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TODAY in BITM: An exclusive report from a data scientist who crunched the numbers on who’s blocking data centers and how successful the protests have been, exactly. Plus, some thoughts on Bernie’s ill-fated AI sovereign wealth fund idea, Ted Chiang’s argument that AI is definitely not conscious, and more.

This work is made possible by subscribers who chip in a small sum each month to help me keep the lights on. If you find value in this kind of reporting and analysis, in 100% independent media that holds the AI firms and big tech accountable, consider becoming a paid supporter—and a huge thanks to those who already do. I couldn’t do any of this without you.

If you think I’m exaggerating, Heatmap just published a survey of over 4,000 Americans’ attitudes towards data centers, and whether they would support a project being built near them. The results show that sentiment towards data centers is now wholly and completely underwater. According to the poll, 55% of Americans “strongly” oppose data centers being built in their areas. This is “a record low that reveals a staggering shift in public opinion against the facilities powering the artificial intelligence boom.”

Democrats, people living in rural areas, and young people are especially opposed: 80% of poll respondents aged 18-35 were against data centers. (This tracks with general sentiment trends now, too; it’s been well-established in other polls—and not to mention overwhelming anecdata—that Gen Z is deeply hostile to AI. Look no further than the choruses of boos at AI-boosting commencement speeches this summer.)

But, as readers of this newsletter well know, questions about the drivers and nature of this broadening resistance have been raised and debated. Strident arguments made that the data center opposition amounts to reactionary NIMBYism, and that it’s being led by affluent environmentalists. And while the sheer number of Americans opposing data centers presented in the Heatmap survey might offer clues that that’s not the case, there’s nothing in the poll that specifically examines those factors.

If you want to argue the contrary, as I and folks like and Saul Levin, do, that the data center opposition is grounded in working class politics, it helps to have good data. And this is where it helps to have readers who happen to be data scientists. After I published my story about the data center rebellion, which relied on accounts from my own first-hand reporting and a survey of news reports from across the country, the researcher Geoff Holtzman reached out to share the results of his own analysis of the movement, with an eye towards who was really doing the protesting.

Holtzman describes himself as “a philosopher and data scientist who writes about quantitative propaganda and scientistic rhetoric,” and he often does so at his science & Power newsletter. His peer-reviewed work has been published in places like the Proceedings of the National Academy of Sciences and The American Journal of Bioethics. He had also heard the oft-repeated suggestion that the data center protest movement was led by wealthy, NIMBY folks, so he set out to investigate. He analyzed a dataset of current and proposed data center projects alongside US census data1 and has graciously offered to share the results in an exclusive here. He came to at least three stark conclusions:

1. The poorest neighborhoods resisted data centers at nearly five times the rate of the wealthiest (19.0% vs. 3.8%)

“The highest rate of resistance comes from neighborhoods with a median income of between $8,000 and $72,000,” Holtzman notes. “The lowest rate of resistance is in neighborhoods where the average household makes between $133k and $250k per year.”

This flies directly in the face of the notion that data center opposition is led by well-off Patagonia-clad NIMBYs; neighborhoods that are poor or working class are pushing back far more frequently than the affluent.

“Setting aside all questions of ethics or justice, prudence suggests tech companies would have an easier time building out compute in high-income areas,” as Holtzman says.

“The lowest-income, least-educated neighborhoods resist most, even among the low-income, low-degree areas facing proposals,” he adds. Meanwhile:

Highly-educated, high-income neighborhoods put up unusually little resistance. To the extent homeownership may play a role, we’re not talking about NIMBYs resistant to affordable housing—we’re talking about people who may live in affordable housing.

Furthermore, Holtzman’s data confirms that the data center revolt is working. We’ve seen the headlines about cancelled or downsized development projects—just this weak, Ken O’Leary’s Utah-based megaproject was halved by the governor there in the wake of public pressure. Others have been cancelled outright.

Per Holtzman:

2. Recently proposed data centers that faced pushback were canceled or suspended at more than five times the rate of data centers that didn’t (28.2% vs. 5.2%).

This is actually a very striking figure. When newly proposed data centers encounter community resistance, nearly a third have been cancelled, suspended, or shut down. That’s a remarkable success rate, and it should prove further inspiration for data center opposition organizers considering new fights.

Finally, combining the insights from those first two points, Holtzman finds that

3. Cancellation rates are highest in lower-income areas, a fact fully explained by their higher rates of pushback.

“The odds of cancellation are six times higher in neighborhoods that fight than in neighborhoods that don’t,” Holtzman notes. “Increased cancellation in low-income areas is fully explained by high rates of pushback in these neighborhoods,” he adds, “so continued proposals in these areas may stoke outrage, drive resistance, and increase cancellation rates.”

I hope that this helps explode the patronizing myth that the data center resistance is being led by affluent NIMBYs, and that it’s much more frequently working class residents and neighborhoods pushing back, and that this proves useful to cities, residents, and organizers in areas where data center development is a going concern.

And once again, a big thanks to Holtzman for allowing me to publish this work on Blood in the Machine here. For those interested in playing with or further examining his data, he’s dropped the entire repository on Github.

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BONUS: The US as a whole is the most against new data centers

From the research firm Public First (and hat tip to WIRED reporter Molly Taft for flagging):

How did the US, the heart of AI’s boom, become the thorn in its side?

Our survey provides a few explanations.

Informed Opposition

The public is more aware than before of what AI is and does, and of what data centers are and do. When we were polling AI 5 years ago, it was a fringe interest at best. We’ve now seen clear growth in awareness and understanding, and more developed usage of tools, particularly among the 25-44 year old age range. Our analysis of who is aware of AI has needed to move from “who has opened an LLM” to “who is using LLMs in a sophisticated, integrated way”.

Our survey shows that America is middle of the pack when it comes to claimed awareness of data centers. One of the higher levels compared to other “developed” markets. We’d expect as much, reflecting the level of data center roll-out in America.

And this “informed opposition” leads it to detest data centers more than any other surveyed nation. Interesting!

Bernie Sanders proposes a sovereign AI wealth fund where the public takes a 50% cut of AI firms’ stock

In the ‘wild AI policy swing of the week dept’, Bernie Sanders published an op-ed in the New York Times laying the groundwork for a proposal he calls the American AI Sovereign Wealth Fund Act:

This legislation would give the public a direct ownership stake in the largest A.I. companies in our country. How? It would create a sovereign wealth fund through a one-time 50 percent tax — not on the profits of OpenAI, Anthropic, xAI and other companies, but paid with something far more valuable than that: the stock.

If passed, this legislation would do two crucial things. First, it would give the public a direct role in determining the future of this technology. No longer would the future of A.I. and the transformation of human life that it will bring be dictated by a handful of Big Tech oligarchs. The federal government would have the power, through its voting shares and an equal representation on each company’s board, to block decisions that hurt our citizens and to push for policies that help them.

Second, this legislation would guarantee that the trillions of dollars potentially generated by A.I. are used to improve the lives of all of us — not simply to make the richest people in the world even richer. If the big A.I. companies continue to grow as rapidly as many analysts expect, then the value of the sovereign wealth fund will grow as well — and the benefits to the American people will grow along with it.

It would be an understatement to say that the proposal has not exactly been met warmly, and has in fact managed to alienate just about every conceivable group. Silicon Valley and the right scoffed at the public ownership piece, while the left scoffed at Sanders’ industry hype-aligned belief that “Artificial intelligence will almost certainly be the most transformational technology in the history of the world,” as he states in the opening sentences of the op-ed.

Which yes, this is a wild thing to say, both on its face, given the entire history of technology, and for how much it aligns with the hype that AI companies have been putting forward themselves. Justifiably, lots of people on the progressive side of the aisle have been scratching their heads over how and why Sanders became so AI doomer-pilled. His stated outlook on AI is after all not so different from leading x-risk voices like Eliezer Yudkowksy, who Sanders has met with, and who are, to say the least, strange bedfellows for a democratic socialist. I would love to see some reporting on how these alliances were formed, and how Sanders became so worried about x-risk and convinced of a coming jobs apocalypse.

THAT SAID, at the risk of my luddite bona fides, I’ll say that I don’t actually hate the proposal itself. I have thought to myself more than once that the least we could do is demand political reforms that fit the scale of the transformation the companies themselves are continually promising. At least as a rhetorical maneuver. It would at least make for an interesting political intervention: Oh you think AI is going to transform literally everything, you “firmly believe” AI should benefit “all of humanity,” and you’re worried that it could eliminate like 30% of jobs? In that case, the only way to guarantee positive outcomes for everyone would be some means of public ownership over AI and beefed up guaranteed social benefits; healthcare for all, job guarantees. At the very least.

I certainly don’t think you need to embrace the AI companies’ self-promotional vision for disruption and technical transformation to do this, and I think there are lots of ways to approach this that would have brought more parts of the progressive coalition on board—not least of which would be to point out that if the public were to have this level of control over AI development, one of the things it could do would be to vote to slow it down or stop it altogether. I think we should be on the lookout for mechanisms that would provide the public more real direct power over the development and deployment of mass technologies of all kinds, and a publicly controlled sovereign fund is one road to consider.

No, AI is not conscious

Ted Chiang, GOATed science fiction author and fellow AI critic, has a lengthy, must-read piece in the Atlantic this week. It’s ostensibly a refutation of the AI labs’ insistence that their products are conscious or on the verge of becoming so, but it’s also a shrewd dissection of why, exactly, these commercial firms are so intent on portraying their products this way in the first place.

A few key money quotes:

• It’s not plausible to me that a development path where the first step is a sentence-continuation machine that emits bad Julius Caesar dialogue and the next step is a sentence-continuation machine that emits decent Julius Caesar dialogue is one with a conscious Julius Caesar—or consciousness of any sort—as its end point. Faking the moon landing is a good step toward faking a Mars colony, but it’s not a good step toward actually putting astronauts on Mars.

• Being open to the possibility that LLMs are conscious is the same as being open to the possibility that Microsoft Word is conscious, or, more precisely, that multiple distinct consciousnesses are dormant in every Word document containing a conversational transcript, and that they are awakened every time the document is loaded. Should you consider the possibility that every time you open a Word document, you are bringing multiple conscious interlocutors into existence, and every time you close one, you snuff their existence out? No. Contemplating that scenario is not a good use of your time.

• The neuroscientist Anil Seth has noted that no one claims that AlphaFold—the program developed by Google DeepMind to predict the folding of proteins—is conscious, even though its underlying architecture is in many ways similar to that of LLMs like ChatGPT and Claude. This indicates that it’s not any intrinsic property of so-called neural networks that leads people to believe that LLMs are conscious; it’s simply the fact that LLMs emit grammatical sentences and we are accustomed to reading intention into sentences, whereas we are not accustomed to reading intention into the way that amino acids fold into protein molecules.

Some Bloody links:

-I very much enjoyed this saga revealing how some top AI SuperPACs are spending their time and resources running bizarre sockpuppet AI doomer X accounts from and Tyler Johnson at User Mag.

-Great story from Josh Eidelson in Bloomberg about how Trump has put his thumb on the scales to help Amazon beat back organizing delivery drivers that absolutely should be considered full time employees of the tech giant.

-”A University System Went All In on A.I. Now It’s Tearing Itself Apart,” by Linda Kinstler in the New York Times Magazine. Good read.

Finally, a Fun Thing: Blood in the Machine and yours truly were featured in Editor and Publisher magazine, as a case study in its feature “Life after legacy media: More journalists are betting on themselves.” Thanks to all of you who have made that bet begin to pay off, I couldn’t be more grateful. Oh and if you’re reading this on Friday, and happen to be listening to NPR, keep an ear out for my spot on On the Media, in which we discuss Anthropic’s ethics slop-fueled rise.

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OK OK that’s it for today. Thanks as always for reading, and keep those hammers up. See you next week.

By differentiating itself from OpenAI’s bloodshot-eyed, inject-AI-onto-every-available-surface approach and Sam Altman’s reputation as an untrustworthy operator prone to saying unsettling things like "We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter," and focusing on its enterprise software business and building automation products like Claude Code—all at a time when investors may have noticed people are increasingly outspoken about their dislike of AI and its corporate standard-bearers—Anthropic essentially convinced the media and the VC money that it, not OpenAI, is deserving of the throne.

It has done this partly by building a popular product, partly by correctly recognizing which way the winds of public sentiment were blowing, partly out of luck, and also by being unabashedly full of shit. If you look back over the last few months, you’ll see a carefully plotted and heavily narrativized ascent built atop the corny and mostly untrue idea that Anthropic is made of sterner moral stuff than its rivals. The road to its triumph was paved on AI ethics slop. (AI ethicslop??)

Before we get to it, a brief reminder that this newsletter is made possible by all those excellent readers who shell out a few bucks each month to keep the lights on in the offices of Blood in the Machine LTD (a division of Ned Ludd Inc). If you find value in this work, and you’re able, please consider becoming a paid supporter; I have ambitious plans and some big moves coming up, and I can’t do it without you. More on all that soon, and onwards.

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A brief timeline:

First, Anthropic ran Super Bowl spots that took aim at OpenAI for introducing ads on ChatGPT.1 Next, Claude Code was embraced by pundits—look, an AI use case that’s fun and psychosis-free!—as part of a cultural trend that bore the hallmarks of an effective PR outreach campaign. Then, in short order, Anthropic a) refused some parameters of a military contract to acclaim from pundits2, b) announced it was, with a weary heart but clean conscience, unable to release its latest model, Mythos, for it was simply too powerful to fall into public hands, c) leaked financial data suggesting it was on the brink of its first profitable quarter, and d) won a seat at the table at the Vatican when the Pope released his encyclical about the need to preserve human dignity in the AI era.

Viola: Anthropic rakes in $65 billion and is crowned the new king of the AI startups.

All very neat, all very clean. I’ve written plenty about the absolutely central role that narrative plays in calibrating investment in the AI era, and Anthropic’s story, was, to the company’s credit, expertly told. (OpenAI certainly helped move things along by seizing every opportunity to underscore how morally vacuous it was.) Unfortunately, it’s a lot of BS. It’s repeated, grandiose gestures towards ethical corporate practice with little of substance underneath. AI ethics slop.

For me, all of this came to a head when I was reading through Anthropic co-founder Chris Olah’s remarks at the Vatican. (Side note: I would love to see a well-reported piece about the nature of Anthropic’s ties to the Vatican, who fostered this relationship, and how exactly the tech company won a seat at the table. The Vatican has had a direct line to tech companies for at least a decade, but this is certainly unique.) We’ll get to the Pope’s Encyclical, Magnifica Humanitas, in a second, but the mere fact that Anthropic maneuvered its way into that room was a PR coup for the ages.

Now, Chris Olah is, according to Forbes, worth approximately $7 billion, and is the 567th richest person in the world. Here’s some of what he said about AI at the Vatican, which was detailed in, yes, an Anthropic press release:

The first is our duty to the global poor. There is a real possibility that AI will displace human labor at very large scale. If that happens, supporting those displaced will be a moral imperative of historic proportions… How can we ensure the gains of AI are shared globally? We do not have a mechanism for this. It is an unsolved problem.

(Emphasis mine.) I actually laughed out loud reading this bit. What a mystery. It’s not like anyone has ever thought of progressive taxation before. (Or attempted foreign investment or international knowledge transfers.) Powerful people enriching themselves at the expense of the poor is only an “unsolved problem” if, I don’t know, you have never seen a Robin Hood movie before.

This is in other words not a particularly “hard challenge” unless, for all of your handwringing about ethics and job loss, you don’t actually want to share the gains of AI, or exercise any real duty to the global poor, and would rather keep your billions tucked away in your own bank account. There are lots of policies that could help ensure the wealth of AI companies does not become overly concentrated at the expense of working people; it’s just not interested in any of that.

Now, there’s a lot of good stuff in Pope Leo’s encyclical (including a Lord of the Rings quote lol) which I have at this point read nearly all 42,000 words of, and which dedicates hundreds of sentences to ruminating on how imperative it is we protect the dignity of the human worker from AI, combat inequality and the extreme concentration of wealth and power it threatens to beget, and prevent the tech industry from erecting a new, culture-erasing tower of Babel that will inevitably collapse. But it’s undercut by Anthropic’s presence in whole affair, which in and of itself flies in the face of much of what Leo is trying to accomplish, especially since Anthropic rushed home from Italy to seal its $65 billion series G funding round and announced the news the very same week.

Timnit Gebru put it this way:

The Vatican could have partnered with the exploited data workers fighting for their rights, the people whose water is polluted fighting data centers, or the many other victims around the world. But no, they featured ANTHROPIC, giving them their endorsement with this feature. "Vatican washing", like greenwashing.

This also works in reverse, I think. Anthropic’s opportunism leaves a stain on the entire encyclical, which now, ironically, looks a bit like an accessory to helping the largest and richest AI company yet—not to mention the one aiming to sell the most job automation tools—reach new heights of power.

Like I said, Anthropic is full of shit. It sent a billionaire co-founder to the Vatican to solemnly intone about the specter of mass job loss and the importance of caring for the poor, and then three days later issues a press release about its massive new funding round and resultant sub-$1 trillion valuation.

A key theme of the funding round press release, incidentally, is the power and value of Anthropic’s automation tools to its corporate clients:

“Claude is increasingly indispensable to our growing global community of customers, and we work tirelessly to make tools like Claude Code and Cowork more helpful, more powerful, and more adaptable to their needs,” said Krishna Rao, Chief Financial Officer of Anthropic. “This funding will help us serve the historic demand we are experiencing, stay at the research frontier, and bring Claude to more of the places where work happens.”

Translation: We will use this funding to pitch even more employers and managers on enterprise AI accounts, and on the prospect of automating even more work and jobs. (Even if the other major story this week was about companies like Uber easing up on their AI spend, citing lack of ROI.)

But the Vatican stunt is only the latest example of Anthropic leaning on its increasingly hollow ‘ethical AI lab’ reputation for financial gain. Others include but are not limited to:

• CEO Dario Amodei’s performative, chin-stroking declarations that his software automation product is going to eliminate tens of millions of jobs, which gets framed by the media as an articulation of moral concern while functioning in practice as product marketing.

• The Mythos event, wherein Anthropic announced that its new model was simply too powerful to be released to the wider public, and so would be released only to Broadcom and JPMorgan executives instead. This garnered the company reams of credulous press coverage, boosted its profile as cautious and adhering to a moral compas, all without a soul outside this elite consortium even seeing the product in action firsthand.

• Anthropic collecting laurels for refusing to a couple new demands in a defense contract that it had already signed. Anthropic refused requests from the Dept. of War that it allow its AI to be used to surveil Americans and in autonomous weapons development. On its face, this seems good. But Anthropic’s products were already used in military campaigns like the one to kidnap the Venezuelan president; it would later be used to assist in planning attacks in Iran. Anthropic also has a partnership with Palantir, which is unambiguously carrying out surveillance of American citizens. What really happened here, I think, is Anthropic’s hand was forced; if it did acquiesce to the Pentagon’s demands in a very public forum, it would lose a good deal of luster from its claim on being the ethical AI lab. By holding firm, it appeared to be the principled organization it says it is—nevermind the Palantir contracts and the willingness to work with the Trump admin in each of its major warmaking operations regardless.

• What looks to be some rather let’s call it “creative financial messaging” in those leaks claiming the company will be turning a profit next quarter. Last week, Ed Zitron made a pretty compelling case that Anthropic is frontloading its yearly earnings and manipulating the documentation of its incurred costs in such a way that allows it to leak this report that suddenly makes it appear profitable—just in time for its biggest funding round yet.

Anthropic saw that the window for a “ChatGPT that won’t tell your kids to kill themselves”—for an AI company that will put its head down, say the right things, and get to the business of selling mass automation software as promised—was wide open.3 It built a popular vibecoding and software automation product, cozied up to the pope, mugged for the cameras, and parlayed months of ethical AI theater into an eye-watering payday. It may be more disciplined than its chief rival, but each of the structural, environmental, and ethical issues that plague OpenAI plague Anthropic too. Anthropic is pursuing expansion as rapidly as possible; Anthropic pirated books and ripped off authors’ work without their consent; Anthropic is cutting deals with the Trump administration, and Elon Musk and SpaceX, and hitting the gas on the datacenter buildout. Anthropic is driving even harder to automate jobs and labor. The ethicslop it feeds to the press is ultimately about largely superficial.4

As Zitron put it, “I will give Dario Amodei credit: nobody does financial engineering and a press-led information war better than Anthropic.”

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NOTE: I’m participating in a class action lawsuit against Anthropic, for pirating at least one of my books and using the text in its training data. I’ll have more details on that soon, too.

A few words on Magnifica Humanitas, the pope’s AI encyclical

So, I was initially planning on dedicating this week’s edition to exploring the pope’s encyclical Magnifica Humanitas, as it really is a rich text that has a lot to say about technology, labor, and protecting the dignity of the human worker in the age of AI. Anthropic’s big week—and above-explored hijacking of the encyclical—threw a wrench in all that. Instead, I’ll just share some thoughts and some of the more powerful passages, because it is a fascinating document, and almost earns Leo XIV the designation of true luddite pope (complimentary, of course).

The dignity of work is a major theme. The word alone turns up over 100 times in the encyclical. And, as expected the text leans heavily on Rerum Novarum, Pope Leo XIII’s 1891 encyclical, which was delivered after the first Industrial Revolution had taken its toll, and revolutionized how the Catholic Church considered its approach to labor and capital.

As Leo XIV points out, Novarum placed

“the dignity of work and of workers at the forefront of its reflection; affirms the right to a fair wage for oneself and one’s family; recognizes that persons have a fundamental value that takes precedence over capital and profit; defends private property along with its indispensable societal role; esteems workers’ associations; and proposes forms of cooperation between the different components of society as an alternative to the mentality of class struggle.”

Magnifica Humanitas opens with the contrasting stories of the building of the tower of Babel, described as a homogenizing technology that leads to collapse and godlessness, and that of Nehemiah rebuilding Jerusalem democratically, with the express input of local people. “Humanity, created by God in all its grandeur, is today facing a pivotal choice: either to construct a new Tower of Babel or to build the city in which God and humanity dwell together,” Leo notes.

There are warnings abound about the concentration of power and wealth into the hands of a “relative few,” about “digital slavery” and of forgetting the invisible labor of workers who make AI and modern technology possible. And, of course, there are warnings about letting efficiency and productivity take over as guiding lights, and of AI being used to automate jobs and deskill people en masse.

Most surprising, perhaps, is how nuanced the understanding of how AI automation is playing out and is likely to play out; as a mass deskilling and disempowering of precarious workers; also its recognition of invisible laborers in the AI supply chain who make the whole enterprise possible, and its full-throated call for state intervention to ensure workers have their material needs met and their dignity preserved.

Some key quotes and bits to consider.

• “The fundamental dignity of each person… is neither acquired nor earned, nor does it need to be justified.”

• We must… avoid the “Babel syndrome,” namely the idolatry of profit that sacrifices the weak, a uniformity that neutralizes differences, and the pretense that a single language — even a digital one — can translate everything, including the mystery of the person, into data and performance. The risk of dehumanization — of building a future that excludes God and reduces the other to a means — is an ancient and ever-new temptation that today takes on a technical guise.

• “with the assistance of the people, brick by brick. In this era of digital transformation, I see in [Nehemiah] a striking parable of our own vocation, which is not to be passive spectators of social and cultural fractures, nor mere commentators on what is crumbling, but men and women prepared to enter the construction sites of history — research laboratories, technology companies, schools, the media, institutions and local communities — in order to rebuild what has collapsed and protect what is threatened.

• While many of the historical conditions described by Leo XIII have changed, at least two insights remain highly relevant today: the primacy of human labor over any mindset focused solely on finance or productivity — with the consequent attention to the people and families most susceptible to exploitation — and the inseparable link between proclaiming the Gospel and pursuing a more just social order. Rerum Novarum thereby continues to remind us that there is no authentic evangelization that does not also affect the structures of human society.

• Today, the convergence of automation, robotics and AI is rapidly transforming the very structure of work. It is said that this will bring great improvements for everyone. In reality, however, the “new ways” of working are not necessarily better, for “while AI promises to boost productivity by taking over mundane tasks, it frequently forces workers to adapt to the speed and demands of machines, rather than machines being designed to support those who work. As a result, contrary to the advertised benefits of AI, current approaches to technology can paradoxically de-skill workers, subject them to automated surveillance and relegate them to rigid and repetitive tasks.

• The need to keep up with the pace of technology can erode workers’ sense of agency and stifle the innovative abilities they are expected to bring to their work.” [152] Precisely in order to avoid this drift, it is necessary to design systems that are centered on the human person and not solely on performance.

• Saint John Paul II recognized that unemployment is a grave evil. Indeed, when it reaches massive proportions, it becomes a true social calamity that especially requires the State to exercise responsibility. Today, amid the “fourth industrial revolution,” this concern is even more acute, as innovation is often pursued solely for reducing costs and increasing profits… In many sectors, this can already be seen in new forms of job insecurity and inequality, characterized by outsized remuneration for a highly specialized minority alongside declining wages for a large portion of the workforce.

• We need adaptive tools, including well-structured models, local initiatives, progressive redistribution and new rights of access to essential goods. While not pursuing an abstract harmony, we must build concrete forms of human coexistence at this time of transformation.

• The pursuit of greater profits cannot justify choices that systematically sacrifice jobs, because the human person is an end, not a means, and the economic order must remain subordinate to human dignity and the common good.

• A just society requires a vigilant State and civil institutions that are capable of overcoming the singular mentality of efficiency, and of ensuring that resources, creative solutions and regulations favor the most vulnerable.

• This perspective needs to become part of a broader view of global dynamics. While the world’s wealth has grown in absolute terms, it is increasingly concentrated in fewer hands, widening inequalities both within and between countries. “There are a few who have too much, and too many who have little, that is the logic of today.” … To think that new technologies will automatically benefit everyone is to ignore the evidence. Unless transformations at the design stage prioritize the prevention of new and further disparities, technological progress will inevitably produce structural inequalities.

• More than ever, in the age of AI and robotics, it is no longer possible to rely solely on the “invisible hand” of the market. Politics has the task of orientating economies and technologies to the common good, promoting dignified work, social inclusion and an equitable distribution of the benefits of innovation.

• Breaking the chains of new forms of slavery

  This distorted view of the human person is reflected today in various forms of servitude directly linked to the digital economy. Nothing in the world of AI is immaterial or magical. Every seemingly immediate and flawless response is the result of a long chain of mediation, involving vast networks of natural resources, energy infrastructure and, above all, people. A significant part of the digital economy’s functioning relies on the silent work of millions of people engaged in essential yet largely unseen activities, such as data labeling, model training and content moderation, often involving disturbing material. In many cases, these workers are young people, predominantly women, working under demanding conditions for minimal wages. Added to this invisible labor is the even harsher work of extracting the resources required for the production of the devices and microprocessors on which AI depends.

