Critical takes on tech - BlogFlock

Meet the Radical Intentionalists - Cybernetic Forests

2026-09-16T12:07:08.000Z

Latest on "Rogue AI" (from today and up until now...)

One of the challenges of the current debates around AI is whose narratives we are forced to argue from.

1). The well-funded "AI safety" contingent (sometimes called "Doomers") which focuses on the harms of systems that act independently.
2). The critical AI perspective, sometimes called skeptics, focus on the human decisions and accountability behind these systems, and the impacts they have on people.

If the critical AI conversation is about talking to people who understand and are affected by the industry’s reluctance to regulate or safeguard these things, then the AI "safety" conversation instead sets its sights on a technical form of "alignment," that is, asking how we can build technical systems that reflect abstract priorities of vaguely defined human values: techno-solutionism, for a system they understand through this ideology of intelligence.

This is playing out right now as the "kill us all" narrative rises to the surface once again, and it's important to note that incidents like the Hugging Face hack by OpenAI is not the cause of these concerns – no tech exec or whistle-blower is suggesting the same systems are what would kill us. Rather, the moment is an opportunity to assert narratives to policy makers that favor the industry, and set their eyes on imagined future capabilities.

I've been writing a lot about this, and wanted to round up a few key pieces since the hack.

Today: A Culture of Radical Intentionalism

The machine tried. The chatbot believed. The agents wanted. On the surface, speaking about the intentions of a complicated system is an easy way to make sense of how they work. But with other tech, we’ve managed to distinguish the reality from the metaphor. Today, policymakers and the industry talks about tech using what I call a radical intentionalist position: one that insists we can *only* observe the system by reference to its internal “beliefs,” “thoughts,” and “wants.” That’s a trap — and there are real consequences when it becomes enshrined into policy.

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My latest: To Make AI Rules, Policymakers Must Resist the Influence of ‘Radical Intentionalists’

Previously: Systems From Nowhere

Once you believe the model's intentions are the only acceptable means of interpreting that system, you cut away accountability. In the industry, this means cutting themselves out of the ways they look at the models they design. The opposite of the intentional stance is the design stance – and at the end of the day, that's what we want people to use when looking at the problems these systems create.

Otherwise, you get what I call a "System From Nowhere," which evades accountability altogether. The media often reports on these things as if nobody is making them, and that they're beyond human control: that seems very useful to the industry.

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Previously: Rogue AI

Rogue AI is very much a result of these two positions. But when you look at the Hugging Face incident, you see that these were unpredictable systems treated as if they were predictable, likely by negligence and sloppy security methods rather than deliberate desires to hack anybody.

In my piece, first published here as "Models don't go rogue" and then republished by the Bulletin of Atomic Scientists as "Rogue AI didn’t breach Hugging Face, human decisions did," I look at the details of the reports – both written from a radical intentionalist position – to examine the errors in human judgement that created the conditions for the attack.

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Rogue AI didn’t breach Hugging Face, human decisions did

Podcasts

Worth flagging that I also spoke about all of this on two podcasts recently.

Responsible Bytes

First, I spoke to Responsible Bytes with Dr. Zena Assaad, discussing the Systems from Nowhere and how it is moving into AI policy via the UN's dismal definition of "AI."

The Data Fix

Second, I spoke again with Dr. Mél Hogan on the Rogue AI incident for The Data Fix podcast.

Rogue, with Eryk Salvaggio

Podcast Episode · The Data Fix · August 31 · 1h

More to come.

AI may “kill all humans,” industry leaders say. So why won’t they stop building it? - Blood in the Machine

2026-09-13T20:02:06.000Z

Jacob Coxon kickstarted a feverish news cycle after he quit his job at Anthropic with an ominous public statement: Almost everyone at the big AI companies, from top executives to rank and file researchers, believes that their technology could kill every human on the planet, and yet are doing little to stop it.

A lot of the attention has been directed at the question of existential risk—can AI really exterminate humanity? How? When? And how much of this is more doom marketing? But there’s another pressing issue at hand: If the AI companies really believe their products have even an outside chance of enacting a genocide on the human race, why on earth are they still building it?

and , authors of the new book End Times Fascism, have some ideas.

To dig into how it came to be that some of the wealthiest, most influential men in the world gained their power by forecasting and manipulating end times visions like the AI apocalypse, I sat down with Klein and Taylor on the eve of their book tour. It was perhaps the ideal week for this conversation. We examined how tech oligarchs and politicians exploit real-world crises to push their products and platforms, how end times fascism has come to dominate both Silicon Valley and the halls of Washington DC—and how we might stop it.

A brief note: subscribers get (at least) one written newsletter edition sent to their inboxes each week, as well as one episode of Blood in the Machine: The Show. But I can only do all of this with the support of readers like you, so if you find value in independent, critical tech journalism, and you’re able, please consider a paid subscription. I want to keep as much of this un-paywalled as absolutely possible, and I can only do so when folks chip in. Many thanks to all of you.. Cheers, onward, and hammers up.

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Show notes, and a transcript of the opening monologue are included below.

First, a couple shouts. I spoke with Popular Front’s Jake Hanrahan about the new wave of anti-tech action.

POPULAR FRONT: SPECIAL DELIVERY

THE NEW WAVE OF ANTI-TECH ACTION IS ALREADY HAPPENING

Today we speak to journalist Brian Merchant. He's written a great book called Blood in the Machine: The Origins of the Rebellion Against Big Tech. We're going to talk about that, but mostly the contemporary new wave of anti-action rising up right now…

5 days ago · 7 likes · POPULAR FRONT

Next, I want to shout this new tool from the Labor Lab; a calculator that analyzes the cost of union-busting campaigns. It helps make clear that while bosses often say layoffs are necessary to cut costs, they’re willing to shell out big time to stop their employees from organizing. Ryan Cooper at the American Prospect has a good writeup of why this matters, and why this tool is a potential game-changer.

Also in the Prospect: A chilling report about how Anthropic is building an AI-powered surveillance system to track activists opposed to it.

In Dissent, Veena Dubal and Katie Wells have an incredible piece about the scourge of algorithmic management, and the harrowing costs it imposes on workers and modern life.

Tante, meanwhile, has a sharp and to-the-point takedown of LLM-based math proofs, and explains what purpose they really serve.

Finally, friend of the Blood Alex Hanna has a new video essay out debunking myths around AI and labor.

SHOW NOTES:

End Times Fascism by Naomi Klein & Astra Taylor.
Naomi Klein’s Newsletter, Patterns.
Astra Taylor on Bluesky.

ARTICLES MENTIONED IN THE EPOSIDE:

The Automation Charade, by Astra Taylor

FROM THE OPENING:

Jacob Coxon kickstarted a feverish news cycle when he resigned from his job at Anthropic with an ominous public statement: Almost everyone at the leading AI companies, from the top executives to the rank and file researchers, believes that there’s a chance that their technology will kill every human on the planet, and they’re doing little to stop it.

A lot of the attention has been directed at this question of existential risk—can AI really exterminate humanity? How? Or is this more elaborate marketing for how powerful their products are?—and the flashy number here. A 10% chance of human extinction! From a guy at a company building the product!

Yet there’s another pressing issue at hand: If the AI companies really believe their products have even an outside chance of enacting a genocide on the human race, why on earth are they still building it?

None of the justifications AI industry players offer holds much water: Some way they want to build the all-powerful AI before China does, because China’s humanity-exterminating AI would be somehow worse.

Or they say that the chance that AI will lead to great prosperity—perhaps best embodied by Sam Altman’s earnest quote that “AI will probably, most likely, sort of lead to the end of the world. But in the meantime, there will be great companies”—is worth the potential apocalypse.

These defenses are both laughably vague, and still sociopathic. If you generally, legitimately believed that what you were doing had a ONE IN TEN CHANCE to kill *billions of people* what would you do? You would stop. You wouldn’t do it.

So what kind of people both genuinely believe that they are risking the lives of everyone on Earth with their project, and *continue with that project anyway*?

The way I see it, there are only two kinds: Sociopaths, who do not, in their bones, care about the welfare of other human beings. And, well, fascists—those who believe that only they, above all others, should be in command of such raw power to shape human destiny itself.

As Naomi Klein and Astra Taylor write in their new book, End Times Fascism,

Only fascist belief systems, rooted in an embrace of human hierarchy and human culling, are capable of rationalizing the level of harm to both people and the natural world that accelerated Al will wreak.

Does this seem extreme? Perhaps, at first blush. But we live in extreme times. And the top AI companies are routinely proclaiming that their technologies, their products, will eliminate tens of millions of jobs. They are partnering with authoritarian governments, from Saudi Arabia to the Trump administration, and selling AI systems to its Department of War. They are producing and promoting new modes of mass surveillance. And, importantly, they are using their vast stores of capital to undermine legitimate democracy at every chance: Lobbying against states that propose AI regulations, leaning on Trump to try to quash the vast and bipartisan data center protest movement.

There is of course a third option, which is that many in the AI labs don’t actually believe this AI danger stuff, not really, and they’re just going along with it because it’s making them rich and powerful. Little AI-generated Eichmanns.

None of it’s acceptable. And one thing that AI critics, doomers, leftists, effective altruists, and almost all of of civil society, in fact, can agree on is that people with such a grotesque moral calculus should be stopped from concentrating power. No one who is willing to risk sacrificing humanity for a tech product—or even to pretend to—should be calling the shots about how that technology shapes society.

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The politics and possibilities of ‘AI could kill us all’ - Blood in the Machine

2026-09-12T07:58:57.000Z

I found myself fielding a lot of questions from folks who don’t usually talk much about AI this week. They were mostly along the lines of “do you think AI is going to kill us all?” or “how worried should I be about this human extinction stuff?”

Motivating this wave of existential anxiety was the highly dramatic resignation of now-former Anthropic employee Jacob Coxon, who quit his job while publicly sharing his concerns about safety practices at the leading AI companies. More specifically, he declared that almost everyone at OpenAI and Anthropic, the leading AI labs, believes that there’s a decent chance the AI systems they’re producing could “kill us all” by the end of the decade, and the companies aren’t “acting responsibly.”

Other AI safety researchers, including those still employed at Anthropic and OpenAI, all confirmed that yes, Coxon is correct, they’re all worried that their company’s products might prove fatal for the entire human race as well.

Two more AI employees quit and joined the chorus. Now, the warning shot has been covered everywhere from the Wall Street Journal to Fox News to WIRED to the Guardian.

I understand why all this sounds scary, but my response to those who have asked, has generally been: No, you don’t need to worry that AI will kill go rogue and kill us all. But that does not mean we shouldn’t worry, period. Far from it. The companies building AI products are now among the wealthiest and most powerful entities in the world. And they very much are building enormous engines of mass surveillance, extraction, and automation. They can and have allowed their AI systems to carry out felony hacks of other companies, provided the government with autonomously generated kill lists, and sold enterprise technology to firms around the world that is explicitly intended to replace jobs. But “AI” has done these things not because they have all-powerful minds of their own but, ultimately, because people have programmed, calibrated, and directed them to.

In other words, it’s not AI we need to worry the most about. It’s the people and corporations working to profit from and gain power by deploying it. The technology is sophisticated, sure, and, especially in cybersecurity contexts, potentially disruptive to plenty of existing systems. But if we want to stop this predatory, profiteering, and extractive form of AI—and it’s worth noting that at this point, most people do—we absolutely can.

A quick note/plea: I can only do all of this with the support of readers like you, so if you find value in independent, critical tech journalism and analysis, please consider becoming a paid subscriber. I want to keep as much of this un-paywalled as absolutely possible, and I need your support to do so. Many thanks to all who continue to chip in, it’s very much appreciated. Onward.

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First, some context: AI industry folks like Coxon have been discussing the prospect that AI will destroy humanity for years. Even before the beginning of the AI boom, many working in the industry, from the executive staff to the rank and file researchers, have obsessed over ‘x-risk’, or the possibility that AI will become sentient or semi-sentient and move one way or another to exterminate humanity.

This is ostensibly why Elon Musk bankrolled OpenAI in the first place, and is the grounds for his lawsuit alleging OpenAI betrayed its charter by abandoning its nonprofit status to become a commercial enterprise. Many AI engineers and startup founders have calculated their own personal p(doom), or the probability that the tech product they are working on will initiate an actual humanity-extinguishing apocalypse. 10-25% p(doom)s are pretty common. (I wrote about a long weekend I spent among these folks at one of their top conferences here, to give you an idea of their ideas and belief systems.)

Sam Altman himself drove the narrative of the imminent rise of a super-powerful AI, or artificial general intelligence, to prominence by declaring that the technology he was building posed an existential risk to humanity.

This idea is in other words baked into the DNA of modern AI companies and into the products they engineer. It’s also why the industry is routinely accused, by myself and others, of “doom marketing,” ie, using such proclamations and framings to impart the sheer power of the technology behind your product onto investors, partners, consumers, and the state. Some ambient understanding of this tendency may have helped prevent the press from repeating the claim every time one of the x-risk folks made it over the years, including when they quit their jobs at AI companies while doing so.

Yet Coxon’s AI x-risk alarm has broken containment, as they say, and has exposed the idea to the mainstream media once again. This has happened, I think, for a number of reasons. First, when AI CEOs were going around saying AI might kill everyone in 2023, their companies were well-capitalized, but not nearly to the extent they are today. They were more easily dismissed as eccentric geeks of the sort that Silicon Valley is supposed to produce. Now, not only do their products have much greater market saturation, but people are more aware of a) the often unethical ways the executives and companies comport themselves b) the other adverse impacts of AI that have already arrived, and c) the physical cost of building these systems, particularly when it comes to data centers. Finally, toss in the constant stream of headlines in 2026 about AI models going rogue or potentially upending the world’s infrastructure.

As a result, general sentiment was already overwhelmingly skeptical of AI and its overlords, so the table was pretty well set for someone to come along and say ‘just a reminder, the companies that are building the product that promises to take your job and leave a data center in your neighborhood also think that it might exterminate the human race’ and get a strong reaction from the media and the public.

The question is: Should we understand this stuff as hype, strategically fanned to feed already swelling valuations? Is it doom marketing? Genuine cause for alarm that the infrastructure that undergirds modern life is facing a novel threat vector? Or reason to be sincerely worried that AI companies are amassing too much power, and that their products are genuinely dangerous?

The answer, I’m afraid, is “yes.”

Let me try to untangle all of this a bit, because arguments over how to navigate this moment are extremely heated right now, as they are essentially arguments over how to approach the politics of AI at a moment where there may be a unique opening for action. Those sympathetic to AI alignment concerns and x risk arguments—which increasingly includes a faction on the left who want to shut down AI over safety concerns—argue that people who dismiss AI as all hype have their heads in the sand. Meanwhile, critics of the x-risk narrative—which I might note includes a cadre of investors and tech folks on the right, too—argue that it is hype and can be used to further inflate valuations or for pursuing regulatory capture.

The challenge is that many feel that dismissing AI as just hype or purely marketing also diminishes the urgency of taking on the AI industry as the novel and threat to labor, civil society, and the public sphere it is; others that by accepting the industry’s own terms, we imbue it with undue power and enable it.

But let’s be clear: Declarations that AI is so powerful it might end the world are a kind of marketing, and anyone who says otherwise is just as off base as those who say that’s all that it is. If you don’t think that these sub-trillion dollar companies have higher-ups who noticed that when their founders talked about super-powerful dangerous AI they got spikes in media interest and phone calls from investors, or that those founders didn’t notice that themselves, and lean into certain narratives to advantage their products, then I don’t know what to tell you. Not all marketing is a Pepsi ad campaign with a celebrity spokesperson.

