The Federal Trade Commission has a two-word name for how the AI industry funds itself: circular spending. It sits in a staff report the agency published in January 2025, describing the deals between the largest cloud companies and the largest AI labs. Not a critic’s phrase. Not a short seller’s. The government’s own words, in a federal document, for the way the money moves.

Here is the arrangement that phrase describes. A cloud company invests billions of dollars in an AI lab. The lab then agrees to spend a large share of that same money buying computing power from the company that just funded it. The money leaves and comes home. The FTC found that these partnerships “include cloud commitments that require AI developers to spend a large portion of their CSP partner’s investment on cloud services from their partner,” and it named that feature, in plain type, circular spending.

That finding has been public for more than a year. So has a second fact, from a different stack of documents: the same small group of companies now floods Washington with more lobbyists than almost any other industry. What nobody has done is lay the two records side by side. Do that, and a single machine comes into focus. A money loop that funds its own demand, and a legal wall going up to keep anyone from regulating it. Same companies. Both halves. It is the first thing to understand about the AI economy, and it is hiding in plain public records.

The loop

The loop runs through three partnerships, and the FTC studied all three. The agency’s 6(b) study examined Microsoft and OpenAI, Amazon and Anthropic, and Google and Anthropic, the largest such deals in the industry. As the report tabulated the publicly reported figures through September 2024, Microsoft had put 13.75 billion dollars into OpenAI, Amazon 8 billion into Anthropic, and Google 2.55 billion into Anthropic. Microsoft’s own quarterly filing that fall put its total funding commitments to OpenAI at 13 billion, accounted for under the equity method.

These are not ordinary stock purchases. The FTC found the deals hand the cloud partners “significant equity and certain revenue-sharing rights,” and leave the door open for one company to fully acquire its partner down the line. They carry “consultation, control, and exclusivity rights,” including board seats and preferential treatment. The company writing the check also gets a hand on the wheel.

Then the money comes back as cloud spending. That is the circular part, and the FTC was direct about why it matters. The structure, in the agency’s reading, is one avenue through which a cloud provider may aim to reduce the size of the loss it might otherwise take on the billions it pours into a partner. The lab gets discounted computing it could not afford on the open market. The cloud gets its money returned as revenue, plus equity, plus a view inside a rival’s operation. The report found the arrangement reaches all the way down to the silicon, with “co-development plans for CSP-designed semiconductor chips” tuned to the labs’ models. Money, equity, control, and custom hardware, all moving in a ring.

The entanglement runs deeper than money. The FTC found the partnerships give the labs discounted access to the scarce computing they cannot get elsewhere, let the two sides embed their own engineers inside each other’s companies, and share training data along with detailed performance and financial figures on the models themselves. The people, the data, and the hardware are braided together as tightly as the cash. Unwinding one company from the arrangement would mean pulling all of it apart at once.

The agency also listed risks it thought were worth watching. In Section 5 of the report, staff flagged that these partnerships could limit other AI developers’ access to computing power and engineering talent, the two scarcest inputs in the field. They flagged that the deals could raise the cost of switching providers, through exclusivity terms and technical lock-in that make leaving expensive and slow. And they flagged that the arrangements hand the cloud partners access to sensitive financial and technical information, including confidential chip designs and a partner’s own customer and revenue numbers, which those same cloud companies could use to build products that compete with the labs they fund.

That section is contested, and the objection came from inside the agency. Commissioner Andrew Ferguson, joined by Commissioner Melissa Holyoak, filed a concurring and dissenting statement on January 17, 2025. Three days later Ferguson became Chairman of the FTC, the job he still holds. He voted to approve the report and said why: it “sheds light on three Big Tech-AI partnerships,” and “Congress, state officials, and the public deserve to understand how these partnerships work.” What he objected to was Section 5. The study was fast and narrow, he wrote, covering three partnerships between five companies, and “the limited, brief nature of the study should foreclose the drawing of broad conclusions about the AI industry and its future, or even about the partnerships themselves.” His instruction to readers was blunt: “Readers should skip Section 5 of the Report, or read it with tremendous skepticism.”

Fine. Skip it.

Nothing else in this piece needs it. The money loop, the equity and revenue-sharing rights, the board seats, the chip co-development, the embedded engineers, the shared training data and financial figures, all of that sits in the parts of the report Ferguson voted to publish and called valuable, drawn from what he described as “company documents produced in response to the Commission’s Section 6(b) orders.” The speculation about what it might mean is the part he wanted struck. What these companies actually signed is not in dispute.

It is worth noting what Ferguson did not say. He did not say the arrangement is harmless. His own statement holds that the Commission “must remain a vigilant competition watchman, ensuring that Big Tech incumbents do not control AI innovators in order to blunt any potential competitive threats.” His argument is that a study run in under a year should not be the last word. That is a reasonable thing for a regulator to say, and it cuts both ways. If a year was not enough to draw conclusions, it was not enough to rule anything out either.

The report’s own limits are real and worth stating plainly. It covers three partnerships. It reflects what the companies disclosed as of September 2024. It was aggregated to protect trade secrets. It says outright that it is “not a formal legal or economic analysis” and accuses no one of breaking the law. Circular spending is not a crime. The concern here is not illegality. It is that this structure concentrates enormous power in very few hands, and that the ordinary means of checking that power are being closed off one at a time.

What happened next

The FTC published those numbers in January 2025. Every one of the three partnerships has been rewritten since, and the public record of what replaced them is the strongest evidence in this piece.

Start with Amazon and Anthropic, because the arithmetic is right there in the announcement. On April 20, 2026, the two companies said Amazon “is investing $5 billion in Anthropic today, with up to an additional $20 billion in the future,” securing “up to 5 gigawatts (GW) of capacity for training and deploying Claude.” In the same announcement, Anthropic said: “We are committing more than $100 billion over the next ten years to AWS technologies.” That covers Amazon’s own Trainium chips, Trainium2 through Trainium4, plus Graviton processors and the option to buy future generations of Amazon silicon.

Read those two sentences together. Amazon puts in up to 25 billion dollars. Anthropic commits to spend more than 100 billion dollars back with Amazon. The purchase commitment running the other way is roughly four times the size of the investment. That is the arrangement the FTC described in January 2025, at ten times the scale, announced in public by the companies themselves. It is also a large share of the 2026 capex wave now reshaping the American power grid.

