity.

We may ultimately need a different way of measuring successful AI economies—not simply by how much venture capital they attract, how many data centers they build or how many AI companies call them home, but by how effectively they enable existing businesses and new entrepreneurs to create value from the technology.

The geography of entrepreneurship is not disappearing. AI is redrawing it. And the regions that understand that distinction may have the greatest opportunity to shape what comes next.