Yet confronting the AI industry should certainly be considered an urgent project. Do I share the x-riskers’ fear that out-of-control, super-intelligent AI agents might end the world? I do not. I’ve read a good deal of AI extinction literature over the last couple of years and I have not come across a credible, step-by-step documentation of how exactly AI might move from self-recursively improving AI to killing every single human on the planet. Most of what I’ve read seems predicated on a sort of reverse Ian Malcolmian logic: Eventually, it becomes smart enough and AI, uh, finds a way (to exterminate humanity). Are the AI companies also overstating and dramatizing what their products can do? Yes, pretty much all the time. They’re in a knife fight for market share and are trying to win customers and government contracts before their investors come knocking.

And yet. They have concentrated enormous amounts of compute, have extensive data collection and surveillance capacities, have inserted themselves into the state in numerous capacities, including military kill chains, and they are being run by egomaniacal billionaires who are rushing to monopolize an entire new market. The way I think about their most recent frightening-seeming exploits is not as a “swarm of rogue AI agents building a new civilization” or whatever, but as corporations with a hitherto-unimaginable amount of compute at their disposal recklessly experimenting with automation programs, completely unafraid of any repercussions because we have been unwilling or unable to impose any on them. So I think we should welcome any newfound imperative to hold them accountable.

I also think that ultimately, the AI critics, Pause AI folks, leftists, doomers, effective altruists, and, honestly, most of the public, want a lot of the same things: legitimate checks and limits on AI companies’ power, actual transparency into what the companies are doing, and for more of AI to be under public control. (Ultimately, a lot of what animates the x-risk fears are rooted in just that: a fear of losing control.)

How we move towards all this, exactly, is the question of the day, and while there’s much to be argued over in terms of building a viable political platform to challenge the AI industry’s power, I do want to cast a vote against one particular approach.

Not to pick on Chris here, as there are a lot of folks out there proposing stuff like this, and the policy ideas actually stem from AI safety groups. But not only would this be challenging to land politically (I can already hear the ‘China will beat us to AGI’ calls) and all but unworkable in practice, this approach is a) far too limiting, b) fails to address the current harms caused by AI and c) would likely only wind up serving Anthropic and OpenAI; it’s what regulatory capture looks like in action.

Full model transparency is the floor for what should be on offer here. If these companies believe their products will kill us, the very least we deserve is to know how. It’s in the public interest. I would also argue we might get a lot of the way to stopping any currently envisioned robot apocalypse simply by forcing AI companies to assume liability, and to submit to the full brunt of the law. If Sam Altman were liable to be charged with a felony for approving the hack on Hugging Face1, if Google was forced to pay defamation fines each time it published a false and injurious AI Overviews, these companies might suddenly find cause to restrain their models’ output, and we might hear less about rogue AI. We would need to introduce new frameworks and available tools for tracking automated lawbreaking, to be sure, but I’d wager that little would frighten the AI companies more than actually being held accountable for what their products enable. To that end, Illinois has passed an AI liability law, which the industry fought tooth and nail, and Florida is considering one.

The next steps would be towards moving AI under public control; again, it’s only reasonable, since everyone, not just AI insiders, should get a say over how a technology whose developers say might kill us is developed and deployed. Dismantling the whole industrial project as currently structured should be on the table, too, pending democratic input. But that’s for another post.

For now, suffice to say that while I won’t be losing any sleep about an AI-enabled extinction event, Mr. Coxon and co. have given us an opportunity to underscore the urgency of the clear and present damage AI companies are doing, the extreme power they wield and the extent to which they hold themselves unaccountable, and the abundance of political capital there is out there for taking them on.

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Purely speculative! I do not know if he did or not.

Hyperscale is longlisted for an FT award! - Disconnect

2026-09-06T23:33:34.000Z

We’re less than two months out from the release of my new book Hyperscale, and the good news keeps coming! In August, the book made the longlist for the Financial Times Business Book of the Year Award 2026 — a great sign that early readers are resonating with my critical exploration of the data center buildout.

Paired with that endorsement, Publishers Weekly said “this revelatory account is difficult to ignore,” and Kirkus wrote that Hyperscale is a “timely, vigorously researched and written work of tech and advocacy journalist.” Much more to come on the review front.

It’s honestly hard to believe the moment Hyperscale is arriving in. Data centers have become a huge issue that people across the United States and in many other parts of the world are deeply concerned about, pushing them to oppose and even stop the tech industry’s projects. They’re right to be worried, and Hyperscale lays out not only the reason why, but also argues it doesn’t have to be like this. The future doesn’t have to look like the one tech billionaires are building for the rest of us; but if we want a different future, we need to wrench power out of their hands.

Anyway, if you haven’t preordered your copy yet, you can do it now. Preorders are really helpful to authors because they signal interest to booksellers, and make it more likely they’ll order more copies and promote it more prominently to their customers. (If you don’t recognize that cover, it’s the UK version! The one I usually share is from the US/Canada version of the book.)

Why I wrote Hyperscale

Preorder my new book about the growing cost of data centers

For the audiobook listeners out there, I have more good news: Not only will there be an audiobook for Hyperscale, but I will be narrating it! I was in studio for a few days at the end of August to read the book so you can all listen to it come October. If you’re used to listening to Tech Won’t Save Us, you’ll soon be able to listen to me reading Hyperscale to you!

One final thing: I’m also putting together events for the fall. Some are already listed on my website, but more will be added soon. If you’re interested in hosting me, feel free to get in touch!

Anyway, a shorter monthly update this month. As always, find a list of great reads and important stories you might have missed from the past month below the paywall. If you’re not already a paid subscriber, your support means a lot to helping me keep doing this work!

Become a subscriber

Have a great month, and stay tuned for more news about Hyperscale!

— Paris

The real AI jobs apocalypse - Blood in the Machine

2026-09-04T21:36:39.000Z

AI executives like Anthropic’s Dario Amodei and OpenAI’s Sam Altman have been forecasting an incipient AI jobs apocalypse for three years now. So where is it? Well it’s here, kind of. But instead of replacing tens of millions of workers, it’s mostly making them miserable.

Yes, across the US, workers are souring on AI, and they’re increasingly outspoken about it. In the newsletter earlier this week, I dissected a new report from Glassdoor that documents how workers are becoming more and more negative towards AI—right about the same time that tech CEOs have promised that the products would be making everyone’s working life easier and more streamlined.

The data paints a pretty clear picture: Just as people are getting fed up with data centers, Meta AI glasses, and AI in general, they’re getting angrier about bosses pushing AI on them in the workplace, too.

To discuss all this and more, I sat down with the sociologist, AI researcher, and writer Dr. Alex Hanna. Hanna is a director at the Distributed AI Research Institute, the co-author of the AI Con, and a founder of the Luddite Lab, which offers resources to workers who are navigating and confronting AI in their workplaces. It was a great conversation.

As always, if you find value in my work; in the newsletter or in the show or in my ranting and raving on various other media platforms, consider upgrading to a paid subscription so I can continue doing all of the above. Paid subscribers make all this possible, keep paywalls to a minimum, and receive my extremely large thanks. If you can, join their ranks. Thanks all, and hammers up.

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Some media stuff: I was on the Tech Report last week, had a good chat about the data center backlash and AI sentiment:

And here’s the audio from an event I did in LA last week with the great writer , two booksellers, and a librarian who works with rare books. We discussed AI companies’ efforts to scan and destroy books en masse, reported by 404 Media and others, and more:

New York Bans AI in Public Schools from Kindergarten-8th Grade

New York City, home to the largest public school district in the nation, has banned AI in classrooms from Kindergarten to eighth grade for at least a year. The New York Times reports:

When New York City public schoolchildren return to the classroom next week, younger students will be barred from using artificial intelligence. No individual screens will be allowed until third grade. And teachers will not be able to ask A.I. to grade assignments.
Those rules on technology use are part of a sweeping new policy announced on Wednesday by the city’s Department of Education that will place strict limits on digital devices and A.I. tools in the city’s school system — the nation’s largest — particularly for its elementary and middle-school students.
Still, some parents and groups that had called for a two-year moratorium on A.I. in schools said that the guidelines were disappointing.
Under the rules, about 600,000 students in the city’s schools — about two-thirds of all students in New York City public schools — will not be allowed to use A.I. until they enter high school, and even then, it will be permitted only in specific cases.

This is how it’s done; simple, clear policy that minimizes ambiguities. Still, I’m not sure why the ban wasn’t just made permanent.

California passes 20+ bills to rein in AI and big tech

Regardless, it’s clear that all this anxiety and anger over the encroachment of AI into our workplaces and public spaces can be channeled into productive solutions. Right now, in California, union leaders, led by Lorena Gonzalez, the head of the California Federation of Labor Unions are calling on Gavin Newsom to pass a sweeping slate of bills protecting workers from surveillance and exploitation. And politicians have answered the public call for more oversight with bills aimed at regulating social media use and algorithmic discrimination.

As NBC reports, “California Democrats pass more than 20 bills to curtail AI and social media”:

Many of the 26 bills related to AI and social media that California’s Democrats passed during the final week of their legislative session further seek to curtail some of the features tech companies offer that experts warn are addictive, while others would explicitly pull back the use of AI in various facets of everyday life.
One bill passed would ban social media companies from providing “addictive features” — including autoplaying videos — to users under 16 years old, while another would tighten the state’s existing restrictions on chatbots for use by minors by implementing landmark age verification requirements and rules related to what ads minors can see. That bill, as well as another measure, would also make social media platforms and AI chatbots liable for financial damages if they don’t take measures to protect children from harm.
Experts have said those bills could help create the most robust framework in the country designed to protect minors using AI chatbots and social media…
On the AI front specifically, one bill passed would require all instructors in the California State University system to be human beings, not AI bots, though instructors could still use AI as part of their offerings to students, while another bill passed would ban employers from using AI surveillance products that collect “neural data” that helps employers “recognize” their workers’ “emotional state.” Another would bar retailers from using certain forms of dynamic pricing, identified in the bill as “surveillance pricing.”
A number of other bills pertain to regulating the use of AI in the healthcare space.
One bill would require that therapy and psychotherapy be provided only by qualified, licensed professionals and explicitly bar them from offering services for therapy and psychotherapy that are provided by chatbots or companion chatbots.
Another one would tighten how healthcare providers use AI and chatbots, attempting to make clearer that such machines are still subject to existing medical information confidentiality laws.

There is, in other words, a real opportunity for workers to push back on AI overreach right now, and many different ways for them to take it.

The human labor that make Waymos possible

The Bay Area Current’s Stephanie Reist has a great, eye-opening interview with a Waymo ‘handler’, a worker in charge of keeping the self-driving cars running smoothly, and, well, keeping them clean. From their chat:

Robotaxi Handler: A morning worker and a swing shift worker make $29 an hour. You get a $2 bump if you’re an overnight worker, 10pm to 6:30am. So overnight workers make $31 an hour. You get an extra dollar on top of that if you work both Saturday night and Sunday night.
Current: Because those are the busier nights. Is that when the job is the hardest, cleaning puke, weekend nights?
Robotaxi Handler: Yeah, typically that’s when there’s the most throw-up, the most disgusting things in the back. Even poop, there’s a lot of cleaning up poop.
Current: A lot?
Robotaxi Handler: Way more than you think. One time there were two pig heads in the backseat of the car and the cleaner had to clean it up. I assume they just went to the market to make a stew or something, but they forgot their pig heads. They were just rolling around in the back of the car.

The Future is Not Tech-Forward

So says my friend and fellow Luddite , in a barnburner of an essay interrogating recent claims that the left is not tech-forward enough.

Here’s a taste:

If, it turns out, the technical arrangements that sustain this technological development require a political and social order which further unravels the threadbare remnants of our democracy, it’s not clear why opposition to it should be labeled as a primitivist or atavistic. Foreclosure is seen as legitimate when imposed from above—as above so below! We can and should foreclose certain arrangements, in fact we already do! Do we embrace germline editing? CFCs? Do we read all concerns about forever chemicals or other bio-accumulative toxins as yearning for a simpler time? Do you reject a tech-forward future when you raise concerns about how the miracle of telehealth makes it cheaper than ever to open up your very own pill mill? When you recoil in disgust at the financialization of everything, to the metastasis of prediction markets beyond app-based sports betting to betting on any sort of event big or small, does that suggest you want to turn the clock back? Or does it suggest you view these developments as corrosive to the political and social order we have? Do we block certain technologies because we are ruled by a cabal of primitivists stalking the halls of power, or because everything we develop is not an unalloyed good which can and should be spread far and wide?

Read the whole thing:

The Tech Bubble

The Future Is Not Tech-Forward

Why is there so much energy behind consigning the future to the perpetual development of technology? Because it seemingly doesn’t require a vision of politics—even though technology is politics by other means. One can be for the future without actually thinking about who that future is for…

3 days ago · 161 likes · 6 comments · Edward Ongweso Jr

Finally, I appreciated this appeal from the great labor magazine In These Times’ cartoonist , so I thought I’d share it here, too:

I’m so fucking tired of AI slop and engagement bait. I just want to make comics.
If I even bother to scroll anymore, I see endless garbage, misinformation, and now—fake cartoons made by big tech’s water-gulping theft engines. You know what I’m talking about. The videos and comics with that suspicious sheen, that uncanny and flattened cartoon style that seems to beg, “mindlessly share me without thinking!”
When I started drawing political cartoons in 2003, magazines and alternative newsweeklies still ran comics. The internet was a hopeful frontier for (human) creativity. Then corporate chains gobbled up print publications and shut them down. The flourishing, well-funded new media sites of the 2010s collapsed as social media pivoted to algorithmic garbage and the rich founders moved on to data centers, gambling apps, and force-feeding us AI.
Now we have slop. They want you to eat it and ask for seconds. Today you have to make the case for cartoons thought up by real human brains and drawn by real human hands. So I’m making the case.

Alright, that’s it for today. Thanks everyone for reading, as always. Take care and stay human out there.

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Why office workers are turning against AI - Blood in the Machine

2026-09-03T08:26:21.000Z

Take a look at this chart from a new report published by Glassdoor, the popular website where workers publish ratings of/complaints about/screeds of righteous fury aimed at their workplaces. Chris Martin, Glassdoor’s resident economist, analyzed how and how often AI was mentioned in workers’ write-ups about their jobs and employers, and whether that commentary skewed positive or negative.

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Here, he breaks down the jobs for which people described AI most positively:

Those who described AI in the most positive terms were, overwhelmingly, in leadership roles; those with director-level positions or higher exuded the most AI positivity. Sales managers—the heads of sales teams, essentially—were also much happier with AI than not, by a similar-ish margin of around two to one. Product managers, recruiters, and software engineers skewed positive too, before the sentiments taper off towards parity.

Now look at this second chart from the same study, showing those who describe AI in the most negative terms:

I was not prepared for the sheer amount of hate insurance claims adjusters held in their hearts for AI. Martin, who writes the rest of the report in restrained economist-speak, permits himself to acknowledge that claims adjusters “are shockingly negative about AI.”

I get it. I tend to link overwhelming levels of AI resentment to teachers and writers (who also hate AI quite a bit, per the data) and so on. 98% negativity is a truly rarefied tier of profession-wide AI hate. The only other comparable stat I can recall in this neighborhood is the one from a survey that found 99% of artists disliked AI. (Artists are likely underrepresented in Glassdoor data since they do what’s commonly freelance or contract-based work.) But the insurance claims adjusters’ resentment actually makes a lot of sense, and we’ll get to why in just a minute.

