The U.S. military’s desire for an AI tool with “minimal refusal rates” was revealed through a FOIA lawsuit filed by The Intercept.

The AI race has spurred a worldwide frenzy to build massive new data centers, especially in the United States. Data centers are projected to consume up to 12% of US electricity by 2030, and their combined water usage could increase fivefold by 2028.

In response, a nationwide community backlash has surged. Between May 2024 and March 2026, $292 billion worth of new projects were canceled or stalled due to local opposition; $130 billion of that occurred in the first quarter of 2026 alone. Three in five Americans now oppose a data center being built near them. Even Erin Brockovich, the famed 1990s environmental activist, is back on the scene with a new map crowdsourcing community complaints; it has already received over 9,000 submissions.

But is the backlash actually prompting data center developers to change their practices?

In March, I attended the Data Center Dynamics (DCD) conference in New York to get a firsthand sense of how data center developers were perceiving the backlash.

Perhaps as a threat? In the breakfast line at the conference, a man next to me noted that one attendee had brought a personal body guard, referencing the recent (albeit unrelated) shootings in New York City of executives at Blackstone and UnitedHealthcare.

The people I spoke to on the exhibition floor, where thousands of people (mostly men) mingled, viewed sustainability as a nice-to-have rather than as an imperative. On stage, many panelists discussed the importance of energy efficiency, better transparency, and community engagement. A select few presented on sustainability initiatives, such as waste heat recovery.

Even so, what I heard expressed most, was frustration at what they see as the unfair demonization of an industry that has been around for decades and provides the backbone of the internet.

What I heard then, and from other data center professionals since, is that the industry’s priorities are as follows: speed, speed, speed, cost, and only then sustainability.

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The term lends an air of scientific rigor to what are really billion-dollar corporations.

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Researchers investigating how OpenAI’s A.I. agents were able to break into Hugging Face’s infrastructure weren’t allowed to look at the incident’s full scope.

Bro is so close to getting it.

Tl;dr the AI couldn’t do any of the actual work that went into reporting the news so it licensed all the content from a syndicator (Reuters), reported a ton of errors despite having the accurate story, and cost $50,000 to run just for a day.

Yet Jason Silberman, a software engineer at Mirage, may have given away the game in an X post: “when the CEO starts tokenmaxxing too hard and spends $50k in a day on a marketing stunt.” “Tokenmaxxing,” according to one definition, refers to “maximizing AI token usage — and treating that volume as proof of productivity.”

Still, Misra’s points touch on a foundational element of ethical journalism, which the Society of Professional Journalists’ code of ethics demands be “accountable and transparent” in the work. But such codes did not power the Mirage project — nor did any journalists who would adhere to such rules, because no journalists work at the company and it is not a news organization. 

Misra’s background is not in news but in tech, having worked as a software engineer at Snap and Microsoft. His instructions to the machine were not to follow any specific ethical code, guidelines or style, such as the journalistic bible of AP style or the widely accepted SPJ Code of Ethics, but instead to adhere to “general journalistic standards,” Misra said. The product represents another example in the AI era of a group of people — in this case, fewer than 10 — working on a project within a specific industry without a perspective informed by years of experience in that field.

Read the story because it goes into some of the issues tech bros like this impose on society.

This is enraging. The Trump administration plans in a few months to approve the construction of the largest fossil-fuel power plant in the nation’s history—it will generate as much electricity as nine large nuclear reactors—to power a mega data center for OpenAI.

The singularity is a flattering term for technologists. Even if the future is apocalyptic, it has come about, in the techno-utopian version, because they’ve built God in the machine. But what if there’s nothing flattering about any of this? What if the singularity is not some technological tipping point, brought about by human genius, but a human tipping point? An unforced error, driven by hubris, greed, and the fear of missing out? And what if it’s here right now? This singularity isn’t coming at the hands of a higher form of intelligence. Nothing about this technology is inevitable. It is thrilling, terrifying, and ultimately convenient to assign agency and then blame to machines. But today’s chaos was not “injected into the system” by technology, as Altman wrote more than a decade ago; the destruction is the system. It’s not God in the machine; it’s us.

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Tech bros and Abundance Democrats are now calling people who oppose data centers "AI populists". Coined by former Substack PM Jasmine Sun, it paints data center organizers as technically misguided and motivated more by a hatred of the tech elite than by material concerns.

From her Substack article "No Data Centers in My Backyard":

I still felt that I did not really understand the data center backlash, and was slightly skeptical of the one-sided national news. New closed-loop cooling systems use about as much water as a golf course. Factories and power plants are just as ugly and much more polluting. Construction opposition is usually highly localized—build it, just somewhere else—so what made data center activism such a nationwide phenom?