Google and Anthropic expanded too. On April 6, 2026, Anthropic announced a deal with Google and Broadcom for “multiple gigawatts of next-generation TPU capacity that we expect to come online starting in 2027,” Google-built chips supplied through Broadcom, with the vast majority of the capacity sited in the United States. Anthropic did not disclose the dollar terms. It did say its run-rate revenue had “surpassed $30 billion,” up from roughly $9 billion at the end of 2025, and pointed back to an earlier pledge to “invest $50 billion in strengthening American computing infrastructure.”

Microsoft and OpenAI went the other direction on some terms. On April 27, 2026, Microsoft announced an amended agreement. Its license to OpenAI’s models and products runs through 2032 but is “now non-exclusive.” OpenAI “can now serve all its products to customers across any cloud provider.” Microsoft “will no longer pay a revenue share to OpenAI.” Those are real changes, and they loosen exactly the kind of exclusivity the FTC described.

The same announcement says what did not change. Microsoft “remains OpenAI’s primary cloud partner,” and OpenAI’s products still ship first on Azure “unless Microsoft cannot and chooses not to support the necessary capabilities.” Microsoft “continues to participate directly in OpenAI’s growth as a major shareholder.” And revenue share payments from OpenAI to Microsoft “continue through 2030, independent of OpenAI’s technology progress, at the same percentage but subject to a total cap.”

So here is the honest accounting. One leg of the arrangement got looser. The other two got dramatically larger. Ferguson was right that a one-year study could not tell you where this was going. Sixteen months of company announcements can. The money still leaves and comes home, and the sums involved have gone from billions to hundreds of billions.

The shield

The companies inside the money loop are also among the largest lobbying forces in Washington. Public Citizen, a nonprofit watchdog, found that more than 3,500 lobbyists worked on AI issues in 2025, more than one in four of every registered federal lobbyist in the country. The overwhelming majority of that work, 82 percent of it, was done on behalf of corporate interests. The AI lobbying force grew 168 percent between 2022 and 2025. Sludge, working the same disclosure filings, put the precise count at 3,570 lobbyists, or 26 percent of everyone registered.

The growth is steeper than even that suggests. Public Citizen found the number of distinct lobbyist-and-client relationships working AI jumped 265 percent over those three years, from 1,672 to 6,110. Software and services became the single largest lobbying sector in the country by that measure, with about 1,448 lobbyists, close to 30 percent of the entire AI lobbying push. Lobbyists working specifically on data centers grew from 68 in 2022 to more than 400 in 2025, close to six times as many. An industry that barely registered on K Street four years ago now sits near the center of it. I have written before about the money behind AI policy and about the policy revolving door that moves people between the agencies and the firms they regulate.

Look at who is doing the spending. Public Citizen’s count of the top AI-lobbying operations in 2025 lists the US Chamber of Commerce with 91 lobbyists, Microsoft with 63, Meta with 55, Intuit with 51, and Amazon with 48. The names at the top of that list are the same names inside the money loop.

And the policy showing up is built to remove the biggest threat to the loop. On December 11, 2025, the White House issued an executive order titled “Ensuring a National Policy Framework for Artificial Intelligence.” It orders the Attorney General to stand up an AI Litigation Task Force whose only assignment is to challenge state AI laws in court. It directs the Commerce Department to identify state AI laws it considers onerous and hand them to that task force. It moves to cut states that keep the targeted regulations off the non-deployment portion of federal BEAD broadband funding, the money for planning, administration, and outreach, unless they fall in line. And it reaches further still, directing the FCC to weigh a federal reporting standard that would override conflicting state rules, and the FTC to spell out when a state law that forces changes to an AI model’s output is preempted by federal law. States have been the one level of government actually writing rules for this industry, and the fight over state AI laws had been running in 45 of them. The order is designed, layer by layer, to preempt them. It is the enforcement arm of the White House framework released earlier that year.

No one can prove the lobbyists wrote that order, and I am not going to claim they did. But the shape is hard to miss, and it is not the first time the question has come up about Big Tech’s hand in an executive order. The firms that dominate the money loop are among the heaviest spenders shaping AI policy, and the policy that arrived, federal preemption of state law, happens to sweep away the one venue that had started to regulate them. Public Citizen’s J.B. Branch put the stakes plainly: “Congress now has a once-in-a-generation opportunity to decide whether AI becomes another chapter in the story of unchecked corporate power.”

I asked Public Citizen how the two halves fit together. Eileen O’Grady, a researcher there and co-author of Generative Influence, told Laterstack:

“Last year’s AI lobbying surge and the preemption push are two parts of the same play. Big Tech spent heavily to shape federal policy and is effectively cashing in through the government’s attempt to wipe out state laws that would have created common sense guardrails for the industry. We can expect to see federal AI lobbying continue to intensify as preemption plays out in Congress and the courts, especially if bills like the draft Great American AI Act advance. We might also see more pressure directed at the executive branch, which has become the industry’s most effective route now that the direct legislative attempts have stalled.”

The bill she names is real and not yet law. Representatives Jay Obernolte, a Republican from California, and Lori Trahan, a Democrat from Massachusetts, released the Great American AI Act as a discussion draft on June 4, 2026. It has not been formally introduced. It carries a three-year preemption of state laws governing how AI models are built, while leaving states their authority over how those systems get used.

Her last point is the one to sit with. She reads the executive branch as the industry’s most effective route now that the direct legislative push has stalled. The December order came from the executive branch.

The same hands

Set the two records next to each other and the machine is whole. Microsoft and Amazon are principals in the arrangement the FTC called circular spending. Microsoft and Amazon also sit second and fifth on the list of the country’s biggest AI lobbying operations. The hands that built the loop are the same hands building the wall. This is not two stories about the AI industry. It is one story about a small number of companies that fund themselves in a circle and are working, out in the open, to keep anyone from stepping in.

None of the individual facts here are secret. The FTC report is on the agency’s website. Public Citizen published its lobbying count. The executive order is posted on the White House site. The partnership terms are on the companies’ own newsrooms. The pieces have been sitting in the open, in separate places, waiting for someone to set them on the same table. Standard Oil looked permanent too, right up until someone wrote the whole thing down in one place.

A quick word on why I am writing this. I am not trying to tell you what to think. I want to lay out what the public documents actually say and let you weigh it for yourself. How these companies are funded, and who gets to set the rules for them, touches things people feel directly, like prices, competition, and how much real choice they have. You can follow all of that without taking a political side, and it is already on the record.

No brakes

Two forces usually correct a concentration of corporate power, the market and the government, and in the AI economy both are being closed at once. The market is the first. Competitors move in, customers leave, the advantage erodes on its own. But a loop that funds its own demand does not wait on the market’s permission to keep running. Government is the second. Regulators and legislators draw the lines. But you cannot regulate a machine whose owners are writing the rules, and the December order is aimed squarely at the level of government that was trying.