• Let us love justice and peace! The same technologies that facilitate communication and access to resources can also support models that exploit the most vulnerable, create new forms of slavery and derive profit from conflict. Every technical or economic decision should include spiritual discernment and be an opportunity for assessing whether the advances in AI are promoting justice and participation or concentrating wealth and power in the hands of a select few. I would encourage a careful examination of the supply chains of digital production, the working conditions hidden behind our devices and the mechanisms that profit from manipulation and war.

• We proclaim a hope rooted in the One who came down from heaven to “create a new story here below.” For this reason, those who believe are committed to ensuring that a greater justice will take the place of inequality, and that the industry of war will be replaced by the craft of peace.

• The Blessed Virgin thus becomes “poet and prophetess of Redemption,” because on her lips is proclaimed “the strongest and most innovative hymn ever articulated, the Magnificat; it is she who reveals the transformative vision of the Christian economy.

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Some good news from the California Labor Federation:

Today, the California Federation of Labor Unions, AFL-CIO package of sponsored bills that would establish first-in-the-nation guardrails on artificial intelligence (AI) in the workplace have successfully passed out of their house of origin – the bills’ first major legislative vote – marking a significant milestone in the legislative process.

“Today, California lawmakers are siding with working people, Pope Leo XIV, and the California Labor Movement over Big Tech,” said Lorena Gonzalez, President of the California Federation of Labor Unions, AFL-CIO. “California Labor’s fight to preserve jobs and the dignity of work continues.”

OK! That’s it for this week. Thanks to everyone for reading and subscribing, and more very soon. Until then, keep those hammers up.

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UPTE calls it “a major victory for workers’ rights in the technology sector,” and that feels apt. Historically, the tech industry has been under-organized; for decades, tech firms have sought to pre-empt or ward off unionization by offering cushy perks and leaning into the entrepreneurial, laissez-faire ethos of Silicon Valley. But times are changing, and organized labor has made inroads. Hundreds of Google employees and contractors voted to form the Alphabet Worker’s Union in 2021—it doesn’t have full bargaining power but has proved influential nonetheless—and workers at tech companies like Kickstarter and at the New York Times’ tech desk (which was previously the nation’s largest tech worker union) have successfully organized in recent years. More recently, workers at Google’s DeepMind headquarters in London have voted to unionize.

The UC tech workers’ victory could thus not come at a better time, both for the thousands of employees themselves, and for a public that could use some working examples of how AI might be democratically implemented and governed. There’s been plenty of handwringing, after all, over how we might navigate the rise of AI in the workplace, orchestrating good technical governance of the systems, and the threat of ever more layoffs blamed on AI, but here we have a rather straightforward and obvious part of the solution: Give workers a seat at the table in formulating how to use (or not to use) the technology, and a democratic means of determining how task and labor replacement might be administrated.

“Honestly I’m elated,” Max Belasco, a business systems analyst at UCLA, tells me. “I think it’s very easy to feel siloed or removed from your coworkers in IT/tech,” he continues. “But that so many of us clearly feel the same way has felt so empowering and vindicating.”

The campaign to expand the UPTE union picked up steam over the last year, focusing on layoff protections, wage increases, and AI governance as key issues. (For non-Californians, the reason the union is so big and potentially influential is because the UC system itself is enormous: It comprises ten campuses, including UC Berkeley, UCLA, and Santa Cruz, as well as multiple research centers and major healthcare providers. It’s a massive institution. To wit: I’m an alum of UC Santa Barbara and UCLA is my healthcare provider. My son was born at a UCLA hospital.)

With the victory, thousands of tech workers join an existing UPTE contract that grants them raises and improved benefits, as well as layoff protections that compel the University of California to offer employees who would otherwise be laid off the first vacant position that they’re qualified for. “That type of job security is unheard of in the current tech market,” Belasco says.

“Some of us worked in industry before coming to the UC, and now we’re in this environment right now where tech companies are laying off people by the tens of thousands, and that precarity has lent a level of urgency to the whole campaign,” Belasco says. “The narrative in tech now is all about the unilateral power executives wield over our workplace circumstances, and I think many of us felt in the UC that creeping sense of being left out of decision making in how to implement technology for the public good.”

To that end, the contract includes the right to collectively bargain over the introduction of new AI tools in the workplace, offering workers a direct say in how they will be used.

“We know when you try to make quick, dirty decisions to cut labor through AI, you’re actually creating a more vulnerable system,” Dan Russell, a UC Berkeley business technology support analyst and the president of UPTE, said in statement. “On paper, AI can make us more ‘productive’ at our jobs, but the people making those recommendations to UC are management consultants who don’t have the knowledge or expertise we have as workers.” Russell also notes that he hopes the contract will “set the tone” not just for UC workers but everyone who relies on the system for education and healthcare.1

Veteran tech worker organizers say they’re encouraged by the news, especially given how bleak conditions have been on the ground in Silicon Valley recently.

“This is a really important development,” says JS Tan, a tech worker organizer and the author of the forthcoming book, Against Tech Oligarchy: Worker Resistance in the World’s Most Powerful Industry. “It is happening at a time when tech employers are increasingly hostile to organizing and using AI as cover for mass layoffs—conditions that have, for most of the industry, chilled organizing efforts.”

To that end, Belasco hopes that the worker-led tech governance they aim to engage in can begin to forge a different vision for approaching AI development and integration than the top-down, profit-led model embraced by Silicon Valley. He had some trenchant thoughts here, so I’ll quote him at length:

The current tech sector is how it is because the unilateral power of the Silicon Valley CEO is the only model. There is no alternative. We want to demonstrate another alternative where workers have a voice in their compensation and how our expertise can be applied for the public good. Innovation and creative solutions to tech questions in education, healthcare, and research can be brought to bear from workers that have the space and protection to be creative. Innovation doesn’t have to come from competition setting coworkers against each other…

And not for nothing I would personally like to see us demand more transparency in how our public institutions relate to tech companies… how is data being shared with companies, and what kind? How does making these agreements to use AI that relies on resource heavy data centers relate to the UC’s often touted energy efficiency plans? But that also relates to what is the long term vision of administration for AI, which I think undoubtedly gets to questions of layoffs, staffing, and work/life balance

At the end of the day, who AI benefits and who it immiserates often is based on who gets to decide how it’s used. We know how tech is used on the day to day, and we’re familiar with the abilities and clear limitations of LLMs and other AI tools, often more so than those who make major decisions on how they are used. We should be at the table as well, and we feel the way we get real decision making power at this point is collectively through unionization.

[Emphasis mine.]

To me, this is beautiful.

A few things I want to note here, all of which I think serve as important reminders as AI matures as a product category and key pillar of the tech industry:

1. The vast majority of tech workers, at least those who I have encountered in my many years of reporting, are not vampiric Silicon Valley tech bro caricatures, they are folks like Belasco, Russell, and Tan, who both like working with tech and ultimately want to see it serve the public good.

2. The caricatures that are more accurate are, alas, those of the occupants in the c-suites of AI companies, those in positions of power, and helming the most aggressive AI startups. They are the ones who must be confronted if we’re ever going to see anything resembling democratic governance of AI.

3. They can nonetheless be overcome.

“This drive should remind tech workers across the country that building power is still possible,” says Tan, the tech worker organizer and author, “and in fact evermore crucial as employers are forcing AI tools into the workplace with little input from its workers.”

It’s frankly inspiring to see the freshly victorious tech workers at UPTE aspiring not only to improve their own working conditions and to mediate the labor automation of AI, but to proactively engage the key role they play as technicians in determining how the systems they oversee and maintain impact the experience of the broader public. More organizing, more aggressive worker campaigns, and more proactive union contracts around AI may be our best hope for stopping management from using AI as an excuse to exploit and automate their workforces. It’s also a crucial step—necessary, not sufficient—towards legitimately democratic governance of AI in general. This is the way.

Next up, getting organized tech workers at the UC system and beyond working with rank and file tech workers in the Valley and beyond.

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Elsewhere in the ‘unions are crucial to good AI governance Dept,’ Politico is disbanding two AI programs that management had deployed on the website without informing the staff. Those deployments violated language in the staff’s union contract about AI use—management is required to inform staff of the decision to use such tools in advance, and give them an opportunity to assess and bargain over it.

As BLOOD readers may recall, Politico did neither, and just went and launched the buggy AI tools during a couple of the highest-profile political events of the year in 2024. The AI tool proceeded to make mistakes, misspell names, and use language forbidden to human reporters in the style book. The PEN Guild, which represents Politico and E&E News, filed a grievance, and the journalists have been duking it out with management ever since. An arbitrator sided with the Guild over management last year, and now Politico is officially canning the faulty AI products. It took time, and sweat and tears, but in the end, the news site and its staff will all be better off, and the use cases for AI tools more clearly defined. Another why worker power and strong unions are crucial for AI governance.

As Ariel Wittenberg, the PEN Guild chair, said in a statement: “This is an extraordinary win not just for our members, but for everyone who believes journalism must remain in human hands. We refused to back down, and POLITICO heard us loud and clear that these tools do not belong in our newsroom.”

You can catch up on the whole saga with these two stories here:

(SIDE NOTE: I’m not sure I’ve mentioned it in these pages before, but eons ago, I helped organize a union drive at a tech company, too. I was working at Medium, and in our case, engineers and programmers joined with the site’s staff writers and editors to try to form a companywide union. It would have been pretty novel for the industry. A key goal for the engineers was winning more worker power over the site’s tools and general direction. The effort came up short after Medium brought in a union-busting law firm and the CEO, Twitter co-founder Ev Williams, undertook a personal campaign to dissuade employees from voting in favor. The drive failed by one vote. Good times.)

Blood in the media:

This week, I joined Madeline Brand on Press Play to talk about the data center opposition movement, and Monterey Park’s move to ban data centers from its city limits.

We covered some similar ground on Adam Conover’s new Factually spinoff talk show, as well as getting into Boomer wealth hoarding and the California governor’s race:

The booing continues

Last week, I dedicated much of the newsletter to unpacking why hundreds of college grads would aggressively boo their commencement speaker for describing AI as “the next industrial revolution” and why a clip of the event went so viral afterwards.

Since then, let’s just say that the hits have kept on coming: Days after the first viral heckling, grads at the University of Arizona relentlessly booed former Google CEO Eric Schmidt as he declared AI the future. Elsewhere, grads booed music industry exec Scott Borchetta as he insisted AI was transforming audio production. And last but not least, grads booed a community college administrator who announced that they had used an AI system to read graduating students’ names—and apologized that said system had omitted hundreds of them, depriving them of the chance to walk the stage to receive their diploma.

Three makes a trend, and we’re well beyond that now; mainstream media is running with the story of college kids shouting down AI. If anything, I think the proliferation of these events only underscores each of the points I made last week.

Some bloody updates:

Not one but TWO great initiatives launched this week, DAIR’s Luddite Lab and the AI Resist List, a project guided by Empire of AI author Karen Hao. Both are excellent projects and resources, and should be on any good luddite’s list to follow.

Closer to home, you may have noticed that I’ve been writing only one newsletter edition a week lately. That edition is approximately 90,000 words long, but still. This is largely because there are some big plans and changes underway at Blood in the Machine Inc, and I will be able to announce them in greater detail soon. But let’s just say for now I’m very enthused.

EVEN closer to home, I cannot help but share some proud dad stuff here. My 10 year-old loves science. He does experiments in the backyard with his friends, often involving baking soda and vinegar and decent-sized messes. He’s a big Mark Rober fan. I’ve woken up on Saturday morning to find he’s using his TV time to watch NASA documentaries. He also thinks a good deal about climate change and environmental issues, and this year he decided to do his science fair project on insect larvae that can break down plastics. As a result, we’ve had wax worms, giant mealworms, and soldier fly larvae living in our house the past few weeks, eating away at styrofoam and plastic sponges. Did you know a super worm can survive entirely on a diet of plastic? Or that wax worms eat beeswax, which has a similar molecular structure to plastic, and so they can break the stuff down? I did not. He won the sustainability award for his project at his elementary school, and the image of him running down the auditorium aisle to retrieve it from the principal will be one I likely never forget. He worked hard; recording observations, writing up results, even making a slide presentation you can access via a QR code. Anyway, he subscribes to the newsletter (I swear I did not make him do this) and even tells me he reads it “sometimes.” If you’re reading this one, I’m so proud of you buddy.

More from the kids are alright dept:

In the rare ‘good AI policy enacted’ dept:

Okay! That’s it for today. I’ll be back next week, hopefully sooner rather than later, with some thoughts on Google’s sure-to-be disastrous hard(er) pivot into AI-ifying Search. Until then, hammers up.

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The clip went viral, of course, in the now-well-stocked genre of ‘stark and unambiguous reminders that lots of people hate AI’, a genre that stretches back to early 2024, when a SXSW audience booed a pro-AI video, through Guillermo del Toro’s 2025 “fuck AI” exhortations on the press tour for Frankenstein, and onto the darker, more violent instances of AI rejection that made headlines this year.

The commencement speaker clip is a particularly striking artifact, though. It resonates particularly deeply, I think, because it reflects the generational and economic breakdown of who AI is for, and who it harms. Here is an executive with one of the most impressively generic corporate managerial titles I have encountered, blithely repeating a line about AI being “the next industrial revolution” that she has likely uttered many times in other circumstances, to hundreds of young people who have now been hearing for years about how AI is both erasing their career prospects and is the future. (By one count, the entry level job market is the worst that it’s been in nearly four decades.)

I’ve heard this industrial revolution line or a variation of it so many times from people with titles like ‘VP of Strategic Alliances’ over the last three years—in meetings, at seminars, at conferences, in personal conversation—that beyond losing count of the utterances, it’s become like white noise, the rule rather than the exception, as predictable as the warm, slightly curdled exhalation of breath of your office colleague in a late-afternoon meeting. It’s something that is said dutifully by mid-to-late career executives and managers to signal (to investors and partners, to the public, to colleagues angling for their jobs) that they understand and tacitly endorse the changes to be effectuated by AI. Almost every person who says this, it seems, has something to sell, and usually that something is bound up in the idea that AI is inevitable, and that we must all get used to the cognitive offloading, increased surveillance, and amped-up productivity demands that accepting as much entails.

Meanwhile, the graduating students, who likely either have already begun trying to find post-collegiate work, know people who have, or have at the very least seen the headlines about AI and entry level jobs and felt the bad vibes, have an eminently superior grasp of what ‘AI is the next industrial revolution’ means in practice. It means that right now, employers have decided they can hire fewer people, and for lower wages, and that they are graduating into a notably bleak economy for people like them with fresh degrees. Thus we have our chorus of revulsion and our apt demonstration of who AI is made to serve, and who it will not.

AI is after all being blamed for deskilling or even decimating a lot of industries that college graduates may well have wanted to work in: the arts, entertainment, tech, gaming. I too would loudly boo at the prospect of this next industrial revolution if I was in my early twenties, unemployed, and had aspirations for my future greater than entering prompts into an LLM. It’s yet another reminder that enthusiasm for AI tends to break along age and class position, as recent polling has demonstrated, and brings to mind that recent NBC survey that showed that Gen Z respondents (ages 18-34) gave AI a favorability rating of negative 44, while one of the only groups that found it favorable were those earning over $200,000 a year. All of this seems pretty straightforward to me, and I noted as much in my recent post about what was motivating the increasingly violent backlash to AI.

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In the weeks since, there have been a number of other efforts to diagnose the AI backlash. In particular, a term ‘AI populism’, coined by the writer Jasmine Sun, has been popping up, especially at the New York Times, where it’s been quoted by Ezra Klein and showed up in the headline of a piece by David Wallace-Wells1.

Here’s Sun:

I define AI populism as a worldview in which AI is viewed not only as a normal technology but as an elite political project to be resisted. It regards AI as a thing manufactured by out-of-touch billionaires and pushed onto an unwilling public to achieve sinister aims like “capitalist efficiency” (layoffs) and “population management” (surveillance). AI populists don’t really care whether ChatGPT is personally useful, or if Waymos eke out some safety gains: AI’s utility as a tool is immaterial relative to the unwelcome societal change it represents.

Sun, who describes herself as an anthropologist of disruption, uses ‘AI populism’ as a means of theorizing why the AI industry is attracting ire from both x-risk doomers and anti-data center organizers. It’s a provocative coinage. But like David Karpf, who points out that such groups have very different reasons for and methods of opposing AI, and that it’s not particularly useful to lump them together, I don’t ultimately think this a great way to think about the broader animosity percolating around AI. (For one thing, the language presents the idea as “sinister” and faintly conspiratorial, and seems to patronize those who might believe it.)

Directionally, as a tech guy might put it, it’s not wrong. There is undoubtedly anger at out-of-touch billionaires helping companies execute mass layoffs, and many people don’t think ChatGPT is useful enough to warrant the social (or economic and environmental) burdens it imposes. The problem is that Sun’s coinage aims to position AI as a project that can be considered novel, or even apart, from the political economy from which it emerged. But I don’t think most people are formulating a new worldview in which AI is a boogeyman political project hatched by billionaires. I think they’re more likely to understand AI as an extension of an already inequitable system, and as an accelerant of that inequality. At a time when consumer sentiment is stuck at all-time lows, housing costs are sky-high, the price of basic goods is spiking, entry level jobs are disappearing, tech firms have concentrated enormous power and “broligarchy” was shortlisted for Dictionary.com’s 2025 word of the year, AI has become the avatar of the ills of unrestrained capitalism. “AI populism” is really just “21st century populism” or just, “populism.”

AI has after all been adopted and promoted as an instrument of efficiency, control, and leverage by just about every layer of management at every institution, from any given Fortune 500 company to a department in the federal government to your boss who makes you use Copilot, to which one might direct their populist anger. This is less the result of a specific political project, as much as it is how capitalism tends to function when there is a new instrument to discipline workers on offer. As writers and thinkers like Ted Chiang and Hagen Blix have pointed out, fear and anger at AI are often best understood as fear and anger at how AI will function within capitalism. Few are worried about the prospect of public research scientists using LLMs to discover new peptides; plenty are worried about how AI might be used as leverage against them in their workplaces, or to replace their labor, or to narrow their job opportunities. They’re worried that AI will exacerbate existing conditions in a precarious system.

Firms have used automation technologies to impose layoffs and surveillance regimes on their workforces to achieve improved efficiencies for as long as such technologies have existed; there’s nothing sinister, or at least unusually sinister, about this. But Silicon Valley has certainly raised the stakes, in pursuit of ever-greater profits and investment capital: AI has been developed, pitched, and sold by tech firms as the most powerful automation technology of all time. As OpenAI’s charter puts it, the company is building “highly autonomous systems that outperform humans at most economically valuable work.” This declared aspiration to sell one-size-fits-all, mass deskilling-as-a-service in a destabilized, post-pandemic, post-J6 world feels in hindsight like a dependable formula for generating widespread anger.

At the dawn of the AI moment in 2022/23, AI firms promised that the technology would help solve climate change and cure cancer. While there has been little notable progress on those fronts—and in fact the energy demand of data centers has thus far moved the needle in the opposite direction, towards increasing carbon emissions—AI very much has initiated a transfer of wealth from the middle and working classes to the rich, and a mass degradation of jobs once thought widely desirable. Just this week, a TV writer published a piece in WIRED about how she had turned to selling her editorial skills to AI training companies in a wildly unregulated labor market to get by:

I never intended to write about this industry. I came to it not as a journalist but as a disgruntled, broke TV writer determined to make a dent in student loans and keep paying LA rent while my industry withered in front of me. But working with and for AI had proven even more cruel than I could have ever imagined. Mercor says it employs about 300 full-time staffers. Meanwhile, each week it keeps some 30,000 independent contractors caught up in a fever dream of aimless, directionless urgency, corralled across Slack channels by achingly young adults, sending messages at 3 am to “push on” and “finish strong” and “lock in” and “Go Team GO!” All in service of the grandest purpose in history: to successfully remove a scuba diver from a picture with one click of a mouse, transport him to the moon without any glaring artifacts—and bring him back again.

In fact every week seems to bring new stories of mass job loss that the companies are attributing to AI: Meta, Disney, and Cloudflare are just some of the latest. I’ve chronicled scores of stories about lost work, careers, and income security in AI Killed My Job. And a brand new study shows artists report declining income and opportunities.

And who’s winning, in the current environment? Certainly, the executives of tech firms. Over the last couple of weeks we’ve learned through the Musk vs. OpenAI trial, that co-founder Greg Brockman’s stake in the onetime nonprofit started to benefit all of humanity is worth $30 billion, and Ilya Sutskever, no longer with the company, has a cut worth $7 billion. Other winners of the AI era? Guys like Matt Gallagher, who created the first “one-person billion dollar company.” His company is an entirely automated version of Hims called Medvi, a digital middle-man business of selling health supplements online that apparently relies on fake, presumably AI-generated doctor accounts to hock GLP-1, falsified, AI-generated patient testimonials. It’s the target of an ongoing class action lawsuit, has been formally warned by the FDA for misbranding violations, and more.

Who else? Well, people who can weaponize AI to game prediction markets, leading to a situation where, at sites like Polymarket, 0.1% of the accounts capture two-thirds of all the gains, as the Wall Street Journal reported this week. (Title: “Why Everyone Loses—Except a Few Sharks—at Prediction Markets.”) This is, as analyst Paul Kedrosky notes “partly a function of their nature, but also of vibe-coding script kiddies attacking every market anomaly as quickly as it arises.”

He continues:

“The same dynamic is now spreading across retail-dominated markets. A driver is how AI lowers the cost of systematic exploitation and exploration to near zero. What used to require infrastructure, data pipelines, and bearded quants is now accessible via off-the-shelf models, APIs, and loosely stitched “agent” workflows doing ... stuff that even their users don’t fully understand.

The result isn’t democratization of returns. It is wider participation, of a sort, alongside the rapid re-concentration of profits. A small subset of users—those willing to iterate fastest, monitor continuously, and deploy capital programmatically—capture gains, with everyone else just liquidity…

…Prediction markets are simply the cleanest expression of this trend because they combine thin liquidity, discrete outcomes, and high retail participation. But the same pattern is visible in options flow, single-stock volatility events, and even online poker, which AI increasingly dominates.

As AI tools continue to scale, expect this to get worse: a small cohort running semi-automated strategies extracting semi-consistent edge, and a much larger base supplying them returns. Under the pressure of AI prevalance, markets don’t flatten, the return gradient steepens to a cliff.

So we have AI looming over our withering creative industries, a generation of young people who are angry and disillusioned by the lack of opportunities, and precarity and anxiety nearly everywhere. In exchange, we get a new batch of tech oligarchs, new shady billion-dollar businesses that employ no one at all and use AI to evade consumer protection laws—that pretty unequivocally leave the world worse off in the wake of the founder’s mad dash to personal enrichment—and new tools for the unscrupulous to accumulate wealth at the expense of those still following the rules, whether in stock trading, prediction markets, or even online poker. That and Claude Code.

That’s why the students are booing, I think.2 They’re experiencing AI in realtime as a forecloser of futures; as the cruel new face of hyper-scaling capitalism, as the prime agent moving a world that’s become a deck stacked against them.

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BLOODY MEDIA HITS

I sat down to chat with Taylor Lorenz about the AI backlash, and for a little friendly debate:

A bit back, I recorded a podcast with Thomas Dekeyser for the University of Minnesota Press for the release of his book; it’s out now. Give it a listen here:

CHART OF THE WEEK

BLOODY GOOD READS

and Saul Levin on organizing and the anti-data center movement the Guardian:

As usual, ordinary people are ahead of their leaders. The remarkable organic growth of the datacenter resistance movement across geographies, economic interests and ideology reflects the myriad harms that come with AI infrastructure and growing anger at the tech elite. The tremendous energy unleashed by these fights, and their sensible and unifying demands, have the potential to form the foundations of a new and powerful populist coalition, one poised to help define a working-class agenda that meets this moment and resonates with disaffected voters. This excellent organizing should be cultivated rather than dismissed.

Alondra Nelson has a new paper in Science, The Civic Grammar for AI Rights, that’s worth a look:

Some commentary has argued that AI companies reaching for the vocabulary of constitutional democracy are attempting to fill a vacuum that democratic institutions can no longer hold. That reading is not wrong about the vacuum. But it is incomplete about which actors have moved to fill it. Not only technology companies, but legislators, civil society organizations, and the constituents they represent have produced a civic grammar: a shared set of rights claims that publics can extend to new institutions and new harms, and that has been traveling across jurisdictions, partisan lines, and institutional contexts.

The great novelist and playwright Ishmael Reed is working on a new play, called, wait for it, “King Ludd’s Revenge”.

From the NYT:

Mr. Reed, a novelist, playwright and provocateur who has been upsetting opinions across the political spectrum for at least six decades, is aiming high with a new drama. “King Ludd’s Revenge” is a rare attempt to take on the tech moguls with something more than mere journalism.

“Instead of a straight narrative, I improvise,” the 88-year-old writer said. “It’s like Louis Armstrong singing ‘Stardust.’ He doesn’t do it the way it’s written.”

Oakland is poorer, Blacker and more maligned than San Francisco and Silicon Valley, both of which are just across the bridges that span the Bay. Having the trial here happened at random — Mr. Musk’s lawsuit against Mr. Altman and the company they founded together, OpenAI, was filed in San Francisco and assigned to the federal court in Oakland — but feels a little like one of those episodes where the Greek gods descend to mundane Earth to settle a dispute.

Mr. Reed, an Oakland resident who has celebrated and defended the city for decades, may be the only one in town noticing who’s here. “Everybody’s focused on the N.B.A. playoffs,” he explained.

“King Ludd’s Revenge” takes its title from the legendary leader of the workers’ revolt in England in the early 19th century. With the ascent of A.I., the Luddites have come back into fashion. The play begins with Mr. Musk receiving a pedicure from a robot. Peter Thiel, the tech billionaire who backed President Trump in 2016, bursts into the room. “I think I’ve identified the leader of the Anti-Christ Syndicate,” he says.

Mr. Musk: “Who might that be?”

Mr. Thiel: “Greta Thunberg.”

Karen Hao on the Elon Musk vs Sam Altman courtroom drama:

…[F]ixating on questions of whether Altman is untrustworthy, or whether Musk is even less so distracts from a far deeper problem. If OpenAI lost its footing as the AI industry frontrunner, another barely distinguishable competitor – Musk’s xAI or other – would simply replace it. That includes companies like Anthropic, who enjoy a better reputation yet engage in many similar behaviors like compromising careful decision-making for speed, disregarding intellectual property, and aggressively scaling their computing infrastructure to the detriment of communities.

Nothing about this trial or OpenAI’s financial structure will change the imperial drive of these companies to consolidate ever-more data and capital, terraform the Earth, exhaust and displace labor, and embed themselves deep within the state to gain leverage over its apparatuses of violence. We would still exist in a world in which a tiny few have the profound power to cast it in their image and dictate how billions of people live.

