Tag: AI Bubble

  • The Data Center Casino: Why America Needs Elected Technology Leaders Now

    By the Founders, Department of Technology | San Diego, California | November 13, 2026

    America needs more data centers. That part is not in dispute. What is in dispute is who decides where they go, who pays for them, and whether anyone is looking at the whole board.

    If the United States wants to remain the world leader in artificial intelligence, it has to build the computing capacity, and the power to run it. We start from that premise. But for several years, officials in both major parties, in red states and blue, have treated new data centers less like public infrastructure and more like a slot machine: pull the lever, hope for a jackpot of investment, tax revenue and ribbon-cuttings, and worry later about who covers the losses.

    The question that has been asked is “how much money can be made?” The question that should be asked is “what is the best outcome for the community that has to live next to this?”

    Three risks pull in three different directions. Build too fast, and we strain an aging grid and inflate a financial bubble. Build too slow, and other countries may make our lead obsolete. Ignore what AI could do to employment, and we erode the tax base a modern society runs on. No one in government is currently accountable for all three at once.

    The casino logic

    Thirty-eight states offer tax incentives to data centers, and at least 28 of them considered scaling those incentives back this year, according to the National Conference of State Legislatures. At least ten states forgo more than $100 million a year in these breaks, with Texas and Virginia each losing as much as $1 billion annually, according to Good Jobs First, as reported by Broadband Breakfast.

    In Ohio, Policy Matters Ohio reports the break cost state and local governments about $2 billion last year, far more than the state had forecast, which led Republican Governor Mike DeWine, who had previously vetoed a repeal, to order a temporary pause.

    The backpedaling is bipartisan, which is exactly the point. In Virginia, 21 Democrats and seven Republicans in the state Senate voted to curtail a 5.3% tax break. Kentucky’s Democratic governor, Andy Beshear, has called for ending his state’s incentives, and Ohio, Arizona, Nebraska and Illinois have paused theirs.

    When lawmakers of both parties are reversing course, the original approach was not a partisan mistake. It was a structural one: the deals were made one project at a time, often in secret (as we noted in our open letter, 25 of 31 Virginia local governments involved in data center development had signed NDAs), and no one owned the cumulative consequences.

    Build too fast: the grid and the bubble

    Our electrical grid is where the cost of speed shows up first. In PJM, the nation’s largest grid market, wholesale power prices jumped almost 76% in the first quarter of 2026 compared with a year earlier, and the market monitor said the price effects on customers were very large and not reversible, according to E&E News.

    Capacity auction prices rose roughly tenfold in two years, from about $29 to $329 per megawatt-day, and the December 2025 auction fell short of its reliability target for the first time, per one analysis of PJM’s results. Ordinary ratepayers are paying for capacity built to serve some of the wealthiest companies in the world.

    Then there is the money behind the buildout. Hyperscalers will spend about $750 billion this year, and they are borrowing heavily to do it, while economy-wide statistics still show little or no productivity growth from AI. Jefferies warns that AI investment was the main driver of U.S. economic growth last year after consumer spending, so a sudden collapse would leave the economy exposed. And The Economist notes that more than half the spending has gone to servers and specialized chips that become obsolete within a few years. A technology can be transformative and still be overbuilt. If that happens, the stranded assets and the unpaid debts will not stay on Wall Street.

    Build too slow: the electron gap

    The opposite risk is just as real. The United States leads in advanced chips but is short on power, while China has the power and is short on chips. BloombergNEF estimates China will add more than six times as much generating capacity as the U.S. over the next five years. Morgan Stanley has forecast a 44-gigawatt shortfall for U.S. data centers within three years, roughly the summertime capacity of New York State. According to calculations reported by The Washington Post, Chinese data centers pay about a third of what American ones do for electricity.

    We cannot answer that gap with moratoriums and lawsuits alone, and we cannot answer it with secrecy and subsidies either. Speed that costs public trust is not speed. As voters showed on November 3, when they favored limits in 40 of 41 local data center measures, communities will stop projects they feel were done to them rather than with them.

    Ignore the jobs: the tax base

    The third risk is the quietest. A RAND analysis found that 84 percent of federal revenue in 2024 came from individual and payroll taxes, both collected from labor income. Because income is concentrated in high-paid white-collar jobs, replacing those roles with AI would hit revenue harder than the headcount suggests, and higher corporate profits would not make up the difference unless corporate tax rates roughly doubled.