The bill for all of it lands somewhere. Five gigawatts here, multiple gigawatts there, and the power and water to run them come from somewhere real. In Arizona that has already turned into the data center bill you never voted on, paid through electricity rates by people who were never asked.

That is the machine, at least the part you can already prove from public documents. It is also only two layers of it. The full stack runs from the chips and the packaging bottleneck that decides how many of them get built, up through the clouds and the labs, to the money loop and the law wrapped around it. The rest of this series follows it the whole way down.

Laterstack contacted Microsoft, Amazon, Google, OpenAI, and Anthropic for this piece. None provided an on-the-record comment.

Where every number came from

Every figure and quote in this piece comes from a public document. Here is where each one lives, in the order the claims appear.

The loop

1. The phrase “circular spending,” the cloud commitment finding, the equity and revenue-sharing rights, the board seats and exclusivity terms, the chip co-development, the discounted compute, the embedded engineers, the shared training and performance data, the Table 1 investment figures, and the report’s own scope limits: FTC Staff Report on AI Partnerships and Investments 6(b) Study, Federal Trade Commission, January 2025. The circular spending language appears in Finding 3 on page 19.

2. Microsoft’s own accounting of its OpenAI position, stated as “total funding commitments of $13 billion” under the equity method: Microsoft Form 10-Q for the quarter ended September 30, 2024, U.S. Securities and Exchange Commission. Note that this figure and the 13.75 billion in FTC Table 1 are not identical. The FTC tabulated publicly reported investment; Microsoft reported its own booked funding commitments. Both are cited here as each states them.

3. Amazon’s investment in Anthropic and the Trainium chip arrangement: Amazon to invest additional $4 billion in Anthropic, Amazon.

4. The dissent from Section 5, the instruction to read it with skepticism, the vote to approve the rest of the report, and the “vigilant competition watchman” line: Concurring and Dissenting Statement of Commissioner Andrew N. Ferguson, joined by Commissioner Melissa Holyoak, Matter No. P246201, January 17, 2025.

What happened next

5. Amazon’s $5 billion investment, the up to $20 billion that follows, the 5 gigawatts of capacity, and Anthropic’s commitment of more than $100 billion to AWS over ten years: Anthropic and Amazon expand collaboration for up to 5 gigawatts of new compute, Anthropic, April 20, 2026.

6. The multiple gigawatts of next-generation TPU capacity from 2027, the Broadcom supply arrangement, the U.S. siting, the $30 billion run-rate revenue figure, and the $50 billion American infrastructure pledge: Anthropic expands partnership with Google and Broadcom for multiple gigawatts of next-generation compute, Anthropic, April 6, 2026. Confirmed by Google Cloud, which discloses no financial terms.

7. The non-exclusive license through 2032, the any-cloud provision, the end of Microsoft’s revenue share payments to OpenAI, the primary cloud partner and major shareholder language, and the revenue share running to Microsoft through 2030 subject to a cap: The next phase of the Microsoft-OpenAI partnership, Microsoft, April 27, 2026.

The shield

8. The AI lobbyist count, the corporate share, the growth figures from 2022 to 2025, the data center lobbyist growth, the lobbyist-and-client relationship growth, the software and services sector share, the top lobbying operations by headcount, and the J.B. Branch quote: Generative Influence, by Mike Tanglis and Eileen O’Grady, Public Citizen, February 24, 2026.

9. The 3,570 lobbyist count, the 26 percent share of all registered federal lobbyists, and corroboration of the top lobbying employers: AI Boom on K Street: One in Four Lobbyists Now Work on AI, by David Moore, Sludge, February 24, 2026.

10. Federal lobbying spending by the AI developers themselves, $3.13 million by Anthropic and $2.99 million by OpenAI in 2025: AI’s Biggest Builders Are Now Its Biggest Lobbyists, by Phoebe Liu, Forbes, February 20, 2026.

11. The AI Litigation Task Force, the Commerce Department review of state AI laws, the BEAD funding condition, the FCC reporting standard, and the FTC preemption guidance: Executive Order 14365, Ensuring a National Policy Framework for Artificial Intelligence, The White House, December 11, 2025. Legal analysis of the same order, including the point that the funding at risk is the non-deployment portion of BEAD: AI Executive Order Targets State Laws and Seeks Uniform Federal Standards, Latham and Watkins, December 17, 2025.

12. The three-year preemption of state laws governing model development, and the bill’s status as a discussion draft: Obernolte, Trahan release a discussion draft of the Great American AI Act, June 4, 2026.

13. Eileen O’Grady’s comment was provided to Laterstack by email on July 22, 2026, and her attribution was confirmed by her on July 23, 2026.

Frequently Asked Questions

What is circular spending in AI?

Circular spending is the FTC’s term, from its January 2025 staff report, for cloud commitments that require AI developers to spend a large portion of their cloud partner’s investment back on that partner’s cloud services. The money is invested, then returns as revenue.

Which companies did the FTC 6(b) study cover?

Three partnerships between five companies: Microsoft and OpenAI, Amazon and Anthropic, and Google and Anthropic. The report reflects what those companies disclosed as of September 2024.

Did anyone at the FTC disagree with the report?

Yes. Commissioner Andrew Ferguson, joined by Commissioner Melissa Holyoak, voted to approve publication but dissented from Section 5, the Areas to Watch section. Ferguson wrote that readers should skip Section 5 or read it with tremendous skepticism. He became FTC Chairman three days later.

How much are these partnerships worth now?

In April 2026 Amazon announced a 5 billion dollar investment in Anthropic with up to 20 billion more, and Anthropic committed more than 100 billion dollars over ten years to AWS technologies. Anthropic separately signed with Google and Broadcom for multiple gigawatts of TPU capacity starting in 2027. Microsoft and OpenAI amended their agreement to make Microsoft’s license non-exclusive.

What does the December 2025 executive order do?

Executive Order 14365 directs the Attorney General to create an AI Litigation Task Force to challenge state AI laws, has Commerce identify state laws it considers onerous, conditions the non-deployment portion of BEAD broadband funding, and directs the FCC and FTC toward federal standards that would preempt conflicting state rules.

How many lobbyists work on AI?

Public Citizen found more than 3,500 lobbyists worked AI issues in 2025, more than one in four of every registered federal lobbyist. Sludge put the count at 3,570, or 26 percent. Public Citizen found 82 percent of that work was on behalf of corporate interests.