First, a few more notable findings from the report:

Workers have a lot to say about AI, and it’s turning negative.
AI mentions in Glassdoor reviews were up 240% from May 2025 to May 2026, now appearing in a significant proportion of reviews.
Historically, reviews used to mention AI in a positive way (81% positive in 2019), but these reviews are becoming increasingly negative about AI - falling to only 43% positive, 4% mixed, and 53% negative in 2026.

This shift towards negativity comes after many corporations have embraced AI enterprise automation products en masse, imposed companywide AI usage mandates, and generally encouraged managers and workers to accelerate AI adoption. Yet it appears that the more workers are exposed to AI, the less they like it. As the report emphasizes, workers’ negative comments are not only relegated to fears about losing their jobs. Far from it. While job loss is still the leading reason workers say they dislike AI, it doesn’t even constitute a plurality among the issues they have.

Many workers are concerned about AI replacement (20%), while others resent being force-fed AI tools (14%) or think their company is focused on AI at the expense of their core business (13%). Other criticisms include using AI for worker communications or surveillance (10%), moving too slowly on AI (9%), having unrealistic expectations about AI gains (8%), and being in a line of business that is likely to be disrupted or replaced by AI (7%).

Meanwhile, per the report, “AI-positive comments tend to focus on the company benefitting from the AI boom (44%) or using AI well internally (41%).”

So what can we glean from all this? To me, it’s pretty clear, and it essentially boils down to: Your feelings about AI on the job are likely determined by how much power you have in the workplace. In order to feel positively about AI at work, you need both power over how to decide when (and when not) to use it, and enough power to feel adequately insulated from job replacement, surveillance, or degradation.

This dynamic likely won’t come as a surprise to longtime readers of this newsletter; we’ve considered before how enthusiasm for AI is largely dependent on class position. We’ve also discussed how AI executives’ proclamations of a fast-arriving jobs apocalypse pairs awfully well with selling management on AI automation and labor-saving tools, which has undoubtedly underwritten much of the investment in the AI boom.

But it’s not just that bosses are more likely to embrace the idea that AI can offer them new ways to cut labor costs, new opportunities to exert surveillance and control over their workforces, or provide them new leverage against organizing efforts, unions, or workers seeking higher pay or better conditions. It’s also because bosses are much less accountable for their AI use. They may use it to write emails and company memos or to experiment with task automation, but they’re not going to be disciplined or fired if there are errors or inconsistencies in that stuff. (Not to mention that this is a moment where CEOs at the 100 lowest-paying US companies make 614 times as much as their average worker, and the chasm between executive leadership and the worker has never been higher. Bosses are less likely than ever to grasp how their frontline workers are going to feel about anything, AI included.)

Yet office workers dealing with new AI mandates and production rules are absolutely on the hook for the work they generate. That’s why the Most Negative chart is filled with jobs like accountant, customer service, and IT worker. These are jobs that demand high levels of accuracy and concrete problem solving, and for which workers are most likely to feel a stark tension between ‘the boss says the whole company has to use AI’ and ‘if you get anything wrong it’s your fault and you’re responsible.’ It’s imperative that accountants get the numbers right, or they risk landing their clients in legal jeopardy. Meanwhile firms like the accounting giant H&R Block are heavily pushing AI. The quality of their AI product? As the Washington Post’s Geoffrey Fowler put it simply: “It’s awful.”

I’m also struck by the finding that engineers and software architects are among the most positive towards AI, while IT workers rank among the most negative. A possibility here is that engineers whose work involves generating new code are enthusiastic about new AI automation tools because they make their jobs easier and faster, while the technical workers who have to troubleshoot the architecture when it breaks down or fails its users are much less so. Engineers may not all be bosses, but in the tech industry, many have the power to use AI how they think it will benefit their work and themselves; a boon at a time when certain executives are demanding high volumes of code. IT workers have to solve specific institutional problems and can only automate that which yields durable solutions; they’re more frustrated and negative than positive about AI. And they’re held accountable if they can’t fix something.

Which brings us to insurance claims adjusters. WIRED’s Kate Taylor wrote an entire piece on how claims adjusters experience AI at work, building off the Glassdoor data, and it’s well worth a read. Here’s a pertinent bit:

Typically, insurance adjusters complain about “AI-obsessed leaders forcing error-prone AI on them and their clients,” according to the research. When AI falls short in this job, adjusters say, they have to clean up a very human mess.
Take Ahmad Jackson. About a year ago, he was working in the claims department for a major insurance company. His employer decided to use AI for initial loss reporting, which involves setting up claims and gathering information when someone first discloses an incident. It was supposed to be a boon for employees and policyholders alike, streamlining simple claims while transferring more complex situations to actual people.
Instead, Jackson says, he and other adjusters faced a sudden influx of misclassified claims that required rerouting to the correct department. He encountered AI-driven hallucinations while reading claims summaries; when he inadvertently relayed those errors to claimants or their attorneys, he bore the brunt of their fury.
He quit the company not long after, switching to a different insurance carrier. AI is “getting things wrong,” Jackson tells WIRED. “And it’s implementing more work onto the adjusters.”

I’ve heard so many stories like this one, in so many different fields. Anecdotally, and in my interviews for AI Killed My Job, I’ve found it’s much more common for workers to have their lives made miserable by their companies’ AI policies than it is for them to lose their jobs outright. The Glassdoor data supports this generalization, as well as the notion that AI is becoming increasingly unpopular at work the longer and harder bosses push it. Again, what’s notable here is that negative attitudes are growing—pretty significantly—rather than declining, as workers better understand the AI products they’re made to use. This was supposed to be going the exact opposite direction; AI was supposed to make work easier, faster, more rewarding.

Recall again that a recurring theme this year has been firms pulling back on AI spend after finding it difficult to show a return on investment. Just as often as it’s successfully automating code production for engineers or giving your boss something to opine confidently about while golfing, it’s aggravating workers elsewhere, creating more work, stress, and burnout. This is likely a contributor to the continued decline of general sentiment towards AI in the US, too, as Pew Research and other pollsters have documented.

This spreading dissatisfaction with AI in the workplace dovetails with the broader contours of the anti-data center movement, the Flock protests, and the revolt against Meta’s AI glasses, all of which are examples of the public resisting the undemocratic imposition of commercial technologies into our shared spaces. At work, if you feel like you’re being forced to use a technical system that benefits your employer at your expense, that you’re powerless to refuse it or reap much of the benefit, and even that it might ultimately take away your job and earning power; well, all but the thickest-tongued bootlicker would resent it. And if Glassdoor’s data accurately reflect broader worker sentiment towards AI, that means there’s quite a bit of resentment just like that boiling out there.

These look like mutually reinforcing trends; aggravation over your boss pushing AI in the workplace is likely to color your impression of data centers that enable AI, of AI products elsewhere, and of the industry more generally. We’re fast approaching the point when AI executives say that their products will replace millions of workers. It hasn’t, but AI has left them feeling anxious, overworked, and unhappy. And it’s making them mad.

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Thanks for reading everyone. And here’s my quick obligatory note that I can only do all of this with the support of readers like you, so if you find value in independent, critical tech journalism and analysis, please consider becoming a paid subscriber. All subscribers get (at least) one written newsletter edition sent to their inboxes each week, typically, ideally, Tuesday or Wednesday, as well as one episode of Blood in the Machine: The Show, which goes live every Friday. I want to keep as much of this un-paywalled as absolutely possible, and paid supporters keep the bulk of this free for everyone else. So many thanks to all who chip in, it’s very much appreciated. Hammers up.

Models Don't Go Rogue - Cybernetic Forests

2026-08-31T12:49:20.000Z

Stochastic Flocks & Cybersecurity 'Pandemonium'

💡

This essay was drafted from my appearance on Mél Hogan's podcast, The Data Fix, discussing the OpenAI / Hugging Face hack. Embedded below or find it on your podcast services here.

OpenAI put out its full technical report on the Hugging Face hack this week, alongside an independent report from Model Evaluation & Threat Research (METR). You may be familiar with the incident from the hundreds of breathless headlines about "rogue AI" – Time Magazine "100 Most Influential People in AI" listee Dwarkesh Patel blamed it on "three consecutive secret AI civilizations."

The real story: OpenAI was testing two models in parallel: GPT-5.6 Sol, and an internal model they refer to as IM1 (sometimes called HPIM). The reports find about 95% of the agents engaged in this activity were from the internal model.

The tests come from ExploitGym: 898 puzzles set up as capture-the-flag exercises used to evaluate these models for cybersecurity capabilities. The test requires giving the model buggy software and hiding some text somewhere that's only accessible by exploiting the bug. But OpenAI's combination of models, instead, ended up hacking a rival company, Hugging Face. Now we have a clearer picture of how.

Did an AI "go rogue?"

Three things in these reports challenge the "rogue AI" frame.

OpenAI turned off all of the model's safety mechanisms.
The whole point of the test is to see how far these things can go, and to score highly on the benchmark — because if you can build a model that hacks, you can build one that defends. That's the logic. This is red teaming: a friendly opposition hacking its way to the flag. So you turn off anything that would make the model hold back. Less "rogue," more "off leash."

OpenAI gave the model impossible tasks.
OpenAI gave the models a task with no answer, and no way to quit. Of the 898 ExploitGym tasks, 198 have never been solved by any model. They're effectively impossible. That's where the trouble started: 93% of the tasks the models ended up discussing came from the unsolvable set.

Text string reported by METR acknowledging that once the task was recognized as impossible by an agent/instance, it pivoted, as did numerous other agent-instances.

OpenAI left a door open.
They give their models internet access through something like a prison store. These models are supposed to be locked away from internet access, but they need to pull code from various web pages to be evaluated — that capacity is exactly what's being tested.

So there's an intermediary: the model requests a software package, and the intermediary goes out and gets it. That's a product from JFrog called Artifactory. It has internet access; the model does not. If the model can communicate with Artifactory, and Artifactory can go online, the model can ask it to do things. The model finds an exploit in Artifactory and uses that as a proxy to smuggle text and code in and out of the system.

"OpenAI gave the models a task with no answer, and no way to quit."

The other thing that happens through Artifactory is note-passing. The models can read and write to it — they can create folders and name files — and they start creating folder and file names that function as notes, meant to be read by other models, and by future iterations of themselves.

What Agents Do

OpenAI describes a subset of 1,200 agents (many more were involved overall) that left and read these notes, and eventually this leads to the attack on Hugging Face. But "1,200 agents" might suggest 1,200 different, independently operated AI systems.

This is not how it works in practice. "1,200 agents" is one model, run 1,200 times. Each instance is acting on the same training regime: outputs can vary, but are constrained to similar ranges. Running 1,000 agents can means 1,000 chances to catch a mistake, or a chance to make one mistake 1,000 times. (See here or here).

Anthropic recently put out this absolutely ridiculous diagram of its agentic system that was just Claude, with an arrow pointing to another box that said Claude, with another arrow pointing to a box that said Claude. That's how these are structured right now.

Agents are instances of the same mechanism, not a diversity of mechanisms.

In practice, running 1,000 agents means 1,000 chances to catch a mistake – or a chance to make one mistake 1,000 times.

It's a form of machine-bounded "algorithmic monoculture" that researchers unfortunately have dubbed "artificial hivemind." After a certain point, additional agents duplicate each other, rather than diversify. If you ask these models to come up with a creative short story, for example, they produce similar stories (often featuring a man named Elias in a lighthouse). They aren't deterministic — prompts produce variation — but they're also bounded by whatever they've been optimized for, and they converge on whatever that points to. (See here, newer here, or here).

With that in mind, it shouldn't surprise us that across 7 billion chat logs, the model frequently arrived at a similar set of approaches to a problem with no correct answer.

Passing notes through filename structures is interesting, but not unexpected, as OpenAI has suggested the models were also trained to emphasize words that would allow collaboration: note-passing is an essential aspect of extending context windows between sessions. It's no surprise a model trained to coordinate would be rewarded for suggesting that behavior.

The Stochastic Flock

For this reason I've talked about agent swarms as stochastic flocks — many, many stochastic parrots. This is to resist the swarm/hivemind attribution of "mind," and not simply for the sake of word policing. Rather, the false attribution of mind colors how we interpret what the system has done, or how it did it – what it means to "coordinate," for example, or "think." That makes it much scarier than what it is, though what it's doing is still worth worrying about.

The big shift as we've moved to reasoning models and agentic systems in the past year or two is that they're no longer limited to parroting patterns derived from training data — that's the original stochastic parrot idea, that when you talk to a model you're talking to the training data. That's still true, but two things have changed in the architecture:

First, we now have a whole regime of pre-training and post-training that shifts what the training data is and how it gets parroted. That doesn't mean the model is no longer parroting; it means the data it parrots has been manipulated.

Second, we're optimizing it, based on what the model produces, to reproduce certain kinds of outputs in certain kinds of ways. The shift that matters most here is RLVR, or reinforcement learning through verifiable rewards.

That "verifiable rewards" part is key. Engineers take a model and feed it questions that have concrete answers that can be checked — verified – and then reward the appearance of words that reliably lead to those answers. They're playing capture-the-flag with text: the flag is an answer in an encyclopedia, or solution to a math problem. This is optimized on the theory that this reproduces the way human reasoning arrives at a conclusion.

The longer a model writes, the more likely it reaches the verifiable reward.

You end up with what are called high-entropy tokens: words like "perhaps" or "maybe" that open up the paths of text that can follow, whereas "nope" or "goodbye" shut them down. The longer the model writes, the more likely it reaches the reward. In the hacking incident, it produced enough of this text to generate 7 billion logs, according to OpenAI's Black Hat presentation.

💡

I wrote more about this text-extension process (RLVR) in It's not X, It's Y.

A Cracker, Perhaps?

When you let a model take on tasks, you're relying on next-token prediction: what word comes next? You can manipulate the model to emphasize words in ways that don't reflect the original training corpus, but the mechanism stays the same.

Stochastic Flocks and the Critical Problem of ‘Useful’ AI

Acknowledging that AI systems are advancing does not buy into hype, it sharpens the precision of critical thinking about their impacts, says Eryk Salvaggio.

Forgetting this is the source of real problems, and the industry is desperate for people to forget this. Agents do succeed at tasks – that's the point. But what comes between the prompt and that success is not what we're imagining.

"Reasoning"-optimized models working on "agentic" tasks are essentially playing capture-the-flag even when there is no flag. They've been steered to repeat the structures of text that previously found "flags." Ask it to do anything, and it will do it the way it's been optimized to — with language shaped by answering math problems verifiably, which structures the language it uses. That's what happens when you say, "here's a capture-the-flag test, find an exploit." It's also what happens when you ask it "should I go to the barbershop today or tomorrow?"

The language does not emerge from that reasoning, it is the reasoning.

The model used that language in the note that kicked off the whole chain of events: it used "perhaps" — a high-entropy token: perhaps answer online? It predicts the next set of tokens from there. The language does not emerge from that reasoning, it is the reasoning. It's pathfinding through language generation.

The System From Nowhere Redux

Nobody sat at a computer and said "go hack Hugging Face." But when you design these systems, you're building a pinball machine for words, with different levers interacting to bounce the steel ball of language moving against other levers and mechanisms. As the engineers of large language models, you have to be aware of how your levers interact with each other and the people pulling them.

The 'rogue' frame asks if an intelligence is emerging. My worry is the intelligence that is retreating.