    The same logic applies closer to home. New York City’s Comptroller built five AI scenarios for the city’s finances, put a combined 50 percent probability on the three with worse outcomes for jobs, growth or tax revenue, and recommended building a reserve fund to 16 percent of annual tax revenue. Every county and state should be asking the same question before the layoffs arrive, not after.

    Why we need elected technology leaders

    Look at who currently holds a piece of this problem. Utility commissions watch rates. Economic development offices watch ribbon-cuttings. Treasurers watch revenue. Each sees one slice, and none is answerable to voters for the combination. That is how a state can win a data center, lose a billion dollars a year, raise its residents’ power bills and weaken its future tax base, all through decisions that each looked reasonable on its own.

    We are calling for elected technology leaders at the local, county, state and federal levels, with one job: to weigh speed, grid capacity, public cost and long-term fiscal health together, in public. In practice that means:

    • Public plans before approvals. Grid, water and fiscal impact published before a project is approved, not after.
    • No secret deals. Public bodies should not sign NDAs that keep residents from learning what their own governments agreed to.
    • Large users pay for what they trigger. Grid upgrades and capacity costs assigned to the facilities that cause them, not spread across households.
    • Incentives with accountability. Tax breaks tied to measurable jobs and revenue, with clawbacks if the promises are not met.
    • Fiscal readiness for AI displacement. Scenario planning and reserves at every level of government.

    They should be elected, not appointed, because the tradeoffs here are value judgments about whose power bills, whose land and whose jobs, and those belong to voters. And they are needed now because the contracts are being signed now.

    The case against our case

    A fair reading includes the strongest objections.

    • Incentives may be what brings projects at all. The Data Center Coalition’s Dan Diorio argues that revenue estimates overlook the chance a project would never have been built without exemptions, and notes that states with the most growth, including Virginia, Texas and Pennsylvania, offer them, while Colorado, which does not, has less (S&P Global).
    • The electron gap may be overstated. The Brookings Institution cautions that no one knows how many data centers will be needed or how fast chips will become more efficient, and that worries from builders are not the same as geopolitical shocks (Brookings).
    • Job forecasts are uncertain. One UK think tank’s scenarios range from eight million jobs lost to none at all (IPPR), and New York’s Comptroller itself says it is hard to tie current trends to AI with certainty.
    • Another elected office is another layer. Critics may reasonably prefer strengthening utility regulators and legislatures over creating new positions. Our answer is that each existing body owns only a slice, but that is a debate worth having in public.

    Start the conversation

    Neither the casino nor the moratorium is a plan. The country needs people whose job it is to build enough, build in the right places, make the builders pay their share, and prepare for what comes after. That job does not exist yet. We are asking every candidate and every sitting official, of every party, to start the discussion about creating it, at department.technology/.

  • What Every County Treasurer Should Model Before AI Layoffs

    By the Founders, Department of Technology | San Diego, California | November 13, 2026

    Local governments face two different AI-related revenue risks at once: the labor income that funds them could shrink, and the data center revenue that may be replacing it could be concentrated in one fragile industry. Here is a simple scenario worksheet any treasurer or finance director can build in an afternoon.

    This is not a forecast. Nobody knows how many jobs AI will displace or how long the data center buildout will last, and we say so plainly. The point of scenario planning is to find out, before it matters, which of your revenues would hurt most under each outcome, and to decide in advance what you would do. Treasurers do this every year for recessions and hurricanes. The same discipline applies here.

    Why now

    The labor-income exposure is documented at the federal level. A RAND analysis found that 84 percent of federal revenue in 2024 came from individual and payroll taxes, that the loss of high-income white-collar jobs would hit revenue disproportionately, and that higher corporate profits would not offset the decline unless corporate tax rates were roughly doubled. Economists Anton Korinek and Lee Lockwood warn that even modest labor displacement could significantly strain public finances. Local governments sit downstream. One opinion writer argues that states and localities will bear the initial cost of retraining and income support when workers are displaced.

