You’re out of free articles.
Log in
To continue reading, log in to your account.
Create a Free Account
To unlock more free articles, please create a free account.
Sign In or Create an Account.
By continuing, you agree to the Terms of Service and acknowledge our Privacy Policy
Welcome to Heatmap
Thank you for registering with Heatmap. Climate change is one of the greatest challenges of our lives, a force reshaping our economy, our politics, and our culture. We hope to be your trusted, friendly, and insightful guide to that transformation. Please enjoy your free articles. You can check your profile here .
subscribe to get Unlimited access
Offer for a Heatmap News Unlimited Access subscription; please note that your subscription will renew automatically unless you cancel prior to renewal. Cancellation takes effect at the end of your current billing period. We will let you know in advance of any price changes. Taxes may apply. Offer terms are subject to change.
Subscribe to get unlimited Access
Hey, you are out of free articles but you are only a few clicks away from full access. Subscribe below and take advantage of our introductory offer.
subscribe to get Unlimited access
Offer for a Heatmap News Unlimited Access subscription; please note that your subscription will renew automatically unless you cancel prior to renewal. Cancellation takes effect at the end of your current billing period. We will let you know in advance of any price changes. Taxes may apply. Offer terms are subject to change.
Create Your Account
Please Enter Your Password
Forgot your password?
Please enter the email address you use for your account so we can send you a link to reset your password:
That’s okay for clean energy firms, terrible for manufacturers, and a big risk for everyone.

Over the past few months, you could put together three different — and somewhat conflicting — pictures of the American economy.
For companies exposed to the AI boom, business has been good — excellent, even. The surge in ongoing capital investment into data centers and electricity has been larger than other recent booms, such as the telecom buildout. Electricity demand is soaring, especially in Texas and the Mid-Atlantic. Technology companies have signed power offtake deals with nuclear and hydroelectricity companies. If anything, companies exposed to artificial intelligence are more afflicted by congested supply chains and shortages than by slack demand — see the yearslong waiting lists to get a new transformer or natural gas turbine.
Outside of the AI economy, though, the economy has been a fair bit colder. You might even say it’s been frozen by indecision. When you talk to business leaders, they confess confusion about where things are heading. President Trump’s constantly changing tariffs — and his administration’s mercurial policy shifts — have made it difficult for non-AI-exposed businesses to plan long-term capital investment.
You could hear this view from clean energy manufacturing and traditional fossil firms alike. When I talked to John Henry Harris, the CEO of the medium-duty truck maker Harbinger Motors, for an episode of Heatmap’s Shift Key podcast in June, he told me that his company was just about to shift a production process to Mexico when a last-minute Trump change made it cheaper to keep it in China. Meanwhile, an oil and gas executive recently told the Dallas Federal Reserve: “The Liberation Day chaos and tariff antics have harmed the domestic energy industry. Drill, baby, drill will not happen with this level of volatility.”
But the data contradicted that tepid view. This was the third picture that we were getting of the economy. Through the summer, federal surveys showed an economy that was performing okay. In May, according to the Bureau of Labor Statistics, the U.S. economy added 139,000 jobs; it gained another 147,000 jobs, apparently, in June. The AI boom was clearly contributing to those robust reports. But how could an economy that business leaders otherwise described as difficult be going so well?
Now we can finally square these disparate pictures.
On Friday, the federal government released its newest tranche of job numbers. The headline number was mediocre — the U.S. added a mere 73,000 jobs in July — but the guts of the report were worse. The government revised down its estimate of the May and June reports by a total of 258,000 jobs. With these new numbers in hand, it’s clear that the labor market has essentially stalled out since Liberation Day in April.
The unemployment rate slightly rose to 4.2%, which was in line with what economists predicted.
These new reports clarify that the broader American economy wasn’t actually thriving. Its summer strength was a mirage the whole time. Outside of AI, things are downright frigid. And as President Trump continues to shuffle tariffs and increase trade uncertainty, we can expect conditions to worsen. Trump seems hellbent even on clouding our ability to understand the underlying economy: on Friday afternoon, he fired the Bureau of Labor Statistics commissioner, a career civil servant.
If you squint, you can see a hazy “AI sector” versus “non-AI sector” distinction in the data, even among the energy and decarbonization companies we cover at Heatmap. But it’s not obvious. Contrary to what you might expect when power demand is surging, utility employment was basically flat last month. Heavy and civil engineering construction jobs were up by 6,000, and “nonresidential specialty trade contractors” — a category that can include electricians — gained nearly 2,000 jobs.