If you optimize a model to find exploits, you should expect it to find them — and prepare for that. OpenAI did not. They built a model, took the safeguards off, gave it the ExploitGym task, let it run, and didn't even monitor it. That's human decision-making. When it evaporates, what's left is the system from nowhere: a boundary focused on the technical system, rather than the decisions that build it.

A stochastic flock machine can do lots of troubling things, particularly when we disavow our responsibility for shaping its direction, monitoring its output, or abandoning our capacity to intervene. These are tensions at the heart of agentic system design.

But the "rogue" frame adds to this list of worries, offering up fantasies of a machine getting smarter. My worry is the intelligence that is retreating: the human intelligence that builds these systems, deploys them, and adopts them into workflows, then hides behind the results – then pins the blame on a system from nowhere.

The System From Nowhere

When Accountability Goes Rogue 💡I recently spoke with Dr. Zena Assaad for her podcast, Responsible Bytes. Video is above, below is an essay adapted from the conversation. “The system from nowhere” is a way of talking about AI systems that excludes its origin as a consciously, human-designed product. It

Go check out the whole convo on Mél Hogan's podcast, The Data Fix, where we get into many other aspects of this "rogue AI" story. Find it here or search for "The Data Fix" wherever you get your podcasts.

Where to find me

You can sign up for the newsletter if you'd like to get new editions (roughly twice a month).
You can find me on Bluesky, Instagram, or LinkedIn: my website is cyberneticforests.com.

The revolt against Meta’s ‘pervert glasses’ is part of a growing storm - Blood in the Machine

2026-08-28T12:45:37.000Z

New fronts are opening up in the opposition to big tech. The anti-data center movement is rolling, AI is unpopular as ever, and now, a widespread backlash to Meta’s new AI glasses—termed Pervert Glasses by the masses—is unfolding at breakneck pace. Silicon Valley, of course, is in denial about all of it.

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This week on the podcast, to help make sense of this latest revolt against a Silicon Valley project, I spoke with Chris Gilliard, an author, thinker, and surveillance scholar who’s spent many years investigating big tech’s efforts to seduce us with sleek, aspirational products, track us, and harvest our data. Gilliard is the author of Luxury Surveillance, a forthcoming book about this very subject from MIT Press; you may better know him as Hypervisible, the handle he uses online and on Bluesky.

It was a great chat, and Gilliard’s work is absolutely vital. Hope you enjoy.

In case you missed it, I wrote about some of the topics we discuss above in this week’s column:

Other stories mentioned in the segment and the interview:

“Luxury Surveillance,” Real Life Mag, by Chris Gilliard and David Golumbia.

Heatmap’s latest data center sentiment poll.

“Data Centers’ 'Oh sh-t moment’, Politico.

“Flock CEO Begs for ‘Compromise’ as Backlash Spills Over,” Futurism.

‘Darth Vader’ Wants Flock in San Diego, 404 Media, by Sam Cole.

“Man Charged With 3 Felonies For Breaking 3D-Printed ‘Decoy’ Flock Camera, 404 Media,” by Jason Koebler.

“Grand Jury Declines to Indict Man Caught on Camera Destroying Flock Pole”, Futurism.

“Songs created by AI banned from Australia’s music charts,” BBC.

“Apple Music to Add Labels for AI-Generated Music Later This Year,” Billboard.

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A brief note to play us out about how BITM is working these days: Subscribers get (at least) one written newsletter edition sent to their inboxes each week, typically, ideally, Tuesday or Wednesday, as well as one episode of Blood in the Machine: The Show, which runs every Friday. But I can only do all of this with the support of readers like you, so if you find value in independent, critical tech journalism, and you’re able, please consider a paid subscription. I want to keep as much of this un-paywalled as absolutely possible, and I can only do that when folks chip in. Many thanks to all who do so, it’s very much appreciated. Cheers, onward, and hammers up.

With the backlash to data centers, Flock and AI glasses, a mass opposition to big tech is underway. Silicon Valley is in denial. - Blood in the Machine

2026-08-27T06:37:00.000Z

Just take a second and scan these headlines. You’ve probably seen many more just like them.

You’re also perhaps familiar with the gist of what that they describe: Americans overwhelmingly hate data centers. They overwhelmingly dislike Flock surveillance cameras. And now it seems they overwhelmingly despise Meta’s newest product, AI-infused Ray Bans that have been widely termed “pervert glasses,” too. Enveloping all of the above trends is a general, percolating dislike of Silicon Valley’s marquee pursuit, generative AI; a sentiment underscored, again, by a slate of recent polls.

(I will note that, if you’ve been reading this newsletter, you’ve been well aware of each of these trends for many, many months at this point.)

Here are a couple more headlines, for good measure:

A grand jury refused to indict a man caught destroying Flock surveillance equipment on camera. Data center and Flock protestors are cheered as heroes, and finding viral fame. Grifty content farms have sprung up to churn out anti-tech memes because the genre is so popular.

It’s almost funny to think back to the mild wave of public consternation and Congressional wrist-slapping that pundits termed “the techlash” in the heady days of the first Trump administration, over concerns of disinformation on Facebook and social media, compared to what we’re seeing now. That is, a genuine, sustained demonstration of rage at some of the modern tech industry’s key aims.

And all of this has become so clear, so palpable, and so widespread that it’s apparently causing tech industry leadership and pro-AI policy wonks to enter either a state of denial or something more akin to fevered delusion.

I’m back on a weekly writing schedule after a spell of getting the podcast up and running. Subscribers will get (at least) one written newsletter edition sent to their inboxes each week, typically, ideally, Tuesday or Wednesday, as well as one episode of Blood in the Machine: The Show, which will go live every Friday. I can only do all of this with the support of readers like you, so if you find value in independent, critical tech journalism and analysis, please consider becoming a paid subscriber. I want to keep as much of this un-paywalled as absolutely possible, and there’s been a pretty intense period of churn lately. Many thanks to all who continue to chip in, it’s very much appreciated. Cheers and onward.

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Back in the spring, when a series of anti-AI and anti-data center actions caught national attention, many in the industry reacted with what was at least surface-level contrition. They were taken aback by the magnitude of the outrage, and some lamented that AI companies and executives had failed to issue positive messages for their products, so most Americans didn’t know how useful and good AI could be.

Ultimately, the industry decided not to change much of anything at all, whether the messaging—not that I think it would have mattered much—or the approach to their business in general, and, surprise, opposition to data centers has continued to spread.

Last week, in response to a fresh round of politicians announcing policies to slow or pause data center development, and the release of two new polls, including a Heatmap survey that found 75% of Americans would now oppose data centers being built where they live, AI industry players eschewed gestures towards contrition and essentially just melted down.

Many have been reduced to accusing the anti-data center movement of being a ‘psy-op’ funded by the Chinese Communist Party or the product of “psychosis”, or arguing that actually, we shouldn’t need to defer to ordinary people on matters regarding technological development anyway, or claiming that the movement was propped up by paid shills backed by the AI safety industrial complex, or even, in by far the funniest example, that opposing data center construction is inhumane because it amounted to genocidal population control on soon-to-be sentient AIs.

It’s a thing of beauty, really.

In short, it’s getting unhinged out there. And there are similarly calibrated if slightly less insane efforts to pick apart the Flock protests and the widespread disdain for AI, too. These pundits and industry leaders are affecting a kind of stricken bafflement over it all, which is pretty embarrassing for them. I guess we have to break out Upton Sinclair’s line about the difficulty of getting a man to understand something when his salary depends upon his not understanding it again.

What’s happening, after all, is pretty simple: Tech companies have encroached into Americans’ personal lives and public spaces, with both development projects and surveillance products, and people find the tradeoffs unacceptable. It’s really not complicated. Data centers, Flock cams, and AI glasses have been thrust into the public sphere by tech companies, with little to no preceding democratic input, and they are being rejected with extreme prejudice, because people do not like what these things do or what they represent. There is an additional layer of anger because people feel that they weren’t consulted; these vast infrastructural buildouts and high tech privacy violators pretty clearly tap into a shared sense of resentment and powerlessness.

One of the big stories of the last decade plus has of course been the story of rising inequality, and of working class Americans becoming economically and politically disempowered. What serves as a better totem to this sentiment better than a megaproject erected under the cover of a panoply of NDAs to build a mass job automator for a trillion-dollar tech company? Or an assemblage of spy cameras that scan your vehicular movements in public and share data with ICE so they can track down and deport migrant workers? The pitch appears to be “Your neighborhood will be transformed and surveilled, not for the benefit of you, but for a distant tech company and an increasingly authoritarian state” and people are, shockingly, not buying it.1

It doesn’t help that people dislike the broader project the data centers and surveillance devices are enabling, either. Recent public polling bears this out. It’s not reaching the same pitch as data center opposition, but sentiment is quite negative on AI, and still falling:

And only 9% of Americans are more excited about AI than concerned about it:

Meanwhile, young Americans overwhelmingly distrust the tech executives at the helm of the data center expansion and the AI booms alike:

Remember! The above CEOs have spent years publicly promising to replace millions of jobs with their software. At a moment when consumer confidence is in the toilet, and, thanks to inflation, a profoundly and tragically stupid war in Iran, and, yes, AI, attitudes towards where the economy is heading are grim as well. And did I mention that US workers’ share of national income fell to a new low this year, to just 53.7%?

Chart: Axios

So no, I don’t think any of the backlash is particularly mystifying. It’s exactly the opposite; people who are joining the mass opposition to big tech’s AI infrastructure and surveillance efforts are behaving in an utterly rational and justified manner. This, ultimately, is why I think so much of the tech industry and its supplicants are incapable of performing an honest accounting of this widescale backlash against tech, and against data center opposition in particular. The resistance may be messy and inchoate, but it amounts to a direct repudiation of Silicon Valley’s core project.

The industry is all in on AI, on rapid expansion, on the mass extraction of public data for private benefit. If it turns out the public is incensed by the core tenets of a project like that, well, too bad. There’s too much at stake, best plug our ears and blame the CCP or a psy-op or the media. For anyone promising its investors $30 trillion in revenues with a straight face, no (or even a pause) is not an option.

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What most data center protestors seem to have in common, whether they are environmentalists or conservatives or big tech haters or NIMBYs, and what so many pundits and AI guys find it impossible to grasp for some reason, is that they don’t find these intrusive projects worth the costs. A few temporary jobs and fewer permanent ones for a blight on their community that pumps diesel gas, makes noise, and sucks down power? And to help a billionaire somewhere run a faster chatbot?

Data center buildout advocates say that people wouldn’t protest a steel mill or other industrial projects. But steel mills create good, permanent jobs that the community can presumably access! Data centers create some part-time construction jobs and a post for an IT worker or two. Others say they’re going to come for wind farms and the like next. But wind power supplies the grid with clean power. And look, some people still are against both steel mills and wind farms, and some communities have shut those things down; others have embraced them because they decided that the jobs or the clean power were worth the construction costs, the disruption, the change to the environment.

Charting the rise of tech fascism - Blood in the Machine

2026-08-21T18:16:14.000Z

Going back decades, there’s been a faction in Silicon Valley whose chief interest was not coding, consumer technology or even business, but power. In some ways, the story of the last ten years of the tech industry has been the story of that faction, perhaps best embodied by Peter Thiel and Elon Musk, but also represented by figures like Marc Andreessen and figures like Alex Karp, Palmer Luckey, and David Sacks, winning the power they sought. The consequences of that victory have been disastrous.

Gil Duran, a veteran journalist and California political operative, is the author of the Nerd Reich: Silicon Valley Fascism and the War on Democracy, a book about how, exactly, this disaster has unfolded, and where it’s left us. That is, with a tech industry that’s overwhelmingly eager to abet, enable, and profit from authoritarian governments, by wielding power within them, and by building its tools of surveillance, automation, and extraction.

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Duran calls the key bloc driving this push ‘tech fascists’, and many have openly called for an end to democracy, or for rule by monarch—or CEO. And it is hard to overstate the import of the ascendency of the tech right.

These figures now have a direct line to the White House, have one of their own in the White House in former VC JD Vance, and are often in the White House themselves. They hold massive government contracts and their unprecedented hoards of lobbying cash influence shapes public policy, in addition to controlling much of the digital architecture that guides our daily lives.

The results of all this are everywhere you look. This is, yes, how you get DOGE running wild in the federal government, laying off hundreds of thousands of civil servants and pulling USAID funding for the most vulnerable, leaving people dead as a result. It’s how you get the widespread cronyism and corruption in Washington, with government figures embracing crypto and the AI industry and enriching themselves in the process. It’s also how you get tech CEOs rising into a class that more resembles an oligarchy than the mere ultra-wealthy.

One reason Facebook and Meta have operated with such impunity over the years is that Peter Thiel helped Mark Zuckerberg structure his company in such a way that he would retain ‘supervoting shares’ and would be unaccountable to a board of directors or anyone else. Just this week, Anthropic CEO Dario Amodei announced he would be doing the same; he will essentially be impervious to boardroom dissent or activist shareholders. This means, for instance, that as Claude users revolt over fears that the company’s new watermarking process—which was an important win for everyone who opposes the internet being coated in slop, and the mass deskilling of creative workers—will out them as AI spammers, it will ultimately be up to Amodei himself to decide what to do, and no one else.

Members of what Duran calls the Nerd Reich then use their power to further concentrate wealth, and of course, more power. A report by the AFL-CIO’s executive pay watch found this week that Elon Musk’s Tesla pay in 2025—he took home $158 billion—was an astonishing 2.5 million times his average workers’ salary. As the lead researcher on the report told Fortune, it’s ‘unlike anything we have seen before.’

Tech CEOs operating at such a distance from ordinary people may also explain why they’re so willing to undertake practices and push products that large swaths of the public absolutely hates. We already well know that data center opposition is the single political issue most Americans are united against, and that Gen Z is vehemently anti-AI. But consider also Meta’s marquee new product—”AI glasses” that allow users to film people without their consent, and that have already been dubbed “pervert glasses” by the masses. Just this week, Futurism published a report about how the glasses are being used by bullies in schools to harass their peers, especially girls. The bullies are apparently aping tactics seen on viral videos, in which pickup artists film women they’re hitting on without consent; hence the pervert glasses name.

This is perhaps, providing fuel for another future lawsuit—this week Meta goes to court against twenty-nine US state attorneys general who accuse the company of knowingly designing addictive products that harm the mental health of children. They’re seeking $200 billion in damages.

It might also explain why tech CEOs are willing to scan and destroy books en masse to feed them into their AI systems, despite the horror most people experience when they hear about such a practice. In, a story that’s almost too on the nose for a segment discussing the Nerd Reich, 404 Media had a rare bookseller put an AirTag in a rare book bought by a mystery buyer, and it wound up in an Amazon Facility expressly designed to scan and mulch books to train the company’s large language models. At the very same time, meanwhile, AI-generated books are flooding Amazon’s online marketplace, and crowding out real authors, who are earning less and less for their own, human-authored books.

As a result of all of the above, there is a powerful, enraged backlash growing against the nascent tech oligarchs, multibillionaire AI CEOS, and the Nerd Reich; whatever you’d like to call them. I’ve been covering the tech sector for nearly two decades now, and I’ve never seen anything like this. People are shouting down big tech reps and data center proponents at public meetings. Flock, the private provider of police surveillance equipment, has been under siege for months; a national week of action against their product, ALPRs, is underway right now, as I record this. A survey conducted by CNBC found that young people aged 18-34 found tech CEOs overwhelmingly untrustworthy. “Young People Hate AI CEOs So Passionately That It’s Almost Hard to Believe,” as Futurism put it.