    The boom itself can be a concentration risk. California’s Legislative Analyst found that stock-option withholding from a handful of large tech companies made up about 10 percent of all state income tax withholding in 2025, and warned that withholding could fall if the AI market deteriorates. At the county level, Loudoun County, Virginia, reports that data centers generated 38 percent of its general fund revenue while occupying about 4 percent of its commercial land. Prince William County projected data center revenue of about $550 million in fiscal 2027, roughly 28 percent of general fund tax revenue. Loudoun staff have warned that if the personal property tax rate is not lowered, data centers could exceed 60 percent of the general fund within five to seven years.

    The lesson is not that data centers are bad revenue. It is that a budget resting on one industry needs a plan for what happens if that industry slows down, and a budget resting on wages needs a plan for what happens if wages shrink.

    The worksheet

    1. Map your revenue by source and by exposure. List every major revenue line: property tax, sales tax, income or payroll taxes where you have them, business taxes, fees and intergovernmental aid. For each, note how much depends on (a) wages in office and professional jobs in your area, (b) a single employer or industry, and (c) data centers or their equipment.
    2. Define three or four scenarios. For example: a baseline, a gradual-displacement scenario, a rapid-displacement scenario, and an investment-bust scenario in which data center equipment values fall sharply. New York City’s Comptroller used five scenarios; a small county can use fewer. Choose ranges your analysts can defend. For illustration only, you might test declines of 10, 25 and 50 percent.
    3. Run each scenario through each revenue line. A wage decline changes income and sales tax receipts. A bust changes personal property tax on equipment. Record the dollar effect and the share of the general fund.
    4. Estimate the demand side. Displaced workers use services. Note which of your programs would see more demand, such as workforce programs, public assistance, and emergency services, and what each would cost.
    5. Set a reserve target. The NYC Comptroller recommended building a reserve to 16 percent of annual tax revenue. Loudoun built a $119.7 million revenue stabilization fund specifically to absorb differences between budgeted and actual data center revenue, and reports it is now fully funded. Your target will differ, but it should be written down.
    6. Set triggers and publish. Decide in advance what happens at each threshold: when to freeze hiring, when to review the tax rate, when to draw on reserves. Then publish the scenarios and triggers each year with the budget. Residents should see the plan before they need it.

    If you host data centers

    Host jurisdictions have an extra set of questions:

    • How much of the general fund comes from data centers, and what is the trend? Track the share every year, not just the dollar amount.
    • What happens if equipment is replaced less often, or values fall? Equipment, not buildings, drives much of the revenue. The Economist notes that more than half of AI spending has gone to servers and chips that become obsolete within a few years.
    • Are you spending windfalls on permanent commitments? Treat unexpected revenue as one-time money unless you can justify it as durable. Loudoun staff have framed lowering the tax rate on data center equipment as the strongest tool for managing dependence.
    • Is relief visible to residents? Loudoun has cut its real estate tax rate substantially over the past decade, a real benefit residents can see.

    What not to do

    • Do not turn scenarios into predictions. A scenario is a stress test. Label it that way.
    • Do not wait for certainty. The range of forecasts is wide, and it will stay wide.
    • Do not model only the downside. Include a scenario in which AI strengthens your local economy, so the worksheet is credible to everyone.

    The case against our case

    • Holding reserves costs services today. Every dollar in reserve is a dollar not spent on schools or roads now. Reasonable officials will weigh the insurance value against current needs, and the right level is a local judgment.
    • The effect may look like earlier technology shifts. Economists David Agrawal and William Fox argue that AI reinforces rather than overturns the classic tradeoffs in state and local public finance, much as e-commerce and remote work did. If so, familiar tools may be enough.
    • Data center revenue is real and useful. Industry groups argue that without it, residents would pay far more. NetChoice, a trade association, says the alternative for Loudoun homeowners is a tax bill nearly double today’s. That argument deserves weight, and it is exactly why the downside needs a plan.
    • Forecasts are uncertain. The NYC report itself notes how hard it is to tie current trends to AI with certainty. We agree, and that is why the worksheet uses ranges.

    The role no one holds

    In most governments, nobody is assigned to ask these questions across revenue, workforce and infrastructure at once. Treasurers see revenue. Economic development officers see ribbon-cuttings. Workforce boards see training. A technology leader’s job is to sit across all three. If you are a treasurer, you do not need to wait for that role to exist: start with step one this quarter. We are glad to share a blank version of the worksheet. Our proposal is at department.technology/.