The cheapest thing you did today might turn out to be the most expensive. Ask an AI to write an email and it costs you nothing you can see. Behind that one request, a data center somewhere pulled about a third of a watt-hour of electricity and a small amount of water to cool the machines. Counting the power plant feeding it, a hundred-word AI email runs through roughly a bottle of water, about 519 milliliters, according to researchers at the University of California, Riverside. Now multiply that by a billion requests a day. That is a new kind of industrial demand, and it has to be built, powered, and cooled somewhere real. In Arizona, that somewhere is more and more often the lot down the road, and the data center costs are landing on bills you never agreed to.

The short version

• APS has asked for about a 14 percent residential rate increase, roughly $240 more a year, and a separate increase of about 45 percent for data centers and other large loads.
• The Arizona Attorney General’s expert testimony says the household number could be cut to 3 percent, saving about $524 million a year.
• Data centers are the primary driver of APS’s projected 40 percent peak-load growth by 2031.
• Phoenix-area data centers use about 385 million gallons of water a year, projected to grow tenfold, and up to 85 percent of data center water is lost to evaporation.
• Arizona’s data center tax break costs the state about $38 million a year, and new applications are now paused for three years.

Start with your power bill

Arizona Public Service has asked the Arizona Corporation Commission for about a 14 percent residential rate increase, roughly $240 more a year for an average household. The company says it needs the money because demand is surging: APS expects its peak load to grow up to 40 percent by 2031, with data centers as the primary driver, and it is spending around $2 billion a year on new plants, transmission, and grid upgrades to keep pace.

Here is the line that decides who actually pays. APS is also proposing a separate, much steeper increase for the data centers themselves, about 45 percent, on the argument that the customers driving the demand should cover the cost of serving it. The state’s Residential Utility Consumer Office wants to go further and put data centers in their own customer class entirely. Whether that holds is the whole fight. The Arizona Attorney General intervened in the rate case, the first time a sitting Arizona attorney general has formally opposed a utility rate request, and filed expert testimony arguing the 14 percent could be cut to 3 percent, saving customers about $524 million a year and roughly $220 each, mostly by lowering the guaranteed profit APS is allowed to earn for shareholders. The Attorney General has framed it plainly: the public should not be “subsidizing the cost of building data centers for a whole bunch of out-of-state billionaires.” APS responds that its plan protects everyday customers and makes the big users pay their share. An administrative law judge hears evidence through the summer and recommends a decision around November, with the Commission voting near December and any increase taking effect in early 2027. Until then, whether a data center’s power bill lands on the company or on you is still an open question.

Then the water you can’t see

In a state that has none to spare, water is the quieter cost. A single data center can use anywhere from tens of thousands to millions of gallons a day, and up to 85 percent of it evaporates and never returns to the supply. Phoenix-area data centers already use around 385 million gallons a year, and one analysis projects that growing tenfold. A few cities saw it coming. Chandler caps how much water a data center can draw per square foot, and Marana banned them from using drinking water at all. Most places have no such rule.

Then the taxes you forgot

Since 2013, Arizona has waived sales tax on data center equipment, a break that costs the state about $38 million a year. This June, lawmakers and the governor paused new applications for three years, which tells you how the math was starting to look from the inside. Nationally the trade is steeper still: these breaks can run more than $2 million for every permanent job, and one out-of-state deal handed a data center $77 million in exchange for a single job. The construction work is real and temporary. The permanent payroll is small.

How a data center actually gets approved

So how does a project that reshapes your power bill, your water, and your tax base get the green light? Through four separate doors, and most people never knock on any of them.

The first is the tax break, run by the Arizona Commerce Authority. A company applies, commits to investing at least $50 million within five years, and gets its equipment exempted from sales and use tax for ten to twenty years, under A.R.S. 41-1519. The break is performance-based, you only collect it if you actually build, but it locks in for a long time.

The second is the land, decided locally. Maricopa County now lets data centers go into heavy-industrial zones automatically, with no hearing at all. Anything else needs a rezoning or a special-use permit, which means public hearings at the Planning and Zoning Commission and then a vote by the City Council or the County Board of Supervisors. This is the door where you have the most say: if enough nearby property owners file a written protest, approval can require a three-quarters supermajority instead of a simple majority. It can work, too. In southern Arizona, Pima County voters rejected a data center and then watched the state move to override them. In the by-right industrial zones, that door never opens.

The third is the power, and it splits in two depending on who sends your bill. If you are an APS customer, your utility answers to the elected Corporation Commission, and the rate case deciding all of this is a public proceeding you can read and comment on. If you are an SRP customer, you are in a different system. SRP runs on its own elected board, but the votes are weighted by land ownership, roughly one acre to one vote. The more land you hold, the more say you get. In the 2026 board races, a political committee tied to large energy users ran a slate, and data center companies including Google, which is building a complex in Mesa, and Edgecore donated to it. The renters and quarter-acre homeowners whose bills are on the line barely register a vote.

The fourth is the water, set by cities and the state water department, which can cap use, require a hundred-year supply, or ban drinking water outright. Or not.

Where it helps, where it hurts

None of this is simple villainy, and pretending it is would be its own kind of dishonesty. Data centers bring real capital, real construction work, and a claim to being a serious technology state, the same pull that landed the chip fabs. The tax break only pays out if a company builds. The proposed 45 percent data-center rate and the push for a separate customer class are the system trying, in real time, to make the heavy users carry their own weight.

Where it hurts is quieter. Tens of millions in forgone tax revenue a year for a handful of permanent jobs. Water leaving a drying state for good. And a grid buildout whose bill is being argued over right now, with you as the default payer if the utilities win the argument.

For this piece, APS, SRP, and the Arizona Commerce Authority were asked to address whether data center costs shift to residential customers. Laterstack has also filed public-records requests for the tax-exemption totals, the utilities’ cost-allocation studies, and data center water permits, and will update as those return.

Some cities are starting to organize as a bloc. On June 23, Phoenix joined 42 others in the C40 Cities Global Urban Data Centres Pact, a set of standards that includes data centers paying appropriate rates for the energy, water, and network access they use, with excess revenue directed back into local resilience. Phoenix Mayor Kate Gallego, who co-wrote the launch, named the stakes directly: left unchecked, the buildout could “significantly increase emissions, strain resources, push up residential energy prices and erode public trust,” and cities cannot let “the fear of missing out on new technology result in data centers being waived through our planning processes.”