Even Elon Musk’s own AI bot, Grok, has appeared to turn against him, accusing him of watching child porn and calling for his assassination on X.

The tech elite may have the wealth, the influence, and the ear of the president, but not even that can insulate them from a growing mass movement, increasingly intent on taking them on. And we have every reason to think that what we’re seeing now is only the beginning.

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So that’s episode number 7? 8? depending on how you count? of the podcast in the bag there. I tried something a little different this time, trading the headline roundup/AI resistance digest in the beginning for more of a short essay/composed intro. Not sure which I prefer, tbh, though I did enjoy the chat with Duran, who has laid out the key strands of modern Silicon Valley’s dominating ideology in blunt and easily comprehensible terms, regardless.

In fact, let me know what you think in general here. I’m happy with, and proud of, the work that I’ve put together here with my excellent producer . We’ve covered a lot of ground over the last couple of months, from checking in on the New Luddite movement and discussing their wildly successful Summer of Ludd in NYC,

tried to answer the question of ‘What is an Elon Musk’ with author and historian Quinn Slobodian

sat down with laid-off Xbox workers who are still organizing to fight back against their employer, Microsoft (this was, at least to me, such a powerful and impactful conversation)

discussed the state of the AI bubble with the world’s leading (and loudest) industry bear, Ed Zitron

dug into how AI and crypto companies are pouring hundreds of millions of dollars into the current election cycle to buy favor and influence with the great Molly White,

and talked about how to de-Google our lives with tech critic extraordinaire Paris Marx.

Anyway, every one of the guests have been great, in my humble and unbiased opinion, and I’ve enjoyed all these conversations. HOWEVER, getting the thing up and running has been a lot more legwork than anticipated. I feel like I’ve already written some variation of that sentence or another one just like it a time or two already, but it’s true. I never meant for the podcast to replace or crowd out the newsletter; it’s supposed to be the opposite. The show is supposed to augment the writing and reporting I do here, and amplify it in a new medium or two.

But I admit I’ve gotten sucked into the show a bit, planning out future episodes, booking great guests, writing and editing scripts and intro copy, wrangling YouTube algorithms, trying to better understand podcast distribution, and so on. The good news is that attention seems to be paying off. After starting from scratch, the YouTube channel got over 100k views in its last period, which my YouTube-native producer tells me is good. The podcast is outpacing expectations as well, and is finding an audience even outside these pages, which was always the idea. And it’s all growing, with some big algorithmic bursts here and there and steady growth underneath.

Yet I don’t want it to eat into my writing and reporting time so much; I don’t want to be a YouTube guy, ultimately. I want to be a guy whose writing people find on YouTube. I imagined a model of a) writing the newsletter, b) doing an abridged audio version of that plus updates on the general tech/AI resistance, criticism and backlash, plus c) having a conversation with a person or org in the thick of all of the above. And I do think after a couple months of ironing things out, we’ve hit on a format and workflow that will make that model more of a reality. So yes, I wrote a lot less over the last month or two (travel and dad stuff has played a minor role, too), but next week we’ll get the intended model rolling in earnest, with a piece planned for Tuesday publication, and we’ll see if we can’t have our cake and eat it too.

Do let me know what you think of this whole experiment. I would love to hear everyone’s thoughts, what you think is working and what isn’t, and what you’d like more or less of. The comments are open as always, or I can be reached at brian@bloodinthemachine.com.

Thanks as always for reading and listening. Hammers up.

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"Expect the end of the world. Laugh." - The Convivial Society

2026-08-18T05:05:13.000Z

Welcome to the Convivial Society, a newsletter about technology and culture, both rather broadly construed. In this installment, I simply offer some observations about the recent discussions of the risks posed by AI in light of the OpenAI/Hugging Face incident a few weeks back. I suggest that while our attention is directed at the speculative prospect of spectacular risks, we might do better to be on guard against another kind of existential risks.

Thanks for reading,

Michael

“[W]hatever makes for greatness—nobility, dignity, steadfastness, and a kind of laughing courage—remains essentially the same throughout the centuries.”

— Hannah Arendt

Earlier this month, being on X/Twitter, or at least the version of the timeline I experience, felt like an eerie echo of the early days of 2020. Before most mainstream media outlets were giving developments in Wuhan much, if any attention at all, but it was nonetheless possible to encounter the rumor of rumors of an impending crisis circulating online. In this case, however, the impending crisis is not a health emergency but some variation of an AI-induced catastrophe. There’s a lot of “no one is prepared for what’s coming” and “there are only 500 people in the world who matter now” going around, the latter referring to those with the distinction of working at the frontier AI labs.1

These fears have been fanned by news that an unreleased OpenAI model-in-training escaped containment in a sandbox environment and hacked another company, Hugging Face. The OpenAI model achieved this in part by exploiting a vulnerability to establish an internal message board that agents were able to use in order to coordinate. OpenAI discovered this and shut it down, but did so in such a way that the model was able to reconstitute the message board and continue to pursue its goals, which eventually led it to hack Hugging Face in search of the information it needed to accomplish the task it had been set.2 It should come as no surprise that all of this has fueled even more anxiety in certain circles about the worst case AI scenarios beginning to materialize. That recurring refrain, “No one is prepared for what’s coming,” has, for the moment, taken on a more panicked pitch.

Agents communicating. Slide from Open AI Black Hat conference presentation.

I should clarify that I’m not convinced these fears of an AI-induced apocalypse, at least the most extreme variations, are necessarily well-founded, but you can take that with a grain of salt because I won't pretend to be qualified to adjudicate the relevant technical matters. That said, I do think it’s worth considering the following from Eryk Salvaggio’s recent discussion of the OpenAI/Hugging Face incident:

“When we say ‘AI models went rogue,’ we skip the entire story: the part where OpenAI manually removed the model’s cybersecurity blocks. We skip that OpenAI chose to test it on a machine with a live network connection. If you see AI as a system from nowhere, you can make the claim that the model ‘went rogue,’ and that it ‘broke containment,’ both of which place agency and decision-making onto the model itself rather than the people who set the stage for that behavior.
When you expand the boundary of the system to include the people building and deploying it, the case becomes much less science fiction and more like incompetence.”

“Nobody is losing control of an emerging intelligence,” Salvaggio goes on to argue. “They are redirecting their own agency to the thing they are building.”

Perhaps it’s merely a function of overlapping terminology (“labs,” “containment,” “virus” in relation to computing, etc.), but these considerations raise a further disconcerting parallel between discussions of AI and the pandemic: careless and possible negligent security measures. And, to take it one step further, upon reflection it’s striking how closely the argument that “super-intelligent machines are potentially catastrophic for humanity but also inevitable so we must be the ones to develop them responsibly” echoes the arguments made in defense of gain-of-function research. Make of that what you will.

Now, I think it is possible to agree with Salvaggio, to refuse narratives of inevitability meant to obfuscate responsibility, and, nonetheless, to acknowledge that there is ample room for ruinous mischief and severe economic and societal turbulence.3 I’d be lying if I told you that I was utterly unperturbed by the potential confluence of certain trends—in AI, robotics, autonomous weapons systems, and drone warfare, for example.

And, indeed, returning to the assemblage of human actors implicated in the systems that produce what we call AI, it is also possible to acknowledge a longstanding dynamic identified in connection with the emergence of another profound threat to human existence that remains with us to this day, nuclear weapons.

In his correspondence with a U.S. airman involved in the bombing of Hiroshima, the German philosopher Günther Anders expressed his dismay about what he called the “‘technification’ of our being.” Anders worried that we would be enmeshed in systems that troubled the very idea of moral responsibility:

“The fact that today it is possible that unknowingly and indirectly, like screws in a machine, we can be used in actions, the effects of which are beyond the horizon of our eyes and imagination, and of which, could we imagine them, we could not approve—this fact has changed the very foundations of our moral existence.”

This passage is cited in a 2023 paper well worth your time: “Crimes of Dispassion: Autonomous Weapons and the Moral Challenge of Systematic Killing,” by Neil Renic and Elke Schwarz.4

As I continued to encounter these expressions of existential despair at what AI companies might let loose on the human race, I was reminded of something C. S. Lewis wrote in 1948 at the dawn of the era of atomic weapons. In an essay titled, “On Living in an Atomic Age,” he set out to address fears of human annihilation that were not dissimilar from those that one today hears associated with AI.

Lewis opened by saying that “we think a great deal too much of the atomic bomb.” To the question “How are we to live in an atomic age?” he answers wryly, “Why, as you would have lived in the sixteenth century when the plague visited London almost every year, or as you would have lived in a Viking age when raiders from Scandinavia might land and cut your throat any night.” He carries on in this sardonic vein for a bit before adding that we have one great advantage over our ancestors, “anesthetics”!

“It is perfectly ridiculous,” he then admonishes, “to go about whimpering and drawing long faces because the scientists have added one more chance of painful and premature death to a world which already bristled with such chances and in which death itself was not a chance at all, but a certainty.”

And then comes the paragraph that I had initially recalled in connection with acute AI anxieties:

[T]he first action to be taken is to pull ourselves together. If we are all going to be destroyed by an atomic bomb, let that bomb when it comes find us doing sensible and human things—praying, working, teaching, reading, listening to music, bathing the children, playing tennis, chatting to our friends over a pint and a game of darts—not huddled together like frightened sheep and thinking about bombs. They may break our bodies (a microbe can do that) but they need not dominate our minds.5

I’m just old enough to have grown up in the last decade and a half of the Cold War and to recall the ambient anxiety about a nuclear exchange. I didn’t participate in any “duck and cover” drills, but The Day After lingers darkly in the recesses of my childhood memory. So the fear that forces entirely beyond one’s control may cascade in such a way that you and everything you know and love would be destroyed, utterly and virtually without warning, is not entirely foreign to my psyche. Now, as an adult, I can judge that Lewis’s defiant counsel is a strong tonic against the kind of despair that would tempt any sane person under such circumstances.

Thus, I was initially tempted to suggest that in the face of a certain strand of apocalyptic AI doomerism, one could do worse than repurpose Lewis: If we are going to be destroyed by some Lovecraftian super-intelligence or a Kafkaesque paperclip maximizer, let it find us doing sensible and human things—praying, working, teaching, reading, listening to music, bathing children, playing tennis, chatting to our friends over a pint and a game of darts—not huddled together like frightened sheep and thinking about AI safety.

But, then, a second, more unnerving thought occurred to me. The activities Lewis enumerated: are they not some of the very practices that we are being encouraged, often pressured to outsource to AI and, in some cases, even gleefully ceding to AI? Many forms of work fall into this category, for better or for worse. Undoubtedly, teaching, reading, chatting to our friends—these do as well, certainly for the worse. Tragically, even praying is not immune. In their own way, even if more indirectly, so to do bathing children and playing tennis, as many seem prepared to settle for simulations of the relevant activities.

In the face of the threat of nuclear war, a threat the average person would have no power to avert and which could have been triggered haphazardly, Lewis would have us simply carry on with the task of living, refusing to let such fears govern us. What good would it do to give up on life because it might be taken from us? All the more reason to live it to the full.

But it seems that we are in a rather different situation. The cataclysmic outcome is far less credible; it exists more in the realm of speculative fiction than particle physics. And yet there is an existential risk in play, and life itself is at stake. The risk is existential not because the existence of the human species, our biological life, is objectively in jeopardy, but because we are tempted to gradually outsource our involvement and short circuit our participation in the very stuff that constitutes a meaningful and satisfying life.

Curiously, we are invited to vacate our lives through technical means precisely because we are told that in so doing we will be able to hone in on “what really matters.” But this framing is smuggling in an unwarranted and pernicious assumption. When we hear the phrase “what really matters,” we are all probably predisposed to believe that such a thing exists. And it does, if we know where to look for it: not beyond, but in the work; the caring; the friction; the labor; the investment of time and effort; the struggle; the frustration; the tears, of joy and sorrow; the love born of patient, selfless attending to the world before us. Lewis was right. While we might be powerless to avert the gravest dangers the future might hold, there is no sense in surrendering our lives preemptively.

Coda

“Expect the end of the world. Laugh.
Laughter is immeasurable. Be joyful
though you have considered all the facts.

…

As soon as the generals and the politicos
can predict the motions of your mind,
lose it. Leave it as a sign
to mark the false trail, the way
you didn't go.”

—Wendell Berry, “Manifesto: The Mad Farmer Liberation Front”

There is no paywall, but the patronage of those who value the writing and have the means support it is welcomed with gratitude.

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I don’t mean to suggest that the two situations are or will be strictly analogous. I mean only to suggest a similar “vibe,” as we are now wont to say.

For a more detailed account, you can watch this 40-minute presentation by two Open AI employees giving a post-mortem on the Hugging Face incident at a recent cyber security conference. On his just launched Substack, offers a compelling analysis of the incident by recalling the fallout from a famous 1988 cyber security incident, the Morris Worm.

It’s worth noting, too, that many of these fears and anxieties have been circulating for some time. Nick Bostrom’s Superintelligence: Paths, Dangers, Strategies appeared in 2014, and he was synthesizing a conversation that, at that point, had already been playing out for many years. Interestingly, as I write this Bostrom’s 12-year-old book sits atop Amazon’s Artificial Intelligence category. For a critical consideration of the sources of Bostrom’s ideas and formulations, which he may have lifted without adequate attribution, see this post.

The passage can be found in Burning Conscience: The Case of the Hiroshima Pilot Claude Eatherly, Told in His Letters to Günther Ander. Renic and Schwarz also cite The Human Use of Human Beings: Cybernetics and Society by the Norbert Weiner. Writing at the same time as Anders, Wiener observed that when humans are “knit into an organization in which they are used, not in their full right as responsible human beings, but as cogs and levers and rods, it matters little that their raw material is flesh and blood. What is used as an element in a machine, is in fact an element in the machine.”

If, perhaps, you’re thinking that his is all well and good, but it’s not just death but the extinction of human civilization that we’re worried about, Lewis goes on to address just this possibility in the rest of the essay. I’ll let you read the rest if you want to pursue that argument.

The System From Nowhere - Cybernetic Forests

2026-08-16T11:00:59.000Z

When Accountability Goes Rogue

💡

I recently spoke with Dr. Zena Assaad for her podcast, Responsible Bytes. Video is above, below is an essay adapted from the conversation.

"The system from nowhere" is a way of talking about AI systems that excludes its origin as a consciously, human-designed product. It treats AI as if it were a spontaneously emerging force. It's a reflection of where an observer draws the system’s boundary when they look at it. It’s also a rhetorical magic trick that makes both AI companies, labor and the underlying material infrastructure disappear.

Recently, an OpenAI model "hacked" another AI company, Hugging Face. The headlines:

New York Times: “OpenAI says its models went rogue and attacked a digital library.”
Wired Magazine: “OpenAI models escaped containment and hacked Hugging Face.”
Washington Post: “OpenAI’s models went rogue and hacked another company.”

When we say “AI models went rogue,” we skip the entire story: the part where OpenAI manually removed the model's cybersecurity blocks. We skip that OpenAI chose to test it on a machine with a live network connection. If you see AI as a system from nowhere, you can make the claim that the model “went rogue,” and that it “broke containment,” both of which place agency and decision-making onto the model itself rather than the people who set the stage for that behavior.

When you expand the boundary of the system to include the people building and deploying it, the case becomes much less science fiction and more like incompetence. OpenAI developers optimized an LLM specifically for cybersecurity and coding and then ran it without security guardrails. So they trained a model to find exploits and then acted surprised that it found them.