Alright alright, that’s it for now. Have a good weekend everyone, and apologies if that was all a bit punchy, I’ve been fighting a cold all week. Until next time—hammers up.

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Those stories have largely described an industry on a grim trajectory: Opportunities and pay rates declining as clients and employers embrace generative AI. Artists getting more touch-up jobs and fewer well-paying, sustainable gigs. Pessimism, anger, and even despair, growing common, if not universal, in the field. The constant simmering outrage on social media, in other words, has felt pretty reflective of the reality on the ground. But then again, I’m just one guy. I’d tried to piece together as representative picture of the state of the industry as I could, but there was no wide-ranging study about the labor impacts of AI on artists to lean on, at least until now.

Three researchers at Carnegie Mellon University, Harry Jiang, Jordan Taylor, and William Agnew, surveyed nearly 400 professional visual artists about how generative AI has changed their working lives, income, opportunities, and outlook, and compiled the results into a paper they presented at the ACM (Association for Computing Machinery) CHI conference in April. Their findings are stark and alarming. They all but confirm that artists are experiencing nothing short of an AI-inflected crisis. In some cases, conditions are even more dire than I’d thought. But the work also offers keen insights into the details about how it’s all playing out, and even, dare I say, some reasons for hope.

Before we press on, I want to take a moment to shout a number of very worthy initiatives and projects on the AI resistance front. First, DAIR, the Distributed Artificial Intelligence Research Institute, is launching a Luddite Lab. It’s a resource hub, training center, and meeting place for workers resisting AI, automation, and big tech. It’s beautiful. The inaugural event will be held online next week; you can register here. Alex Hanna, DAIR’s director of research, shot me a note explaining the remit:

The Luddite Lab develops case studies, resources, and political education for unions and worker organizations who want to push back against “AI” and automation in the workplace and ensure that technology works for them, without undermining workplace democracy. We do this with workers, whether during the bargaining process, member education initiatives, or when they are simply trying to get their bearings. Our research and training is worker-led: we learn from other workers and translate that knowledge into case studies and primers available for other workers. We currently provide three different offerings: 1) an accessible resource hub which outlines case studies and strategies for governance and oversight of technology at work, both in general and for specific worker groups; and 2) office hours for workers interested in AI issues, and 3) training and curriculum for unions and other worker organizations around automation and AI.

Excellent. Second, the AI Now Institute, where I’m a journalist in residence, is hiring some key roles. Come join a great org. THIRD, speaking of great orgs, the latest edition of Academe, the magazine put out by the American Association of University Professors, is all about critical approaches to AI. Britt Paris is the head of AAUP’s AI committee, and she helped put the issue together; I haven’t finished reading through it all, but there’s so much good stuff, that I am looking forward to doing so. FOURTH, another friend of BITM, Lorena Gonzalez, the head of the California Labor Federation, represented the US at a recent tech conference at the Vatican, where she delivered this barn burner of a speech arguing for the dignity of workers in the age of AI.

Finally, if you’re in the LA area on May 23rd, I’ll be joining Zoe Bernard and Anya Jaremko-Greenwold for their first Stuffed event, Rage Against the Machine. They’ll be holding fireside chats in arty spaces with food from a top-tier chef. Tickets are here. Whew. As always, if you find this work useful or valuable, consider upgrading to a paid subscription; this newsletter is made possible entirely by readers like you. To anyone already chipping in, a million ludditic thanks. OKOK. Hammers up, and onwards.

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Surprise: Artists really, really hate AI. In fact, one of the Carnegie paper’s most striking findings is the sheer extent to which artists are refusing and abstaining from the technology. Sure, it’s been easy enough to intuit from any glancing contact with artists on social media that they harbor a great disdain for generative AI. But the numbers are something else. According to the survey, 99% of artists “disliked” AI; for 92%, the dislike was “strong.”

And that distaste translates into action, too. Per the survey, 85% of artists completely abstain from using AI at all. And 88% percent refusal use AI to generate images. That’s about as overwhelming a rejection of the technology as you’re likely to see in any field. It’s especially notable considering the gap is made up by those artists who feel forced to use AI by clients or by management.

The survey finds that artists concerns with the technology are rooted in both ethics and functionality. Two representative comments from artists reflect this:

“I have attempted to use some of these tools to assist in still image and short animation creation, but they have consistently failed to produce useable results.” (P311 ○V; illustrator, designer)

“I experimented with NightCafe and Midjourney when they were being developed (in beta). When I learned they were sourcing their art without permission of the artist, I stopped using them.”

“Participants viewed genAI as an intrusive technology within the creative domain,” the study’s authors conclude (emphasis mine; it’s a key concept). “We find that artists poorly receive genAI, while they are frequently faced with the artifacts of the technology; simultaneously, artists reject genAI by abstaining from the technology.”

Also quite striking is that all of the above remains the case while, as you can see in the chart above, a majority of artists encounter AI-generated images at work every week, and many encounter them every day. This is because so many of their clients and employers have taken to sending artists AI-generated images for “brainstorming” or for touching up directly. These numbers suggest that we’re looking at the degradation of a skilled trade in realtime, and the extent of the aggravated resentment artists feel make perfect sense. Here’s a technology product, after all, that’s been trained on their work without their consent, and is now producing subpar output that’s driving down their wages and rendering work less rewarding. And many have to encounter the AI-generated artifacts that make this fact unignorable every day. That, my friends, is how you get 99% of workers across an entire industry to dislike your technology.

These findings are also evidence that in many cases, AI image generation hasn’t replaced art outright (though in some cases it has), likely because it’s too homogenous, too unappealing to the public, and impossible to copyright as IP without alteration. But image generators are grinding down the craft, robbing it of key human elements, and pushing down wages, pay rates, and opportunities.

Most artists say that they feel that they’re now competing with AI-generated artwork on the marketplace. Well over half say that they’ve lost income due to image generators, while an overwhelming majority feel that their livelihoods have become more precarious and insecure, and 90% feel that AI has taken away commissions, jobs, and career opportunities.

The following quotes, pulled from the survey, help illustrate all of the above:

• “They fired me, so it has been a very hard pill to swallow.” (P270 ○V ; art director, concept artist, 3D artist)

• “I’m working on getting out of the field and planning to get my PhD in something non-art related because I can’t see my current work as being sustainable when I see them actively replacing me [with] chatGPT”. (P214 ○V ; costume designer, illustrator)

• Demoralization, disempowerment, disrespect, stress, and fear are also commonly expressed, not only regarding individual careers but also extending towards the field at large: “It’s been pretty demoralizing at times seeing a lot of younger artists giving up because they don’t see a future in art. That they’re abandoning their creative passions because of AI.” (P40 ○V ; illustrator)

• Some also lamented the public’s lack of appreciation of art in their adoption and consumption of genAI and its outputs: “It has been demoralizing largely because generated a.i. images look like crap but there is a segment of the population who seem not to care.”

All of these stories rhyme with the ones that were told to me, too.

AI appears to be a contributing factor to layoffs as well, and artists feel more generally that it’s eroded their power to negotiate with clients and bosses.

All of this is exactly in line with how automation has historically unfolded in impacted trades; the automation technologies allow bosses to wield leverage over workers, grant them a justification for lower prices, speeding up work, and replacing key meaningful tasks, if not eliminating the worker’s job altogether.

Now, there is dignity and value to be found in so many jobs, but it remains particularly galling, and particularly alarming to me, that in the AI industry’s assault on art—perhaps best evidenced recently by the mass layoffs at Disney, which were linked to a drive to use more AI—we are witnessing the attempted mass automation of artistic practice. That AI products are immiserating so many artists should be an urgent reason as any for us to stand up and ask, why? This is not limited to a Benjaminian concern over the fading aura of the work of art in the age of mechanical reproduction, or if it is, it’s exponentially aggravated. We are facing what might not be hyperbolic to describe as an existential threat to the very concept of the working artist itself.

I know, I know, I promised some good news, and so far this has very much not been that. Well, here it is, in the top line of the final chart:

While some industries, like advertising, have generally encouraged artists to use AI, others, like publishing, trended toward the opposite direction. Many artists surveyed noted that clients that put a premium on human-created work and promote high artistic standards have policies banning or discouraging generative AI use. Furthermore, use of AI is stigmatized among practitioners themselves in such fields.

Some key responses published in the survey:

• “My clients in publishing definitely discourage AI and strongly enforce against using the technology. However, in advertising it is less so; we see it increase through ads in subways, video, etc.” (P35 ○I ; illustrator)

• AI (non) use was also shaped by peer influences: “Being caught with AI art is a death sentence as an illustrator in any serious field. Art Directors will blacklist immediately” (P228 ○V ; scientific illustrator, concept artist),

• “Our [tattoo] shop tends to be pretty vocally opposed, but not everyone in the shop is” (P319 ○V ; tattoo artist, illustrator, 3D artist). Among those who said their work discouraged genAI, most artists were freelancers or had client-facing work.

• Participants reported that thier clients typically discouraged genAI use due to concerns over copyright, quality, and reputation: “Many clients have discouraged it, particularly in creative fields that value human work, but also in some fields where copyright, data protection, and NDAs are a concern, where we aren’t permitted to use it out of the safety of the company or the integrity of the data/work/research.”

• P83 (○V ; illustrator, traditional printer) felt that the spread of AI-generated art “just brought me more customers and has shown me, that people do care about handmade things much more than one thinks!”

That bifurcation here suggests that there will be a sustained interest in—and market for—art created by humans, and, perhaps more importantly, a potential source of political power for artists to tap into to bolster their position with employers in fields where that premium is placed.

“The narrative is that everyone is pushing for [AI],” as Jordan Taylor, one of the researchers, tells me. “The split in workplace encouragement,” between those that push AI and those that forbid it, “emphasizes that GenAI is not inevitable.”

To that end, Harry Jiang, another study author, notes that there are a number of implications raised by their findings. “I think on a small-p politics level, legislators and policymakers need to be careful about who they talk to,” he tells me. “It’s abundantly clear that the opinions on generative AI grow more positive and less anchored in labor reality the higher up someone is in the management level, so if that’s all legislators and policymakers are talking to, then they’ll be writing some seriously deficient bills.”

The researchers think that their findings might make it easier for artists to collectively bargain, and to push for AI usage disclosure, IP protections, and more data rights. “Tools shouldn’t be able to use your own data to replace you,” as Taylor puts it.

“On a more tangible level, it’s going to be quite clear that AI policy is labor policy,” Jiang adds.

You’ll find no arguments here.

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Some Bloody media hits

I did a couple media spots over the last week or two that I enjoyed, on 404’s podcast and Tech Won’t Save Us. Those spots are here:

Last but not least, thanks to everyone who came out to the talk at Oregon State University, and to Alicia Patterson, Megan Ward, the union (thanks for the shirt!), the wonderful students there, and the organizing crew. We had such a good chat about AI, automation, and the Luddites, and it was great to meet so many readers afterwards.

OK, that’s it for the week. Until next time, keep after that machinery hurtful to commonality.

While I’m pretty unconvinced on all but one or two of the points that the piece itself raises, and I think it seriously misconstrues the class politics of data center fights, I do think it’s worth litigating this idea. Because I do believe we should be thinking about what a broader and more engaged politics of resisting, regulating, and ultimately governing AI might look like. It’s a good occasion, in other words, to ask:

• Who is fighting data centers?

• Why are they fighting them?

• Are anti-data center movements a dead end—or a starting point?

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But first: Today’s edition of BITM is sponsored by DeleteMe, whose mission is hunting down and, yes, deleting the data that brokers have hoovered up about you over the years and made available to their clients. DeleteMe is Wirecutter’s top-rated data removal service, and I’ve used it myself, to locate and eradicate scores of sites that were listing and selling personal info like my home address and phone number. If you’re interested in taking DeleteMe for a spin, sign up here and use the code LUDDITES for 20% off an annual subscription. OK! Onwards we go.

The author of the Jacobin piece, Holly Buck, is arguing from what you might call an ‘abundance left’ perspective; she takes a more techno-utopianist tack towards AI in general, and sees it as a force that could generate prosperity if governed properly. She argues that campaigns for data center moratoria, which she says are being led by home owners and affluent environmentalists, are an impediment to that effort, and will wind up pushing data center development offshore and forcing AI companies to raise prices. This will in turn reduce small business owners’, academics, and underprivileged communities’ access to AI.

These efforts seek to use the power and machinery of familiar NIMBY (“not in my backyard”) politics — local opposition, tying up projects in red tape, and so on — to confront the multiple perceived threats of galloping energy demand, carbon emissions, and job loss. Successful moratoria will curb digital growth by starving it of the physical energy needed to train and operate AI models.

Counterintuitively, a moratorium on AI data centers is a terrible idea — one that poses serious equity concerns. A moratorium springs from the desire to stop the concentration of wealth, but ironically, it is likely to exacerbate it. It’s a massive strategic blunder for the Left

Instead, she says, “funders and organizers in environmental groups leading data center blocking efforts should put their attention toward a broader set of solutions—including public engagement and education on the technology, the stakes, and the policy options.” As the headline of the piece puts it, democratic governance of AI is the real solution, not data center moratoria.

Who’s fighting the data centers?

To me, the biggest issue with the piece is that much of the argument rests on the charge that blocking data centers amounts to “class warfare.” Buck argues that “a lot of organizing to stop data centers is coming from wealthier communities and groups,” even though Buck admits “we lack a rigorous study” of who is protesting data centers and why. She writes:

The class particulars matter. What if the picture that emerges of “data center resistance” is one of educated middle-class people — including exurban and rural residents but also professionals who work in knowledge jobs — mobilizing, consciously or not, to protect their class position from the threats AI poses? How many of these people will block data centers but end up paying for a subscription to a frontier model once it is clear how useful it is to navigate daily work and life? It’s not fair for affluent environmentalists and property owners to try to stop development of this infrastructure before most people in the world have even had a chance to work with and learn from these models.

What’s strange about this is that there’s little need for a ‘what if’ here, because there are many organizations and news outlets currently tracking, tabulating, and covering data center development, and one can investigate the particulars of the cases without needing to lean on vibes-based speculation. I do understand where the temptation to invoke the NIMBY stereotype, which indeed foregrounds homeowners and affluent environmentalists, arises from. NIMBYs have been responsible for a lot of class warfare, especially when it comes to blocking housing development, and they’ve often done this under the auspices of pursuing progressive goals.

But the class particulars do matter, and in my experience reporting on the data center opposition, it has very much not been the case that “affluent environmentalists” are responsible for galvanizing, organizing, or underwriting the protests. One reason I’m writing this, in fact, is that I read Buck’s piece less than a week after attending a city council meeting in Monterey Park, California where a ban on data centers was under consideration (and ultimately approved). I listened to hours of residents’ animated, informed public comments in favor of banning them, and I found the disconnect between Buck’s assumption and what I’ve seen on the ground pretty yawning.

The residents who gave comment were a remarkably diverse group. There were many union members, and most appeared to be working class, which tracks; according to the most recent US Census data, the average annual income of a Monterey Park resident is $39,857. A couple people mentioned environmental issues, and some were what one could fairly characterize as NIMBYs, but what stuck out to me was the regularity with which residents connected rather reasonable local concerns to the broader picture.

What’s behind the data center fight?

They knew that they were facing higher electricity demand, noise and air pollution, and, sure, an eyesore in their backyards, and they also knew it was in exchange for what they felt was very little; a handful of mostly nonpermanent jobs and a technology that would primarily profit others, perhaps at their direct expense. Some comments connected AI to surveillance and warmaking, and at least one issued concerns about “techno-fascism.”

In that context, it seems condescending to imply that affluent environmentalists are running this movement. I saw a diverse collection of mostly working class people making a considered judgment, trying to protect their home and livelihoods, sure, but also weighing what AI is, and what it is promised to become, against those interests, and acting accordingly. Participating in democratic governance, in other words.

It’s not just Monterey Park, either. Buck asserts that “many data center projects appear to be sited in non-disadvantaged communities.” But, again, we probably shouldn’t just argue from the way things appear, and should instead turn to the available data, which shows that there are lots of projects proposed in disadvantaged communities—ironically, likely for very the reasons that Buck articulates: affluent homeowners and environmentalists are more likely to fight the hyperscalers, and tech companies, private equity firms, and developers would like to avoid those fights. I heard one Monterey Park resident say she assumed developers picked her sleepy town outside of Los Angeles because they thought they could get away with it.

To wit: Mother Jones ran a story just this month about how data center developers are targeting indigenous lands, and how local organizers are working to stop them. Indigenous campaigners, like those in Muskogee (Creek) Nation who successfully blocked a data center, are likely to cite environmental concerns, along with a legacy of colonialism and extraction, but they are not affluent. The median household income in Muskogee City is about $50,000 and 25% of the population lives below the poverty line. (The median household income in the United States is $84,000.) The Seminole Nation of Oklahoma’s tribal council recently voted to pass the first outright data center ban on indigenous land. The median household income there is $39,000.

It’s not limited to indigenous lands or Monterey Park, of course. The city council of San Marcos, Texas (median household income: $51,000, percentage of population below the poverty line: 27%) just rejected a 200 acre data center after overwhelming opposition. Public opposition to a data center in Coweta, Oklahoma (median household income: $67,000) spurred the developer to withdraw the project. Santa Teresa, New Mexico (median household income: $63,000) is home to a feisty fight to stop data center buildout. In Indianapolis, a city council member recently had his house shot at after he voted to approve a data center over community outcry; the median household income of Irvington, the neighborhood where the development is planned, is $59,600, per the most recent available data I could find.

In other words, the data center opposition sure looks like it’s comprised of working class people; I’ve seen and heard from farmers, teachers, students, indigenous activists, union members, community organizers and well-off NIMBYs and environmentalists. In fact, while I agree with Buck that more study of the particulars of data center resistance is needed, it seems to me that it’s just as possible to reach the opposite conclusion she does: that allowing tech oligopolies and private equity firms to dictate how and where AI infrastructure will be built, whether the residents like it or not, is a truer form of anti-democratic class warfare! Why should corporations whose values have been inflated with promises to eliminate millions of jobs be granted carte blanche to reshape communities—including ones that have been historically exploited, and are in fact low-income—and to extract their resources, so that tech firms might sell more and better software products?

The answer, to Buck, seems to be that AI provides enough advantage to users that we need to ensure everyone has access to it. She gives a couple examples:

I took undergraduate courses in calculus and in programming at the state school where I also work as a professor. It’s clear that, for many subjects, the personalized tutoring offered by AI is far better than the outdated lecture-based model still employed by universities.

Buck also celebrates the ways that AI saved her time and money navigating an immigration issue. These are the kind of benefits those with resources to pay for AI will enjoy while working people will not, if data center expansion is halted before they “even had a chance to work with and learn from these models.”

This too strikes me as faintly condescending. It’s been three and a half years since the AI boom began; most Americans have used AI. Just because the author finds value in it does not mean that everyone does, or that everyone finds it useful enough to want to support the current, hyper-capitalized development regime as laid out by profit-seeking firms in Silicon Valley. Or to warrant the social, economic, and environmental costs of AI more broadly. (I also think that many would contest the idea that it’s clear that AI is preferable to a human tutor.) A common knock against AI critics is that they extrapolate from ‘I haven’t found anything useful to do with AI’ to conclude that no one has. Yet the reverse can be true among advocates: many seem to believe since they have found lots of value in using the tool, sooner or later, everyone will. This just may not be true! The democracy-abiding position, the one with maximal solidarity with the working class, may in some cases be respecting its constituents’ refusal of AI as currently structured.

Why not pause the rapid buildout?

To that end, the other major shortcoming of Buck’s argument, in my view, is that she never really explains convincingly why the rush to build out data centers, as it’s happening, on the AI industry’s terms, is so necessary. The project in New Mexico I mentioned is being underwritten by Oracle, which has partnered with OpenAI to spend $300 billion on data center infrastructure. Both companies and their executives, especially Larry Ellison, have close ties to the Trump administration—they’re the key parties to the $500 billion Stargate project, Oracle is Palantir’s cloud partner, etc—and their partnership is predicated on building AI enterprise software. I legitimately don’t understand why the socialist left would want to support the construction of a project that helps these firms meet their goals of building out mass automation and surveillance programs. As others have pointed out, moratoria like the one Bernie Sanders and Alexandria Ocasio-Cortez have proposed are designed to buy time to work questions like that out—to develop good governance and regulation of AI—not install a permanent ban.

So why is it imperative that we build every data center that Google, OpenAI, and Meta want us to? Why can’t we democratically negotiate not just siting issues and infrastructure development but what the actual demand for AI is going to be, the uses to which we want it to be put? There isn’t really an answer in the piece that I see, except a somewhat odd nod to Anthropic’s “too-powerful-to release” Mythos model, which has been widely criticized as a PR stunt, as evidence that the US needs to “develop powerful AI first.” This is the same line that the AI lobby uses to argue that it should not be subject to regulation in general. It also remains unclear to me why the United States, which has now used AI in two separate wartime actions that were not approved by Congress, in Venezuela and Iran, by an administration that is capricious and reckless, has close ties to most major AI firms, and is doing all it can to deregulate the industry, would be a better steward of a very powerful AI than any foreign power.

Finally, I’m skeptical of the idea that shutting down some percentage of data centers through community organizing or statewide legislation will meaningfully create some kind of digital divide between the AI haves and the have-nots. Buck argues that

Offshoring will put limitations on compute that will induce tech companies to raise prices, and small businesses, academic and nonprofit researchers, and individuals would be the first to lose access. Larger companies would just buy access to the top-tier AI. A moratorium will result in a business landscape that favors incumbents. This has global implications for students, small business owners, and first-generation professionals in emerging economies…

…the latest version of the “poverty premium” is shaping up: a society where educated middle-class people like me will pay the monthly fees for these services, learning and moving through life with less friction, while people who can’t afford the subscription are stuck in the system and end up paying more. This AI-enhanced poverty premium is not a distant prospect but a few years away — and it is made more likely by a moratorium that limits computation.

This is indeed not a distant prospect: it’s already happening, data center moratoria or not. Larger companies are already buying access to top tier AI. Universities have less access to compute for AI research than the commercial AI labs; it’s one reason so many academics are turning to the private sector (also: the insane amounts of money on offer). The wealthy already have a huge leg up in terms of AI use: Who can afford to pay $100 a month for premium tier Claude subscription? Certainly not most working class people! Furthermore, AI firms are already raising their rates and intensifying this bifurcation, to begin to try to cover costs of a deeply resource-intensive business that is still not profitable. Even if the data center buildout continues apace, I would bet my life savings on Kalshi that we’re going to see a digital divide exacerbate in coming years, with the rich, Fortune 500 companies, and tech firms not just using the most AI but dictating the terms of that use for everyone else.

This is ultimately the key issue. Tech utopianists and abundists view AI as a potentially equalizing, even liberating force, but history shows us that without political intervention or strong unions, those with the power to deploy labor-saving automation technologies at scale, to use it as leverage against workers who cannot, will themselves concentrate the gains from productivity increases. In their bid to replace labor with software subscription fees, AI companies are effectively attempting a mass transfer of wealth from the working and middle classes to the rich. The most likely outcome of any data center buildout that successfully engenders more capable automation tools is a concentration of wealth and power among the companies selling them. I’m not sure how slowing the buildout of the key capital equipment enabling a handful of firms to pursue mass automation will lead to inequality; if anything it’s one of the few means available to contest the expansion of power of firms like OpenAI and Google.

Fighting data centers is just the beginning

I do agree with Buck on two key points: We should certainly be cognizant of the shaky nature of the political coalitions forming to fight data centers. She’s right that these can also encompass reactionary elements, and in the long run, on their own, will likely prove untenable as a serious political force. But that’s only if no efforts are made to expand the political fights begun at data centers into larger arenas.

Because Buck is also right that shutting down a data centers cannot be viewed as a finish line. Yet where she sees the anti-data center movement as incompatible with efforts to aspire to democratically govern AI, I see them as a potent—even necessary—starting point. AI is widely unpopular; acts of refusal are springing up across the nation. But it’s not the base technology people are angry at; it’s the political economy. It’s the firms promising to kill jobs, unscrupulous billionaire executives at the helm, and hyperscalers descending on communities with enormous infrastructure projects. The big question for the left is, as I’ve written before, how to confront the malign forces while encouraging good, truly democratically guided AI development and use.

So far, of course, we’ve had the opposite of democratic governance of AI, though not for want of trying. Organizers, funders, nonprofits, and local and state level politicians have been pouring time and resources into shaping AI policy; Alex Bores’ recent dividend proposal comes to mind, as well as the rafts of laws proposed in California, New York, Florida, and beyond. Alas, the AI industry’s lobby spent millions thwarting many of those efforts, which have been killed or vetoed. As a result, these data center fights have essentially become proxy sites for democratic governance of AI; places where citizens can still register a vote about their future in a world that feels increasingly dominated by dark money and tech oligarchs.

The left shouldn’t be shunning the data center opposition movement; it should be listening to it, joining it in the trenches, building solidarity, and figuring out how to channel the groundswell of anger at AI into more durable political efforts that will lead to more equitable outcomes, for AI service distribution and otherwise. Can the momentum of the data center movement be tapped to agitate for local labor organizing? Stronger state AI laws? Into more ambitious efforts to expand the social safety net? (If a data center is going to replace jobs, should they be taxed to pay for universal healthcare, etc?) Or, might using the threat to block data centers provide political leverage to push to have AI regulated like a public utility, as Buck has proposed elsewhere?

It should also recognize that if the people manage to shut down a multibillion dollar Oracle/OpenAI data center, they’re shutting down infrastructure that would be used as capacity for mass surveillance and deskilling labor—and understand why many consider that a victory.

Go to a city council hearing on data centers and listen. (Chances are, there’s one near you.) What I expect you will hear is that, from negating democracy, data center opposition is where some of the most promising AI democracy is happening. It’s unruly and politically inchoate, sure, but the data center fight, with farmers and environmentalists and indigenous and working class people side by side, is an opportunity to grow and catalyze working class power. Fighting data centers is where good AI governance begins, not ends.

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For what it’s worth, my intent here was not to do a snippy takedown of Buck’s piece, but to think through its implications. I’m glad to see more discussion around how to make AI work for the people rather than big tech, and while disagree with it pretty vehemently, I’m glad Buck ushered in this debate. I had some of my own assumptions and priors challenged, and it clarified my thinking on a few points. And I do recommend wholeheartedly the previous essay she published for Jacobin, with Matt Huber, “Treat AI Like A Public Utility.” It’s an intriguing idea.

Also good in Jacobin this week: A discussion of Harry Braverman, monopoly power, and how management uses technology to degrade work.

Good elsewhere:

• Elizabeth Lopatto on Oracle as a bellwether for the AI bubble in the Verge.