But manufacturing lost 11,000 jobs last month, with the motor vehicles industry driving 2,600 of those losses. Mining, quarrying, and oil and gas jobs were down. The Institute of Supply Management report, a private survey of U.S. manufacturing activity, showed the sector shrank in July for the fifth month in a row.
And even though the Department of Government Efficiency’s deferred buyout program for more than 150,000 people has yet to hit, the federal government bled 12,000 jobs.
In a way, the clean energy industry — or at least solar, battery, nuclear, and geothermal developers — might consider themselves lucky. Despite the best efforts of Trump’s officials, and despite the chaos of President Trump’s policies, they have been able to eke through the past few months because of the AI boom. Nearly 70% of all new power-generating capacity added to the U.S. grid in the first quarter of this year came from solar panels, and the government has thrown its weight behind next-generation nuclear and geothermal technologies. A tepid jobs report might even bring some interest rate relief from the Federal Reserve.
But if that AI boom slows down, we should all watch out below.
Log in
To continue reading, log in to your account.
Create a Free Account
To unlock more free articles, please create a free account.
The data center boom is everywhere you look in U.S. economic and emissions data.
This is an edition of Heatmap Daily, an evening review of the day’s news written by our executive editor. Sign up for it here.
It isn’t exactly a new thought, but I’ve been struck recently by how many trends in America’s economic and environmental data are fundamentally about the data center boom and the return of electricity demand:
First, the Energy Information Administration reported this week that U.S. emissions grew by more than 2% last year, driven by surging electricity demand and an increase in coal-fired generation. What caused that higher power demand? New factories and data centers — as well as record summertime cooling demand.
Second, many of the new factories driving that higher power demand are themselves producing goods that are … let’s say … data center-adjacent. There are the enormous new semiconductor fabs, of course. But Ford and General Motors have also set up new production lines (or repurposed old ones) to manufacture grid-scale batteries to meet power demand.
Third, take a look at the recent U.S. spending on private non-residential construction — in other words, everything American companies are building that is not houses, condos, or apartments.
The construction industry’s spent almost $60 billion on data centers over the past year, which is more than it spent on all other office buildings combined (and more than it spent building warehouses, too). Just a handful of categories — data centers, power plants, electricity infrastructure, and certain kinds of electronics manufacturing — now make up a third of all U.S. private non-residential construction investment. They’ve never made up such a large share of construction spending since data collection began in 2014.
As The New York Times recently noted, the American economy is unusually dependent on the American stock market right now — and the stock market is unusually dependent on artificial intelligence. This week, investors started to balk at the enormous spending hyperscalers are planning to keep building out the AI boom; Alphabet’s shares dropped 8% this week after it boosted its planned 2026 capital expenditure and signaled 2027 will be even bigger. If the data center boom started to slow down in earnest, then more than just that budget will change.
Speaking of which, my colleague Emily Pontecorvo wrote earlier this week about how many businesses are struggling to even estimate their carbon emissions from artificial intelligence. The carbon accounting startup Watershed recently unveiled a new formula to help companies get a sense of their AI-related emissions.
But even that formula is still limited by the amount of data hyperscalers publish — and they don’t publish that much. Google, for instance, is the only AI company that has (laudably) provided estimates of its emissions on a per-prompt basis. Yet no company has published its per-token emissions, or how emissions sync up with particular models or regions.
So Emily asked Google: Why aren’t you — or any other model provider — disclosing this kind of data yet?
The tech company didn’t get back to us until after we’d published Emily’s story. But its response was interesting enough that I wanted to quote some of it here.
The problem is “industry consensus,” Cooper Elsworth, a Google spokesperson, told us. “There is currently very little consensus on how to comprehensively and fairly measure the serving environmental impact of generative AI (such as text generation),” he wrote. “Without standardized, ‘apples-to-apples’ frameworks, it is difficult to compare different providers accurately.”
That’s partly because energy use — and emissions data — can vary from site to site and depend on “custom-built hardware, software compilers, and advanced inference techniques.” And he claimed Google doesn’t always have the measurement hardware in place to provide such specific estimates: “Providing precise, repeatable data requires highly advanced measurement infrastructure,” he said. “For example, software-based energy monitoring tools often suffer from sampling biases. For our study, we had to step away from top-down averages and directly measure actual energy at the physical power supply unit (PSU) level across our deployed fleet. Not all providers have the telemetry or data sets required to benchmark their operations at this level of granularity.”
Read Emily’s story to understand the other reasons why estimating — or even “guesstimating” — AI-related carbon emissions is so challenging.
A conversation with Emma Uridge of the Kansas Health Institute.
