The Grid Divide: Which States Will Power the AI Economy—and Which Will Be Left Behind

Artificial intelligence is triggering an electricity demand surge unlike anything the U.S. grid has faced in decades. By 2028, data centers will consume two to three times more power, and by 2030 nearly half of all new U.S. electricity demand could come from AI. The AI revolution is no longer about code or GPUs—it is about gigawatts.

Yet the growth is not evenly distributed. A handful of states are sprinting ahead, positioning themselves as the energy backbone of the AI economy, while others—especially in New England and parts of the West Coast—risk being left behind. This emerging gap is what The Grid Divide defines and measures.

At the heart of the report is the Grid Readiness Score™ (GRS), a first-of-its-kind ranking of all fifty U.S. states based on their ability to power AI-driven load growth. The GRS incorporates five critical factors:

1. Load Tolerance – headroom to absorb new demand.
2. Capacity Flexibility – interconnection and transmission availability.
3. Permitting Velocity – how fast infrastructure can be approved.
4. Resource Mix – balance of reliable and clean energy.
5. Investment Visibility – scale of projects already announced or underway.

The results are striking. Georgia (87), Texas (86), and Virginia (75) lead the nation. Georgia’s rise is tied to the Vogtle nuclear expansion, a streamlined permitting regime, and a flood of new hyperscale investment. Texas benefits from ERCOT’s open market, rapid transmission planning, and over 30 gigawatts of projected AI-driven load. Virginia remains the world’s largest data hub but is beginning to strain under congestion and community pushback.

At the bottom are Hawaii (18), Rhode Island (26), and Maine (29), along with much of New England. Despite deep pools of tech talent, these states struggle with high costs, slow permitting, and limited grid capacity. California also ranks low, dragged down by permitting hurdles, escalating costs, and reliability concerns that are pushing development eastward.

The report emphasizes that this divide is not inevitable. States can climb the rankings if they act decisively. The Grid Divide outlines a five-part playbook for lagging states:

• Anticipate load growth with AI-specific forecasts that map demand at the county level.
• Reform interconnection queues with transparent timelines, standardized costs, and fast-track approvals.
• Accelerate permitting by setting statutory deadlines and pre-certifying corridors.
• Create AI-ready zones with documented access to power, fiber, and water.
• Rebalance resource mixes to ensure hour-by-hour reliability with firm clean energy, storage, and flexible capacity.

The stakes could not be higher. States that deliver reliable, affordable power quickly will capture billions in capital investment, tax revenue, and job creation—not just in data centers, but in semiconductors, advanced manufacturing, and other AI-adjacent industries. States that fail will watch opportunity flow elsewhere.

Ultimately, The Grid Divide shows that the future of AI will not be built where the coders are. It will be built where the power is. The GRS is both a scoreboard and a roadmap—revealing today’s leaders, today’s laggards, and the path forward for states willing to act.

Transcript

Michael: Welcome to AIxEnergy, the podcast where we explore the rising intersection of artificial intelligence and the systems that power our world.

I'm your host Michael Vincent. My guest is Brandon Owens, an energy innovation executive, author, and founder of A-I-X-Energy. He’s just released a landmark report: The Grid Divide.

Brandon, it’s great to have you.

Brandon: Thanks, Michael. Excited to be here.

Michael: Your report opens with a pretty shocking projection: by 2028, U.S. data centers will consume two to three times more power, and by 2030, nearly half of all new electricity demand could come from AI. Paint the picture for us. How dramatic is this shift?

Brandon: It’s transformative. Think of it this way: data centers today already consume more electricity than entire mid-sized countries. Now we’re saying, in just a few years, we could be adding the equivalent of Japan’s entire electricity consumption onto the U.S. grid.

This demand is being driven by two forces. One, the training of ever-larger AI models, which require huge bursts of power over weeks. And two, the always-on inference services that follow, powering everything from chatbots to image recognition around the clock. Together, these loads behave less like a tech service and more like a heavy industry—continuous, concentrated, and extremely sensitive to outages.

Michael: But as you point out, this boom isn’t spread evenly across the country. Some states are magnets, others are stalling. Explain the divide.

Brandon: Exactly. Nine states are on track to host about seventy percent of U.S. data center capacity by 2030. Georgia, Texas, and Virginia are sprinting ahead—they have speed, affordable power, and regulatory clarity.

Meanwhile, traditional innovation hubs—like California and many New England states—are struggling. They face high electricity prices, protracted permitting processes, and congested grids. Developers can’t wait five or seven years for an interconnection approval. So the capital moves to states that can deliver in 18 to 24 months. That’s the “Grid Divide.”

Michael: To measure readiness, you created something brand new—the Grid Readiness Score. Walk us through what it is and how it works.