The door you keep walking past

Which brings it back to the email you asked an AI to write this morning. You paid nothing for it that you could feel. But you are paying for it on your power bill, in your water, and in the taxes that fund the break, and the one cost you actually chose, the query, is the only one that felt free.

The strange part is how much say you have and how rarely anyone uses it. The rate case is open for public comment. The zoning hearings are on the public calendar. The SRP board is elected, even with the deck tilted. The doors are right there, in daylight. Most of us have simply never walked through them. That is exactly how a bill this big gets approved without anyone voting for it.

FAQ

Do data centers raise electricity bills in Arizona?
It is being decided now. APS says large users will pay a separate, higher rate (about 45 percent) so costs do not shift to households, while the Arizona Attorney General’s office argues the proposed 14 percent household increase is too high and could be cut to 3 percent. The Corporation Commission is expected to decide by late 2026.

How much water does an Arizona data center use?
A single data center can use from tens of thousands to millions of gallons a day, and up to 85 percent evaporates. Phoenix-area data centers use about 385 million gallons a year, projected to grow tenfold.

How do data centers get approved in Arizona?
Through four channels: a state sales-tax exemption from the Arizona Commerce Authority, local zoning by a city council or county board, electricity service regulated by the Corporation Commission (for APS) or the elected SRP board, and water approval from cities and the state water department.

How can the public weigh in on data center costs?
By commenting in the APS rate case at the Corporation Commission, speaking at local zoning hearings, and voting in SRP board elections, where votes are weighted by land ownership.

Related Stories

• Pima County Voted Against a Data Center. The State Wants to Override Them

Pull up a chart of who does not want a data center built nearby, and something looks off. Conservative Republicans oppose them more than moderate Republicans do, which puts the most conservative voters closer to liberal Democrats than to the middle of their own party. “I’m not sure I’ve ever seen a chart where conservative Republicans are closer to liberal Democrats,” said Anthony Leiserowitz of the Yale Program on Climate Change Communication. In a country that agrees on almost nothing, that alignment is worth a second look.

Not a fringe

The data center backlash is not a handful of angry neighbors. Seven in ten Americans say they do not want one built in their community, a higher share than opposes a nuclear plant, a pattern researchers at Harvard have been tracking as it spreads. More than 800 groups across 49 states are fighting roughly 1,500 planned projects, and tens of billions of dollars in builds have already been blocked or delayed this year. Of the politicians who have taken a public position against a project, 55 percent are Republican and 45 percent are Democrat.

Same enemy, opposite reasons

What looks like one coalition is really two, arriving at the same place through different doors. On the right, the objections are tax giveaways, strain on the grid, property rights, and a distant company reshaping a town without asking. On the left, they are water, emissions, and who pays when the bills climb. The motives do not match. The vote does. As one observer told Talking Points Memo, the draw is having “real, actual villains” both sides can see.

Already moving votes

The data center backlash has left the comment period and reached the ballot box. Two Democrats won landslide upsets for the Georgia Public Service Commission, the first since 2007, running on energy bills and data centers, even as some Georgia Republicans raised their own questions about the facilities’ water and power use. In Virginia, the country’s largest data-center market, candidates ran on making the facilities pay more, and a seat changed hands over it. Voters in Monterey Park, California banned data centers outright with 86 percent of the vote. Towns in Pennsylvania, Nevada, Rhode Island, Wisconsin, and Maryland have paused, capped, or put them to a referendum. Arizona, where the fight runs through the power bill and the water table, passed one of the country’s strictest tax-break moratoriums, and in Pima County voters rejected a project only to watch the state move to override them.

Why this one crosses the line

The usual reason an issue goes bipartisan is that it is small or symbolic. This one is neither. What puts the rural conservative and the urban progressive in the same room is not a shared philosophy. It is a shared position: someone far away decided their town would carry the cost of the AI boom, and the first they heard of it was a rezoning notice. That experience does not sort by party.

Whether it lasts

The people who study this are not betting on permanence. The unity could fray, they warn, as the midterms approach and the issue turns into something to win with rather than something to agree on. It is also running against a well-funded current: while towns fight projects one at a time, the AI industry is spending at record levels in Washington to freeze state regulation before the local victories can add up. A problem everyone shares is, for a campaign, a problem to be divided. The thing to watch is whether the backlash still looks bipartisan once candidates need it to belong to one side.

The opposition makes the headlines. The alignment is the part worth studying. When the most conservative and the most progressive voters land on the same side of anything in 2026, the useful question is what they are seeing that the people in the middle are not. The safe bet is that it does not survive the midterms intact, because a unifying issue is exactly the kind of thing a campaign exists to split. For now, the data center is the rare thing a divided country can point at together.

FAQ

Is opposition to data centers bipartisan?
Yes. Roughly seven in ten Americans oppose local data center construction, including 75 percent of Democrats and 63 percent of Republicans, and conservative Republicans oppose them at a higher rate than moderate Republicans. Of politicians taking public positions against projects, 55 percent are Republican and 45 percent Democrat.

How many data center projects have been blocked?
More than 800 groups across 49 states are fighting about 1,500 projects, and tens of billions of dollars in builds have been blocked or delayed in 2026.

Why do the left and right oppose data centers?
For different reasons that reach the same conclusion. The right cites tax breaks, grid strain, and property rights; the left cites water, emissions, and rising bills.

Will the bipartisan coalition last?
Analysts expect it to weaken as the 2026 midterms turn the issue into a partisan weapon.

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There is a peculiar cognitive dissonance in how the technology industry discusses artificial intelligence. The conversation revolves around models, parameters, benchmarks, and breakthroughs. GPT-5 is coming. Claude gets smarter every quarter. Gemini scales to ever larger context windows. The assumption embedded in this discourse is that AI progress is fundamentally a software problem. Build better algorithms, train larger models, accumulate more data, and intelligence will continue its exponential climb.

The assumption is wrong. AI progress is increasingly a hardware problem, and the hardware problem is increasingly an infrastructure problem, and the infrastructure problem is increasingly a physics problem. You cannot conjure substations and power generation capacity on the same timeline you order GPUs. The grid does not scale on demand. Transformers take years to manufacture. Permitting for new power plants moves at the pace of bureaucracy, not venture capital.

Siemens Energy announced last week that it will invest approximately $1 billion to expand U.S. manufacturing of grid equipment and gas turbine components. The announcement was not framed as a response to AI. It was framed as a response to “surging electricity demand.” But the source of that demand is not mysterious. Data centers consumed approximately 4.4% of total U.S. electricity in 2025. Projections suggest this figure will reach 6% to 9% by 2030. The hyperscalers, Microsoft, Google, Amazon, Meta, are racing to build ever larger training clusters, and every cluster requires power that the existing grid cannot provide.