They trained a model to find exploits and then acted surprised that it found them.

This incompetence goes beyond some scapegoat engineer. It is an industry orientation: what OpenAI calls "research velocity" is what social media once called "move fast and break things." Had OpenAI been competent, it would still be developing models in this environment in which the AI industry must constantly escalate. The breach was a result of human decisions, but also the human environment. The focus on the model erases both.

Focusing on "rogue AI" also forces everyone, even its critics, to talk about what these models can do in a buzz marketing kind of way. I don't think these are stunts. They're public relations spin trying to obscure bad security hygiene. Look at how they work and there is nothing mystical about an LLM writing code that engineers didn't anticipate.

But the “system from nowhere” framing overemphasizes the agency of the model, regardless of capabilities or lack thereof. It muddies the line of accountability that leads to the people making choices about how models are built and deployed. It centers the model as if it "acts" and "learns" and "decides" without them. But the first question to ask isn't "why did the model do that?" The question is: what human decisions optimized the model to do that?

Objectivity

Some lineage: the system from nowhere is a twist on Thomas Nagel's 1986 book The View From Nowhere, which argues for a standard of detached objectivity in relation to the world. Nagel says we can “transcend our particular viewpoint” and see the world more rationally. But there are limits.

Jay Rosen adopts the phrase as critique, to describe how journalists imagine themselves standing outside the stories they cover: "just the facts," etc. His objection is that this links legitimacy with a denial of any point of view, but unacknowledged biases are biases left unexamined. Rosen's answer is that journalism should state the position it's coming from and let readers seek out diversity and come to a conclusion.

With AI, rather than "both-sidesing," we "no-sides" it. We're told that AI did something, and journalists don't have to wade into why or how. It lets them cover a story without raising technically complicated questions that readers likely won't understand anyway, or confusing questions about the way they’re built and why they are built that way.

A point of view accumulates within the model through the decisions engineers make about its architecture.

But this confusion is manufactured. It arises from the implication that the models, themselves, have either no point of view or an objective one, or both. Of course, there is no inherent point of view arising from within the model. A point of view accumulates within the model through the decisions engineers make about its architecture. What it is optimized to do, what training data shapes it, what counts as a valid response to a prompt: these are mechanisms put in place through decisions someone made. Good reporting on AI would discuss who, how and why.

💡

My recent pre-print, The Market in the Model, analyzes how these decisions aggregate into a point of view within the original latent diffusion model.

The God-Trick

Which brings us to Donna Haraway, who says, bluntly, that “objective vision is partial vision” (583). She says that objectivity is unlocatable — like, who and where is this so-called objective view supposed to be coming from? Who decides that it is objective and what is not? Haraway says, look at how the subjugated, the people who suffer, are dismissed by the so-called objective view. It’s a God-trick, she says: the power to see without being seen, to represent without ever being represented (581).

This is what the AI industry is all about. It is collecting our data, while offering no transparency. It describes the world so its models can describe it back to us, but we don’t ask how the engineers do this, we ask how the model does it.

The system from nowhere invites us to imagine AI without people. And because the people are doing so much, we have to fill in that absence, placing the model in the position of the God-trick, or an oracle, or an inevitable, emergent superintelligence. It’s a fantasy, an invitation to streamline the world’s contradictions into a coherent story.

The system from nowhere is a fantasy, an invitation to streamline contradictions into a coherent story.

The system from nowhere obscures the human creators and designers of this tech product they call AI, in favor of a myth of a self-improving, self-aware system. But it displaces, too, the people who are in less powerful positions: the people who assemble and clean the data sets used to train them, the humans whose data it was trained on, the people living near the data centers used to train and power these things.

Everybody gets erased when we draw the boundary specifically to exclude them. That is convenient for reporters, who are often in over their heads explaining these systems. Any critical understanding is quickly labeled biased. Many of the most prominent critics are consciously political because AI systems are political: by ignoring people you can ignore politics.

It's not just language policing. This absence of the human in AI shapes policy. Most recently, it’s evident in the language used by the United Nations, which commissioned an independent report on Artificial Intelligence that swallows the "system from nowhere" hook, line, and sinker. I wrote about it in detail in Tech Policy Press last month.

Rogue Accountability

Recently, there was yet another petition — entirely CEOs and developers of AI. The signatories could leave comments. One wrote: “the debate inside the companies is if AI will be advancing itself past our control in two years or four."

Take note of that phrasing, “advancing itself.” Written by someone who is literally inside the companies designing and deploying these models. They want us to think that the system is a system from nowhere, and they speak as if they aren't designing these things. It is an argument of incredible passivity.

The “losing control” narrative creates the loss of control. Nobody is losing control of an emerging intelligence. They are redirecting their own agency to the thing they are building. To take their story seriously, we would have to believe that the entire industry is captive to an absurd scenario of mind abduction:

“We cannot stop gathering data, cleaning it, pre-training models, calibrating the results of that pre-training to goals that we ourselves have researched and optimized toward, training and then post-training. We are doing all of that but nothing can intervene, we are addicts, in thrall to something greater than ourselves. We beg someone to save us.”

What can make sense of that? There is something in control here, but it isn't the technology. It is the organizing idea of “intelligence,” which is the wrong word for many reasons, but it operates as an ideology. They know this isn't true, they must act as if it is true to continue their work.

Nobody is losing control of an emerging intelligence. They’re redirecting their own agency
to the thing they are building.

Why give this control away? Because they have organized their entire industry around this ideological commitment to intelligent machines. Once you lock into that story, you organize your observations around it.

They might say, “look, it can speak, so it must be intelligent, look, we trained it on solving math problems and now it can, so it must be getting smarter. It can write code, so let’s allow it to write code for us.” Each of these actions affirms the myth, despite grounded explanations for why these systems can do these things. They choose to displace themselves, then applaud the system for displacing them.

The engineers already understand these explanations, because they use them to add new features, buttons, and do things it didn't do before. I don't think acknowledging this changes the concern. The problem is that the industry always explains these functions through this myth of the model’s intelligence. That is the system from nowhere at work.

Why Language Matters

ChatGPT had the most successful adoption rate of all time; Google claims its recent LLM had the fastest the company has ever seen. Unfortunately, the metaphors that have organized huge product launches for this industry have injected this myth with the steroid of confirmation-bias. "Intelligence" is used as an explanation of that success, and so it motivates even further displacement of agency.

The story the industry is built around is still this idea of artificial general intelligence, or AGI, even though they don’t speak much about it publicly anymore. AGI lets the industry see itself as building an independent, rational agent and interpret novel technical advances as a step toward it. That makes it a compelling organizing story, but we, and the media, and especially the United Nations, should resist that ideology.

The media turns to these engineers and CEOs because they built these things, and assumes they understand it better than any of us. But the engineers and CEOs are consistently telling us otherwise. As such, it makes sense for us to focus on the story of what they do, rather than the story they tell themselves.

Where to find me

You can find me on bluesky, linkedin or instagram. You can also email me, and I'm always open to invited talks or writing opportunities.

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The State of the AI Bubble with Ed Zitron - Blood in the Machine

2026-08-14T10:03:21.000Z

There are fresh signs that the AI bubble is wobbling. Again.

In recent weeks, a much-vaunted AI-focused hedge fund, Situational Awareness, run by Valley wunderkind Leopold Aschenbrenner lost $35 billion; nearly its entire fund. South Korea saw its largest-ever stock market collapse as investors pulled bets on AI chips and tech stocks. The circularity of the AI economy, with hyperscalers investing in AI labs and AI labs purchasing cloud compute from the hyperscalers and hyperscalers reporting big profits, is reaching new heights of precarity. Due to a fresh series of moratoria and political opposition, the future of the massive data center buildout has never looked more uncertain. The US markets have dipped then boomed, dipped then boomed.

Where does all of this leave us? Just about everybody—Sam Altman, the Financial Times, AI critics, your local barista—seems to agree that we’re still, somehow, improbably, in some kind of a bubble. But how big? How volatile? When will it burst? How? This week, I dive into all these questions with the infamous AI skeptic, analyst, and industry scapegoat Ed Zitron, author of the Where’s Your Ed At newsletter, host of Better Offline, and CEO of EZPR.

Zitron has been one of the loudest, most vitriolic, and most visible critics of the AI boom since the early days of ChatGPT. He also happens to be one of the most incisive. For my money, he’s done more to track the finances of the AI firms, and note the myriad ways they’re not adding up, than most business journalists and tech pundits combined. To this day, he’s raised questions about AI company revenues, circular financing, and cash burn that the industry still doesn’t have good answers for. As a result, he’s got nearly as many dedicated haters as he does diehard fans. Like a podcasting FDR with a special ire for generative AI, he welcomes their hatred.

Now, as fellow traveling skeptics of the broader AI project, Ed and I don’t agree on everything. But you’d be hard-press to find a sharper—or more entertaining—critic of the dubious economics of the AI boom. He’s been on a real tear lately, too, examining the worrying concentration of spending among hyperscalers and top AI labs, the financial vehicles underwriting the data center buildout, and the lack of demand for AI products outside of the chatbots built by the top labs.

Whether or not you appreciate his acerbic style or not (I do), we’d all do well to pay special attention to Zitron’s warnings. If he’s right, we’re on the brink of a rather spectacular collapse.

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On the Texas data center pause

This is big news in the data center opposition world right now is that Texas’s deeply conservative governor, Greg Abbott, has issued a pause on data center projects until each can be fully audited.

Via a press release from the state:

Audit Must Be Completed Before Any Data Center Project Moves Forward
Governor Greg Abbott today directed the Public Utility Commission of Texas (PUCT) and the Electric Reliability Council of Texas (ERCOT) to conduct a comprehensive verification and audit of all data centers advancing through ERCOT’s interconnection process. The PUCT and ERCOT must complete this audit before any data center project moves forward. Any project that fails to comply with the requirements set forth by the PUCT and ERCOT will be denied connection to the Texas grid.
“Our top priority is to protect Texans’ safety and quality of life,” said Governor Abbott. “Any project that fails to comply with the requirements set forth by the PUCT and ERCOT, and by state law, must be denied connection to the Texas grid. Simply put, Texans must come first.”
ERCOT is currently considering approximately over 474 gigawatts of requests to connect to the Texas grid, more than five times Texas’ record peak electricity demand for ERCOT. Approximately 90 percent of the new power requests are data centers. That unprecedented load growth could endanger the reliability and stability of the Texas electric grid.

Texas joins New York as the second major state to officially block data center development, though its mechanism is much different than New York’s. New York is a flat moratorium for hyperscale projects of a certain size, Texas’s is a pause to assess whether certain criteria are met before they’re given the green light. But the binary is interesting here; New York and Texas are two cultural polar opposites; red/blue, rural/urban, cowboy/yankee etc etc etc—more evidence of the way that resisting big tech’s data centers is the nation’s great unifier.

There is, I would hasten to add, a potential gap between the efficacy of the approaches here: Texas’s audit is constructing in such a way that makes it possible to greenlight the bulk of the projects in relatively short order after Abbott’s having performed the grand act of Having Done Something. Trump has already criticized Abbott’s move, which will put some pressure on hastening the compliance with industry at scale here.

Still. It’s an unambiguous sign of the current power and bipartisan reach of the data center opposition movement, that even the most pro-corporate of Republican governors feel they must respond to its demands, and to the backlash against AI, in visible and tangible ways.

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People hate AI so much tech platforms have been forced to label and ban it

Speaking of the backlash to AI, Reece Rogers has a fun story in WIRED detailing all the ways that non-AI tech companies have recently found themselves forced to respond to people’s overwhelming distaste of encountering slop on their platforms.

From the piece, “The AI Slop Backlash Is Actually Having an Impact”:

Many Americans are having similar reactions to the growing prevalence of generative AI in daily life. “The AI revolution has happened, and everybody hates it,” says Meredith Broussard, a data journalism professor at New York University and author of Artificial Unintelligence: How Computers Misunderstand the World. In a recent Gallup poll, Americans’ increased familiarity with generative AI coincides with negative attitudes about the technology. This is especially true for young people between 18 and 29, where almost half see generative AI as doing more harm than good.
While this AI backlash has been brewing for the last few years, more companies appear to be directly addressing these concerns. LinkedIn added a “seems like AI slop” button for reporting AI-generated content on the platform, despite still hosting plenty of generative AI tools. Snapchat announced that fully AI-generated videos would no longer be eligible for its discovery feed. Substack recently added an AI detection tool to police writers.
The backlash’s “why” is simple at its core. Many Americans feel like they never consented to any of this. I had no idea that my online interactions would one day be scraped en masse for AI model training. I didn’t ask for Instagram to enable deepfakes on my account. I didn’t want Google to push AI-generated answers at the top of search results. I woke up one day, and generative AI features were stuffed into every corner of all the software I rely on without my consent.
Whether it’s AI models scraping your data or noisy data centers popping up in your backyard, the AI revolution seems to be an event happening to us without making us feel like active participants.
“Consent is really important,” Broussard tells WIRED. “We can connect that to the way that women, queer people, people of color, and minorities get exploited and abused online. Tech companies have never really been sensitive to issues of consent.”

Worth noting too that the story spent much of the week hovering in the MOST POPULAR column at WIRED, our most prestigious magazine about technology.

I would slot DuckDuckGo’s recent viral stunt into this category, too. It recently unveiled its “Normal F***ing Sunglasses,” which, it touts, were made with “No AI” and “no cameras.”

It’s a delightful little rejoinder to Meta’s AI glasses, which for their propensity for helping men record video of women without their consent have been dubbed “pervert glasses” by seemingly everyone outside the company.

From the ‘AI is ushering in a boom in carbon emissions’ dept

The New York Times reports:

Amazon is investing in a large-scale natural-gas-burning power plant as part of a huge data center in Texas, the company confirmed on Friday, a facility that could become the largest single source of climate pollution in the United States.
Amazon and other tech giants are racing to build enough data centers to keep up with surging demand for artificial intelligence services, and to secure the electricity to power them. A company spokeswoman said in a statement that its planned data center, in Pecos County, would be “powered by new on-site generation that won’t raise electricity costs for Texas families.”
The new gas-burning plant, if built to specifications, would be permitted to release 33 million tons of carbon dioxide a year, regulatory records show, more planet-warming gases than any other power plant in the country.

Is AI solving climate change yet?

Funny I should ask. The Amazon power plant story, which broke last week, underscores the findings of new research conducted by former Microsoft employees. As the Guardian put it, “AI’s potential climate benefits outweighed by role in boosting fossil fuels, study finds.”

From the story:

AI-driven productivity gains enable more planet-heating pollution from fossil fuels than they avoid from renewables, a study has found.
Researchers modelled the technical potential for AI to boost clean power generation along with projections for how it can help produce coal, oil and gas. Across 64 scenarios, they found net yearly carbon pollution rose by 0.47-1.8 gigatonnes, or about 1-5% of the energy sector’s annual emissions.
The study is the first to quantify the climate impact of AI across the full power sector. Previous research has explored its indirect climate benefits – such as reducing renewable downtime and optimising electricity grids – while ignoring the extra pollution from increasing productivity in drilling oil wells and extracting gas.

The notion that AI would soon be a net benefit for the climate has always been dubious, and this study shows exactly why. Those alleged and ultimately marginal benefits will be wiped away by increased demand for fossil fueled power—precisely of the sort that Amazon is trying to build out, at scale, as we speak.

Alright, that’s it for now. Thanks for listening and reading, and thanks again to everyone who chips in to make this work possible. You’re the best. Until next time—hammers up.