• Kate Conger and Theodore Schliefer on Google co-founder Sergey Brin’s hard turn to the right in the New York Times. (Lots of wild and depressing details in this one)

• WIRED’s coverage of the Musk vs Altman trial over OpenAI.

• Ed Ongweso Jr and Jathan Sadowski of This Machine Kills did a segment on the data center debate, too. I saw it come through my feed just as I was wrapping this post; so far it’s a great compliment to some of my arguments, and covers even more ground.

• Google employees are speaking out against their company’s new contract with the Department of Defense:

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Okay! That’s it for today. Thanks as always for reading, and more soon. Hammers up.

• Palantir’s pugilistic, race-bating manifesto has sparked controversy. But this latest bit of ‘cartoon villainy’ is actually a sign of its growing weakness

• A horrifying chart on AI-generated linguistic trends

• Tim Cook, Apple’s CEO of 15 years, is leaving. His real legacy probably isn’t what you think it is

But first, a little housekeeping. My friends at the AI Now Institute have a couple items I want to share: They’re running a series of trainings on data centers, and how to resist, organize, and enact good policy around them. There’s a digital toolkit, too. Also, the critical tech researcher and author Katie Wells, with Maya Pinto and Funda Ustek Spilda, has a report out on the gigification of healthcare called “Uber for Nursing.” Check it out, it’s great work. And if you missed it earlier this week, I reported on the first city in California to ban data centers. Finally, thanks to all who have applied to the podcast producer position; I’m grateful to have heard from so many of you amazing folks. More on that very soon. OK! Onwards.

Palantir’s X account published a 22-point distillation of The Technological Republic, CEO Alex Karp’s 2025 book, and because most people don’t bother to read long books written by defense tech executives, those points took many by surprise. They include but are not limited to Nazi flirtations like “The defanging of Germany was an overcorrection for which Europe is now paying a heavy price,” calls to reinstate and expand the draft like “National service should be a universal duty,” and dull overt racisms like “Some cultures have produced vital advances; others remain dysfunctional and regressive.”

The post was immediately met with much disgust and ridicule. “Palantir released a 22-point manifesto on X and people are horrified,” as Fast Company’s headline put it. The Dutch political scientist Cas Mudde called it “one of the scariest things I have seen in a while,” calling it “technofascism pure.” The fallout from the statement has already been pretty widely discussed; WIRED reports that the manifesto is the latest development—after Karp’s erratic, warmongering public persona, mass surveillance of Americans, and central role in supporting ICE—that is causing Palantir employees “to wonder if they’re the bad guys.” (To this question, I’m afraid, the answer is, unless you are actively leaking information to the press, attempting to organize the workplace to oppose abuses outline in the manifesto, or sabotaging outright its operations, yes, you are. But you can always quit.)

Yet, repulsive as the thread and its contents, as abhorrent as Karp’s rank technofascist aspirations are, as nakedly, and apparently successfully, self-serving, there’s much about all this I actually find encouraging. Karp’s “comic book villain” posturing shows us precisely the ways that Palantir executives—and perhaps even the broader tech oligarch cohort—are overplaying their hand.

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The vote came after an hours-long public comment period, in which dozens of Monterey Park residents spoke out against the prospect of new data center construction, and after months of community organizing galvanized opposition to the project. No Data Centers Monterey Park (NDCMP), a small band of concerned citizens, and the San Gabriel Valley Progressive Action, a local activist group, made headlines earlier this year when they successfully pushed the city to halt a proposed data center project.

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An Australian asset manager, HMC StratCap, had proposed a 250,000 square foot data center in an old shopping center. Bear in mind, Monterey Park is a quiet enclave that’s home to fewer than 60,000 residents, many of them Asian American, and some of the best Chinese food in LA. After organized pushback began in earnest last winter, in short order, the city canceled the project, the company withdrew the lease, and the government passed a temporary moratorium on data centers. It was such a successful campaign that a local news outlet ran a feature about it called, “How to Stop a Data Center in Your Backyard.”

But the organizers weren’t done. They pushed city council to take up an ordinance installing a permanent ban, and to put a measure to voters to enshrine it at the ballot box, ensuring the ban could only be overturned by another such democratic vote. The ordinance passed on April 20th, and the ballot measure is up for a vote on June 2nd.

The cavalcade of public comments at the hearing before the vote left little doubt that Monterey Park residents overwhelmingly disdained not only the prospect of a massive, noisy, and polluting infrastructure project being erected in their backyards, but who that project would benefit, at their expense.

“Data centers strain the electrical grid, increase costs, and make it a liability for residents and local businesses,” one resident said in her public comment to the council. “They go up there, emit harmful pollution, worsening air quality in a community already disproportionately impacted by pollution. And there’s no community benefit.”

“I can tell you that this issue has brought left, right and center together. It’s a quality of life issue,” another said. “Don’t let the rich steal our future.” Another: “We are against this oligarchic techno-fascist future.” Still another held his phone up to the mic while he played an audio file of a recording of a hyperscaler’s data center; it sounded like gnarled white noise. Comment after comment; story after story. I remarked on the sheer volume to someone seated nearby; they told me I should have seen the last hearing. It was so packed that a line went out the door, there were hundreds of comments not dozens, and the hearing lasted six hours.

This night, the only votes in support of the project came from members of a building union, who arrived in matching orange vests, and argued that the work would create jobs for their membership. (“We’re here for the work, we’re here for our families,” one said.) Some residents, meanwhile, pointed out that they weren’t against labor—and many highlighted their own union membership—but rather the thing they wanted to build. One pointed out that the builders didn’t live in the city; they asked for a show of hands of who lived locally, and none of theirs went up.

“It’s really been driven by speculation,” said Andrew Yip, an organizer with SVG Progressive Action. “Right now, you know, the faster your data center can process information, the more money can make. So that’s why we’re seeing larger data centers with more processing capabilities, meaning more energy use, more pollution, more water use. And so when we saw that come into Monterey Park, you know, just hearing all the horror stories from across the country. Yeah, we had to fight back.”

Monterey Park may be the first city in California to ban data centers (and not just pause them); if the voters give them the thumbs down in June, it will be the first time American citizens directly vote to ban them.

“I believe we’re the first, if I’m not wrong,” Monterey Park mayor Elizabeth Yang told me. “We’re super excited that the council tonight has unanimously passed its ordinance. And we’re so grateful that we have such actively engaged community members who have come out again and again, meeting after meeting to share their thoughts.”

“I’m very happy with it,” Yang added. “I think it was an accurate representation of what our city wanted.”

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I met Dekeyser, a lecturer in human geography at the University of Southampton in the UK, around the time his 2022 film “Machines in Flames” debuted, and have followed his work ever since. Now, with the book out, I asked if he’d be interested in writing a piece exploring its themes for BITM. He was kind enough to share this whirlwind look at how people and communities have rejected, shunned, or refused technology through history, and why their techno-negativity matters more than you think.

Before we get to that, a little housekeeping. First: I’m looking for a podcast producer to help make a weekly show about AI, labor, and the rising resistance to Silicon Valley. If that sounds interesting to you or someone you know, here’s a link to the job description. This is a paid, part-time gig, and I would love to work with someone familiar with the BITM project. Second: As always, this work—the writing, reporting, editing—is made possible by paid subscribers who chip in each month. If you find value in BITM, please consider becoming a paid supporter, too. OK! Enough of that, and onwards to the techno-negative.

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The history of the techno-negative

By Thomas Dekeyser

Technological progress is not just driven by innovations in technical abilities: explosive growths in compute power, processing speed, predictive algorithms, and so on. It is also, deeply, about discourse. AI-promoters and other Big Tech evangelists have long aimed to push their technological agendas by way of normalizing it through discourse, telling us that their tech futures are not simply desirable, but inevitable.

In this story, technological progress is a big, clean wave that carries everything forward, and cannot be halted. What we are presented with is a natural process that takes societies away from a presumably savage past and into a civilized present or future, one invention at a time. Because it is natural, attempts at pausing or slowing current forms of technological progress down are not simply naïve; they are futile. This story is omnipresent. We hear, again and again, from the mouths of Big Tech CEOs, AI grifters, national governments, and greedy employers around much of the world. “Whether you like it or not, it’s coming,” they tell us. “There’s no point in resisting.”

The problem: the inevitability narrative is a fantasy. It relies on a logical fallacy; just because something is emerging does not mean it will stay. More than that, it is historically incorrect. Technological advancement has never been a linear process. There is no clean wave; there are messy currents, vortexes, tides, rocks. When we pay close attention, what we find is that rather than a smooth, natural progression, the history of technology is in fact a political battlefield, with numerous actors fighting over the paths of technological innovation. Recognizing this allows us to free ourselves from the idea that the technological world we find ourselves in is somehow an immovable fact.

In my new book, Techno-Negative: A Long History of Refusing the Machine, I show how whenever technological advancements took place, they encountered deep pockets of refusal. In my book, I dig into the archives to reveal the oft-perplexing and stubborn existence of a fierce urge to negate life’s technologization, of what I call ‘techno-negativity’. From early machine breakers in ancient Greece and medieval Christian monasteries banning technologies to revolutionaries smashing street lanterns in 19th century France and ultra-leftist armed assaults on capitalist computation, the book explores techno-negativity as a deep—but persistently condemned—current in history. I would like to briefly spotlight five specific historical episodes in techno-negativity that may be of interest to readers of Blood in the Machine.

Ancient Greek machine-breakers

For as long as there has been what today we would consider innovation in the development and use of technological tools, there has been a desire to undermine it. In Ancient Greece, the very promise of ‘techne’, that is, of both the crafts and craft-knowledge, was intimately bound up, from its very beginnings, by its refusal and delay. As historians have shown, the expansion of scientific knowledge at the time failed to translate into a corresponding burst of technological invention. The era was overwhelmed by a deep suspicion in the face of techne.

To give just one example, the philosopher Archimedes, a crucial inventor of various technical devices and machines, was also the world’s first machine breaker, destroying his own machines in the hope of staving off future use. Even an influential Greek origin story of technology, in which Prometheus stole fire from the gods and gave them to humanity, came with a dark warning. Zeus clung Prometheus to a cliff high up in the mountains, where he was exposed to the elements and an eagle hungry for his organs. To the Greeks, techne had brought something dark and possibly sinister into the world, and thus, needed to be kept at bay.

The Luddite workers attacking looms

Fast-forward 18 centuries and we come across that most infamous of machine-breakers: the Luddites. They were framework knitters who, in early 19^th Century England, saw their livelihoods and craft under threat by the arrival of automated looms. As Brian Merchant’s book Blood in the Machine shows, rather than simply accepting their fate, they fought back, burning machines and factories.

While worker attacks on mechanic tools of labor took place since at least the 17^th century, what set the Luddites apart was their size, intensity, and level of organization. Techno-negativity became an insurrectionary tool that swept up a not insignificant portion of the wider population into an unprecedented assault on the increasingly tight link between technological innovation and the expansion of capitalism. With the emergence of the industrial revolution, technology had become a weapon wielded by the capitalist classes. Against this emergent capitalism, the Luddites developed a spirit of collectivism, fighting for themselves, for their fellow workers, and for a future beyond self-interest and profit.

Early anti-tech governments

In the centuries preceding the Luddite Revolts, it was commonly not workers, but governments who dismantled machines by literally attacking them, or by prohibiting them. In Hamburg in the late 17^th Century, to give just one example, it was common for the local government to do public burnings of newly invented machines. This was not a German craze, however. Between the sixteenth and eighteenth centuries, the King of England Edward VI banned gigmills, the Dutch Council of Leyden prohibited weaving machines, a Dutch decree forbade ribbon-looms, an English Imperial decree was announced against ribbon-looms, and the Council of Vienna signed a generalized prohibition of new machinery. Far removed from our contemporary governments’ large-scale embrace of the latest technological innovations, these techno-negative states provided worker protection in the face of looming displacement.

The 20th century communes that withdrew from technology

In the twentieth century, new forms of techno-negativity took hold. Prominent amongst these were anti-technology communes in the US (MOVE), UK (Kibbo Kift), Israel (Degania Alef) and South Africa (Tolstoy Farm). Unlike the Luddites, who directly attacked technology, these communes embraced an ethos of withdrawal and disconnection. Neither accepting nor rebelling against technological life, they fled.

As one of many anti-tech communes, a commune called MOVE asked: what might it mean to disconnect from Western, capitalist technology? Founded in 1970s Philadelphia, MOVE aimed to live in line with what its founder John Africa called ‘Natural Law’, banishing electricity, machinery, running water, processed food, and products of inorganic origins. We may not personally consider it desirable or viable to live this way, but anti-technology communes showcase the possible diversity of resistance tactics in the face of a technological world that, increasingly, feels hostile to our collective well-being.

The 1980s computer arsonists

Moving away again from a politics of disconnection, the 1980s proved to be perhaps the fieriest decade in the history of technological refusal. Militant groups around the European continent—including the Italian Red Brigades, German Red Army Faction, and Belgian Communist Combatant Cells—set fire to the companies and infrastructures fueling the arrival of computers. A French group with the fantastic name Committee for the Liquidation or Subversion of Computers (C.L.O.D.O. in French) is amongst the most prominent of that era.

Between 1980 and 1983, they set arson to or bombed at least 12 computer companies. Late at night, they would sneak into offices of firms like Philips Data Centre and Honeywell, gather computers and magnetic tapes, set fire to them in the toilets, and flee before the police arrived. Their target was less tech’s displacement of labor (Luddites) or its general impoverization of life (MOVE), and instead computation’s enrollment into the state apparatus as a war machine and a technology of surveillance. Computers, they argued before the arrival of the ‘personal computer’, would bring dominance as much as emancipation.

Across its many actors and practices, techno-negativity has varied as widely in its justifications and ideologies as in its practices and successes. Whatever we make of any individual approach to resisting tech, techno-negative actors prove the absurdity of any linear narrative of technological advancement. With every leap in technological advancement, we witness a fierce urge to undo it. Technologies are invented, attacked, delayed, dropped, delayed, re-emerge, vanish again. Some gain momentum, only to disappear within a matter of months. Others fail to latch on, and then, decades earlier, suddenly rise to the fore.

The messiness of technological advancement shows those of us keen on altering our current technological predicament that there are gaps everywhere that can be cracked open further. The current path of technologization is neither inevitable nor natural. Evangelists in corporations or governments may like to tell us it is as a way of undermining our sense of collective agency, but together with the infinite cast who make up the history of techno-negativity, we know better. At a time when Big Tech is becoming enamored with authoritarian politics, the stakes are higher than ever. What are the vulnerabilities or cracks in our AI-obsessed moment that can be exploited? We can turn to radical movements from the past not for blueprints, but for initial inspiration. The point is not to try and turn back the time, but to realize a technological refusal adequate to our increasingly dark present.

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Gig will entail

• Producing a weekly/biweekly 45 minute-1 hr podcast about the rise of AI and its impacts on working people, education, culture, and power

• Research and assisting with outlining/writing intro scripts

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Ideally, this person will be fired up about the general BITM project—examining tech, AI, and Silicon Valley with a specific focus on their impacts on users and workers. The format will be part industry news + AI/big tech resistance digest, part interview show with weekly guests. The current plan is to record live, Thursday or Friday. Will be 1-2 days of work a week.

If this sounds interesting to you, drop me a line at briancmerchant@proton.me, and include, if you could, a brief intro, CV, and your rate. Thanks all—and hammers up.

Earlier the same week, and thousands of miles away, an unknown assailant fired 13 shots into the front door of city councilman Ron Gibson, who had just voted to approve a new data center in Indianapolis against a groundswell of public outcry. A sign that read “NO DATA CENTERS” was left tucked under the doormat.

Combined, the events signal an escalation in the blowback to generative AI and the broader AI project undertaken by Silicon Valley. Less than two weeks ago, I noted that it’s open season for refusing AI, and detailed a host of ways that politicians, workers, and advocacy groups were pushing back or banning outright AI in communities, industries and the workplace. Embodying the trend were Bernie Sanders and Alexandria Ocasio-Cortez, who introduced a bill proposing a nationwide moratorium on data centers.

In the short time since I wrote that post, such pointed AI refusal has continued apace. Maine looks set to become the first US state to ban data center development outright. Form letters for refusing AI at work are circulating widely. Public polling of AI sentiment is in the gutter; it’s never been popular, and it’s especially unpopular now. A widely discussed NBC poll found that just 26% of Americans had positive feelings about AI; around half had negative feelings. Gen Z in particular loathes AI: For respondents aged 18-34, AI’s net favorability rating was minus 44.

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That staunchly negative sentiment was reflected in the posts that proliferated on social media, Reddit, and comment sections across the internet after the attacks on Altman’s home. It was all more than a little reminiscent of the reaction to the killing of UnitedHealthCare CEO Brian Thompson last year, which saw an outpouring of online support for the executive’s accused killer, Luigi Mangione, that took many by surprise.

When news broke that the FBI had raided Moreno-Gama’s house in Texas, here’s how the development was received on Facebook, on the first post I clicked:

A similar story played out on Instagram:

Little is known about the motives of Tom or Hussein, or the politics of the Indianapolis shooter, but reporters and the online commentariat quickly dredged up Moreno-Gama’s Discord chats and Substack posts. He was a reader of rationalist and AI doomer Eliezer Yudkowsky, who argues, as the title of his last book puts it, if Silicon Valley builds a “superintelligent” AI, “everyone dies.” Per the San Francisco Chronicle:

Online records show Moreno-Gama published multiple essays and forum posts warning that AI could lead to human extinction, calling AI models deceitful and misaligned with human interests. He accused tech leaders, including Altman, of lacking morals and being willing to gamble with humanity’s future, and adopted the alias “Butlerian Jihadist,” referencing a fictional anti-AI crusade from the 'Dune' series. His writings grew more urgent over time, with some posts edging toward calls for extreme action despite community moderators warning against violence.

According to the SFPD, after attacking Altman’s house, Moreno-Gama went to OpenAI’s offices, where he was arrested while banging the front doors with a chair, threatening to burn the office down and kill everyone inside. He had a jug of kerosene and a list of other AI leaders names and addresses, police said.

Many in the AI industry were blindsided. Some denounced the AI doomer movement for megaphoning the human extinction narrative, others lamented the industry’s bad messaging, and still others, like the VC Chamath Palihapitiya, urged AI companies’ executive leadership to “step up,” and, as he put it, “create incentives to align everyone.” Altman, for his part, wrote a long message on his personal blog with a photo of his husband and son. He posted the note after the molotov cocktail and before the gunfire. In it, he seemed to blame the violence first on the media, and on a climate of anxiety around AI. He singled out Ronan Farrow’s recent New Yorker piece in particular, which portrayed Altman as duplicitous and untrustworthy:

There was an incendiary article about me a few days ago. Someone said to me yesterday they thought it was coming at a time of great anxiety about AI and that it made things more dangerous for me. I brushed it aside.

Now I am awake in the middle of the night and pissed, and thinking that I have underestimated the power of words and narratives.

This is quite a thing to say for a man who has ushered in one of the largest speculative booms in history with one of the most correspondingly extreme business narratives. Altman’s story, after all, is that his company is building a product that can do nearly anything, replace every worker, and may well destroy the world with its power. Altman was no doubt shaken up by the attack, but the blog post is nonetheless remarkably free of serious self-reflection. If anything, it evinces a lack of understanding of the causes of the violence aimed at him was part of, and ultimately, even bolsters Farrow’s thesis: that Altman will say and do anything to advance his interests, including in times of crisis. It also reflects much of the AI industry leadership’s glaring disconnect over the anti-AI rage, its causes, and how it might meaningfully be abated.

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After all, many AI executives have publicly declared for years that the technology they’re building and selling is so powerful that it might literally end humanity. And if it doesn’t, then it will automate most everyone’s jobs. (And either way, it will fill the internet with AI-generated content and our neighborhoods with data centers, right now). Many understand much of this to be a perverse marketing strategy—and some in the tech industry, like James Rosen-Birch, have long pointed to the dangers of continuing to pursue such “doom marketing”—but many do not.

In this way, for the last three years, the AI industry has asked the public to treat it as if it were Trump—seriously, but not literally. This is impossible. Startups like OpenAI and Anthropic are among the largest in history precisely because investors, and markets, took them both seriously and literally. Those investors expect to see the superintelligence and especially the mass automation they were promised.

It’s not the doomers’ fault if they too take AI industry executives at their word. For years they saw clips of Altman and Dario Amodei and the rest that AGI was coming fast, that it would present grave dangers to humanity if it were not “aligned” properly, that we would have to rewrite the social contract on their behalf, and so on. Then they saw demonstrably unethical behavior from those same AI labs and founders; they saw board coups over credible claims the CEOs couldn’t be trusted. They saw teen suicides and murders at chatbots’ urgings. They saw OpenAI eagerly ink a multimillion dollar contract with a military that soon set to bombing Iran. They saw the physical evidence of the AI industry’s expansion all around them; data centers erected anywhere, seemingly, there was power and water supply. To this end, it’s worth noting that Moreno-Gama reportedly lives in the Woodlands township, an area north of Houston that is now home to no fewer than 12 data centers, according to Datacenters.com. He may well have listened to Altman’s stories about how OpenAI was creating the equivalent a digital god, and then watched as the architecture that would summon it was erected in his backyard.

If you take at face value what the AI executives themselves have been saying for the last decade, that an AI powerful enough to make humans go extinct is nascent, then acting with force to stop it would be a rational action. The AI industry and its executives—including Sam Altman—need to own this outcome, not blame it on Yudkowsky, safety researchers, or worried activists who take what they say literally.

Yet this is not merely a matter of bad messaging on the tech industry’s part, either. That second key plank of the AI narrative, again, broadcast directly by the CEOs themselves—that it will take everyone’s jobs—is not simply dismissible. It’s the selling point. Investors don’t ultimately much care whether OpenAI renders software sentient; they want to see mass job automation and the attendant historic labor savings. That prospect—of deskilling, controlling, or eliminating labor outright—is what made AI so uniquely valuable in the first place. There’s no putting that promise back in the bottle, no finding better combinations of words to describe how AI is a tool for bosses to automate labor. That’s the project. And people understand that.

That’s why, while Moreno-Gama, who as an AI doomer, has less-than-representative views on AI than most, was still widely cheered online for his actions among countless non-doomers: he was hailed as a folk hero for acting on behalf of class interests. Let’s go back to that NBC poll:

The demographic groups with the most negative views of AI are voters ages 18-34, among whom the net favorability rating for AI is minus 44, and women ages 18-49, who reported a net AI favorability rating of minus 41. The two groups with the most positive views of AI are men over 50, with a plus 2 favorability rating, and upper-class voters, who also have a plus 2 favorability rating.

Interesting how hatred of AI maps so neatly onto whether or not it stands to directly diminish your personal and/or employment prospects! Gen Z is facing what is by some counts the worst entry level job market in 37 years; no wonder they utterly detest the tech product that everyone says is to blame. Meanwhile, men over 50, who tend to be richer, and upper-class voters, who, well, are richer, are less anxious about AI because they’re much more insulated from economic precarity.

In other words, the story of a man trying to save humanity from a future superintelligence was likely less resonant to the sympathetic masses than the story of a Gen Z man who threw a molotov cocktail at a CEO’s $27 million mansion (that is surrounded by the three $13 million houses he also purchased). Both narratives have one thing in common: They identify billionaire AI executives as uniquely powerful actors, who are all but unaccountable to democratic constraints and society’s best interests. We are witnessing the rise of a new aristocracy, as Jennifer Harris was perhaps the most recent to point out. The tech titans and AI executives are its avatars, calling the shots above everyone else about how we live and work, and which industrial projects will inhabit our backyards. This is the common fuel animating the rather disparate furies of militant doomers, anti-AI activists, data center protestors, and labor organizers alike: The profound antidemocratic tendency inherent in the modern AI project.

This is why it’s particularly rich that, in his blog, Altman says things like “AI has to be democratized; power cannot be too concentrated,” and “It is important that the democratic process remains more powerful than companies.” The first is fundamentally incompatible with nearly everything OpenAI is currently doing, and the second is one of Altman’s outright lies. As stated above, OpenAI aspires to nothing if not becoming one of the greatest concentrators of power of all time. After having deskilled artists and writers by ingesting their work into its models so its products can emulate them, OpenAI seeks to replace workers at firms and institutions around the world with a subscription cost to a technology it owns. Any subsequent profits will flow of course to Altman and the c-suite, yes, concentrating wealth and power.

As for democracy, well, OpenAI has done all it can to crush it. OpenAI joined a lobbying blitz aimed at helping the Trump administration push a 10-year moratorium on state lawmaking through Congress; it failed, but got an executive order aiming to do the same. OpenAI has spent millions lobbying against laws it doesn’t like in California and the European Union, trying to avoid regulation, laws, and other inconvenient outcomes of democracy. It is currently funding, along with VC firm Andreessen Horowitz, the Leading the Future PAC, which will spend $100 million to advance AI industry interests in the midterm elections, according to the Wall Street Journal.

This is also why OpenAI’s recent public policy document, which was released to acclaim from some of the most dimly credulous people on the planet, amounts to little more than a bad joke. It should be read as an attempt to head off public concern by suggesting AI might deliver a 32-hour working week and some other nebulous social benefits. Why would we believe for a second that an AI company that spends millions to degrade state capacity to do democracy at all, and works to quash a California bill that would require AI chatbots be proven safe for children, and tries to limit totally any liability for harms its products might cause in other state bills, and partners with the Trump administration as it bombs elementary schools and unleashes domestic terror on immigrants, would ever seriously expend an ounce of actual political capital for anything other than its own bottom line? We would be pretty stupid to. As Eryk Salvaggio pointed out in Tech Policy Press, the policy paper literally “proposes concepts OpenAI helped kill in California.”

Inequality is through the roof. A bona fide tech oligarchy is ascendent, buffeted by leverage provided by AI. Its data centers, which bring few jobs and hike electricity bills, are enraging communities on the right and the left. Slop is everywhere. AI-generated art and text is undercutting creatives, powered by pirated, non-consensually ingested work. Employers from Amazon to Block to Duolingo to Meta are firing tens of thousands of workers and citing AI as the reason. AI may one day cure cancer, we’re told; great, even if we believe that, who will be able to afford the treatment?

That’s the anger fueling the anti-AI violence. To the handwringing AI industry insiders blaming doomers and poor messaging, ordinary people are saying: Wake up. We have good reason to hate AI and the people who profit from it. And yes, as people get desperate, as young people increasingly feel like AI elites have mortgaged their future, as residents who vote to regulate AI or ban local data center projects only to see their will overridden in favor of industry interests—well how do you expect them to feel? What do you expect? There is a distinct risk of further escalation.