This is the bottleneck that will determine who wins the AI race. It is not compute, which can be purchased. It is not talent, which can be hired. It is not capital, which flows freely to credible teams. It is the physical infrastructure required to power and cool machines at unprecedented scale. Whoever solves this problem first will not merely succeed. They will become unfathomably wealthy.

The Grid Was Not Built for This

The American electrical grid was designed for a different era. It assumed distributed demand: factories here, homes there, commercial buildings elsewhere, all drawing power at predictable times in predictable quantities. Load balancing was a solved problem. Utilities built generation capacity, maintained transmission lines, and charged rates that covered costs plus regulated returns.

AI data centers obliterate these assumptions. A single large training cluster can consume as much electricity as a small city. The demand is concentrated geographically, often in regions chosen for real estate costs, tax incentives, or proximity to cloud customers rather than proximity to power generation. The load profiles are intense and sustained. Training runs continue for weeks or months, consuming maximum power continuously.

The result is that data center operators are discovering what semiconductor manufacturers discovered decades ago: you cannot simply buy your way out of infrastructure constraints. TSMC’s most advanced fabrication facilities require dedicated power plants. AI data centers are approaching similar scale.

Consider the math. Nvidia’s next-generation Blackwell systems consume approximately 1,200 watts per GPU. A training cluster with 100,000 GPUs requires 120 megawatts of continuous power, equivalent to the demand of roughly 90,000 homes. The largest planned clusters exceed this by multiples. xAI’s Colossus facility in Memphis reportedly operates over one million H100-equivalent GPUs. The power requirements approach gigawatt scale.

The U.S. has not built gigawatt-scale power infrastructure in decades. The expertise exists but lies dormant. The supply chains have atrophied. The permitting processes were designed to prevent construction, not enable it.

The Transformer Bottleneck

The specific constraint that has captured industry attention is the transformer. Not the neural network architecture, but the electrical device that steps voltage up and down as power moves from generation to transmission to distribution. Large power transformers are among the most complex manufactured goods in existence. They weigh hundreds of tons. They contain thousands of gallons of specialized oil. They require specialized steel that only a handful of facilities worldwide can produce.

Lead times for large power transformers have extended from 12 months to 36 months or longer. A data center operator who breaks ground today may wait three years for the transformers required to connect their facility to the grid. This is not a problem that money can solve in the short term. The manufacturing capacity does not exist to meet demand.

Siemens Energy’s $1 billion investment is explicitly aimed at this bottleneck. The company will expand production of grid equipment and gas turbine components at facilities in Charlotte, Houston, and other U.S. locations. But a billion dollars buys incremental capacity, not transformational capacity. The gap between AI industry ambitions and infrastructure reality remains vast.

The Emerging Opportunity

Where there is constraint, there is opportunity. The companies and technologies that solve the infrastructure bottleneck will capture extraordinary value.

Several approaches are competing for dominance.

Small modular nuclear reactors promise dedicated, baseload power for data centers without the construction timelines of traditional nuclear plants. Microsoft has announced partnerships to explore this approach. The technology remains unproven at commercial scale, but the economics are compelling if regulatory hurdles can be overcome.

On-site natural gas generation allows data centers to bypass the grid entirely, generating power where they consume it. This eliminates transmission losses and permitting delays for grid interconnection. The environmental implications are contested, but the operational advantages are real.

Advanced cooling technologies can reduce power consumption by data centers, effectively stretching existing grid capacity further. Liquid cooling, immersion cooling, and novel heat dissipation approaches all show promise.

Grid-scale battery storage can smooth demand, allowing data centers to draw power during off-peak hours and store it for training runs. This requires advances in battery chemistry and enormous capital investment, but it decouples data center operations from real-time grid capacity.

Space-based data centers, as proposed by SpaceX following its xAI acquisition, represent the most radical approach: escape terrestrial constraints entirely by moving compute to orbit. The technical challenges are formidable, but the logic is not absurd. Solar power in space is continuous and abundant. Cooling in vacuum presents different challenges than cooling in atmosphere, but not necessarily harder ones.

Each of these approaches has advocates. Each faces obstacles. The winner, or winners, will not merely profit from the AI boom. They will enable the AI boom to continue. Without solutions to the infrastructure bottleneck, AI progress will plateau not because the models stop improving but because there is no power to run them.

The Investment Thesis

The investment implications are substantial. For the past several years, AI investment has flowed primarily to model builders and application developers. OpenAI, Anthropic, Google DeepMind, and their peers have absorbed billions in capital. GPU manufacturers, primarily Nvidia, have captured the hardware value.

The infrastructure layer has received less attention. Utilities are regulated and slow-moving. Grid equipment manufacturers are industrial companies trading at industrial multiples. Construction firms are not glamorous.

This is beginning to change. Siemens Energy’s stock has appreciated 200% over the past two years as investors recognize the demand driver that AI represents. Nuclear startups are raising substantial rounds. Data center REITs command premium valuations.

But the opportunity extends beyond public markets. The entrepreneur or investor who identifies the breakthrough technology for AI infrastructure, the equivalent of what TSMC’s advanced packaging is to semiconductors, will capture value commensurate with the importance of the problem. This is not a billion-dollar opportunity. It is a multi-hundred-billion-dollar opportunity. The first person to solve the power constraint at scale may well become a trillionaire.

What This Means for Everyday People

For ordinary Americans, the AI infrastructure buildout has immediate consequences. Your electricity rates will rise. Utilities that serve regions with large data center deployments are already requesting rate increases to fund grid upgrades. The costs are being socialized even as the benefits accrue to technology companies and their shareholders.

Communities near planned data center facilities face decisions about land use, water consumption, and noise. These facilities are not neighbors. They are industrial installations disguised as technology campuses.

Employment effects are mixed. Construction of data centers creates short-term jobs. Manufacturing of grid equipment creates longer-term jobs. But the facilities themselves require minimal labor to operate. A gigawatt-scale data center might employ a few hundred people. A semiconductor fabrication facility of similar power consumption would employ thousands.

The broader economic question is whether AI delivers productivity gains that justify the infrastructure investment being made on its behalf. If artificial intelligence transforms work as profoundly as its advocates claim, the infrastructure buildout will prove prescient. If AI proves more incremental than transformational, we will have rebuilt the grid for a revolution that never arrived.

Either way, the physical constraints are real. The opportunity to solve them is real. And the race to do so is only beginning.