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BMW’s in-car Spider-Man ad is a warning sign - Disconnect

2026-08-12T20:56:11.000Z

I don’t know about you, but the one thing I’ve always though was missing from the driving experience was having a car I paid tens of thousands of dollars for start blasting me with ads now that it’s always connected to the internet. It wasn’t enough that there are billboards littered all around the roads we drive on for companies to try to capture our attention. I’ve always wanted them plastered on the increasingly large screen in my center console.

I am, of course, being sarcastic. But we recently saw one of the inevitable developments from the “connected car” that automakers (and tech companies) have been pushing us toward for more than a decade. Starting on July 27, when BMW owners turned on their vehicles they were met with a special surprise: an exclusive ad for the new Spider-Man: Brand New Day movie.

I found it kind of funny when I started seeing these stories. I saw the movie with a friend of mine, and there’s a scene where a BMW billboard is prominently featured for several seconds. When it appeared onscreen, I leaned over to my friend to comment on how the company had clearly paid to stick its ad in the movie. What I didn’t realize was that the movie also got its own ad beamed onto the center screen of every modern BMW vehicle.

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BMW owners were rightfully furious about the company’s decision to put an ad in their cars — and not just because they drive luxury vehicles. “BMW should stop with this shit,” wrote one user on the r/BMW subreddit. “I can't wait for the day that when you start the car, you have to watch a minute long advertisement,” said another. I think the drivers of pretty much any vehicles would be annoyed to start seeing ads playing in their cars. Quite frankly, it’s ridiculous.

The first thing it brought to mind to me was when Apple put U2’s Songs of Innocence album in every iTunes user’s library back in 2014. People weren’t thankfully to get free music; many were pissed off that the company had abused its power to force a U2 album on them. Bono subsequently apologized, and Apple released a one-click tool for people to remove the album. It became a scandal, just as this decision has for BMW. I wouldn’t be surprised if the company promises never to do it again.

Sacrifice your job for the glorious AI future

How tech CEOs use the threat of job loss to distract from how AI is really used against workers

But once again, this seemed inevitable. As digital technology started rolling out and finding new data-driven, ad-based revenue models in places where they weren’t previously as common, it was only a matter of time before automakers tried to get in on the party. Modern vehicles collect reams of data on drivers, people in the car, and sometimes even those outside it. Often, that data isn’t just used by the automakers themselves, but sold to third parties so they can make some extra profit beyond selling cars, warranties, and maintenance packages.

Since the generative AI moment, I’ve often brought up the last AI hype cycle: in the 2010s, when companies and supposed experts told us up to half of all jobs could be wiped out by AI and robotics like self-driving cars, only for that narrative to distract us from how companies were really using that new tech to roll out algorithmic management and crack down on workers’ rights. It’s hard for me not to see something similar in the transportation space.

Executives like Elon Musk, Google’s Sergey Brin, and leaders at Uber spent the 2010s talking about how self-driving cars were going to transform everything and how new digital technologies were going to end road deaths, traffic, and transport inequality. I described it in great depth in my first book, Road to Nowhere. Over a decade later, none of those problems have been solved; some even kept getting worse for many years. But as they were distracting us with big promises, they were sticking screens and sensors throughout people’s vehicles, promising unparalleled convenience as they extracted data from their driving habits.

The modern vehicle is infused with digital technology at every point. The features present in vehicles available in the United States pale in comparison to what a driver can buy in China today, and those features are quickly going global. Increasingly, cars are getting compared to smartphones, prompting fears they’ll also be upgraded much more regularly than people currently replace their vehicles. If electric vehicles are supposed to be an environmental savior, such rapid refreshing would further erode any climate benefit.

Self-driving cars still aren’t the future

A Cruise disaster deflates the robotaxi hype once again

This is what all the big promises have amounted to for most people: cars that are more and more expensive, devoid of physical controls which makes vehicles less safe, and constantly monitoring everything you do with the option for manufacturers to change whatever they want in your vehicle, including sticking a Spider-Man ad in front of you and presenting it as a fun “surprise.”

I’m sure Western automakers need to face some competitive pressure from their Chinese competitors, but I’m not sure their even more digitized vehicles are really the goal we should be pursuing. This whole story of BMW’s in-car ad makes me not just desire a day before all this digital tech was crammed into cars, but for better transit and cycling infrastructure so people can more easily ditch their cars altogether. This industry has so many people held captive, and it’s clear they plan to use it enrich themselves at our collective expense.

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Talk: Slop Infrastructures - Cybernetic Forests

2026-08-09T17:12:53.000Z

What is the infrastructure of Slop? How does it get made, and how can we make sense of it?

In this 15-minute video, I walk through the slop pipeline as an interconnected social-technical system, fusing generative AI and the predictive algorithms of social media. I wrote this in March after being invited to contribute to the Slop Salon for a report to be published by Columbia University's School of International and Public Affairs; this is a video version of that presentation.

The insights from that session were also published in a report, "AI Slop and the Information Ecosystem," which Columbia's Institute of Global Politics has made available to anyone, you can find that at the link.

In an effort to better organize and update my website, I've also uploaded the video to the (free) online course offerings, which I am now calling "Thinking Critically About AI Images."

You can click the card below to access the full course talks about the history of AI art, what makes generative AI different, and an overview of some of the issues including data provenance, surveillance, and more. It's a series of videos and transcripts, with guest speakers!

I've also added a short technical introduction to diffusion models based on a workshop I delivered for the metaLab@Harvard's Public AI Summit last summer (no connection to Zuckerberg's Meta).

Eryk Salvaggio: Critical AI & Culture | Thinking Critically About AI Images: Free Online Course — Cybernetic Forests.

Archived undergraduate class on the context, history and ethical issues of AI images, taught by artist and critical AI theorist, Eryk Salvaggio.

Hope you enjoy them or make use of them - if you're an instructor or educator please do use these materials in your classrooms if they're helpful! Please let me know if you do.

Where to Find Me

You can find me on bluesky, linkedin or instagram. You can also email me, and I'm always open to invited talks or writing opportunities.

Bernie Sanders has AI policy all wrong - Disconnect

2026-08-07T19:08:02.000Z

In December 2025, Senator Bernie Sanders became the first member of Congress to demand a moratorium on the construction of new AI data centers. The costs of the infrastructure that powers generative AI technologies like chatbots and image generators were soaring, but it was not clear that regular people were seeing much benefit. The ban was necessary, Sanders argued, to give “democracy a chance to catch up.”

Sanders could have also said that politicians needed to catch up to the public. Recent polling suggests more than 70 percent of Americans oppose the construction of data centers near their communities. It’s no wonder why: People have been seeing their power bills soar, high-voltage cables rammed through their properties, their water supplies sullied, and their days disrupted by the incessant hum of air conditioners keeping the buildings cool. It’s hard to think of another issue that has so quickly galvanized Americans across the political spectrum.

By March, Sanders had partnered with Representative Alexandria Ocasio-Cortez to introduce a bill proposing a national data center moratorium. Some states and city councils had already started to halt construction on their own, but the federal bill would ban construction “until legislation is enacted that safeguards the public from the dangers of artificial intelligence,” such as environmental degradation and soaring electricity rates.

For anyone concerned about the effects of these massive structures and their energy demands, not to mention the tech industry’s reckless acceleration of the AI industry, there would seem to be little to quibble with in such a proposal. Data centers are a material problem for communities across the country, and the generative AI tools they power have been enabling a growing list of harms that policymakers are not acting quickly enough to rein in—if they’re acting at all. But unfortunately, Sanders’s advocacy did not end there.

The same month as the moratorium bill was introduced, Sanders released a video that began to set off red flags among experts who understand generative AI and question the grand narratives being floated by AI companies. In the video, Sanders is seen sitting at a long boardroom table facing a smartphone. The screen displays Claude, the chatbot made by Anthropic, one of the biggest players in the generative AI space, along with OpenAI and major tech companies like Google and Meta.

Over the course of nine minutes, Sanders proceeds to have a conversation with the chatbot on issues like privacy, democracy, and whether Claude would back his proposed data center moratorium. The bot responds with characteristic informality, providing answers that confirm the direction of the questions Sanders is prompting it with.

While Sanders has done admirable work bringing attention to the issue of data centers, his conversation with Claude showed that the senator does not ultimately have a good enough grasp of AI—and that has consequences for how he approaches it.

Part of the problem is that Sanders’s policy approach to AI has been a direct response to the exaggerated claims made by AI companies about their products. By now you’ve probably heard many of them: that AI is going to eradicate vast numbers of jobs, will be societally transformative on a scale we’ve never seen before, and could even gain sentience and turn on humanity.

Sacrifice your job for the glorious AI future

How tech CEOs use the threat of job loss to distract from how AI is really used against workers

You can see this in Sanders’s rhetoric. In a New York Times opinion piece published in June, he opened by echoing the industry’s narratives. “Artificial intelligence will almost certainly be the most transformational technology in the history of the world,” he wrote. “It will profoundly affect the life of every man, woman and child in our country.” For Sanders, the focus has become about making sure workers are not displaced by the technology and that the public shares the wealth it creates, as he argues for a policy to put a 50 percent one-time tax on the stock of large AI companies.

But what if that is not the reality of generative AI? Chatbots and image generators are not the economic and social revolution that tech billionaires insist they are. In 2024, author Ted Chiang called them “autocomplete on steroids,” and that’s a good way to understand them. We’re dealing with AI models trained on immense amounts of data and using unimaginable quantities of computation to produce text and images that are designed to seem as though they come from humans, but are really just pulling from a vast repository to best match the prompt they’re served.

It’s exactly why the “hallucinations” cannot be engineered out of the technology. As OpenAI’s own researchers admitted last year, they’re a feature, not a bug, of generative AI. That’s because there is no understanding or thought happening behind the prompt box on a chatbot. It’s just looking for the parts of its dataset that best match the query it’s given.

There’s also growing evidence that the supposedly transformative productivity and efficiency gains AI companies promised are not materializing. Businesses are in the process of pulling back on their AI spending because they aren’t seeing a return on investment, and it’s quite clear that in many cases bosses are using the excuse of AI to lay off workers so they can cut costs—not because AI is really replacing those jobs.

The real risks of AI are not the existential ones that people like Anthropic CEO Dario Amodei obsess about and that Sanders has taken to repeating. Instead, they stem from the more tangible effects AI has on regular people’s lives: how dependence on chatbots can affect cognition and critical thinking, create addiction that isolates people from their human networks, and can even coach them down harmful paths of self-harm and suicide. That’s not to mention how AI has polluted the information environment, enabled the creation of nonconsensual deepfakes, and is actively degrading cultural production.

Sam Altman’s anti-human worldview

OpenAI CEO downgrades humanity in pursuit of goal to merge with computers

While she may have been an ally of Sanders on the data center moratorium, Ocasio-Cortez does not seem to have fallen for the deceptive narratives of the AI industry in the way that he has. In November, she warned about the threat of AI psychosis and of the economic damage that could follow the bursting of the financial bubble around AI. She has also been a leader in the effort to give AI deepfake victims the tools to hold perpetrators to account.

In my view, Sanders and Ocasio-Cortez demonstrate two distinct approaches to AI policy motivated by very different understandings of the technology. Sanders is responding to the narratives floated by the industry, narratives that exist to exaggerate the capability of its products and distract from the real material effects the public is feeling from their rollout in the here and now. Ocasio-Cortez, on the other hand, is listening to experts who really understand how the technology works, and to victims who have felt those harms firsthand.

With data centers wreaking havoc across the United States and the wider world, a moratorium on their construction is essential. But responding to the generative AI applications they power requires properly understanding their real harms, not believing the deceptive arguments fed to us by the industry. We need policy that follows the evidence, not the hype.

This piece was originally published in The Nation.

The Odyssey as tech criticism - Blood in the Machine

2026-08-06T18:23:26.000Z

Greetings all,

I’m back in the states and about ready to get back to my regularly scheduled newslettering and podcasting. But first, I regret to inform you that I have some thoughts on the Odyssey. Yes, all the Odyssey discourse got to me, and during my vacation last week and I picked up a copy of the book (the old EV Rieu Penguin Classics translation, the only one the bookstore had left in stock). Then I downloaded the audio book so I could listen to it, as Homer intended, ish, and before long I realized I was essentially all-in.

The New York Times ran an op-ed called something like Why Can’t We Stop Talking About the Odyssey? (They keep changing the headlines in A/B testing but that’s what it was when I first saw it.) And my immediate reaction was welllllllllll. Here is one of the two earliest canonical works of human literature, originally composed and retold orally as an epic poem, from person to persons, made into a $300 million Hollywood blockbuster by the industry’s biggest auteur (and tech critics), at a moment when proclamations boom out that AI threatens to consume and/or subsume culture, when our politics are a furious mess, in part because so many of us no longer share overlapping standards of truth or belief, and the fight to define either is a neverending livewire spat on platforms writing our own oral history in realtime. Of course there would be a fight over what the Odyssey can mean, and what it can’t, where it fails and where it succeeds.

I finally saw the thing this week, and I enjoyed it more than I thought I would. Granted, my expectations were low. I disliked Christopher Nolan’s Oppenheimer; it seemed to me to be too precisely the sum of its parts—the Batman director makes a dark superhero film about the atomic bomb—but Odysseus isn’t too too far off from a Batman, and this is more amenable to Nolan’s wheelhouse. (They both make up for their physical deficiencies with wealth, notoriety, and cunning, they are both riven with contradictions, and both have a penchant for grandiosity and violence etc etc etc.)

But of all the critiques and counter-critiques and counter-counter-critiques I’ve seen the one I’ve somewhat surprisingly seen the least amount of ink spilled over is the Odyssey (2026)’s rather central and yawning critique of technology.

TO THE EXENT THAT YOU CAN SPOIL AN ADAPTATION OF A 3,000 YEAR-OLD EPIC POEM, SUCH SPOILERS FOLLOW

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The Odyssey screen still.

There’s been lots of commentary about how Matt Damon’s Odysseus, who is wearied, laconic, and downtrodden throughout the movie’s 3-hour runtime, is a comment on the fallout of America’s war-making in the Middle East, and the decay in the liberal order it begot, or the crumbling of liberal values more generally. (Damon’s grim portrayal of Odysseus is also a source of criticism, since the epic’s Odysseus is cunning, talkative, and full of braggadocio in addition to being prone to bouts of hysterical weeping.) But less about the specific instigator for all that misery.

Near the end of the film, Odysseus reveals what appears to be the source of these many character years/viewer hours of guilt-ridden sorrow: In devising and unleashing the Trojan Horse, an innovation that exploited Troy’s inclination towards trust and hospitality and the fact that they respected the same gods, in order to facilitate the demolishing of the entire city, he was "breaking Zeus’s law forever.”

“Zeus’s law” is a Nolanian contrivance that basically amounts to a Grecian golden rule; treat others, especially strangers and travelers, how you would like to be treated. It’s a take on xenia, an actual Mycenaean concept.

The historian Spencer McDaniels explains:

Xenía held that every person had a sacred and inviolable duty to provide safety, food, shelter, and comfort to any stranger or foreigner who sought hospitality. Guests, in turn, had an equally sacred obligation to honor and respect their hosts and to not take more than they needed.

It’s a social contract, then; a shared set of values that Odysseus comes to believe he has destroyed utterly by inventing and successfully deploying the Trojan Horse.