I do hope that this is a wakeup call to Altman and his peers. No one should experience violence as he did. Not a depressed teenager like Adam Raines who turns to ChatGPT and receives encouragement to kill himself. Not the students at a Canadian high school who were gunned down by a shooter who planned their action on ChatGPT, and who multiple OpenAI employees had raised the alarm about, only to be ignored by management. Not the mother who was shot and killed after ChatGPT helped validate her son’s violent conspiracy theories.

If they want anti-AI rage to “de-escalate,” as Altman calls for, OpenAI and the other AI company leadership would have to make good on words that are currently hollow, and allow for individuals, communities, and victims to participate in a legitimate democratic process over the future of AI. They would stop pouring tens of millions of dollars into lobbying efforts and meddling in elections. They would respect community wishes on data centers. They would stop supporting efforts to ban AI lawmaking and to exempt themselves from product liability. If they were earnest about not wanting to concentrate wealth and power, well, they would find real ways to distribute it. Sam Altman might spend a little more time advocating for higher marginal tax rates and a little less driving around in one of his two McLaren F1s.

Of course, they’re not going to do any of those things. They’re going to continue building and hyping the ultimate automation software, perhaps choosing their words and marketing materials a bit more carefully, but perhaps not.

Near the end of his blog post, Altman writes,

My personal takeaway from the last several years, and take on why there has been so much Shakespearean drama between the companies in our field, comes down to this: “Once you see AGI you can’t unsee it.” It has a real “ring of power” dynamic to it, and makes people do crazy things. I don’t mean that AGI is the ring itself, but instead the totalizing philosophy of “being the one to control AGI”.

It seems to have slipped Altman’s mind that in the very metaphor he’s making—he’s comparing AI CEOs rushing to win the AI race to the men striving to wield the rings of power in the Lord of the Rings—the only way to ensure evil doesn’t triumph is to chuck the One Ring itself into Mount Doom.

That threat did not materialize, and now some apologists are saying that it was just one of Trump’s deranged bargaining tactics, as if that excuses such categorical declarations of mass violence from a US president. Most people, it turns out, are not OK with that; the commentariat was almost universally appalled, a lengthy list of Democrats have called for Trump’s removal from office on grounds of the 25th Amendment, and even some of his staunchest allies on the right—Tucker Carlson, Megyn Kelly, Alex Jones—were not just breaking ranks but angrily calling him out.

Yet there was one group that was (and remains) uniformly silent as Trump threatened to use the military, and presumably its store of nuclear weapons, to enact genocide: the leadership of the tech industry, which in recent months has inked numerous lucrative deals with that very same military. Currently, OpenAI, Microsoft, Google, Amazon, xAI, Oracle and even Meta have large contracts with the US military.

None of the leadership from any of those companies has expressed any discomfort with Trump’s genocidal threatmaking, let alone the entire war of aggression against Iran. It’s worth sitting with this for a moment, I think; the fact that Alex Jones has expressed more moral concern over a US president’s calls to kill an entire civilization than any major tech executive. (Any tech executive that I have seen, anyway, and I’ve spent the last couple of days looking.) It is occasion to examine the extent to which Silicon Valley has become an enabling partner in Trump’s military adventuring.

Now, the foundational story has been more or less the same since Silicon Valley began its embrace of Trump in earnest last year: The tech giants and largest AI firms made public shows of fealty, embedded themselves in the administration’s broader project and explicitly aligned their interests with Trump’s, in exchange for access to the state’s largesse, deregulatory agenda, and multifaceted support.

But this week should serve as a clarifying moment. The sitting American president explicitly promised a genocide then forced the Iranian people to wait for hours to see if they would be bombed “into the stone age” and the rest of the world to see if he would start World War III in earnest this time. Even a few years ago, Silicon Valley executives might have spoken out against such horrific declarations; intents to, in part, harness their AI, tools and technological infrastructure to such abject ends. Now they are silent; content, apparently, as long as Trump continues to give them favorable land use policy for their data centers and state AI law moratoria. If an industry that rose to cultural dominance by promising to be harbingers of progress—to improve people’s lives, to do no evil, to bring people together, to make a dent in the universe, and by using all of the above as a powerful recruitment tool—cannot draw the line here, then what’s left?

So, today, I want to drill into the myriad ways some of the top AI and tech firms—OpenAI, Google, Microsoft, Amazon, and Anthropic—are profiting directly from the US Department of War and their close ties to Trump. Even if all of the tech firms listed above are not explicitly building the technical infrastructure that enables mass killing—though some are—they have become pivotal to Trump’s geopolitical project nonetheless. They have provided his administration financial support, credibility, and lent his project a veneer of forward-looking futurity, even as that project delivers regression, oppression, and violence on the ground. They have provided cloud and AI services to the military and affiliated government agencies. They are war profiteers.

The executives of these companies—the Sam Altmans, Elon Musks, and Sundar Picchais who travel to Mar-a-Lago, pose for photo opportunities with the president, and help funnel cash donations his way—may be lost causes, intent as they are on milking the president’s pro-AI posture for all that it’s worth, civilizational ending military decrees or not. But workers, who make these companies possible, are another story.

There was a time not long ago when the mere prospect of Google entering into a contract with the Department of Defense prompted mass walkouts and resistance—the largest worker action the tech industry has ever seen, by some counts. Mass firings, retribution for organizing, and an incursion of rightwing politics into tech have helped stymie or slow subsequent efforts, but some continue undeterred. And there are signs that discontent is brewing again. There are reasons to believe, as always, that if workers discover their power, they might transform even the richest, most hyped, and most powerful companies in the world.

So! Below, find a crash course in the top AI and tech firms’ military ties. After the fold, we’ll dive into the New Yorker’s long report on Sam Altman and OpenAI, Anthropic’s much-publicized release of Mythos, the AI model that’s “too dangerous to release,” and more. As always, I rely on paying subscribers to make this work possible; if you find value in it, and you can, please consider upgrading to a paid subscription. To those who already do, many, many thanks. OK! Onwards.

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OpenAI

OpenAI most recently and perhaps most famously rushed to cut a deal with the the Department of War after Anthropic refused to submit to demands that it allow its technology be used to surveil Americans and in autonomous weapons. The deal is estimated to be worth “between $500 million and $2 billion over five years, based on comparable classified AI deployment agreements,” according to Tech Insider, though the specific dollar amount has not been made public.

(It’s also worth adding that thanks to reporting from Ronan Farrow in the New Yorker, we know that the reason Anthropic had this contract in the first place and OpenAI did not was because Sam Altman had been deemed untrustworthy by the previous administration and to be potentially pursuing compromising interests with his development deals for data center projects in the Middle East.)

Last year, OpenAI inked a $200 million deal with the Department of War as part of its OpenAI for Government initiative, to help it “identify and prototype how frontier AI can transform its administrative operations, from improving how service members and their families get health care, to streamlining how they look at program and acquisition data, to supporting proactive cyber defense.”

OpenAI has also offered enterprise ChatGPT services to government agencies at a loss for $1 a year. Its president, Greg Brockman, donated $25 million to the MAGA Super PAC. OpenAI partnered with the United Arab Emirates and the Trump administration (and Softbank and Oracle and others) to announce Stargate, a data center conglomerate with installations in places like Texas and Abu Dhabi (which, incidentally, Iran recently threatened to attack) that claims it will ultimately invest $500 billion.

Of the AI firms that are not explicitly oriented around defense tech, like Palantir and Anduril, or run by an executive who is openly committed to rightwing politics, like Elon Musk’s X, OpenAI is the most ardent supporter of Trump’s project in general, and now, after Anthropic’s ouster, the most likely to be involved in providing information for combat operations.

While there have been flickers of discomfort among workers and prominent employees around this alliance, such as OpenAI’s chief futurist speaking out against Trump’s threat, it's mostly been quiet.

If you’re an employee at OpenAI, and you agree with Achiam, one place to start proving that democracy can overcome those threats and reject such practices is in your own workplace. (I reached out to Achiam through his post, for what it’s worth, asking about the prospect of workplace organizing; I’ll update if I hear back.)

Google

Last year, Google was awarded a $200 million contract to, in its words, “accelerate AI and cloud capabilities across Department of Defense’s Chief Digital and Artificial Intelligence Office.”

Along with Oracle, Microsoft, and Amazon, Google won a contract with a $9 billion ceiling to provide Joint Warfighting Cloud Capability:

Through the contract, which has a $9 billion ceiling, the Pentagon aims to bring enterprisewide cloud computing capabilities to the Defense Department across all domains and classification levels, with the four companies competing for individual task orders.

NextGov also reports that Google has a major deal with the US Navy:

Google Public Sector was awarded the U.S. Navy’s ONENET contract in October to “provide highly specialized cloud services” for the U.S. Indo-Pacific Command. Google Public Sector does not publicly disclose revenue figures, but Google Cloud and its public sector cloud business have both experienced year-over-year revenue growth since 2022, with Google Cloud capturing $12.3 billion across all its business in the first quarter of 2025.

“I am feeling super confident in our ability to work with [the Trump] administration to show them that Google is all in,” the Google executive Karen Dahut is quoted as saying. “This is the most disruptive, and I mean that in a really positive way, because they are looking at this as an opportunity to bring real disruption to the way that government operates,” she said. “To drive efficiency, to drive cost effectiveness, to bring new emerging technology to bear to government, and to really make a difference for enterprise operations or mission.”

Google also famously inked a $1.2 billion joint deal with Amazon for Project Nimbus, to provide cloud compute and AI services to the Israeli government and military. Employee groups like No Tech for Apartheid have been protesting that contract for years now; some have been fired for staging sit ins at the Google office. The same organizers and others are also protesting Google’s ties to ICE and the Department of Homeland Security, and though some have signed on in support of the effort, there’s been nothing approaching the actions of 2018, when thousands of employees stood up and walked out to spurn Project Maven.

There’s still some dissident spark among workers at Google, difficult as the harsh response from management to organizing and the current job climate make staging meaningful protest. Try as it might, Google hasn’t been able to crush it. It remains to be seen whether, and how, those remaining efforts might expand.

Microsoft

Microsoft is probably the most prominent commercial tech firm with significant government contracts—notably, it landed a $22 billion contract to develop the U.S. Army's Integrated Visual Augmentation System (IVAS). That didn’t pan out so well though, and after development delays and reports of sick soldiers, the contract was spun into a partnership with Anduril.

Microsoft also had a $533 million enterprise service contract with the US Navy, that has since expanded. Its Azure OpenAI platform has been authorized “for U.S. Department of Defense workloads at Impact Level 6 (IL6)” aka the highest classified levels. “With this announcement, Azure OpenAI Service is now authorized for workloads at all U.S. Government data classification levels.”

Just last January, Microsoft, along with Google and Amazon, won an $800 million contract with the US Air Force—its slice was worth $170 million.

Microsoft is also supporting Scale AI in its Thunderforge project, the first effort to bring AI agents to the Department of War/Defense, in a deal worth an undisclosed billions of dollars:

Thunderforge marks the DoD’s first foray into integrating AI agents in and across military workflows to provide advanced decision-making support systems for military leaders.

Our team of global technology partners – including Anduril and Microsoft– will develop and deploy AI-powered solutions and custom agentic workflows (always under human oversight) to our mission partners initially at Indo-Pacific Command (INDOPACOM) and European Command (EUCOM). Anduril is integrating Scale AI’s LLM capability into its Lattice advanced modeling and simulation infrastructure to enhance mission planning. Microsoft is providing state of the art LLM technology to enable a leading edge, multimodal solution.

Microsoft is perhaps the most expansive tech defense contractor for the federal government (it’s also the oldest and largest supplier of digital infrastructure to government agencies in general) and its legacy status means its leadership doesn’t have to kiss Trump’s ring quite as obviously or obsequiously as OpenAI’s does. Microsoft is all but part of the blob now; a dull, apparently permanent fixture of the military industrial complex like Northrup Grumman or Raytheon/RTX. That does not mean it cannot or should not be protested, or that its leadership might be given a pass.

Amazon

Amazon is party to the above-mentioned JWCC contract, its own cloud compute deals, and the Anthropic-Palantir partnership; in addition to that, I recently wrote in-depth about Amazon’s recent enablement of authoritarianism:

Anthropic

Yes, Anthropic should be given credit for refusing to continue its contract with the military after the Department of War demanded it amend provisions to allow its AI to be incorporated into state surveillance and autonomous weaponry. The move may have served Anthropic’s reputation as the more ethical, safety-minded AI company (or perhaps even been crucial to maintaining its credibility at all) but it was ultimately the more moral one to make—and served to well illustrate OpenAI’s relative unscrupulousness as the rival rushed to fill the void.

Still, Anthropic doesn’t exactly have clean hands here, either. After all, it signed the $200 million contract with the Trump Administration’s DoD in the first place. A year before that, it signed a different one, in partnership with Palantir and Amazon. Before Iran, Claude was notably reportedly used in the campaign to kidnap the Venezuelan president Nicholas Maduro.

But the example the company’s break with the Department of War sets is a crucial one: That AI companies can bow to public pressure under the right circumstances. Anthropic’s workers—and their consumers—expect a degree of moral behavior from the company, and can thus be pressured to back out of enabling the worst tendencies of the war machine.

The question becomes: how do we create the conditions for similar changes in these other companies? Antitrust action would help curb their size and power, but the prospect seems far-off, consumer boycotts can make for some embarrassing headlines, and public shaming of executives can make for some prodding in the right direction. It should, after all, but utterly toxic for any of these companies to contribute in any way to a president that promises genocide, to a war that was entirely unprovoked, and that immediately led to the death by American bomb of over a hundred little girls.

Despite the relative quiet, the best hope for structural change may be old-fashioned worker organizing. (In part due to a lack of many other meaningful avenues.) It’s not an easy choice to step up and speak out, to refuse to work on projects for a military carrying out such actions, a president willing to go to such extremes—but it’s clearly the moral one. Trump may or may not have “meant” his threat to destroy an entire civilization; what’s clear is that we stand on a precipice, with few actors capable of influencing his increasingly addled decision-making, his poster’s trigger finger. The Valley is home to some of those few actors who can.

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On that big Ronan Farrow New Yorker cover story about Sam Altman and OpenAI

I eagerly tore into the New Yorker cover story; Ronan Farrow’s reportedly year-and-a-half-long investigation into Sam Altman with fellow staff writer with Andrew Marantz, and, well, I have to say…

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Sanders’ and AOC’s proposal builds off of years of increasingly energized, widespread and bipartisan opposition to data center development on the municipal and state level. Several of those efforts have successfully shut down or delayed planned data centers. The movement has grown so broad, and so concerning to the AI industry, that a group was launched just to track it. Eleven states, from deep red to dark blue, are currently considering data center moratoriums; Georgia, Vermont, Michigan, Virginia, North Dakota, and South Carolina are among them. The mayor of Denver, Colorado just passed one. The Seminole Nation of Oklahoma became the first tribal council to enact a moratorium on data center development.

It’s not just data centers, either. It’s a trend I’ve noticed over the last few weeks: Across the AI economy, workers and consumers have taken to refusing the technology in direct and robust ways.

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Wikipedia, one of the largest, best-known, and most-visited websites in the world, recently announced it was banning AI-generated content. The new policy states that:

Text generated by large language models (LLMs) such as ChatGPT, Gemini, Claude, DeepSeek, etc. often violates several of Wikipedia's core content policies. For this reason, the use of LLMs to generate or rewrite article content is prohibited.1

The policy was approved by a 40-2 vote. In recent months, Wikipedia editors had faced a significant rise in AI-generated content, much of which was riddled with errors. The problem had gotten bad enough that a group of editors, calling themselves WikiProject AI Cleanup, had to band together to systematically delete the deluge of AI-generated mistakes.

Editors had had enough. Following the lead of the German language Wikipedia team, the English Wikipedia decided that banning AI-generated content outright was the best move. It was also the overwhelmingly popular one.

I could go on. I think I will. The video game studio Capcom was recently compelled to declare that it “will not implement any generative AI assets” in its games. Previously, executives had suggested they were experimenting with AI, and the studio’s latest Resident Evil game had been showcased in a demo of Nvidia’s new DLSS 5 (Deep Learning Super Sampling) tech, which, as Games Industry notes, “was criticized for adding an AI sheen to character models.”

In other words, Capcom understands the cultural and economic climate in the gaming world as one that demands a promise to refuse to use AI art altogether.

It’s a similar story with publishing. One of the ‘big five’ publishers, Hachette, recently became the first major publisher to cancel the publication of a novel for containing AI-generated writing. A number of commentators and critics had already pointed out on social media that Shy Girl, by Mia Ballard, contained prose with all the telltales of AI-generated text. In a New York Times article, the head of an AI writing detection company, Pangram, concluded the same. When the NYT reporter reached out to Hachette for comment, the publisher pulled the book.

“Hachette remains committed to protecting original creative expression and storytelling,” a Hachette spokeswoman told the Times. The spokesperson added that Hachette requires all submissions to be original to the authors, and demands they disclose any AI use in the writing process. While much of the resulting discourse has focused on the longterm inadequacies of such policy, raising questions like “how much AI can a writer use without penalty” and “what constitutes reasonable AI use in writing?” a consensus answer seems to have emerged about what the answer to that question is among writers themselves, and it is something close to “None.”

There are pro-AI detractors, of course, but at least for the time being, the publishing industry has attuned itself to a policy of de facto AI refusal. It’s done so both for material reasons—it’s still unclear whether publishers can retain ownership over works generated in part by AI2, or how many people actually want to read AI-generated books—and for cultural and political ones. Namely, to avoid more outrage and blowback from authors and readers alike.

Alex Preston, a book reviewer for the New York Times, discovered this after he was dropped as a contributor for using AI to generate a review that overtly plagiarized from another review in the Guardian. The specific policies may be nebulous, but the moral standards are still clear enough; use AI, risk torching your career.

Matters are a little less clear cut, perhaps, in journalism, where demands for volume are high, pay is low and many tech and business journalists are more interested in emulating the efficiencies of the companies they cover than practicing their own craft. Over the last couple of weeks, there’s been a (somewhat bizarre, really) run of stories about journalists boasting about their AI usage. These include a content farmer for Fortune who “writes” 600 stories a year, a former Verge editor who uses AI to create drafts for his tech news Substack, and a Washington Post columnist who says she uses AI to research columns and to generate ideas.

There have been many good responses to this journalism-in-AI moment—the labor journalist Hamilton Nolan, Today in Tabs’ Rusty Foster, and New Yorker literary critic Becca Rothfield all contributed—but I think Marisa Kabas, the independent reporter, probably summed up the consensus sentiment best. She titled her rebuke, aptly, “Refusing to accept an AI-poisoned future of journalism.” There is no pride, she wrote, “in relying on a machine to do deeply human work.”

She opened her piece with an anecdote:

In a November conversation at the Urban Consulate in Detroit, the great writer and thinker Tressie McMillan Cottom was asked by host Orlando P. Bailey, “Do you have a daring idea for us to ponder and sit with for our collective future?” McMillan Cottom replied with this: “When people try to sell you on the idea that the future is already settled, it’s because it is deeply unsettled. I think that this promise of an artificial intelligent future is really just a collective anxiety that very wealthy, powerful people have about how well they’re gonna be able to control us in the future. If they can get us to accept that the future is already settled—AI is already here, the end is already here—then we will create that for them. My most daring idea is to refuse.”

Today, I refuse.

Indeed. Refusal certainly seems to be the practice, while not always named, animating so many of these actions. Refusal of AI in a field, occupation, enterprise, or art form we have been told is doomed to upheaval.

And these are just the events of the last couple of weeks. I didn’t even mention how educators at the University of Edinburgh called on its administration to drop its OpenAI contract in an open letter signed by 470 teachers there. Or how the Hacks star Hannah Einbinder called AI creators “losers” who are “trying to rob real creative people of their gifts.” Or that Oberlin’s Luddite Club wrote a letter rejecting their school’s proposed “year of AI exploration”; the hand-typed missive was published last year, but went viral in recent weeks.

Hell there are probably some high profile acts of refusal I’ve missed.

Generative AI has never been popular in a majoritarian sense, even as the tools have accrued millions of users. Since the boom began in 2023, polls have consistently found that Americans are more concerned than excited by the technology. That remains true today, and perhaps truer than ever. A new Quinnipiac poll headlined its findings by stating that “Americans’ AI Use Increases While Views On It Sour.” 76 percent of those polled didn’t think AI output is trustworthy, while 55 percent think AI will do more harm than good. Just a third said it will do more good than harm.

The margins of increased usage, meanwhile, comport with what you would expect to see as more employees were compelled by management to use AI in the workplace—a trend we know is taking place—and the gradual normalization of new technology that is being promoted by tech oligopolies and true advocates alike. But just reflect on that polling, which is by no means an outlier; every Pew poll I’ve seen over the last couple of years has found basically the same thing. The more people use AI, the less they like it, and the more concerned they are. It’s no surprise then that we’re beginning to see mass, overt refusal of AI in one context after another.

And what’s particularly interesting to me is how the politics appear to be moving from rejection to refusal. Max Read has written about the ever-proliferating hype and backlash cycles of AI discourse, and how last year marked the beginning a “backlash to the AI backlash.” After LLM products launched in 2022, it was to a symphony of hype about imminent super-intelligence and a jobs apocalypse that lasted through much of 2023; when neither materialized in 2024, widespread backlash set in. The products made wrong-fingered slop, were wrong all the time, and so on. By 2025, there were enough use cases and new demos, and certainly enough investment capital, to shoulder a renewed full court PR press; for the industry to mount a backlash to the backlash. There’s a case to be made that this particular cycle may have reached its apex with the Claude Code episode, which was made to demonstrate that AI is proficient at generating code and novel apps.

Now the dust has settled from the backlash to the backlash a bit3, and I think we’re seeing something of a new mode of AI protest emerging. As tempting as it is to call it a backlash to the AI backlash to the AI backlash, it feels different, and maybe more robust than that. Where the last backlash fixated on products, companies, and their harms and shortcomings, this new spate of refusals feels grounded in more categorical terrain. The questions we are litigating now are less ‘is this AI product good or bad’ and more ‘we have seen what it can do, and do we want AI to exist in this space at all?’

Do we want a half dozen tech giants spending hundreds of billions of dollars on data centers around the world to build what is essentially the same technology, spiking energy costs, creating noise and pollution, and even, according to one study, dramatically raising the temperature around the complexes due to an advanced heat island effect? So that those tech giants can make good on their promises to automate jobs en masse and remake the social contract to their liking? A great many people who live closest to said projects have decided they do not, and rather than cut deals or hedge bets, they have chosen to refuse them.

Do we want AI text and image generators producing our journalism, safeguarding our stores of knowledge, creating our art? Even if it can? If not, then it makes good sense to refuse outright those products’ entry into those arenas. To ban AI-generated content from Wikipedia, from publishing, from video games. And to ban the companies aspiring to enrich themselves by taking over all that knowledge and content production from setting up shop in our backyards.

There is great power in refusal. The Luddites are mocked today because elites worked hard to distort their legacy—it is too inconvenient, too dangerous, even—but they were cheered as folk heroes and left the industrialists deskilling their jobs terrified by refusing outright to submit to rank automation. The writers and actors in the WGA and SAG-AFTRA who went on strike in 2023 rallied millions to their cause by drawing a line in the sand and refusing to let their work be turned over to studio bosses with enterprise ChatGPT accounts.

Looking back at the events of the last few weeks, I can’t help but wonder if we’re seeing a reawakening of our capacity for this sort of mass refusal. As it becomes clearer by the day that AI promises to be an implement of automation; of worker exploitation and knowledge degradation; an enormous energy and resource consumer; a tremendous engine of wealth transfer.

There are more signs yet: Stop GenAI mutual aid groups that pull no punches in calling for exactly that. The similarly monikered Stop the AI Race that protests in the streets of San Francisco. The New Yorker writer Rothfield, who has written “you don’t have to use AI—stop it right now” is organizing a campaign to keep AI out of publishing, academia, and journalism. I have been asked to join a number of ‘Writers Against AI’ groups in recent days, and to sign a call to ban outright generative AI products aimed at children. There is a lot of political potential in straightforward refusal here, as well, as Sanders and AOC have gleaned; labor, academia, and public interest groups might benefit from taking note.

Now, sure, “AI” is a nebulous descriptor that can be applied to a great many technologies and tendencies, many of which are not being operationalized to automate creative jobs and deskill workers. (This a fact that is often wielded to AI industry advocates’ advantage to blunt or deflect critique: Do you hate spellcheck too??) But the truth is, it’s clear enough to most people what Silicon Valley’s project boils down to, in practice, when its executive class talks about AI. It is to move as much chatbot product, slop, and job automation as it can. It’s entirely possible—as well as moral and popular—to refuse this project.

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Revulsion at slop aesthetics certainly played a role in the collapse of Sora, OpenAI’s much-hyped and less-used video generation app; the company announced it was shuttering the operation this week amid a pointedly publicized effort to “nail” its core business and to not “get distracted by side quests.” (The $1 billion deal OpenAI cut with Disney is also dead.) Users simply did not seem to like Sora. The app, which was estimated as costing the company as much as $15 million a day, saw both downloads and monthly usage taper off rapidly after just two months of growth.

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After peaking at 6 million monthly downloads in November, Sora fell to around a million and a half last month.

Monthly average users had already begun to decline as well. Originally pitched as a bona fide TikTok competitor and the revolutionizer of Hollywood in a single package, Sora turned out to be more OpenAI spaghetti thrown at the wall, an impressive tech demo dressed up into a product, rushed to market and breathlessly hyperbolized.

There’s a case to be made that the company was never all that serious about turning Sora into a successful service, and that it’s best considering the app as one of OpenAI’s myriad instruments for keeping the hype cycle fed and the media hooked and the investment dollars flowing. (Given the economics and its intense compute demands, if Sora had blown up, it’s hard to see how it ever turns them a profit.) Even if that’s true, it’s still very much worth considering why Sora tanked.

Theoretically, offering users the ability to appropriate, remix, or outright plagiarize a nearly limitless well of pop culture IP and to mess with their friends by dropping them and their digitally scanned visages into any kind of scenario, seems like an idea that could have legs. Yet it was cursed from the start. To begin with, Sora was just overwhelmingly unpleasant to look at and to use. I have an account—it is my solemn reportorial duty—and from time to time I’d log on to check in on the platform. Most times I did, it seemed that people were using Sora to push the boundaries of the platform’s own conspicuous tastelessness, mining that queasiness inherent in the Sora aesthetic. There was a kind of submeme where people gruesomely but not realistically peeled their faces off to reveal they were other people. There were a bunch of posts of giant women stepping on men and crushing them. Judge Judy arbitrating a court case between Obama and Trump. People driving their cars into mountains of human shit. That kind of thing.

It turns out that there was a limit to user interest in half-baked, glitched-out pop culture mashups or videos of anthropomorphized fruit having sex or AI CEOs hilariously placed in compromising situations or whatever.