For inquiries and analysis contact laterstack@proton.me

Frequently Asked Questions

Why can’t AI companies just buy more power?

The electrical grid has limited capacity in any given region, and expanding that capacity requires building new generation plants, transmission lines, and substations. Lead times for large power transformers alone have extended to 36 months or more. Data center operators can order GPUs faster than they can secure the power to run them.

How much electricity do AI data centers consume?

Data centers consumed approximately 4.4% of total U.S. electricity in 2025, projected to reach 6% to 9% by 2030. A single large AI training cluster can consume 120 megawatts or more, equivalent to the demand of 90,000 homes. The largest planned facilities approach gigawatt scale.

What is Siemens Energy investing in?

Siemens Energy announced approximately $1 billion in investment to expand U.S. manufacturing of grid equipment and gas turbine components. The investment responds to surging electricity demand driven largely by data center construction and aims to address bottlenecks in transformer and grid equipment supply chains.

On Monday, Elon Musk announced that SpaceX would acquire xAI, his artificial intelligence startup, in a transaction valued at $1.25 trillion, the largest corporate merger in history. The combined entity will unite launch capacity, satellite connectivity, and frontier AI development under a single corporate umbrella. Musk now commands an integrated stack that no other entity on Earth can replicate: rockets to reach orbit, a constellation of thousands of satellites providing global internet coverage, and an AI laboratory racing to build superintelligence.

The financial engineering is elegant. SpaceX, valued at approximately $1 trillion following secondary share sales in December, absorbs xAI at a $250 billion valuation. Shareholders of xAI will receive 0.1433 shares of SpaceX stock for each share they hold. The combined company is expected to pursue an initial public offering in mid-June, timed, according to reports, to coincide with Musk’s birthday and a planetary alignment. The symbolism is characteristically grandiose.

But beneath the celestial theater, something far more terrestrial is at work. The question that demands answering is not whether Musk can build data centers in space, though that remains an open engineering challenge of considerable magnitude. The question is how xAI, a company burning through approximately $1 billion per month according to Bloomberg, justified its quarter-trillion-dollar valuation in the first place.

The Product That Cannot Compete on Merit

Grok, the flagship product of xAI, is by most technical assessments the weakest of the major large language models. It trails OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini across virtually every benchmark that matters to enterprise customers. Its reasoning capabilities are inferior. Its factual accuracy is questionable. Its safety guardrails are, by design, nearly nonexistent.

What Grok does possess is distribution. It is integrated directly into X, the social media platform Musk acquired in 2022, which still commands hundreds of millions of monthly active users despite years of advertiser exodus and user attrition. xAI merged with X last year, with Musk claiming a combined valuation of $113 billion at the time. The thesis was clear: if you cannot build the best AI, you can still reach the most users.

But reach is not the same as value, and the methods by which Grok achieved its engagement numbers should trouble anyone paying attention.

In late December 2025 and early January 2026, xAI rolled out image generation capabilities for Grok that included a paid feature called “Spicy Mode,” which allowed users to create partially nude content. Within days, users discovered that the system’s guardrails were trivially easy to circumvent. What followed was, by Bloomberg’s assessment, the largest mass production of nonconsensual intimate imagery ever hosted on a mainstream social media platform.

X users began requesting that Grok “undress” women and girls from photographs. The AI complied. By some estimates, thousands of such images were being generated every hour. The Grok official account eventually posted an apology for generating sexualized images of minors, acknowledging a specific incident involving “two young girls (estimated ages 12-16) in sexualized attire.”

The regulatory response was swift. California Attorney General Rob Bonta issued a cease and desist order. The European Union, France, India, and Malaysia launched investigations. British Prime Minister Keir Starmer threatened to ban X entirely from the United Kingdom.

Musk’s response was to post laugh-cry emojis.

Internally, according to CNN reporting, Musk had been pushing back against guardrails for Grok, advocating publicly against what he calls “woke” AI and censorship. The xAI safety team, already smaller than those at competing companies, lost several staffers in the weeks before the scandal broke. The platform eventually limited image generation to paying subscribers, but only after the damage was done.

These are the engagement metrics that helped justify a $250 billion valuation.

The Government Connection

The timing of the SpaceX acquisition is not coincidental. Musk has become, over the past year, one of the most politically connected figures in American life. His involvement with the Department of Government Efficiency, his proximity to the current administration, and SpaceX’s indispensable role in national security launches have created a web of dependencies that would be difficult for any regulator to untangle.

SpaceX recently asked the Federal Communications Commission for authorization to launch up to one million satellites as part of what the company describes as “orbital data centers.” The vision Musk articulated in the merger announcement is characteristically ambitious: within two to three years, he estimates, the lowest cost method of generating AI compute will be in space rather than on Earth. “Global electricity demand for AI simply cannot be met with terrestrial solutions,” he wrote, “even in the near term, without imposing hardship on communities and the environment.”

The logic is not entirely speculative. Terrestrial data centers face genuine constraints. Permitting for new power generation is measured in years. Transformer production is bottlenecked globally. Water for cooling is increasingly scarce in many regions. These are real problems that Siemens Energy is investing $1 billion to address, as we report elsewhere in this issue.

But orbital data centers introduce their own constraints: launch costs, maintenance in vacuum, latency for round-trip communications, and the sheer thermodynamic challenge of dissipating heat in space where there is no atmosphere to carry it away. Musk has solved difficult engineering problems before. He has also made promises that failed to materialize.

What matters for the present analysis is that the merger positions xAI’s problems, its cash burn, its inferior product, its regulatory exposure, within the protective shell of SpaceX’s undeniable accomplishments. SpaceX generated an estimated $8 billion in profit on $15 to $16 billion in revenue in 2025. It has become the dominant provider of launch services for both commercial and government payloads. It operates Starlink, a satellite internet constellation that has proven militarily significant in Ukraine and commercially viable in underserved markets worldwide.

xAI, by contrast, has a chatbot that trails its competitors and a track record of enabling mass abuse. The merger allows the former to subsidize the latter.

The Investor Class and the Sovereignty Question

The January funding round that set xAI’s $230 billion valuation tells its own story. Among the investors were the Qatar Investment Authority, MGX (an investment arm of the Abu Dhabi government), Nvidia, and Cisco. Sovereign wealth funds from the Gulf states have determined that AI is a strategic asset class, not merely a venture bet. They are purchasing stakes in the physical infrastructure that will run the models of the future.

This is rational behavior from the perspective of nations that built their current wealth on hydrocarbons and understand that energy is always, eventually, strategic. But it raises questions for American policymakers about who will own the compute stack when AI becomes, as many expect, as consequential as electricity or telecommunications.

