The AI Data Center Backlash Is Going Bipartisan

There was a ridiculous amount of huge AI news this week.

Google moved Demis Hassabis into its chief scientist role. Jeff Dean and three legendary researchers also left Google to build Discovery Loop. Multiple models topped ARC-AGI-3. And OpenAI agents built a secret message board to coordinate their work.

And yet…

The growing revolt against AI data centers may be the most important AI story of the year, because this issue is seemingly reshaping political opinions across the US, and people who normally disagree on everything are uniting around one key thing: they hate data centers.

Here’s what happened

Reporter Jasmine Sun went on The Ezra Klein Show after she returned from a 10-day, four-site reporting trip through Wisconsin and Michigan, interviewing activists, workers, officials, and residents to understand how local data-center fights became a bipartisan revolt against the AI buildout. And she explained why the fight has spread far beyond complaints about ugly buildings, noise, water, or electricity.

Here’s what she found:

• Opposition to datacenters has become overwhelmingly bipartisan. Roughly 70% of voters reportedly oppose a data center near them, while more than 100 state and local moratorium proposals are circulating.
• The physical complaint is straightforward. These facilities need enormous amounts of electricity to run their chips; they’re loud, and very ugly.
• The power problem is real. AI clusters require significant new generation and power plants. When projects arrive faster than utilities can expand the grid, residents worry about higher rates and public infrastructure being rebuilt primarily for one wealthy customer.
• The water problem is more complicated. Cooling does use water, but Jasmine says most newer data centers use closed-loop systems that recycle it and consume far less than popular comparisons suggest. Water has still become a sticky symbol for communities already worried about who gets access to scarce resources.
• Secretive negotiations made everything worse. Nondisclosure agreements kept officials from explaining proposed projects, allowing rumors and distrust to fill the gap.
• People do not believe the promised benefits. Datacenter companies offer jobs, tax revenue, and protected electricity rates. But residents shaped by previous corporate failures increasingly respond, “I don’t believe them.”
• AI has no powerful public constituency. Housing has future residents. Auto plants have workers. Renewable energy has environmentalists. But most normal people still see AI as useful software, but not essential infrastructure worth transforming their community for.
• The “permanent underclass” fear makes the bargain feel insulting. Some AI insiders openly predict automation could concentrate wealth while leaving a large group with less work, mobility, and political power. Residents are being asked to supply the land, water, and electricity for infrastructure that its own builders warn may make them economically disposable. NO thanks.

All that is why many people do not want this anywhere near them. They see concentrated costs and uncertain benefits: noise, new construction, more transmission lines, possible rate increases, few permanent jobs, tax breaks for the developer, and decisions made basically without the public’s consent.

This is based on real conversation, with real people, in real areas where this is happening. Some of these people may use ChatGPT every now and then, or even every day, and still reject the idea that enjoying an app means their town owes the AI industry (“a handful of billionaires”) land, cheap resources, and political deference.

Why this matters

AI politics IS kitchen-table politics, and with a midterm election in the US coming up, people’s opinions on data centers impact the entire economy that runs on AI. This debate touches utility bills, farmland, construction jobs, local democracy, and whether a small town can say no to a trillion-dollar industry (ish?).

Now, Sun does stress that some datacenter deals can be net positive for communities: a developer may be the only buyer willing to spend $30M to clean up a contaminated industrial site, for example. Old factories emptied out by empty promises can be re-utilized, and already-cleared land can become productive again. The real fight is over whether towns get a transparent, fair share of that upside.

So what if team “pause AI” wins the polls? Jasmine says you can’t really stop datacenters at the physical location level (so moratoriums = no go), because they’ll just move elsewhere (out of state, overseas, or even into space).

Basically, AI infrastructure is becoming geopolitical bargaining power. Countries that host scarce compute can negotiate for access to frontier models and cybersecurity support. And if US moratoriums simply push that leverage toward authoritarian states, America may end up with LESS public control over AI, not more.

Our take

That said, the AI Futures Project’s new pacing proposal said regulators could slow frontier development by requiring labs to devote most of their compute to serving existing models and toward testing how to monitor and control powerful systems. So in actuality, the same infrastructure communities are fighting over may become the throttle government uses to control how quickly AI advances.

But to us, the part that resonated was Jasmine’s point that the industry keeps treating this like a marketing problem, when it’s a product problem. AI is problematic.

To regular people, probably like you, AI doesn’t feel like critical infrastructure yet. It probably feels more like a toy, or an occasional productivity boost, and one that certainly isn’t worth $1 trillion+ in chips. In my opinion, that’s because we’re trying to “scale” before we solve the five critical problems of AI:

1. Alignment (we still can’t “read its mind” to make it helpful, not harmful).
2. Hallucinations (it still gets some things wrong, depending on the model).
3. Context size and memory (it can only process so much info at a time).
4. Continual learning (every new chat starts over, versus it adapting to you).
5. Inefficient to run (it takes too many computer chips and watts for high IQ).

To solve any one of those, in my opinion, you have to solve ALL of them, at the same time, because they’re all symptoms of the same problem: the architecture. Scaling more datacenters will not fix this. Clearly, it is literally unsustainable.

So get ready for the first AI question every candidate has to answer after primaries: “Who pays for AI, who gets to decide where it’s built, and who gets the benefits?”

Editor’s note: This article originally appeared on our sister publication, The Neuron.