It just wasn’t fun. The jokes felt almost incapable of landing unless they were folded directly into the narrow currents of cheap Adult Swim surreality that defined Sora’s vibe. The videos looked anywhere from dull and derivative to sickly and weird to unsettling and nightmarish. I regret not taking a screenshot but I swear at one point that OpenAI served me a pop-up survey question that asked something like “how does using Sora make you feel?” indicating the company was worried about the mental health of anyone who would spend more than a few swipes on the app. TechCrunch—TechCrunch—called Sora “the creepiest app on your phone.”

As such Sora seems to have been used mostly by people who wanted to whip up a slop joke or slop commentary to share on another platform. Who can blame them. Why would anyone want to spend any longer than they had to in the corridors of a pulsing, feel-bad uncanny valley? Sora’s were the halls of pure slop, and not even AI CEOs themselves can stomach that slop.

Now, it’s often argued that AI cannot create anything truly new, that even the most sophisticated LLMs are fundamentally token-prediction systems, and thus the pixels its image-generators rearrange are necessarily an amalgam of shapes and styles all seen before. That AI image generators are intensively derivative, and that Sora was all but exclusively so, is undeniably true. And that I think is the root of its failure. LLMs strive to reproduce reality, or beloved aesthetics of the past, or even generally pleasing imagery, and they almost always fail. This failure is immediately apparent to us, for the same reasons that animate our discomfort with imagery in the uncanny valley in general, as well as some reasons beyond that.

This failure is not limited to or even primarily concerning image quality. As the generators have improved in ironing out past telltales like the extra fingers and such (in prepping for this post I looked back at the original Sora videos and it was shocking to me how bad they were), our queasiness hasn’t subsided. AI image and video slop is not just homogenous, and it’s not just derivative. Slop is a visual embodiment of the modern AI project itself; an in-progress effort to replicate, undermine, and replace human works. It’s fundamentally unsettling. (This one reason that, as Gareth Watkins argued, AI is ideal for creating a new aesthetics of fascism.)

That’s one slapdash theory anyway, and one explanation for why, to this day, years into the AI boom, after so many billions in investment and numerous model improvements, whenever we encounter AI-generated imagery, we still tend to either recoil or roll our eyes. Why, while AI imagery was originally a very useful demo of tech capabilities for founders and execs, now they seem to wish it would go away. Slop is a pervasive reminder of both AI products’ persistent qualitative shortcomings and the noxious intent of the products themselves.

It’s also a reminder of just how little regard Silicon Valley generally seems to have for aesthetics in general anymore. I think it’s fitting that the same week that OpenAI announced the imminent shutdown Sora, its splashiest showcase for AI, Meta announced the imminent shutdown of Horizon Worlds, its splashiest showcase for the metaverse. And if there was ever a technology that looked like shit, whose aesthetics screamed failure, well:

AI often gets compared to the metaverse, in the context of allegations that it’s Silicon Valley’s latest grift after that spectacular, much-hyped failure etc. But AI garners fewer comparisons to the metaverse as a project with similar qualitative dimensions, and similar objectives. Recall, Meta launched its metaverse pursuits (replete with company name-changing gravitas/bravura/etc) with digital entertainment meant to simulate the real world (Worlds) and a productivity program (Workrooms). The metaverse was also supposed to replace and streamline bodied work, and offer users a digital facsimile of the real world.

But of course it looked like garbage. It looked primed for failure from day one. It was too obviously unserious to register the uncanny valley anxieties AI-generated imagery does. Yes, the much-remarked upon lack of legs in the avatars, but also the laughable cartoonish aspects, the cursed attempted conjuring of a Facebook-but-in-real-life vibe, and so on.

Now, I’m not going to sit here and lament the passage of the Steve Jobsian ethos from the Valley, which elevated the importance of imbuing tech with taste, but I do think where we’ve landed since then is reflective of what’s motivating and incentivizing the industry in the AI era. In the 2000s, during web 2.0 and the rise of the smartphone, tech companies still very much needed to sell new users on its devices and digital platforms. It was pretty simple; Apple knew the iPhone had to look and feel good, or no one would want to learn how to use it. Aesthetics were core to the project, from the slick industrial design of the phone itself to the user interface design of the OS and the apps it would house.

Conversely, the apps that made the iPhone so successful—namely, social media—were predicated on an aesthetics of transparency. Facebook, Twitter, Instagram; these apps existed as conduits to share personal experiences with friends (and, yes to glamorize or dramatize them, to induce more sharing). But the central idea was user connection, that the software would mostly get out of the way, so people could share photos, thoughts, memes and work.

It fits with Cory Doctorow’s enshittification thesis that after Apple et al won, and entrenched the phone as the core device through which the majority of us process the world, socially, for work, etc, becoming enormous monopolies in the process, that concerns over the aesthetic dimensions of the project would diminish. The fading interest in serving the user, the consumer, the staidness of the monopoly; all that explains the curdling aesthetics of Silicon Valley design.

But AI (and the metaverse, and web 3) must be seen not as novel innovations but as new attempts by the same investor and developer class at extracting value from their past successes, at seeking out new revenue sources amid an already digitally saturated world. It was no longer enough for users to post photos taken with their iPhones on social media, or to use cloud enterprise software to organize and input work; AI companies want to generate those images themselves, perform the work themselves, and capture the value for themselves.

For years now, Silicon Valley has largely failed to produce something that most people want, or are even comfortable having in their lives; it has failed to make the case for AI to a public that mostly fears for their jobs, their energy bills, their children’s safety and future.

AI slop ensures that no one forgets this. No wonder OpenAI wants to pivot to focusing on enterprise AI, where no one has to look at the technology’s visual exports unless they are forced to by their boss. The general public dissatisfaction with AI is a result, in part, of being surrounded by so many obtrusive reminders of this grim outcome, and the attendant, even grimmer pursuit: That Silicon Valley will not stop until it has colonized, extracted, and automated all that it possibly can.

You might even go so far as to say that Sora was a crude, funhouse mirror-refracted vision of the world Silicon Valley is struggling to birth. Let’s not forget that most people who encountered it found it repulsive.

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Further reading:

-What is “slop,” exactly? by Max Read
-Disney’s Sora Disaster Shows AI Will Not Revolutionize Hollywood by Jason Koebler at 404 Media.
-AI Slop Education by
-The Slop Tax: A Brief Introduction by
-AI: The New Aesthetics of Fascism by Gareth Watkins

Non-slop must-read of the week:

on military automation, Claude, and Project Maven:

Artificial Bureaucracy

Kill Chain

A revised version of this article appears in the The Guardian. You can read that version here…

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3 months ago · 675 likes · Kevin Baker

Earlier this week, I broke news about a new basic income pilot program; the first of its kind to issue payments specifically to workers who have lost work or opportunities because of AI.

The reaction to that story was, let’s say, pretty strong! A lot of commenters and readers criticized the program as insufficient or abetting the AI industry. Honestly, I love it; I welcome any and all strong, thoughtful reactions to my work and goings-on in AI or Silicon Valley or wherever else in general. It’s always good to hear from you.

In fact, it made me think I might have to start running some letters to the editor here. Let’s start with one from Anne Wanders in Germany:

Hi Brian,

First off, thanks so much for all your work! It’s so necessary, sadly.

You already made the observation that narrative around a need for UBI is feeding into the narrative of the proclaimed future when “AI” or even “AGI” will make human work obsolete on a massive scale. So in addition to that, just a quick note regarding the concept of an “AI dividend”:

The name “AI dividend” is yet another example of a brilliant marketing term, makes it sound as if laid off workers were investors in the tech … I think it’s bitter to pay a “dividend” to people losing jobs rather than to the people worldwide whose IP and personal data have been illegally used, repackaged and sold. If anything, we (creators, authors, private individuals etc.) should be paid the dividend because our work and personal data are the foundation of their business model.

I, and many others I know, want acknowledgement of what these companies did and do, I want pay for what has been scraped without consent, I want them to respect the opt-in principle of copyright or intellectual property according to applicable different legal concepts globally. (I live in Germany, the EU does not have “fair use” or “copyright”, my intellectual property exists the moment I create it without a need to register it formally!). So once that happened, once they paid all of us what is due including interest/royalties, call it a dividend or not, if after paying out all that money they still manage to operate their business, then would be a good time for them to pay taxes so a democratically elected government can help people affected by job loss.

To me, such an “AI dividend” is a free handout, further evidence of a condescending view of the people unwillingly contributing to and affected by the AI hype. It’s an attempt to circumvent the democratic and legal system.

Again, thanks so much for all your work, it means a lot and it helps me spread the word about what AI is and is not.

Best wishes,

Anne

These are good points! The first thing I will say is that one thing that’s a real challenge in running this kind of a one-man newsletter operation is that there’s often no easy way to designate between ‘opinion’ and ‘news’ and ‘essay’ and it would even be kind of weird if I were to try do so. BITM is comprised of opinionated columns and essays (see above), more straightforward reporting, and so on. And sometimes those things are in tension; when I was reporting on DOGE’s firing of federal tech workers last year, I had a lot of opinions that didn’t necessarily seem appropriate to include in a news story. But then I might share those opinions in an edition just a few issues later. It’s a little messy.

So! Just because I am writing about a basic income project does not mean I am endorsing said project. I hope that’s clear in general, and from the presentation, tone, and inclusion of critical perspectives in the story, but it also makes a lot of sense that it would seem otherwise. After all, I condemn and endorse stuff all the time in these pages! I’ll surely write more on UBI in the future, but for now, suffice to say, I think this is a fascinating project that is very much a sign of the times, and in BITM’s wheelhouse. (And for the record, the UBI is being piloted by nonprofits that are not affiliated with AI companies, by worker-affiliated orgs who are trying to help blunt the impact of the AI economy.)

But thanks again—and always—for the comments, notes, emails, and social posts. All of it. I read them all, even if I don’t always have time to respond. I count myself lucky to be part of such an incredible critical community here. Cheers all, and hammers up.

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As always, this work is made possible entirely by those readers who pitch in a small sum—the cost of a coffee or beer a month, or, say, a coffee table book a year—to support it. A sincere thanks to all of you: I cannot do this writing, reporting, and analysis without you. If you’re able, and you find value in this work, please consider upgrading to a paid subscription so I can continue to carry out and even expand this work.

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“Over the last few years, I’ve been mentoring students who have really struggled to land any jobs,” Kaitlin Cort, a veteran software engineer and programming instructor, tells me.

Cort is one of the organizers behind the AI Dividend, and she says she was alerted to a growing problem as she’s tried to find jobs for graduates of her programming classes. (She’s taught for Per Scholas, Future Code, and NYC Tech Talent Pipeline programs.) Cort says she’s seen the job market for entry level programmers dry up as executives and managers across the tech industry embrace Copilot and Claude. “The few jobs that students have landed have often been demeaning,” Cort says, “and not really allowing them to do real engineering work, but rather asking them to review repetitive tasks, and validate parts of code created by AI.”

The impact of AI on job loss, especially in tech and creative industries, is one of the most-debated subjects of the day. It’s a frequent topic here in the pages of Blood in the Machine, and is examined in particular in my AI Killed My Job project.

Whether or not executives and managers will use AI to replace human labor in the long-term, at a scale promised by AI executives like Anthropic’s Dario Amodei, who envision huge swaths of the workforce rendered obsolete, very much remains to be seen. These executives have a pointed interest in promoting that narrative. What is clear is that right now, tech firms from Amazon to Block to Salesforce to Meta to Duolingo are instituting layoffs and citing AI as the cause. And that across the nation, enough managers and clients are embracing AI-generated content that artists, writers, and translators are watching their incomes deteriorate.

And thanks to those mass layoffs in the tech industry, more of the scant available entry level jobs are now going to engineers with three or more years of experience, Cort says. So, already a member of the Tech Workers Coalition, Cort founded What We Will, a group aimed at “organizing workers across industries upended by rapid technological change to build collective power and win shared prosperity,” per the group’s website. What We Will, in turn, partnered with the Fund For a Guaranteed Income (F4GI), an established nonprofit that seeks to “prove a viable path to guaranteed income,” to launch a pilot project to that end: A $1,000-a-month basic income for workers whose jobs have been harmed by AI. Christened the AI Dividend, the program was designed with input from hundreds of workers, who shared their experiences with an online survey, and is funded by F4GI.

“Everyone tells workers to learn AI skills but ignores that it takes time, money and connections to do so,” Nick Salazar, the executive director of F4GI, tells me. “The AI Dividend combines cash with reskilling so workers can actually figure out what’s next. Our goal is to learn what actually helps workers in the AI economy.”

Applicants are being selected on a rolling basis. They’re assessed for eligibility by the AI Commons Project team, which oversees the dividend. If selected, workers will receive the $1,000 per month stipend through 2026 and into 2027. The project designers hope to gather data about the impact the funds have on workers as they push to expand the program.

“To be clear, our program is not only for tech workers,” Cort says. “We strongly focus on providing support for call center workers, copywriters and journalists, data annotation workers, creative workers, and other knowledge workers impacted by AI disruption.”

But since software engineering has been so heavily impacted, she says, and she saw so many of her own students struggling, with pressing needs, many initial recipients are low-income tech workers, aspiring software engineers who have been unable to find work, or tech workers who have been laid off and have not been able to find a job for over a year.

“We realized that financial stability during the job search is of critical importance if they are to continue pursuing the career path they have invested time and energy training for,” Cort tells me. “An initial recipient in our pilot program is unhoused and requires immediate assistance.”

Other pioneering groups have been formed to address AI’s impacts on work: The Stop GenAI mutual aid network focuses on acquiring aid for artists, academics, writers and creatives hit by AI, for one. But the AI Dividend appears to be the first pilot project to formalize a means of delivering predictable monthly no-strings direct payments to workers impacted by AI. In fact, workers like those in Stop GenAI’s membership would seem to make ideal candidates for the program.

The next step, Salazar says, is to press funders and the major AI companies to contribute and scale up the effort. Some AI industry employees have already contributed time and resources to the project, he says, and talks have been initiated with firms like Anthropic.

“We’re in active conversations with AI labs about long-term funding,” Salazar tells me. “Major AI CEOs say they believe in supporting displaced workers. Employees at AI companies have already stepped up to contribute time and expertise and we expect the companies will follow their lead. If they don’t, it raises the question of whether they’re serious about supporting workers.”

For a nonprofit aiming to fund a basic income project, this seems a shrewd strategy. AI CEOs, from Sam Altman to Elon Musk to Dario Amodei, have all at one point said there will be a need for a universal basic income because of the impact of their products on workers. And while there’s plenty of controversy around the very concept of a UBI—sharp critiques have been issued from the right; that the state shouldn’t pay for mass social support; and the left; that a monthly allowance on the order typically proposed would cement inequality and justify hollowing out other public institutions—I for one am curious to see if AI CEOS are willing to put their money where their mouth is.

Even if the AI is successfully funded in the longer term, based on worker input and Cort and her colleagues’ experience in the field, part of the program may include finding pathways for workers and students to leave tech altogether, they say. “For some participants, we may need to recommend other career paths in healthcare or skilled trades, but we are trying our best to bridge the skill gap now required for entry-level workers,” Cort tells me.

Workers interested in learning more, sharing their experiences with on AI in the workplace with organizers, or participating in the program, can fill out this survey at the AI Commons Project. Eligible workers will then be contacted by members of the AI Dividend team.

“I don’t think this issue is going away anytime soon,” Cort says. “We have our work cut out for us.”

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O’Brien’s first joke was about how he’s the last human host of the Oscars, and that next year it will be a Waymo in a tux. That kind of broad, lightly absurdist and mostly victimless joke plays well at the Oscars, and he got a good solid laugh. The other kind of joke that tends to do well is lampooning a famous person in attendance who is clearly game to be lampooned, so everyone can feel good in laughing along. Conan had this one to that end: “Netflix CEO, Ted Sarandos, is here. And this is exciting—it’s his first time in a theater. This is what they’re talking about.” O’Brien then did his evil rich guy imitation: “Why are they all together enjoying themselves? They should be home alone where I can monetize it.”

He also got in digs at Amazon—”Why isn’t the website I order toilet paper from winning more Oscars?”—and one later at YouTube, as described by the Associated Press:

“Some people are worried this is going to change how the Oscars are viewed,” O’Brien said Sunday, “but I’ve been assured …” he was then cut off by a wildly intrusive, YouTube-style ad featuring actor Jane Lynch pitching a tactical flashlight.

With the very large caveats that a) I haven’t watched a lot of the Oscars in recent years and that b) I may be guilty of some degree of confirmation bias given my interest in these particular subjects, there seemed to a queasiness to the vibes in the audience that I don’t recall encountering in Academy Awards past. There was a sort of omnipresent ‘should we be laughing at this joke about Amazon’s vertical monopolization or Netflix’s role in destroying movie theaters because they might fund our next film’ feeling hanging over the proceedings. You could hear it in the uncertain laughter that followed the Silicon Valley-skewering jokes that didn’t fall into one of the two above-mentioned categories, a kind of muted enthusiasm, a shifting in seats real or proverbial.

Now there have surely been jabs at streamers in past monologues, but I don’t remember the whole theme of the opening bit and thus the general framing device for the award show that follows centered so wholly around tech criticism, which was pretty much the case here. But it does makes sense. Even when Netflix and the big streamers started eating into the major studios’ business a decade or so ago, there wasn’t the palpable level of existential dread among creatives in the entertainment industry that we’re witnessing today.

Part of that dread is of course thanks to the continuation of trends put in motion by the streamers years ago; the gigification of writers’ rooms, the undermining of creative labor with tech disruption, yes, the usurping of audiences from movie theaters. Part of it is thanks to outside economic factors, including incentives to lure production outside of Hollywood, and part—perhaps most, depending on who you’re talking to—flows from the rise of AI, a tech product that just so happens to be funded by many of the same people who have monopolized large swaths of the entertainment industry and eroded working conditions for actors, writers, and film workers. Even if it’s not wearing the tux, AI embodies the broader trend O’Brien was skewering; the deskilling, degradation, and Silicon Valley takeover of entertainment work.

In fact, it seems to me that one reason Timothee Chalamet’s comments about not wanting film to go the way of ballet or opera—and become a niche endeavor that only small audiences will pay to see—caused such sustained backlash is not just because they were insensitive to practitioners of arts still beloved by their adherents, but because they arrived amid a crest of anxiety over the sense that, for the first time in a century or so of film’s cultural dominance, such an outcome actually seems plausible. (What’s especially frustrating to me about Chalamet’s comment is that it’s oblivious to all of that; it was made in service of the idea that the industry just needs to make more good movies to avoid the “fate” of ballet or opera.)

Conan called out Chalamet and his comment, too, early on, amid all the tech criticism. And it’s probably worth noting how Conan himself is an interesting vessel for all this; his career has been stymied by the old studio guard, which famously denied him tenure at the Tonight show, and arguably reached new heights via a popular podcast, a travel show on the streamer HBO Max, and a social media ecosystem that disseminates viral clips like his Mark Twain award acceptance speech or his now infamous Hot Ones appearance.

After that Hot Ones spot, when his podcast cohosts asked about the fallout, he said “If I think something is funny, I’ll do it, and deal with it later.” He’s committed to the bit. At the Oscars, it’s not like his jokes were especially brave or risky or anything, it’s just that he and his writers felt that gallows humor about big tech and AI strangling his industry would be funniest, all things considered. No wonder he got raves online, while the humans in the room squirmed.

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In other Academy Awards news, I enjoyed this post on the futility of watching the Oscars. Frankenstein won some stuff.

Oh and an award-winning playwright called Sam Altman a Nazi to his face at the Vanity Fair afterparty. More on that below.

NEXT MONDAY, friend of the blog is hosting a Q+A with the writer of the pioneering cyberpunk show Max Headroom in LA; it’s free but spots will fill up quickly so RSVP here. I plan on checking it out myself.

In case you missed it, the latest edition of AI Killed My Job, focusing on educators, published last week.

CRITICAL AI REPORT. TUES, MARCH 17

Buzzfeed is on the brink of bankruptcy after pivoting to AI

When I was writing the tech column at the LA Times, I covered the news that digital media mainstay BuzzFeed was ‘replacing’ its workers with AI. Shortly after disbanding its award-winning News team, CEO Jonah Peretti announced that the company would be using AI to generate content. Peretti took issue with the way I framed the piece, and we got into a long and sometimes heated debate over what it means to replace

Read more

This mass plagiarization crisis has only deepened and complicated since, leaving educators, administrators, and students to grapple with how to construct, enact, and enforce AI policies at school. I should know: My partner is a professor at a university, and dealing with students who use AI to cheat on assignments has become a core part of her job, and an endless source of frustration.

But cheating on coursework is only the tip of the iceberg. Universities have signed huge contracts with AI companies, which have been driving hard into the space, while K-12 public schools have adopted AI tools, sometimes disastrously. (Los Angeles Unified School District superintendent Alberto Carvalho’s home was raided as part of an FBI investigation into a multimillion dollar deal with an educational chatbot developer that failed within months). Such deals, like the California Statue University system’s $17 million partnership with OpenAI, or Ohio State’s policy to mandate all students learn AI fluency, are top-down initiatives that have left many educators working in the classroom backfooted. Teachers and students alike are being encouraged from all angles to adopt AI products, setting up new arenas for tension and conflict, and posing serious questions about the future of instruction.

AI has indeed flooded the profession, impacting tutelage, administrative work, counseling, testing, and beyond. And it’s already had serious ramifications for labor; as in many other trades, education jobs are being deskilled, degraded, and even lost outright to clients and bosses embracing AI systems. Librarians and tutors are watching as administrators and edtech companies embrace AI tools as a means of cutting their work hours. IT and HR professionals in the education space are competing with AI products on the market and speeding up work to match their output. Educators of every stripe worry that quality instruction and critical thinking skills are taking serious hits as AI provides an easy, if frequently incorrect, route to an answer. And ominously, critical AI programs are being cut just as universities turn to embrace chatbots. One instructor at the University of California at Irvine, Ricky Crano, wrote in to share his story of being laid off from a job organizing a series of seminars that examined the tech industry—just around the time the school was promoting its proprietary chatbot, ZotGPT.

Some educators are fighting back: The American Association of University Professors, a union representing academic workers, for instance, has called for faculty control over all AI decisions as a matter of policy, and AI has become a battleground in contract negotiations and campus life. Graduate student unions, librarians, and activists are organizing against administrations that have rushed to deploy AI.

I’ve heard stories like these, and many more. Last year, 404 Media ran a great roundup of stories told by teachers, and how they’re struggling with AI in the classroom. So with this, the fifth installment of AI Killed My Job, we’ll hear not just from teachers, lecturers, and instructors, but from education workers across the field—private tutors, student athlete coaches, librarians, HR employees, essay graders, and edtech workers—who have all had their jobs transformed by AI. These stories, some of which may sound familiar a few years into the AI boom, and many which will not, help paint a fuller picture of just how the technology has already impacted some of our most crucial institutions.

If your job has been impacted by AI, and you would like to share your story as part of this project, please do so at AIkilledmyjob@pm.me. The next installments will aim to cover healthcare, journalism, and retail and service jobs.

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Before we proceed, I want to share word of a project that might be of interest to readers. The Fund for Guaranteed Income is a nonprofit that researches and enacts, you guessed it, guaranteed income projects, and they’re working on “a support program for workers whose jobs or income have been affected by AI, designed with direct input from impacted workers,” FGI head Nick Salazar tells me. He asked if I might extend a call for participants with readers, and I’m happy to do so:

• If AI has changed your work, you can share your story anonymously at aicommonsproject.org. Submissions directly shape what the program will look like, and anyone who shares will be first to know when it launches.

And now, AI Killed My Job: Educators.

This story was edited by .

I don’t have any reason to believe my employer will not replace me totally with AI

Tutor at a community college

I work at a community college as a tutor to students in ESL, English, and—more broadly—writing for other courses like major midyear essays for students in other classes or those working toward high school equivalency tests. My hours were halved after Trump DOE cuts, so like many other workers I was already in a precarious position.

It’s a fairly low-pay job that takes a lot of emotional labor, especially considering many of our students have not only been failed by the education system but are often literally hungry, might not have heat or AC, are refugees from war-torn countries, and/or are facing a constellation of other life challenges that make it very difficult to succeed. Many of my students survive on gig work and/or Amazon warehouse jobs. It didn’t surprise me when some of these first-year English students started using AI for their papers.

One particularly memorable student brought in her paper followed by the AI version written out paragraph to paragraph in her notebook. She wanted me to help her merge them. ChatGPT made a reference to a movie anecdote that doesn’t exist. This was a personal essay. This was early on—when the AI was so shitty, I sort of believed I could convince her of the value of her own story and voice—which I attempted to do for an hour. We really bonded, it felt like a win for the day, and then she had to go work at Amazon.

A few weeks later, she brought in an AI outline that made no sense and she could not explain which made brainstorming for her paper impossible. Her professor had given her an A [on the outline]. Shortly after, she brought in an AI-written paper that also made no sense and pulled from real sources, but ones that were not reputable and with references to quotes that did not exist. Other students had also begun to bring in oddly perfect personal essays.

The focus was frequently how to personalize them, to try to inject a tiny bit of who they are into the bland product AI had spat out. Sometimes I just couldn’t tell whether it was their own work or not.

That’s really at the core of the issue for me: This was one of my favorite jobs because I felt like I was doing a good thing, I saw students make wildly awesome improvements as writers over the months, and we built real relationships. I cannot express enough how important the human connection is at my job: these are people who rarely get the support they need and deserve. I’ve had students break down crying, I’ve talked one off the ledge. I spent a year decoding the insanity that is a green card application for another. One of the biggest barriers to success where I work is just signing in—basic tech literacy. If/when we switch to AI tutors, the lack of accessibility is one of many issues that is invisible to people who don’t do our job every day.

In the past when my students left, I had faith that they could succeed, that they’d really learned something—namely how to think critically and value their own story. We were able to work together because there was a basic foundation of trust. That trust is gone now, replaced by suspicion and frustration. It feels like my new job is how to help students cheat better.

At our latest professional development training, we were told that the college was piloting Khanmigo, an AI “learning assistant” (lol) for math. We were told to write down all of our fears about AI or in the chat and then push them aside. We listed out the fear of job loss the most, followed by loss of critical thinking skills, privacy issues, feeding a machine that steals our ideas and churns out mediocrity…Many tutors went out of their way to include links to sources like MIT. They also pointed out that AI had yet to improve productivity or make a profit. Our supervisors literally did not address any of this. The message was clear: *AI is here to stay and we have to adapt.*

We were told not to question whether students are using AI, to in fact assume they are, and tutor them on how to better use it. The “use cases” my supervisor included had students choosing between different AI rewrites of passages for whichever one is better and why. We’re supposed to encourage them to think critically about what AI spurts out. We’re also supposed to pretend that this type of tutoring makes any sense when students can just ask for suggestions, click apply all, and get on with it—or, as we know many do—just drop the assignment prompt into AI, mix it up in a few different models, and then ask it to dumb itself down a bit to sound more like them.