Brandon: The GRS is the first composite index ranking of every U.S. state’s ability to power the AI economy. We looked at five components:

1. Load tolerance—does the state have enough headroom to absorb new demand without risking instability?

2. Capacity flexibility—can new projects actually plug in, given transmission and substation constraints?

3. Permitting velocity—how fast can infrastructure be approved?

4. Resource mix—how reliable and clean is the state’s generation portfolio?

And 5. Visibility of investment—what’s the scale of data center projects already announced or underway?

We scored each factor on a 0–100 scale, then averaged them. That gave us a state-by-state readiness ranking.

Michael: So who’s on top?

Brandon: Number one is Georgia with a score of 87. It’s remarkable—Atlanta actually overtook Northern Virginia in 2024 for annual data center absorption, which stunned industry observers. Georgia benefits from the new nuclear units, adding more than two gigawatts of firm carbon-free power. On top of that, permitting can move in months, not years.

Texas is right behind at 86. ERCOT is planning for more than fifty gigawatts of new load growth by 2030. The state’s competitive market attracts investment, and its Senate Bill 6 formally brought data centers into reliability planning.

Virginia, long the “Silicon Valley of Data Centers,” scored 75. It still hosts the largest hub in the world, but grid congestion and community pushback are slowing growth.

Michael: And at the bottom?

Brandon: Hawaii scored just 18, Rhode Island 26, Maine 29. These are small, expensive, or isolated grids—unlikely to ever host massive AI campuses.

More concerning are New England states broadly. They score in the 20s and 30s. Despite having world-class universities and tech talent, they’re hobbled by high costs, slow permitting, and limited interconnection capacity. California is also in the low 30s. It has demand, but siting hurdles and reliability issues are pushing new projects to Oregon, Nevada, and Arizona.

This is the essence of the divide: talent is one thing, but in the AI era, power is destiny.

Michael: One thing I liked about your report is the state-by-state depth. Can you give us an example of how a state turned itself into a frontrunner?

Brandon: Take Georgia. Three years ago, it was a secondary player. Then hyperscalers like AWS, Google, and Microsoft announced billions in new investment. The state leaned in—new nuclear reactors gave it carbon-free capacity, Georgia Power adjusted its resource planning, and the legislature kept permitting streamlined. Result: 70 new data centers are now in the queue.

Or Iowa—ranked 71. It leveraged its years of wind power growth and attracted Facebook, Apple, and Microsoft campuses. Even though it’s a smaller state, its resource mix and permitting speed punch above its weight.

Michael: Let’s turn to the roadmap. For states at the bottom—New England, California, others—what can they do to climb the rankings?

Brandon: We outline five pillars in the report:

1, Anticipate load growth with AI-specific forecasting. Stop relying on incremental demand models. AI arrives in blocks—hundreds of megawatts tied to a single substation. States need county-level forecasts updated annually.

2, Reform interconnection. Many states have backlogs that stretch years. They should create fast-track lanes with guaranteed timelines, transparent dashboards, and standardized upgrade costs.

3, Accelerate permitting. Establish statutory shot-clocks for approvals, and pre-certify corridors for transmission.

4, Create AI-ready zones. Market shovel-ready sites with documented access to power, fiber, and water.

And 5, Rebalance the resource mix. Blend firm clean energy—like nuclear, hydro, geothermal—with flexible gas and storage. AI requires reliability hour by hour, not just annual averages.

If states do these things, they can shift their position in just 12–24 months.

Michael: So what’s really at stake here?

Brandon: Jobs, tax base, and competitiveness. Data centers bring billions in capital expenditure. They often catalyze surrounding industries—semiconductors, electrical vehicles, advanced manufacturing. If a state can’t deliver electricity, it risks missing out on the next industrial wave.

Think of it this way: competing for AI projects is really competing for the future economy. And the binding constraint isn’t talent, it’s power.

Michael: Where does this go next? How will the Grid Readiness Score evolve?

Brandon: We’ll be updating the GRS regularly, incorporating new announcements, statutory changes, and project completions. Scores will move as states reform—or as they slip further behind.

Long term, I see this becoming a scoreboard that governors, utilities, and investors track closely. It’s a way to measure not just ambition, but delivery.

Michael: Brandon, this has been a fascinating conversation. Where can listeners find the full report?

Brandon: They can subscribe at AIxEnergy.io. The report includes the full rankings, detailed state profiles, and the underlying dataset. It’s essential for anyone making decisions in the AI-energy space.

Michael: Perfect. Brandon Owens, author of The Grid Divide, thank you for joining us.

Brandon: Thank you, Michael. Always a pleasure.

And thanks to all of you for listening to AxEnergy. If you enjoyed today’s episode, share it with a colleague, subscribe, and join us next time as we explore the convergences shaping our energy future. Visit AIxEnergy.io. Until next time.