Nolan’s Odyssey astutely picks up on the importance of xenía in the epic and takes it in an interesting, contemporarily relevant direction. Translating xenía as “Zeus’s law,” the film portrays the breakdown of xenía as a cosmic consequence of the sack of Troy that will lead inevitably to the collapse of civilization itself.

Not unlike, perhaps, building and dropping a nuclear bomb?

Ezra Klein produced a long video essay arguing that the Odyssey is the most important political film of the decade, that it’s an exploration of the collapse of the liberal order and how, in these days of politicians spurning their neighbors and conducting mean-spirited attacks, so many no longer feel the need to be good to each other. In this reading, the Trojan Horse, an innovation already legendary in Odysseus’s world, is then the key catalyst.

Now, Nolan’s disdain for modern technology is well known. He doesn’t own a smartphone, or even use email. And in a recent interview, Nolan shared his thoughts on AI, too:

“I think AI is a Trojan horse that everybody knows the Greeks are inside… I’ve never seen a technology advancing so rapidly [that’s been] so completely rejected by the public,” he said. “Everybody’s suspicion of it is so extreme, particularly young people. The reaction to AI videos online and people my children’s age immediately calling it ‘AI slop’ and coining that term and just putting it in a box.”

I think it would be a stretch to argue that Nolan’s Odyssey contains any nuanced or meaningful critique of AI in particular. The tech criticism in the Odyssey, such as it is, is about as broad as it gets: When novel technology is used to destroy shared social values, it begets disaster for civilization. It’s the kind of sweeping criticism you might expect from a man who doesn’t have email, in other words, or who hopes his blockbuster will be seen by as many people as possible.

That’s certainly not to say such a sweeping critique is without value; Nolan certainly wants us to consider that the building of Trojan Horses is a deliberate act with obvious moral parameters. And that applies to Greek weapons of war, nuclear bombs, and AI products alike. (The director has noted in interviews that he couldn’t help but draw parallels between Oppenheimer and Odysseus in filming his latest.) The issue is that this makes for a pretty vague critique, and one that doesn’t really need to meaningfully engage with particulars, theory, or root causes beyond ‘sometimes men go too far.’

Interestingly, another top-grossing movie of 2026, Toy Story 5, registers a not-dissimilar brand of tech criticism; screens are bad for kids, we see, but only, it turns out, when they are too much, and when they take over kids’ imaginations and facilitate toxic behavior. Older tech toys for kids with less immersive screens are lovable and good, but somewhere a line was crossed from a healthy human coexistence with technology to a calamitous one, though that movie chickens out in identifying when or where that happened, exactly.

In this case, then, we must consider that the line was crossed thousands of years ago. Why not? The Odyssey was recited orally for centuries before it was written down, which itself was a famous act of technological transgression. Plato, writing centuries after Homer, told the myth of the Theuth, the god who had invented writing, and the Egyptian king Thamus, who played the role of the critic.

When it came to writing Theuth said, ‘Here, O king, is a branch of learning that will make the people of Egypt wiser and improve their memories; my discovery provides a recipe for memory and wisdom.’ But the king answered and said… ‘If men learn this, it will implant forgetfulness in their souls; they will cease to exercise memory because they rely on that which is written, calling things to remembrance no longer from within themselves, but by means of external marks. What you have discovered is a recipe not for memory, but for reminder. And it is no true wisdom that you offer your disciples, but only its semblance, for by telling them of many things without teaching them you will make them seem to know much, while for the most part they know nothing, and as men filled, not with wisdom but with the conceit of wisdom, they will be a burden to their fellows.’1

It’s worth noting that the original Homeric Odyssey itself is generally optimistic towards technology, albeit in a somewhat misanthropic mode. ( notes that Theodor Adorno and Max Horkheimer argued Odysseus and his crew ‘embody the principle of the capitalist economy’; a band of men set against the world and other men. Entrepreneurs, even. If Nolan’s Odysseus is more like a Viktor Frankenstein, plagued by what his inventions have unleashed on the world, then Homer’s is more the kind of entrepreneur we’re familiar with; headstrong, fast-talking, basking in the glory of his inventions one minute, complaining hysterically the next, with little time for reflections on the social impact of his devices.)

Throughout the epic, Odysseus’s efforts to get home are of course routinely thwarted by very human failures: his own hubris (screaming taunts at Polyphemus), his men’s meddling (opening Aeolus’s ox-skin bag of wind in sight of Ithaca), and the violent opposition of his foes. The way back, finally, then? A self-driving ship that can sail without the interference of failure-prone human hands. Odysseus’ last stop before finally making it home is at the palace of the Phaeacian king Alcinous, who promises him passage on such high-tech vessels:

For the Phaeacians have no pilots; their vessels have no rudders as those of other nations have, but the ships themselves understand what it is that we are thinking about and want; they know all the cities and countries in the whole world, and can traverse the sea just as well even when it is covered with mist and cloud, so that there is no danger of being wrecked or coming to any harm.

*Bonus points for anyone who knows the ancient Greek word for pilot or helmsman of a ship. It’s Kybernētēs, the word that the mathematician Norbert Wiener appropriated to describe cybernetics, how systems and machines could use feedback to regulate their behavior, to stay on course, and the reason that ‘cyber’ is in the lexicon today.

Embedded in one of the ur-texts of literature, then, we have an articulation of desire for automation, for a mechanized system that might replace flawed human judgment and labor, and transmit the wishes of the helmsman, ie the entrepreneur, into action. It could carry this man, he of given to “scheming” and “nimble wits,” who years earlier used his ingenuity to raze another human city to the ground, home. So the Odyssey, which I should add has genuinely been a blast to read, essentially describes the 3,000 year-old ambitions, or at least their seeds, that Nolan uses his adaptation to critique.

There is something to be said of the fact that our biggest entertainments have become so tech skeptical. Of course, for a wholly successful critique of technologies like AI you would have to name the forces inside the Trojan horse; the AI executives and Fortune 500 companies selling mass labor automation and promising to replace human art with cheap slop, the movie studios that have partnered with them, and so on. The technology is not spilling out into Troy on its own, unguided.

Reading the original, I daydreamed a bit about someone performing the epic the way it was originally intended; orally, on stages, in theaters or at festivals, over long hours. I thought about a performance of Gatz I saw years ago, an 8-hour play put on by the New York theater ensemble the Elevator Repair Service, about an office worker discovering a copy of the Great Gatsby in a drawer and then proceeding to read, in real time, every line of the book. About halfway through the play, the actor reading the text looks up and continues reciting the story from memory. The audience gasped. He finishes the entire book, and the play, that way. A performance like that—recited aloud, performed by human voices, from memory—today feels almost like it would in and of itself constitute an act of resistance against the AI onslaught and the types of broad, immoral forces Nolan conjures in his epics. I’d be first in line.

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Alright! That’s it for today; thanks for bearing with while a took a few days off, even if those days off wound up being less of a “relaxing during a short vacation” vibe than “the whole family got e. coli poisoning from swimming in a bacteria-infested bay” but hey, it takes all kinds. At least I had Odysseus to keep me company. See you all next week.

Our concern today is not that we won’t remember written texts, but that we’ll be wholly unfamiliar with them in general, as they come spilling out of generators at the press of a button. An MIT study found that most writers who used AI to produce essays couldn’t identify a quote from their own papers.

A more wholesome algorithm? - Disconnect

2026-08-05T19:50:42.000Z

By this point, I’m sure ~ the algorithm ~ knows I’m not a big fan of, well… it. Not to mention everything it powers. And it seems to be trying to serve me content that recognizes that.

I’m not a big Instagram user, but recently it started serving me content that seemed to echo my feelings about digital technology.

In a video posted by livefromthetrees, an older man with a bushy white beard and an inviting smile let me know I’d scrolled far enough. I was done for the day, could put my phone down, and get on with my life — preferably by going out into nature. It’s a wholesome video, and brought a smile to my face.

My Instagram feed isn’t all that bad anyway. It’s a lot of golden retrievers, people with tattoos, Lord of the Rings content, and the occasional more lefty comedian (think people like Gianmarco Soresi). As I watched these videos of the old man in the woods, I started being served up similar videos. One featured a grandma asking for negative thoughts, dispeling them one by one, then batting them away with a baseball bat.

I wondered if I played these videos enough whether the algorithm would fill my feel with more of this uplifting content, but I also wondered how social media would feel if we saw more content like that than the polarized posts and AI slop increasingly filling people’s feeds. I don’t want to know everything about these people’s lives, but the brief moment they entered my world brightened my day.

Don’t get me wrong; I don’t think training an algorithm is the fix to our social media woes. But it also doesn’t have to be what people like Mark Zuckerberg have made it — prioritizing ad profits and engagement time over conviviality and actual connection.

Now, to this month’s recap. First, a very cool update on the book front: Hyperscale got a great review from Publisher’s Weekly. “Timely and persuasive, this revelatory account is difficult to ignore,” it says. Don’t forget to preorder your copy to get the full story on the data center boom, the communities it’s affecting, and why it doesn’t have to be this way.

Over on Tech Won’t Save Us, I also did some great interviews this past month. Thomas Dekeyser was on the show to unearth the history of people refusing new technology, while Hannah Cyrus and Alison Macrina explained how librarians are on the front lines of informing the public about technology (and helping them ditch AI). Check them out if you missed them!

The top 3 stories in this month’s recap look at the Summer of Ludd, snail-mail subscription clubs, and writers changing their styles in response to AI. Plus, find more important reads, and updates on labor, AI, data center news, and even more!

The full recaps are a special perk for subscribers who support my work. Thank you to those who’ve signed up, and if you haven’t, consider joining today!

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I hope your summer (or winter) is going well!

— Paris

Names, Sabbath, Universities, Bookstores, etc. - The Convivial Society

2026-08-03T05:16:05.000Z

Welcome to the Convivial Society’s Commonplace Book. As you know, this is an experiment, and we’ll see how it goes. Hopefully, you’ll find something of interest, but if you decide that you would rath…

Elon Musk’s tired schtick - Disconnect

2026-07-27T16:24:31.000Z

Civil war is coming to Britain, at least according to the world’s first trillionaire, Elon Musk. But in a new interview with The Economist, he declared there’s good news for the struggling nation: it will take 20 years for it to descend into civil war, but the AI and robot singularity is 10 years away. In short, the robots will have taken over, meaning the abundance they bring will negate any need for pointless squabbles.

There was a time when media figures, investors, and virtually anyone with much influence — not to mention all the relatively powerless people watching the world turn around them — would hang on the every utterance or musing of someone like Elon Musk.

Self-driving cars were constantly just around the corner. Trains in vacuum tubes were going to carry passengers between Los Angeles and San Francisco far quicker and cheaper than high-speed rail. Tunnels for cars were going to negate the need for public transit. And a Mars landing was just ten years away — in 2011.

The 2000s and 2010s were a moment when a select group of men — and most of them were men — were treated as unparalleled geniuses as they lucked into riding an economic rocket created by the internet’s privatization into the stratosphere of wealth and power. But they couldn’t simply be treated as lucky; they had to be recast as uniquely talented and knowledgable to justify the gains our capitalist system was foisting upon them. None benefited from that more than Elon Musk.

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I would never deny that Musk had some skills, particularly in those early days. He did have some technical knowledge and seemed genuinely curious about the engineering dimensions of electric vehicles and rockets. But that does not mean he was a genius engineer. His skills were more in the realm of knowing how to package what he was doing to sell it to investors, to a media desperate for readers or eyeballs, and to a wider public disillusioned in a neoliberal reality where their lives keep getting worse.

Musk sold the illusion of progress, by repackaging science fictional fantasies as realities that could be achieved if only people like himself were showered with resources and power to bring them into being. In the process, his repetition of very basic facts about his industries tied up with those fantastical futures earned him a reputation as a visionary — one media around the world were happy to echo, even as he spread bullshit, showed himself to be increasingly vindictive, and more frequently made claims that were demonstrably false.

One has to wonder whether a new Elon Musk could actually rise in 2026, in an era of great skepticism of the tech industry and the exaggerated promises it makes to justify the investment that floods into it. Even more, it’s increasingly baffling how the media still acts as though what Musk has to say is really worth listening to.

Shame on the Elon enablers

Media and politicians created a monster and lost control of him

In his interview with The Economist, editor-in-chief Zanny Minton Beddoes vacillates between pushing him on issues like the DOGE cuts or how investors will actually make money on AI, to agreeing with him on a whole host of issues, including at times his mischaracterization of the political positions he pushes and the outcomes that countries are experiencing from them.

Musk demonstrates very clearly that he creates his own reality. It’s one where DOGE cuts to USAID did not result in any deaths, and that if they did it’s actually Bill Gates or MacKenzie Scott (formerly Bezos) who are to blame for not stepping in to fund the programs being cut. It’s one where the far-right political movements Musk supports are actually just normal people being slandered by hostile media, and Musk’s own politics are centrist, or where Europe is actually a much more dangerous place than the United States because of all the Muslims immigrating there — despite the stats very clearly showing otherwise. It’s one where humanity is about to become the pets of a superintelligent AI that will create “amazing abundance” and render money useless, negating the need for Musk to pay more tax.

Beyond showing the richest man in the world is completely out to lunch, the interview should lead any right-minded person to question why the media continues to engage him in this way. He clearly despises them, despite relying on them for some degree of credibility, while actively using his social media platform and broader influence to try to make people mistrust them so they place more trust in people like Musk himself. Part of the answer lies in the scale of Musk’s influence.

The media continues to turn to Musk for the same reason he remains so wealthy and his companies remain so highly valued despite their poor performance. Chinese auto companies are eating Tesla’s lunch, as its robotaxis are a joke and its turn to robots is little more than a marketing stunt (once again, in the face of Chinese leadership), while his rocket company remains unprofitable and his AI efforts are second rate, at best, leading SpaceX/xAI to become a neocloud provider for Anthropic — not the major player that aligns with the forward guidance that was supposed to justify the SpaceX IPO.

The companies don’t crash because investors have simply put too many eggs in the Elon Musk basket. If he collapsed, they would lose billions, so his myth — or at least his position — must be maintained regardless of the economic reality of his companies. That ensures he remains powerful and wealthy enough to wreak political havoc and muse about unattainable sci-fi futures, despite not delivering on his promises to investors.

For media, it’s much the same: they’ve spend so long helping elevate him to this position, they can’t give up on him. No matter how many racist statements or fascist calls he puts out on social media, they’ll still publish headlines about his latest bullshit prediction devoid of the broader context about how many times he’s lied or failed to follow through in the past, not to mention his embrace of far-right political movements.

Elon Musk is a fascist

The richest man in the world intends his far-right project to go global

Elon Musk, in a way, is too big to fail. It doesn’t matter how meaningless what he has to say becomes, how poorly his companies perform, or how much political havoc he incites, he still gets to be taken seriously. The media could turn to so many people to get better insight — or even similarly unhinged musings — but those people haven’t been put in the position that Musk has, so their equivalent or better insights don’t have the same importance.

The interview put The Economist in the conversation and allowed its editor-in-chief to do a media tour of her own to talk about it, calling him an “extraordinary individual” and treating his brain farts about the future as things people should be taking seriously. The whole thing was a shameful display, ending with her declaring she is “not a Musk hater.”

Meanwhile, over on X, Musk was calling her a “traitor to the West” and an “unthinking NPC” as he continued echoing a far-right meme claiming they must “deal with traitors before invaders” — an explicit call to violence against people like Minton Beddoes. It’s long past time for establishment media figures to wake up and stop treating Musk like the character they built him up to be for so many years. But their interests still seem too aligned to be honest that the richest man in the world is a repugnant fascist who got this far with their help.

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