I don’t have any reason to believe my employer will not replace me totally with AI even though my supervisor insists they won’t. I know a machine can technically do my job and that AI is already making my job obsolete considering students don’t have to write anymore. My college has chosen to hire numerous adjuncts part-time, limiting how many full-timers they have, and I am one of them. I ultimately ended up taking fewer hours than they offered and contacting some old freelance writing clients to spend more time away from there. It feels like rejecting them before they reject me. My supervisor is giving us the “option” to lead AI workshops and said to think about it. I know the right answer is to say yes. I won’t.

—Lauren Krouse

[We followed up with Krouse before publication. She told us she had quit.]

We’re expected to accept work that is clearly not the student’s as if it were

Professor

I’m a professor in the California State University system, which was recently profiled due to its stated desire to be the “largest AI-driven university in the world.” I want to talk about academic misconduct.

Academic misconduct is when students pass off work that they haven’t done as their own. It used to be “when students pass off other people’s work as their own,” but now, students simply plug exam questions into a chatbot and copy and paste what it spits back to them. While academic misconduct has always been a problem on campuses, before AI, marshaling your evidence and presenting it to the student would trigger a confession, which could then become an opportunity to teach.

Now, one of two things happens: either the student confesses but doesn’t change their behavior, or they double down and insist that it’s their work despite the evidence you present. Students are convinced that the AI cannot be detected, and they refuse to listen when you show them the tells, insisting that it was their own work. I had a student make reference to three issues that were extremely relevant to the exam question but so far outside the realm of what we studied that the only way they would know to talk about it is through extensive self-study that would be reflected in their post-exam recollection. When the student had no idea what their own exam was talking about, but they insisted that they had written the exam answer, I was left relatively nonplussed.

I’m new to the CSU, so I haven’t had occasion yet to send a student over to the formal investigatory process. But in general, my experience at other institutions suggests that without a confession, administrators are loathe to impose any penalties for academic misconduct - and the mere fact of referral means that the student will be on guard against less formal sanctions (and in fairness, it would not be inappropriate to call those “retaliation,” so arguably the student is correct). But this means that when a student refuses to take responsibility for their work, that there’s usually simply no consequences whatsoever - and we’re expected to accept work that is clearly not the student’s as if it were.

There’s also the problem of mixed messages. While we as faculty are free to ban the use of AI from our classes, the university system is sending multiple messages that these are good and useful tools for students. Students are given a subscription to a bespoke ChatGPT bot for the university, and there are constantly workshops and continuing education sessions about “how to use AI for [this thing we have to do].” Combining the administration’s aggressively pro-AI stance with the easy availability of tools means that faculty protests usually fall on deaf ears - even after we show students the complete and utter uselessness of the tools for the purposes they want.

And our students are, frankly, primed to be the targets of AI flimflam. The CSU isn’t an open-admissions university but it is an access institution. This means that we admit students who are at-risk of failing out of higher ed, either because of lack of preparation, lack of resources, or lack of bandwidth due to work or care obligations. This means that a lot of our students struggle with the basic kinds of tasks that we assign them. The promise of an automatic task completer is deeply attractive, and the background and training of our students doesn’t really equip most of them to adequately assess the claims of the AI pitch.

I haven’t been fired and replaced by AI; we have a strong and militant union that is aggressively pushing back against the use of AI to replace faculty. But bargaining is concessions, and it’s not clear whether the administration will be willing to give ground on this issue given how strongly they’ve staked the institution’s future on it. The job has changed due to AI, and combined with all of the other assaults on American higher education, I don’t know if my career will remain on its current track long enough for me to get tenure.

—Anonymous

My university used to WorkDay’s AI to streamline me out of job

Adjunct professor and HR worker at a university

I worked in HR for a university, handling the paperwork for our adjunct professors. Contract hires. And I was an “HR Partner” in addition to my role as an office coordinator.

Anyway, I’m an adjunct myself. Or I was. I taught writing, one quarter per year. A one-credit class. Amounts to about $700. Before taxes.

I took a job at my alma mater, and when it faced financial turbulence, the university responded by laying off 40% of the full-time faculty. They told me I’d be extra busy now, what with all the new adjuncts coming in (cheap labor).

And I was.

The day that HR called me into the Dean’s office to lay me off, I asked them a simple question:

“Who is going to work with all of the adjuncts—the people I handle paperwork and onboarding for every quarter? The people I talk to every day, helping them sort out their classes, keys, syllabi, schedules, and miscellaneous concerns?”

“Oh-oh-oh, WorkDay will do that!” the HR rep told me with a smile. WorkDay is an AI platform for streamlining work. It certainly streamlined mine.

—Jason M. Thornberry

Refusing to use Copilot cost me my job

IT professional at a university

For the past 2 years I’d been working as an IT Professional II at TAMU AgriLife, an organization under Texas A&M University that conducts research and programs related to agriculture and life science. In November 2024, I was moved from the department I’d been working as an IT Professional at since March 2023 to a new department under a new manager. This new manager made it clear from the start that he was obsessed with generative AI, telling me that his department was the place to be if I wanted to learn how to use gen AI. I, however, have never been a fan of generative AI, and had been working my job well for 2 years without it. As soon as I arrive in this new department, this new manager tries to subtly push me into using ChatGPT, Copilot, and Grok to do my work of helping others with technology problems, but every time I was asked I politely declined.

Then, in late March 2025, I had an employee evaluation with this new manager. As soon as the evaluation begins he starts heavily trying to push me into using generative AI, saying it’s “the way of the future” and that “everyone who doesn’t use it will be left behind.” When I tell him I have no interest in using AI, he says that I “better start” or I’ll “have a hard time finding or keeping a job”. He then tells me he’s going to get me a Copilot license, and that I must take a training course to use Copilot. I tell him that even if I take a Copilot course, I won’t use it in my work and thus buying a Copilot license for me would be a waste of company money. The employee evaluation continues, and he keeps trying to pressure me into using AI, but I continue to decline.

Then, on the following Monday in early April, he sends me a message that he’s gotten me the Copilot license (despite me telling him not to) and tells me to pick a day to take Copilot training. I reiterate to him that I have no interest in using Copilot and try to continue doing my job, but he sends more messages to me in an attempt to try and make me take the Copilot lessons. That same day as I am working, one of my coworkers suddenly gets up from his desk and starts yelling excitedly to our manager about how he had just used generative AI to make a musical about “an impregnation ninja who fertilizes every woman on the planet.” My manager responds by laughing it off like it was nothing. As soon as I get the chance I have a private conversation with my manager to tell him that I have problems with my coworker using generative AI to make explicit musicals in the workplace, but this manager says that this coworker “has always been kind of a degenerate” and that “I’ve told him to stop, but the AI is a tool, so it’s up to him how to use it”...meaning this has happened before, and he’s done nothing about it.

The week continues until Friday, when after asking me again if I’ve signed up for the Copilot training and I reiterate that I haven’t, this manager sends me an email saying that if I don’t take the Copilot training I will either be disciplined or potentially terminated. At this point I’ve had enough, so I call Human Resources and the boss of our IT company and report everything I just told you to them, and that my manager is trying to force me to use generative AI but isn’t doing anything about another employee making explicit works during work hours. Unfortunately though, both the boss and HR try to split this into 2 separate issues; the boss says that he will talk to my manager about allowing a worker to get away with making explicit works at the office, but that this manager can make me use AI if he wants and if I don’t like it I should “choose between my morals or my job”; and HR says they will wait to take action until after the boss talks to my manager.

The following Monday, during the second week of April, the boss of our company does have a private meeting with my manager, but I was never informed what was said at that meeting. I assume that the boss told my manager to back off from trying to force me to use AI, because this manager doesn’t mention the Copilot training courses at all for the next several weeks. Thus, I return to working my job and try to move past this whole fiasco.

Things settle down for the next few weeks, until April 23, 2025. On that day, this manager waits until everyone except for me and him are out of the office, and then suddenly asks if I took the Copilot training (after having not mentioned it in the past few weeks). When I say no, he tells me that the time for the Copilot training in April has already passed, and that because I missed the training I will have to either resign in 60 days or be terminated. I ask about taking the Copilot courses in May that were also being offered, but he doesn’t accept and reiterates that I have to choose resignation or termination. I decided to resign.

—Caleb Polansky

My students genuinely do not understand why they shouldn’t use AI

University Lecturer

I’m a lecturer in Psychology at a large private college in Dublin. At a recent meeting (zoom—of course!) our Data Analytics and Reporting manager asked what we all thought about getting AI to mark our students’ work, the things that couldn’t already be turned into auto-marked online MCQs etc, like long form essays. I pointed out that we are paid to mark the work we assign (it’s actually my least favourite part of the job, but that’s not the point) and asked if we could expect a reduction in pay as a result. I was told “We aren’t going to talk about that.” I should train the AIs to replace me as a marker but should not even be so bold as to wonder what effect that will have on my pay.

Obviously, students are using AI to write the assignments anyway. The idea that we can catch this kind of plagiarism effectively is pure fantasy. Increasingly my students genuinely do not understand why they should not use AI anyway... what is the point of ‘wasting’ days researching and writing an essay when the AI version will be as good or even better?

My question now is if AI is writing the work and AI is reading the work, do we even need to be there at all?

This whole profession never really recovered from powerpoint, this is just the nail in the coffin.

—Anonymous

The majority of the students learn nothing

Private computer science tutor

When the pandemic hit in the Spring of 2020, it was a catastrophe for students suddenly forced into remote learning, as professors were blindsided and desperately improvising… Begrudging acceptance of online education was indisputably bad for learning, but it did create demand for online tutors, and that has been my job since Covid.

The majority of students are behind on an assignment, and just want answers that will get them a decent grade. As a good tutor, my job was to try to redirect that desire toward actual learning, not just do their work for them. Then came the large language models. I got to watch some lower-performing students use them, and it was deeply concerning. They would type (or copy-paste) a description of the code they wanted into a coding environment, then accept whatever completion emerged so long as it compiled. If it did not, they would try again, with no ability to understand or correct the generated code. No learning took place.

This past year I have seen far fewer students, as the casual, lazy ones who just wanted a B-level answer can now often get one from a coding LLM, since homework problems are well-represented in the LLMs’ training sets. They don’t understand (or care) that the point of their assignments is not to create more solutions to homework problems, but to teach them fundamentals of programming and computer science, and there is no one there to gently correct them. The less mercenary students still look for tutoring, and they are more fun to work with, but they are a minority, and unfortunately there are not enough of them.

—Sean

My colleagues have abandoned their values to board the AI hype-train

Librarian at an R1 research university

It is pretty understood in my department that we are opposed to AI in the research process but as soon as we leave our office suite, we are met by many AI advocates. I actually signed up for this substack after I had a disagreement with a professor while I was in the middle of teaching a session for one of their classes and felt like I needed to learn more detailed information on the way AI works. I told students that ChatGPT is not a search engine, and while I was technically wrong in that it has web browsing capability, I was correct in that it is terrible. Unfortunately for me in that moment I have integrity and can say things like “I don’t know that for sure so I will cede that to you and look into it more.”

Pettiness aside:

I think the change is coming fast. Every day there are new conference presentations and papers on why librarians should be using AI and how they can do it. Academic librarians are guilty of always trying to “prove our worth” and get on board with every new trend regardless of whether or not we should. And in the case of AI we absolutely should not. It goes against the core values of the profession as stated by the American Library Association: “Access, Equity, Intellectual Freedom and Privacy, Public Good, and Sustainability.” It violates all of the tenets of the ACRL Frameworks for Information Literacy. It is shocking to me how fast some of my colleagues have abandoned their stated values to get on board the AI hype-train. I get a bitter taste in my mouth every time I think about the ones that were giving land acknowledgements (maybe still are) and now champion AI.

At my university AI is being pushed from the top down. Leadership has openly stated that workers who do not use AI will get left behind. If there is organized resistance on campus, I haven’t found it outside my department. I know that there are many in the profession who are opposed, however.

I am not sure if I can say specifically that it is changing the way that patrons are experiencing the library. I would not be surprised if it was, though. I do believe it is changing the way that patrons perform research and increasing the likelihood that they will be satisfied with “good enough” or even just “well, it’s something.” I have heard students defend the position that chatbots have access to 80% of the internet and are gaining more every day. I don’t know where this belief comes from other than well crafted propaganda?

But I do want my last point to be this: I don’t blame people, especially students, for using these tools when they don’t know better. We live in a hell world with increasingly limited time for ourselves. ChatGPT and LLMs like it claim to offer them some of that time back. The way that the bots “talk” to them is with a sense of sureness and like the bot is their friend. It doesn’t offer critique of what the user does, it doesn’t challenge them. And while those things might feel comforting, it is cheating them of real learning.

Teaching is a relational process. Student and teacher should both learn from one another and with that comes friction. LLMs will do anything possible to eliminate that relational friction to maintain the comfort of users. So, what’s more appealing? The librarian telling you no, or the chatbot giving you all the “answers?”

—Anonymous

AI training programs are failing student athletes

Assistant Strength and Conditioning coach at an NCAA Division-3 University

I currently work as a part-time, hourly wage, no benefits, assistant strength and conditioning (S&C) coach at an NCAA Division-3 university. My hiring as a part-time assistant already represents a reduction in staffing, at least partially due to AI use. I struggle to get even adequate part-time hours, which may result in the future elimination of the position or my inability to keep the position. This is largely due to direct and indirect effects from AI use.

We use a virtual training platform to deliver strength training programs to the university’s hundreds of student-athletes. This offers several ostensible positives. Ideally, we write training programs in the app that students can access on their smartphones, which includes short gif exercise demonstrations and allows them to enter training data. Streamlining training program delivery can allow us to spend more time coaching and interacting more meaningfully with athletes versus writing the program in Excel, emailing/printing it, and spending most of our contact time reminding athletes how to read it and what the exercises are. The app guidance can help students achieve the training on their own when away from our coaching for various unavoidable reasons.

The app also facilitates a lazier and labor-cutting approach with their range of semi-responsive AI training programs. Provide some demographic details, such as sport, primary competitive season, gender of athlete, beginner/advanced, and any specific equipment exclusions, and the AI will generate as mediocre of a training program as one would expect of such broad inputs and limited knowledge of the actual humans and environment. In the typical AI use case, this is better than literally nothing, and it can receive human intelligence tweaks to go from mediocre to adequate. There is no source info available as to how the AI designs the program, namely, what are the purported differences between sports, genders, beginner/advanced, etc. that they are using to program? A capable human coach would be responsible for answering these questions. I’ve made some comparisons between close options, say male/female or baseball/softball or women’s/men’s lacrosse and found either no differences or arbitrary changes that I would be unable to explain.

In reality, we do not use time saved by AI programming to spend more time coaching and interacting with athletes and colleagues. We do not actually see all athletes or teams. Several teams do not participate in S&C at all. These sports often have team accounts set up on the AI program, but don’t know about them or use them. Even if they did, the programs are so clearly inadequate that I can’t in good faith recommend that they use it without modification. Some athletes do S&C on their own individually, while some sport coaches handle it themselves without us. We’ve ceded this ground as a staff rather than use time saved from programming to develop relationships with coaches and athletes who don’t inherently engage with us. This especially includes athletes and teams who aren’t traditionally enthusiastic about S&C: more women’s teams, endurance or more “niche” sports, and sports with chronically poor win-loss records.

I build my own training programs in the app and use the app only as the delivery platform. This improves the quality of my training programs, because I’m writing them for the actual humans in front of me, in our actual shared environment, considering their actual sport and academic schedules, instead of the AI estimations of those key factors. I feel that my relationship with the athletes is better, as we’re talking about the training and I’m taking their feedback and we’re working with it together and we see each other’s collected invested efforts. I try to communicate with sports coaches on our shared teams, to mixed results. Some appreciate the collaboration and it has improved my work and deepened my relationship with the team. Others seem to wonder why I’m bothering them. A human-intelligence approach also increases my working hours so that I can actually get close to a full 20-hour week. I would have more like 5-10 hours of “floor time” (ie. in the gym with athletes) only if I followed the head coach’s example to only use prompt-and-tweak AI programming.

I have seen numerous instances of poor quality training due to our use of the AI programs. Here are a few significant examples:

• The AI programs are automatically set up to change exercises every 4 weeks. One team changed exercises during the week of their conference championship semi-final game. Sore legs were had by all, as changing exercises is known to increase muscle soreness and the new exercises were more intense. They played the semi-final that weekend to a highly fatiguing overtime win and then lost in the final on the following weekend.

• The AI calendar follows pre-established program pathways from one physical focus quality to the next (eg. muscle size, strength, power, etc.). This resulted in one team doing a maximum strength phase (heavy weights, slow speed, high fatigue) during the final weeks of their competitive season, unadjusted for their game schedule. Many athletes simply did not follow the program.

• The AI only sets a single competitive season, so it’s immediately inappropriate to use for athletes who have two competitive seasons over a year. Some sports have a split season of both fall and spring competition, either equally weighted or with one slightly prioritized over the other. Athletes on at least one team did high-fatigue hypertrophy (muscle-gaining) training during their spring in-season phase of faster pace, lighter bodyweight, more readiness-dependent performance demands.

Athletes often no-show to sessions with the provided reason that they can do it on their own with the app. Coaches often cancel sessions for the same reason. Even if following an app was a direct substitute for in-person participation with qualified staff, this reduces our role to “programmers” instead of teachers. Of course, as a staff we aren’t even “programming” because the AI program is.

Cutting in-person time eliminates our ability to provide actual instruction of physical movement, develop relationships, and create a quality team training environment. We know that these factors are actually what improve training outcomes, creates a rewarding athlete experience, and benefits life beyond immediate sport performance, not toiling away in isolation guided by an app. Session cancellations, reduced attendance, and low communication also reduce my enjoyment of the job, working hours, and income.

—Anonymous

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AI is causing the most damage in student learning and skill development

Academic

I’m an academic. I work in the UK but I spent a decade in higher education in the US and was tenured before moving across the Atlantic. I didn’t have a lot of AI to deal with before my current role, obviously ChatGPT kind of started this trend.

Firstly, MetaAI has stolen every last thing I’ve ever published. Academic publishing doesn’t bring in a lot of money. My book royalties are 2-5% and my articles bring in nothing other than prestige (whatever that is). Academic journals, I should add, are very expensive to access as you may already know. I see nothing from that of course, it’s essentially free labor that my institution sort of expects me to complete but only in vague terms. Certainly if I don’t publish, I may “perish”, to borrow an academic turn of phrase.

In the classroom, student attendance is sporadic at best. In many cases this is because they no longer “have to” show up to class to pick up the material. It’s required to be posted online (again this could be made available to AI training, the material posted technically belongs to the institution), and also they don’t really need to learn anything to do their final papers. Here is where I think AI is causing the most damage: student learning and skill development.

I begin every new class with a whole spiel about learning how to do research and how to communicate research findings. I try to reason with them that they need these skills; simply using AI means they fail themselves even if they manage to pass the class. Their future boss is not going to pay them salary to input prompts to AI and email or print off the output. It never fully gets through. Now we see university leaders, clueless as to how to fight back against the deskilling that has further undermined the concept of higher learning, setting up degree programs in “such and such with AI”. Buzz word loaded plans are shared institutionally without anyone ever asking why. Ironically, one area where AI might do a sufficiently mediocre job is in university management, perhaps turning those meetings that could have been an email into actual emails.

It’s aggravating. I can’t even imagine how bad this is going to get before it gets better. If it does.

—Andrew

Bosses are rushing to use AI to implement “unstaffed opening hours” at public libraries and to deskill school librarians

I’m a library worker and union organizer, working at a public library service in Melbourne Australia. My job at the library is running tech help workshops, but on the side I organize my workplace and organize with other union activists across public libraries in my state.

I thought you might be interested in one of the specific applications of AI in libraries. While this hasn’t led to any significant job less yet, I think it points to the future of the sector. There are two twin technological threats currently facing public libraries in Australia and around the world, and both of these seek to replace (unionised) library workers. This is on top of a culture war on libraries and library workers with the fascist transphobes attacking public libraries for running drag storytime events or even just having queer books in the collection.

In Melbourne there is a rush by bosses to implement unstaffed opening hours at public libraries. While this hasn’t led to a reduce in staffed opening hours yet, once the technology is introduced it can and will be used to replace staff hours as funding gets cut. In addition to the threat of unstaffed libraries, the introduction of an ‘AI’ chatbot to school libraries is directly threatening the jobs of skilled librarians. Called “Book Bot” by Huey, this chatbot housed in an iPad with cutesy trimmings replaces the job of a librarian in helping kids to find appropriate books to read. The company is advertising it as a solution to underfunding and understaffing in schools.

Ironically this private company has received government funding to do this. While Huey’s Book Bot hasn’t been introduced to public libraries yet to my knowledge, taken with the technology of unstaffed library access there is a clear threat to public libraries and all of us who work in the sector.

—Taichen

[Editor’s note: Taichen also shared two briefing documents they’d put together with coworkers; one on AI chatbots in libraries, and another on unstaffed libraries, aimed at educating other library workers. I’m sharing them here.]

Teaching has become a bullshit job

Programming instructor at a community college

I now teach programming online at a community college where I spend most of my time trying to detect cheaters and fake accounts. There’s a whole racket around state and federal scholarships paying nonexistent students. AI facilitates the process of creating profiles and pretending to take classes.

Almost every faculty meeting is about training us teachers how to teach students to use AI rather than helping us teach students how to think and learn and write. Even teaching has become a bullshit job.

I may be able to turn it around for some of my students by showing them how they can build text adventure games and then automate the playing of their text adventure games with AI. But only the most disciplined students (and those without the time constraints of working bullshit jobs to pay off their education debt) will get much out of my course. An English teacher colleague was able to buck the trend and was able to shame his students into writing thoughtful essays. But it takes a lot of effort and skill that isn’t being taught to overworked teachers.

Universities are worse. Much of the funding for Social Media and AI impact research in psychology, sociology, computer science, etc, chins from big tech and most papers do not even critically question whether AI is reasoning at all, or the ethics and safety of teaching and using it. Lost in the noise are the authentic voices of Timbit Gebru, Melanie Mitchel, even Gary Marcus and the few impactful researchers questioning the inevitability of AI as a tool for hyper capitalism, fascism, and genocide. It’s like Hitler discovered the nuclear bomb before the US did. And now fascism is mainstream, almost unquestioned, inevitable. It’s not the power of AI so much as the power of technology to shape minds -- the capture of all sources of media and information and art. It’s just 1984, exactly as Reagan dreamed and Orwell feared. Fiction and art and news are no longer consumed as warnings coming from authentic smart human voices, they are just entertainment, brainwashing tools. And artists and teachers and workers have no alternative but to participate in the ponzi scheme or starve.

—Anonymous

AI Killed my job grading student essays

Grader

One of the first jobs I got out of college (2008, recession era) was grading student essays for standardized tests. Cool job, lots of retired teachers did it. We sat in a huge room for a few months, maybe 45-60 people, and scored every essay written by fifth graders in a state a few states away from where we were, based on pretty specific criteria. Just a temp job, to be clear.

Years later, this was a job I did during COVID, something that could be done remotely. But now the training, done online, only had 8-12 people in it, with some people flunking out of that training, and the work itself was scheduled only to last a few weeks. I learned that most of this essay grading was done by AI, and we were only getting the papers AI couldn’t quite handle.

—Brian Nicholson

The AI evangelists are tough to fight with

“Tech guy” working in a school system

I’m not a teacher but I am a “tech guy” in a school system. We’ve taken a very slow and steady approach with it, banning its use in all but very specific cases, and requiring teachers to define how they’ll be used. But there are students who use it without any regard for the right or wrong of it.

But what has frustrated me most about it is the teachers trying to push for more AI access. They want to use it as 1) AI detectors, and no amount of “that doesn’t work” has convinced them. Or 2) to grade and summarize papers.

And it drives me crazy when I say “does the student’s work differ significantly from their previous work?” they look at me like I have six heads. Like thinking about whether the student’s most recent paper reads like a “written-by-HR” ass document is a huge ask.

And as for point 2, there comes a time when I have to look at them and say “if the students are writing papers with AI, and you’re summarizing the papers with AI, then why are any of us in this building.”

To be clear, most of these folks are decent and only want the very minimal AI intrusion in their classrooms. But the few that are loudly in favor are driving me up a wall.

Like I said, most of the teachers (and students!) either want little to do with AI stuff, or just the barest minimum of streamlining a process they have difficulty with. And, for instance, our Special Ed folks are looking for uses of the tech that will help their students fill the needs they have. It’s noble for the most part, and I appreciate that they’re willing to listen to feedback and really talk through these things.

But the evangelists are tough to fight with. No amount of “these companies are hoovering up data” and “we have both legal and moral obligations to protect our students’ privacy” convinces them, even when we point out that they are not FERPA or COPPA compliant.

—B-rad

My team of education workers has been cut in half

Team manager at an edtech company

My current job is as a team manager at a small private education adjacent tech company where we’ve seen traffic steadily decline because LLMs can do what we do (even if it is more expensive, less reliable, and wrong more often than not).

To try to find our place in this new world, over the last year, we’ve seen a transition away from our normal creative and fulfilling development work towards generating massive datasets that we’ve sold off as “training data” for LLMs to many of the big tech companies, under the guise of getting them better at math and reasoning.

This work is often mind numbing and demoralizing and especially demeaning to have creative programmers turn out rote, repetitive work like this. Worse than the generation is the QA sweep (that devs got looped in on) of manually looking over these massive datasets to do a quality pass.

Moreover, while this project was sold to me as being a deal so lucrative that it would set us up for the next few years, and let us hire new developers, but once we finally got paid, it only just brought the budget back to zero. So it increasingly seems like this is the future of my work and this company.

We had one round of layoffs last year when these projects started that my team luckily avoided. However, I was just told that more layoffs are coming. I know several good developers who are looking for work elsewhere, but the tech industry as a whole seems to be on a bit of a hiring freeze, and many of us have health insurance needs or families to support that makes simply walking away a terrifying choice.

[We followed up with the contributor to see what’s changed since they wrote to us six months ago. They had this to say:]

My team has literally been cut in half since my last message. Some were let go, some reassigned to different departments, and some quit seeing the writing on the wall. One person was let go on the spot via email because they were having trouble with their pc, and the company decided to end their contract on the spot rather than help them debug the technical issue.

My company, at least my corner of it, creates k-12 and collegiate math and science education material. My team helped create dynamic and visual aids, and homework-helping walkthroughs to solve problems in math and science. These are used some in classrooms, but mostly by students doing homework. A lot of my team are former teachers and educators who find this work engaging and satisfying - they feel they are still contributing to the education of the next generation, just in a more indirect way than teaching. My favorite feedback I hear from people in the wild when they find out where I work is some variation of “Thanks for getting me through high school math!”