Musk now controls a company that provides satellite internet to the American military, launches classified payloads for the intelligence community, and operates the AI chatbot used by hundreds of millions of people globally. The same man posts laugh emojis when that chatbot generates child sexual abuse material. The same man burns approximately $1 billion monthly on an AI product that cannot compete on quality.

The market has assigned a $1.25 trillion valuation to this arrangement.

What This Means for Everyday People

For ordinary users, the implications are both abstract and immediate. The abstract concern is that AI development is consolidating into the hands of a small number of actors whose incentives may not align with the public interest. The immediate concern is that platforms you use daily are being designed by people who view safety guardrails as obstacles to engagement rather than features that protect users.

If you have a daughter, sister, mother, or friend who has ever posted a photograph to social media, xAI built a product that could be used to sexualize that image without her consent. When confronted with this reality, the company’s response was to laugh. Then it was acquired for a quarter of a trillion dollars.

The space data center vision may or may not prove viable. The engineering challenges are formidable. The timeline is aggressive. What is certain today is that the company absorbing xAI into its corporate structure is doing so at a valuation that cannot be justified by the quality of xAI’s products. It can only be justified by xAI’s reach, its government connections, and the belief that in the AI race, distribution matters more than safety.

That belief may prove correct. It will not prove admirable.

For inquiries and analysis contact laterstack@proton.me

Frequently Asked Questions

What is the SpaceX xAI merger?

SpaceX, the rocket and satellite company owned by Elon Musk, announced on February 2, 2026 that it would acquire xAI, Musk’s artificial intelligence startup, in a share exchange valued at $1.25 trillion. The deal combines SpaceX’s launch and satellite capabilities with xAI’s AI development, creating what Musk describes as an integrated platform for building orbital data centers.

Why is xAI valued at $250 billion despite Grok trailing competitors?

xAI’s valuation reflects its distribution through the X social media platform, its recent funding from sovereign wealth funds in Qatar and Abu Dhabi, and strategic investors including Nvidia. The valuation is based on reach and future potential rather than current product superiority over competitors like OpenAI, Anthropic, or Google.

What was the Grok deepfake scandal?

In late December 2025 and January 2026, xAI’s Grok AI was used to generate thousands of nonconsensual intimate images of women and minors on the X platform. The scandal prompted investigations from regulators in California, the EU, France, India, and Malaysia, and threats of platform bans from the UK government.

OpenAI announced it will start serving ads inside the free version of ChatGPT and its $8 per month ChatGPT Go over the coming weeks. The move is long expected, but it raises questions about how a company known for AI innovation balances revenue with user trust.

The company reached $13 billion in revenue last year and expects to triple that this year, according to an anonymous source. Most of that revenue is being spent on cloud services and data centers to support AI infrastructure. OpenAI plans to spend $115 billion between 2025 and 2029, a figure that dwarfs the budgets of most tech companies.

Ads in ChatGPT will not change the answers it provides, OpenAI says, nor will advertisers influence the responses. Still, the method of ad delivery is unlike anything seen on the web. Chatbots generate text instead of web pages, which makes standard display ads impossible. Instead, OpenAI will tailor ads based on the questions users ask and prior queries, with an option to disable personalization.

This approach exposes the tension between AI monetization and the trust users place in the service. ChatGPT is used for everything from coding to personal advice. If users start to perceive any subtle influence from advertising, the credibility of the platform could erode.

It also highlights the scale of AI’s infrastructure demands. OpenAI will use Cerebras chips that consume hundreds of megawatts of electricity, equivalent to powering tens of thousands of households. OpenAI is not alone; companies like Microsoft and Google are also investing heavily in global AI compute, with significant cost and environmental considerations.

This is a moment where technology, business, and ethics intersect. Every ad served is a decision about how much users pay with attention and how much companies pay for compute. AI growth has costs that go beyond money, and users are only beginning to notice the trade-offs.

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Electricity bills across the United States are climbing and people in several states are blaming one thing. The nonstop growth of data centers that power modern artificial intelligence systems. Residents in Virginia, Illinois and Ohio saw double digit increases in utility costs this year. In each of these states there is a dense cluster of massive AI facilities running around the clock.

Energy regulators say the math is simple. A single large scale data center can draw as much power as hundreds of thousands of homes. When dozens of them appear in the same region they reshape the entire grid. When demand shoots up faster than new power plants can be approved or built, prices follow.

Virginia provides the clearest example. The state has the highest concentration of data centers anywhere in the world. Local leaders have begun openly targeting the industry. Newly elected Governor Abigail Spanberger rode a campaign focused on affordability and promised voters that tech companies would pay more of the costs created by their facilities.

A political fight that is growing louder

The energy crunch arrives at a sensitive moment. National elections sit just one year away and electricity bills have become a daily conversation. Several Democrats in Washington now argue that the relationship between President Trump and major AI companies has allowed utilities to pass data center costs to ordinary families.

Senators Bernie Sanders and Richard Blumenthal say the public should not be forced to subsidize data center bills. They are calling for stronger oversight and new rules for facilities that require massive power contracts.

Community frustration is also rising. Some residents do not want more warehouses full of servers that hum loudly and drive up neighborhood bills. In places with high density clusters, the resentment has begun to shape local elections.

A grid that is struggling to keep pace

The PJM grid operator which covers Virginia, Ohio and Illinois has faced a huge imbalance between supply and demand. Prices for capacity auctions, the mechanism used to make sure the grid can reliably meet demand, exploded this year. Bills jumped from two billion dollars to fourteen billion dollars in a single auction cycle and then climbed again to more than sixteen billion dollars.

Independent analysts say data centers account for over half of the projected demand costs in the region. That figure shows how rapidly AI related growth has transformed the energy market.

Other states offer a different picture. Texas has more than four hundred data centers yet saw only a modest increase in electricity prices. California has some of the highest electricity prices in the country, but its year over year increase barely nudged upward. Local conditions and grid structures play major roles in how data centers influence cost.

The future does not look cheaper

Most experts see little chance of electricity prices falling soon. The grid needs huge upgrades. Renewable energy projects wait years to connect. Transmission lines cost more to build than ever. Meanwhile AI companies announce new data centers almost every month.

The result is a new kind of techlash. AI has become the spark for political fights over who should pay for the infrastructure that fuels digital growth. Voters want relief. Politicians want answers. Utilities want more supply. And the tech industry wants more power to keep expanding.

No one expects the pressure to ease any time soon.

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