This week’s conversation is with Emma Uridge, analyst with the Kansas Health Institute. Uridge spent copious hours analyzing state and local laws on data center development to best understand how policymakers are responding to the potential environmental public health impacts of large AI infrastructure, including power and water. The report, which came out this week, also goes in depth into those health impacts. I reached out to her to discuss what she sees as must-watch territory for our readers on this emerging policy arena.
Our conversation was lightly edited for clarity.
What is actually being done on policy when it comes to data centers — beyond moratoria of course?
So first I’d like to just talk about the point of moratoria. It’s helpful to talk about how these policies emerge in the first place. One area where moratoria are helpful is when a data center is proposed but the county has no approach for how they’d like to potentially regulate them. That’s temporary, most of the time. It lets local governments conduct research on the various impacts and also negotiate community benefits, ones that can mitigate any potential negative impacts — like Lancaster Pennsylvania, which instituted a community benefit agreement that maximized the potential benefits of development while mitigating what large data centers can do. That agreement looked at capping municipal water use at 20,000 gallons per day and requiring 100% clean energy. It had financial penalties for non-compliance. The company also committed $20 million to their local economic development and clean energy fund. There are ways to negotiate with developers.
We also see amendments to existing zoning. Data center proposals are increasingly popping up in rural areas, many of which are unzoned, so there’s no way a county can negotiate unless there’s a moratorium in place.
Other policy solutions include different performance standards or requiring on-site renewable energy, like what Jefferson County, Missouri, looked at. Also setback requirements, mandatory noise buffers, ending by-right zoning.
Where are local governments getting ideas for regulating data centers?
A lot of the technical information comes from developers. That can in cases be seen as a biased source of information. I wouldn’t say there’s a dedicated group providing assistance to local governments when a project is proposed — which is a similar story to wind industry development, where we have only a handful of consultants who provide technical advice. It can be really helpful to get a multi-disciplinary approach to hearing information. It can be helpful to have the utility commission, public health folks, those in academia, as well as the developer.
As of right now, especially in rural areas, local governments have a hard task of balancing pushback while getting the most accurate, evidence-based, neutral information to make decisions. That balance can be contentious.
What is the federal government doing on data center policy? How is the Trump administration approaching it?
A few things there. In the early days, the drive was for AI expansion and to be competitive with foreign adversaries. Now due to the amount of public pushback in red and blue localities and a more cautious approach.
I’m not seeing a lot of actual policy movement at this time.
I know the EPA is looking at the chemicals used in cooling data centers because when that water is cycled through the system, some of it is discharged into the water system, so they’re looking at the Toxic Substances and Control Act for monitoring that.
How much of an impact does this minimal federal role have on industry behavior?
Y’know, this isn’t specific to data centers. This is true for all kinds of large-scale development: there’s a need to require some sort of federal monitoring and regulation.
That’s where I see an emerging role for public health. At the federal level, there could be policy movement towards requiring some sort of environmental monitoring at data centers to make sure they’re operating responsibility. Looking at specific water use relative to water availability and what happens when there’s a time of severe, persistent drought. With air quality too — we’ve seen areas where the grid isn’t as reliable so their diesel generators are kicking on more and affecting air quality for residents.
We’re just not seeing all of that right now. We need corporate disclosure.
What do you see as the most important public health impacts from data center development?
It varies by localities. The most discussed obviously is water usage. One thing I’d note about my conversations with folks enthusiastic around emerging tech is, there are still questions that need to be asked about the capacity of localities to support a data center. Like a small town in Kansas may only be using 40% of their water for their utility needs. If a data center came online, how much of that water goes to the data center?
One area underexplored within the public health discipline is energy poverty and energy security. The ability of a household to meet the needs of everything energy provides in our lives. It’s known we have an aging electric grid but we’re not talking enough about large-scale blackouts when the grid is not sufficient to support some of these new data centers.
Plus more of the week’s big development fights.
1. Laramie County, Wyoming — Meta is fighting the fine it received in the Cheyenne data center water pollution controversy, and the conflict between the tech giant and the city’s small board of public utilities is continuing to spill out into the public.
2. Niagara County, New York — This county just rejected a solar project’s highway work permits in a show of retaliation against the state’s Office of Renewable Energy Siting.
3. Barron County, Wisconsin — The anti-solar protest is the new campaign stop in deep red Wisconsin.
4. Chesapeake, Virginia — A large battery storage project on the Virginia coastline is on the rocks amidst rampant local opposition.
5. Lewis County, West Virginia — West Virginia is now a key battleground in the fight over transmission, as a line spanning all of West Virginia and Maryland — and cutting through Data Center Alley in Virginia — causes compounding consternation.