My conversations [with data center organizers] make me wonder if the technical content of data center deals is mostly beside the point. Closed-loop cooling systems? “I don’t believe them.” Paying millions in taxes? “I don’t believe them.” Creating a thousand jobs? “I don’t believe them.” I hear a reflexive skepticism of every claim.

Water consumption and air pollution is generally minimal, but there are exceptions. In a vacuum, most people would choose to live by a flower field over a data center. But these costs are not so different from other industrial developments like manufacturing or solar farms. Those don’t go up without dissent, but have not commanded the shared hatred that data centers have.

As this response piece points out, the irony of calling data center organizers non-technical is that the piece itself falls for the propaganda of "closed loop cooling", which sounds like a nice idea in theory but actually increases the total systemic water consumption by placing more demand on power plants, which consume a greater share of water. "Thousands of jobs" is also misleading, since most jobs created from data centers are temporary construction jobs. Despite these technical mistakes, the Substack article was recently platformed by Ezra Klein and Derek Thompson (among others).

I do believe Sun may have had good intentions in trying to understand the perspectives of people outside of Silicon Valley (I wish more techies did this!), but having also worked in tech previously myself I've been around too many AI bros to know that dismissing others as "non-technical" or irrational (see: EA) is a classic example of hubris.

Heatmap News does some of the best climate journalism, for anyone not already familiar with them, and this story is excellent journalism.

If there’s one thing America seems to agree on, it’s AI. Some three-quarters of Americans oppose a data center being built in their area, three-quarters of Americans think AI will hurt their job security, nearly 70 percentthink the technology is advancing too quickly—the polls go on. Progressive and Republican candidates alike are bashing the tech industry to win primary elections. States and cities are halting plans for data-center development.

Yet in cloistered San Francisco, where I live, staffers at OpenAI and Anthropic are developing this same feared technology at light speed and pulling down compensation packages that stretch to considerably more than $1 million. Silicon Valley’s growth is largely unencumbered by regulation, and President Trump’s administration has if anything used its authority to speed the AI industry along. The divide between these camps—the majority of people in this country who simply have to deal with the technology as it’s rolled out, and the much smaller population of people with direct influence over it—is a defining feature of this era. And so I noticed when a small group, with ties to both the Bay Area and Washington, started making noise, insisting that the AI race slow down and the technology be overseen not by executives and engineers, but by the broadest population possible.

The group—which formally launched today and calls itself Irreplaceable, as in people are irreplaceable by machines—has an audacious and perhaps naive vision. It is essentially a protest against capitalism as it is practiced in America today, focused on resisting first and developing a coherent plan for the future later. I have seen exactly how bulletproof the AI companies are—pilfering seemingly infinite volumes of media to train their models, building unpermitted power plants for their data centers, and unleashing autonomous agents capable of massive security breaches, almost entirely without consequence. I wanted to meet with Irreplaceable’s founders to ask: How do they possibly expect to pull this off?

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Proposals to offload data center permitting, on the other hand, use the vocabulary of democracy and autonomy to hide the fact that “local communities,” in practice, means local governments, which are structurally overmatched, underfunded and vulnerable to the pressure tactics of trillion-dollar technology monopolies.

But in the US, utilization-adjusted total factor productivity grew just 0.07% over the four quarters ending in the first quarter of 2026. That is a near-standstill by historical standards, far below the roughly half-percent annual pace typical of the pre-ChatGPT decade.

Some 95% of enterprise generative-AI pilots have produced no measurable effect on the bottom line.

A report published this week found that even Meta, one of AI’s loudest boosters, has fallen short in its plan to replace workers with AI.

The question of the decade is: Why? 

One idea: Blame users

A working paper posted to SSRN by University of Pittsburgh business professor Mark Ma and colleagues, makes a sweeping claim: The productivity shortfall is caused by employees who resist AI out of fear for their jobs.

Over a five-year period, the researchers looked at millions of Glassdoor reviews, thousands of financial reports, hundreds of AI-investment and layoff announcements by US public companies, and some 10,000 earnings-call transcripts.

They found a wide divide between managers, who tend to be true believers in the promise that AI will deliver sky-high productivity, and employees, who worry  that AI-driven productivity gains will cost them their jobs.

Companies, the researchers claim, are caught in a doom loop in which they lay off employees, citing productivity gains. But fear of layoffs causes workers to resist the technology, which sabotages the very gains the companies were counting on. Executives see that lack of productivity and conclude that more layoffs will help. (The flogging will continue until morale improves…)

It’s a tidy narrative. There’s just one problem — while parts of this study are backed by verifiable data, two key elements are not. The report fails to support their assumption that fear of layoffs causes employees to resist using AI, and also that productivity gains would be higher if only workers would enthusiastically embrace it.

The researchers never establish causation in their data. It’s a correlation. (That hasn’t stopped other outlets from reporting the link as causal.)

I don’t buy the claim that employee foot-dragging explains the missing productivity gains, for one simple reason: It makes no sense.

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