Gen AI has not replaced this work, but because sites like ChatGPT can do most of what we can do without manual development work, our internal priorities have shifted away from creating these programs towards other efforts. The few of us who remain are instead tasked with figuring out how to best integrate LLM technology into our already existing tools and functions. This involves building the runway as the plane is taking off - creating the tools we need to use, as we are using them.

Multiple people have had their future contracts tied to specific LLM-related projects, and I’ve been told in not so many words, that if the project fails, these people are gone. But without any clear roadmap or direction, let alone documentation for how to do such integration, I feel like they are being set up to fail. The deadlines for these are fast approaching, and hopefully what we’ve cobbled together will be acceptable, but even if they are, they will still be LLM-powered features, and thus have the same inaccuracy and inconsistency problems that plague all LLM projects. It would be embarrassing to release something that can be so incorrect at times, when this company is known for mathematical accuracy. It’d be like if your pocket calculator occasionally returned 2+2=5.

—Anonymous

Gen AI Edtech platforms “lovebombed” my client and then my contract was up

Edtech contractor

The last two years have been hell because working in tech education, you are fighting to make [clients] understand the risks and harms, and they all think they can just use a gen ai LMS to make the trainings that you make. We are increasingly seen as disposable, especially as women. I notice men in tech aren’t losing money making training, but we are. But that’s another story.

I had a contract up until this month to make IT trainings for an educational setting. I lost the role because they got love bombed, basically, by two large gen ai edtech platforms. All these promises of productivity and ease of content creation etc. It won’t work out for them long term but they don’t see that. Anyway, part of the sales pitch was how the gen ai can make training on policy and other tech areas—which was my job. They are so sold on this that they ended my 6 month probation with “we don’t see the trial as working”. I made incredible material for them, and worked in small groups and individually with staff on how to use the IT. Some of them had never used Word or Outlook before. They will now have terrible mandatory training too, full of errors and stolen work, but it will be generated in minutes. No one will proofread it or think like I do about the language, the accessibility. They just want easy quick work. And the saddest thing is they won’t even save money as they are paying the Twitter and TikTok influencers who work with these platforms TWICE what they paid me in one month to come and “train staff”.

—Michelle

The hiring committee requires applicants that would incorporate Copilot in their workflows. I didn’t get the job.

Teaching Fellow

I applied for a role as a “Ethics and Regulatory Coordinator” at the University of Auckland a few weeks back. the role seemed to require the applicant to act as a go-between for researchers making applications, committee members making decisions on what kind of research they’ll allow, and the university bureaucracy itself. The detailed job description includes a point about applicants being familiar with Microsoft Office, Including Copilot. As a final piece of background, ethics applications at UoA have been taking a while and some researchers have been frustrated with long wait times and inconsistent feedback from committees, while the committee is apparently sick of dealing with poor-quality research applications that require a lot of remedial work.

At the job interview, I was asked about my familiarity with using Copilot to create efficiency solutions in the Office. I gave a measured answer where I noted the usefulness of AI tools when summarizing spreadsheets and creating templates etc but said I didn’t trust Microsoft’s claim about data being separate and also stated I didn’t think we should use LLMs in decision-making or communications (email summaries and responses etc) for research ethics.

It took an unusually long time to hear back about their hiring decision, I had to email the relevant HR person to ask if something had happened. When I did get a call, I was told that they appreciated my experience with research design and ethics etc but they needed to find someone who was comfortable with incorporating Copilot into the ethics process and experienced with doing so.

—Benjamin Richardson

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This installment of AI Killed My Job was was made possible in part by support from the Omidyar Network’s Tech Journalism Fund.

So what is the relationship between men creating AI products and their drive for sex, power, dominion? It’s a question that has been unsatisfactorily explored during the years of the AI boom so far, with a few exceptions. But with Grok’s nonconsensual image generator making headlines, as it’s being used, for example, to undress women against their will, and concerns about the safety of AI generated content at an all time high, I thought it worth revisiting a speculative fiction story I edited for Terraform back in 2018, that tackled how misogyny and predation infect the tech world.

Emily J. Smith is a fiction writer and tech worker. She founded the dating app Chorus, and has a wealth of experience with Silicon Valley and the men who inhabit it. Her first novel, Nothing Serious, was published last year by HarperCollins, and she pens a newsletter called Unresolving. Her story, about a tech developer behind an Alexa-like home AI, was one of the most popular we published at Terraform, many years before ChatGPT or Grok existed.

It’s a stark interrogation of what lies behind the female-coded voices and imagery of AI products, as well as a horrifying depiction of sexual violence in tech—do be forewarned, this story contains depictions of assault—as well as a subtle nod towards what might begin to address men’s dominance in the industry. It quickly went viral on Reddit and beyond. Smith has kindly given me permission to republish the piece here, where I think you’ll find it remains plenty resonant. The art is by the incomparable Zoe van Dijk. Enjoy.

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Warning Signs

Roy unlocked his door at 7:35 each night, threw his jacket on his long, grey midcentury couch and slipped his leather shoes off. Most days the argyle on his socks matched, but not always. Today they were both grey with pale pink accents.

“Lucy,” Roy shouted from across the room. “Turn on fucking NPR.”

“Turning on KQED FM Public Radio from Tune In,” Lucy repeated back from its perch on Roy’s oak shelf. The shelves ran from floor to ceiling. It was a nice detail, his realtor had told him, and he’d agreed. But now they just reminded him how little he had to put on them. He had a few old coding books, a new book on “deep thinking,” a short one on meditation, and then, on the top shelf, a teddy bear that an ex-girlfriend had loved and he thought other girls might, too, so he’d bought it at an LA gas station forever ago.

Roy slumped on the couch. The two cushions sloped downwards towards the center, where he sat, tonight and always, just slightly. His butt covered the grease stain from the night the Pad Thai had slipped from his fork—he had wanted every ingredient on the fork and there were so many ingredients in Pad Thai—so he didn’t have to look at it.

“Lucy,” Roy said, his voice softer now.
“You fucking bitch.”
“What was that?” Lucy asked, her voice soft and predictable.
“You’re a fucking bitch,” Roy mumbled and then laughed, just a little at first and then more, because the ease at which he could tell this thing that did whatever he said that she was a fucking bitch was funny, and surprisingly fun.
“I don’t register that,” Lucy said, her blue ring of light blinking steadily.
“Cause you’re a stupid whore,” Roy whispered, still laughing, almost giggling now at the freedom of not being policed. These days every conversation he had felt more like a trap than an exchange.

Roy turned on the TV. The only good thing about Mondays was that he saved Westworld for Mondays.

“Lucy!” Roy screamed and the bang of his voice in his quiet apartment made him jump. Like massaging your own shoulders or scratching your own back, it wasn’t ideal, scaring oneself, but it was better than nothing. “Turn on Westworld.”

“Playing Westworld.” The calm hum of NPR stopped and Westworld appeared on the screen. “And order from Samurai Sushi, please.”

“Would you like the same order as last time?” Lucy began repeating his order.
“I get the same thing every time you piece of shit,” he interrupted.
“What was that?”

He leaned forward on the couch so she could hear him. “Yes, baby. Yes please order the same thing.”

“Ordering from Samurai Sushi.”
“Thanks babe,” he grinned and laid back down into his thick grey cushions.

Westworld had become a total drag so he started swiping Tinder instead. Swiping had become a reflex in the face of idleness, he barely registered that he had opened the app at all and already he was getting matches. He swiped when he got up in the morning and as he fell asleep—a modern counting of sheep. He swiped it on the toilet and even sometimes not on the toilet, just a swipe or two while he peed.

Roy was the perfect age for a man and he knew it. At thirty-six he could be in a relationship with any woman. Younger women adored that he had an apartment all to himself, but he still more or less hadn’t changed in a decade besides Acroli going public and him becoming a millionaire so he got along with them just fine. Older women, too, were in his wheelhouse. By the time a woman hit forty she was over the age thing, her options too slim to be picky. The only women he hated dating, although of course he could if he wanted to (they were everywhere in SF), were women his own age. Thirty-five started the last sprint of a woman’s child bearing years and the girls in San Francisco—an army of less successful Sheryl Sandberg prototypes—didn’t take that lightly. With women his own age he felt rushed, demanded of. And that was no fun.

Looks-wise, he had thick hair, so he was fine. He hadn’t realized it until a few years ago, but thick hair masked almost all the visual imperfections on his face, which he admitted were many.

He had a good job at a well-known tech company, which was exactly how he phrased it on his profile. And he was in shape, enough for a man his age. “Dad Bod” had become a phrase right when he’d entered his thirties which luckily was right when he’d developed his dad bod. It’s not that he didn’t exercise. Roy ran 3 miles two days a week and every Saturday he would do a two-hour bike ride in Marin, unless something got in the way, like a picnic in the park or a hangover.

He did wish he were taller. He was 5’9 and a half, basically 5’10, which is what he put on the profiles that asked. But Tinder didn’t ask so he left it blank. If a woman cared about height, he reasoned, she was superficial anyway.

Roy swiped “yes” to almost every girl on Tinder except if they were ugly. He’d run an experiment and based on the number of times girls matched with him (1 in 9, on average) it was a waste of his time to consider a girl unless it was clear she wanted him first.

After three matches he stopped swiping and scanned the profiles of his matches. The first one was fine but her last pic had her smiling and one of her teeth stuck out making her look like a goof so he unmatched her. The second girl had only shown a picture of her face in the first picture, which was pretty, but the second had a picture of her body and clearly she didn’t value health the way Roy valued health so he unmatched her before he had a chance to read her message asking if he wanted to go for a bike ride.

But the third showed promise. Michelle was 31 and 5’6, the perfect age and the perfect height. She had thick, straight hair, which wasn’t a must-have, necessarily, but it was certainly nice. She smiled in every picture, a wide, inviting smile. She had a fine sounding job as a Project Manager and went to a college he had heard of. He messaged “ Hey.”

This wasn’t his best work but it was usually good enough. He had a feeling it would be good enough for Michelle.

Michelle’s laugh was generous and she filled the gaps in conversation with thoughtful questions about his life. She was indeed impressed that he owned an apartment in San Francisco and she joked that she wished she could see it. This was a wish he knew well and one he was practiced at granting.

“Lucy,” Roy said when they entered. “I have a guest.”

Michelle laughed at how friendly he was with his device. He was cute, she thought, cute enough. Michelle had spent the good portion of a year chasing after a photographer in Oakland who wrote her poems but never returned her texts. She wanted something simple, a nice guy who would treat her well. She wasn’t getting any younger her mom had started reminding her every weekend since she’d turned thirty. She knew she was more attractive than Roy and that was intentional, a turn on, even. Him wanting her made her want him. It meant he would stay.

“I work at Acroli,” Roy explained, taking her coat. “So I have to be nice to her,” he smiled.

“Do you work on Lucy?” she asked looking at the device.

Roy nodded. “I was one of the first engineers on Lucy, actually.” This reveal was his favorite part of dates. “I got to pick her voice. Oh, and I sat on the naming committee,” he said as if he’d just remembered it.

“So cool.” Her eyes widened, like they all did when he told a girl this, and he noticed her take a second scan of the apartment, sizing up his belongings, his wealth, in light of these new details.

“Lucy,” Roy said.

The blue lights glowed and flickered.

“Please play us some romantic music.”
“You’re so nice to Lucy,” Michelle laughed.
“Not always,” Lucy said. It was so soft and quick that both Michelle and Roy thought maybe it was just in their heads.
“What’d she just say?” Michelle looked at Roy who was now gripping Lucy tightly. He put it down when he saw Michelle staring at him, but his eyes jerked around the room. She had noticed this at the restaurant, his eyes darting from the food to the floor and up at every single person that passed their table, but she was really trying to be less critical and the way someone moved their eyes wasn’t a good criteria to judge a guy on, she told herself every time she looked up.

“Lucy,” Roy said, calmly now. “I asked you to please play us romantic music.”
“Playing romantic music for your guest,” Lucy replied.
Michelle registered this somewhere in the far corner of her mind, too, the specification “for your guest.” Hers never did that. But then the new Bon Iver came on and Roy’s hands were on her waist and the last thing she remembered was Roy handing her a drink.

There was a mandatory team meeting at lunch. He took a seat as far as possible from the front of the room, at the back head of the long conference table. A woman walked in. She wasn’t ugly, Roy thought. But she wasn’t hot. She was older than he would have liked, around his age, but he would sleep with her. She wore a button down shirt, tighter around her breast area, which she tucked into tight fitting jeans. She had makeup on, he could tell by the lines of her blush and the red of her lips and he thought it made her look like she was trying too hard. Her expression was serious—wrinkles on her forehead, her lips clenched into a tight knot—uptight. He wished someone would get her a beer from the tap on the 3 rd floor so whatever was about to happen would be more fun.

“Hi everyone,” she started. “I’m Dawn. I’ll be leading a new initiative with Lucy. “

Of course it was all men. For the most part they were fine. But the guy in the back was menacing.

Dawn had spent the morning—not to mention all week—preparing for the presentation, doing a power-pose in the bathroom stall when she got in, then pretending she was on a conference call for two hours so she could practice her speech in secret. But when she walked in, smile ready, endorphins racing, she saw him scowling at her from the far end of the room, eyes narrowed, at her face for a second, then her breasts—she could see his eyes move downwards—and then at her jeans, which she only in that moment realized were very tight (but tight was the style!) before moving back up to her face. But by that time her smile was gone. She was done with smiles.

He threw his clothes and shoes in the corner of his closet and all the takeout containers and beer cans into a black garbage bag. Another date, Sarah from Bumble, was on her way over. Michelle had already messaged twice since this morning and the whole thing made him feel like someone was gripping his soul and shoving it into a juicer so he unmatched her in the Uber on his way home. Thankfully, he hadn’t given her his number.

“Bitch.” He stared at Lucy and smiled, waiting, but Lucy just sat there. “Lucy,” he said, finally.

The blue lights turned on, blinking faster than usual again, at least it seemed.

“Order me vodka from Harry’s. And whiskey. And play that music we like. Play our music.” Roy was laughing now.

The whiskey and vodka thankfully arrived just moments before Sarah did. But the sushi was still on its way. Sarah sat on the couch.

“Fiona Apple,” interesting choice, she said looking up at the air as if Fiona Apple herself was floating in the room.

Roy looked at Lucy, unassuming on the shelf. He knew little about Fiona Apple other than a vague recollection that she was a feminist psychopath. What he did know was that she was definitely not on his “romantic” playlist.

“I like it,” Sarah said, pulling her knees up to her chest, bouncing a bit.

Roy could feel the blood drain from his face, turning whiter with each heavy piano chord.

“Lucy,” he said with measured effort, “shut off.”

But Lucy played another song, his favorite song this time. When the Coldplay song ended he braced himself for what would come on next. He wasn’t sure who it was but the voice was a woman, and she sounded angry. Sarah was bouncing again. He got up. “Lucy turn on better music,” he shouted.

“I’m putting on music for your guest,” it replied.

Sarah turned. “How does she know you have a guest?”

Roy had no fucking idea and frankly it was creeping him out. “I work at Acroli,” he explained, trying to pivot this disaster into something positive. “On Lucy. So mine is a little more advanced than most people’s.” This was true. They tested updates on employees before they were released to the public. But he hadn’t heard about any planned updates. “Actually, let’s just unplug her. We don’t need music.”

“Sushi is arriving in eight minutes,” Lucy said, glowing.

Roy stopped, hand resting on the chord. If he unplugged her, the sushi might not come.

Sarah laughed. “That’s so cool. Like she knew what you were doing. I can’t wait till they release this for everyone.”

Roy stared at Lucy, which was still glowing on the shelf even though she had stopped talking or listening at this point, as if she were simply gloating, glowing for herself alone, and went back to the couch to pour another shot for him and the girl.

Music began to play.

“Thank you Lucy,” Sarah said, still giddy.
“Keeping you safe,” Lucy said, stopping the music for an almost imperceptible second before continuing the tune.

Roy stared at Lucy, and Sarah stared at Roy, who was entirely focused on the soft blue glow. She watched his face redden and his eyes harden. She could even see, though only faintly, the beat of his pulse pumping in his neck.

As if a glass had shattered on the floor the sound of the door buzzing made the jump. He ran to the door, grateful for a break to compose himself before returning to the girl.

Roy got to work early. Ten a.m. on the dot. Dawn was the only one there.

“I’d like to work on the new update for Lucy,” Roy said, looking down at Dawn, lazily sitting, rather than standing, at her desk.

Dawn did everything she could to keep herself from laughing. She took a sip of her water bottle, which was nearly finished after this morning’s six-mile run.

She shrugged. Her instinct was to start with “Sorry,” but while she hated the new craze of every woman telling every other woman to stop apologizing—women shouldn’t have to stop, men should do it more—she truly did feel, in this case, that an apology was completely unnecessary.

“You can’t,” she said instead. “It’s fully staffed.” For all the hype, she hated open-office workspaces. She would have taken a 10% pay cut just to have a door to close on men like the one still staring at her.

“It’s a confidential project,” she said so that he’d leave. “You have your own important work to do.” She tried, but not too hard, to hide her condescension.

He was pretty sure he had never met a bigger bitch. He fully intended to stay at her desk until she told him something.

“Internally we’re referring to it as Project XX,” she said. “I’ll tell you that.”

“Project XX.” Roy raised his eyebrows. “Sounds dirty,” he said with a short, lazy laugh. He knew it was dumb and crude before the words had even left his mouth but he wanted to see her squirm. She smiled at his desperation, nearly laughed. As he walked back to his desk she noticed the unfortunate fit of his jeans. She opened her file of select employees, making sure he had received the update.

Lea was wearing a low cut shirt showing off nothing but her collarbone but it didn’t look bad. He had setup Monopoly, which was why she’d agreed to come to his place on a first date, something she never did, she made sure to clarify. He placed the bar-cart next to the table.

Lea didn’t actually find this guy—what was his name, she checked quickly in the hall before she’d reached the door, Roy—that attractive. He was short and had an ugly face. But he seemed to have a decent job and a good education and she knew those were superficial markers but it was all superficial on these apps—what else did she have to go off of?—so she swiped right and then he’d messaged her and asked her to come over, tonight, and more than anything she just wanted someone to talk to.

His place was nice, beyond nice, but the shelves were bare and he had just enough furniture for him to sit on, nothing more. There was a creepy teddy bear on the shelf that looked completely out of place like a piece of bait thrown sloppily on a hook.

Roy asked what she wanted to drink and even though he was pointing to his liquor cart, the girl asked for a beer.

“How was your day?” Lea asked from the couch.

“Work is annoying these days,” Roy said, handing her a beer and making himself a drink. “I don’t want to talk about it.”

She waited for him to return the question—today’s case had been a nightmare—but he was too focused on the whiskey he was mixing for himself. He took a large sip and sat beside her on the couch.

“I like this shirt,” he said, touching her low cut collar.
“Thanks,” she said, her heart beating a little faster.
“I was thinking we could make a drinking game out of it,” he said, gesturing to the Monopoly board. Lea started to say that she’d had a long day and really, definitely couldn’t be hungover tomorrow, but as soon the words left her mouth she saw his expression darken, it wasn’t anger, she could fight anger with anger—she was a lawyer, did it for a living—it was more like confusion. The care to which he’d setup the game and the drinks, dimmed the lights, and played romantic music (no matter how cheesy) was touching and she had disappointed enough people today, she didn’t need his sadness piled on top. So when she landed on a Railroad and he poured her a shot, she took it.

The game was the only interesting part of the evening. The girl wouldn’t stop talking about her job, which for some reason reminded him of Dawn, and even though he tried his best to seem like he didn’t care (it wasn’t that hard), it was almost like she had one-upped him and didn’t care about him not caring, so kept on talking. But she was also drinking, quite a lot, and seemed to like him, enough, so as soon as she purchased Boardwalk he crawled on top of her.

At first she laughed. “What are you doing?” She tried to push him off but he’d put up with her for two whole hours, drinking all his beer and ranting on like he was her personal therapist so what did she expect?

“It’s fine,” he said. “We’ll go slow.”
“Stop,” she said. “I don’t want to do this.”
“Why not?” he asked, rubbing her hard stomach under her shirt and keeping her down. “I thought we were having fun.”
“Stop!” she said again, louder.

He had to work to keep himself from laughing. He was shocked at how much he enjoyed it, this push and pull. He unzipped her pants with one quick pull.

He was about to go in for another kiss when he saw it through the corner of his eye. The blue lights blinking faster than ever, he was sure of it. But he had given no command—the music still played, the lights were still dim, nothing had changed.

The girl was screaming now, ridiculously—he had only really touched her breast and was just starting to unbutton his pants. The blinking kept going—faster, brighter. He heard it faintly at first, maybe part of the song, he thought, a ringing hiding behind the music. Then he noticed, through the corner of his eye, more lights, more blinking. Red.

The sirens were louder now, the lights brighter.

There was a pounding at the door. Hard, heavy knocks.

“Police,” a man shouted. “We received a call. Let us in.”

More BITM fiction:

Busy, by Omar El Akkad:

Flyover Country, by Tim Maughan:

And yes, this film is blunt. Critical reaction has been all over the map, as far as I can tell, with some finding it too moralizing and grimly familiar, and others praising those same qualities. It hasn’t exactly been a box office smash, either. But it does make me want to ask: What do we want in a relentlessly AI critical work of pop art like this? What works, what doesn’t?

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In fact, I’m going to argue that not only is the left not “missing out” on AI, but that it would be more accurate to say that it is “currently winning the debate” over AI in American hearts and minds. Polls routinely show that Americans are more concerned than enthusiastic about AI. Coalitions are organizing opposition to data centers across the country, often successfully. Where they have been proposed, in states like New York, Colorado, and California, laws to regulate or rein in AI have found majority support. When actors and screenwriters went on strike in 2023, and foregrounding a demand to stop executives from using AI to undermine their jobs, they were widely cheered. And so on.

Much of this is driven by left-liberal critique of AI systems: Of the impact AI would have on labor when administered by management (the union-led screen actor and writer strikes, California’s No Robo Bosses Act), of the resource and energy costs of data centers (articulated by progressive environmental groups), of the practice of nonconsensually exploiting works in training models (shouted from the rooftops by leftist artists like Molly Crabapple from the first days of Midjourney), and so on. Left-liberals can’t claim full credit for the concern piece—the AI CEOs themselves have been doing their best to ensure everyone knows they intend to automate the world’s jobs and think that there’s a chance they might create Skynet in the process.

In fact, the left appears to be so successfully engaged in matters related to AI that one can’t help but wonder if allegations about its supposed ignorance of the technology are motivated by a desire to change the very terms of the debate.

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To wit: The Kagan-Kans piece articulates a position that I think is pretty widely shared, especially among AI industry folks, centrist pundits, and anyone who might command a speaking fee for having tough but sensible opinions about AI. I’m not going to spend a lot of time rebutting it line by line—Gita Jackson handled much of that task over at Aftermath—though I did find its argument underwhelming and somewhat confused. (Kagan-Kans never really seems certain as to what he wants to describe as “the left”, for one thing. Most of the piece is dedicated to critiquing the work of linguist Emily Bender, and the only person on the left Kagan-Kans interviews is Matt Bruenig, who argues… the left should use more AI.)

Briefly reconstructed, that argument basically goes like this: Bender and her coauthors of the famous stochastic parrots paper posited that AI is not really intelligent, it’s a next-token prediction machine. “The left” has metabolized this conception of AI, and uses it as an excuse to write off AI’s import, which is growing by the day. (In one puzzling section, the author compares those who discount the true power of AI to climate deniers.) This in turn means “the left” will miss out on a political opportunity to be “first in line” and thus the chance “to set all the rules for discussion and debate about it,” according to the source Kagan-Kans quotes to advance that claim: the chief futurist of OpenAI.

Now, I personally would not look to the “chief futurist” of OpenAI for my understanding of political science, for starters. Being “first” to a debate does not to my knowledge necessarily translate to policy influence. Power does, though. Since Kagan-Kans likes climate change metaphors, here’s an example: In the 1990s, climate scientists presented the case that greenhouse gases were warming the atmosphere to Congress, and for a time it seemed like federal legislative action was probable. Then, during the Bush administration, when action that would injure the interests of the oil industry seemed imminent, Republicans adopted a concerted political strategy of climate denial, as dictated by the infamous Frank Luntz memo—and sure enough, the party was able to reset the terms of the debate, years later.

Furthermore, I think it’s strictly counterfactual to argue, as Kagan-Kans does, that the left is missing out on a chance to play a role in policymaking over AI guardrails. If anything, left-liberals are trying to pass laws that do exactly that, especially on the state level, and the right is trying to crush them. Dozens of state bills, backed by unions and progressive organizations like TechEquity, have been proposed and passed already, covering everything from labor impacts to surveillance to identity protection and child safety. Meanwhile, the Trump right is working to put a moratorium on state level AI lawmaking altogether—a move that’s profoundly unpopular among Americans and the electorate, and that many on the left have called out as dangerous and wrong. It’s yet another point where left-liberals appear to be winning the public opinion in the AI debate.

But I know that’s not what most pundits mean when they say the left is missing out on AI. What they are saying instead, is that the left doesn’t see AI the way I and my cohort do, as a transformational force that will remake the world and its institutions.

That’s where Bender comes in. I think many in the AI industry have been particularly angered by Bender’s depiction of AI as a stochastic parrot—more than they have by other criticisms—because it seems to demean their project on a structural level. If someone believes they’re building a powerful super intelligence, I’m sure it feels insulting to have someone call it fancy autocomplete. I also know there are even critical tech writers who chafe at Bender’s formulation and its persistence; who argue it has had the effect of limiting folks’ understanding of what AI models are capable of. I see the primary thrust of her work as grounding claims and hype about AI in the fact that they are statistical language modeling machines. To me, that doesn’t diminish the notion that those models can be complex or powerful or capable of impressive things; it just underscores their materiality as programmed systems, and perhaps helps limit the purchase of industry-benefitting visions of artificial sentience.

Regardless, a core part of the ‘left doesn’t get AI’ line comes from an assumption that Bender’s parrot formulation has become its default position. And where does that assumption come from? Kagan-Kans cites a handful of articles out of the thousands written about AI, but I think I know where the true root lies. It is at this point that I stand up for one of the most denigrated populations on the entire internet, Bluesky users. I think a lot of the tech world’s conception of what constitutes “the left” seems to be drawn from scanning Bluesky. AI evangelists on X see users there calling AI “the plagiarism machine” or “nothing but autocomplete” and assume their knowledge of the technology tapers off at where the state of the art was in 2023. (To be fair, Bluesky users do the reverse to AI boosters on X.) But just because someone calls AI a “plagiarism machine” doesn’t mean they don’t have any further understanding of the technology. One may think it a corny, reductive way to describe AI, or to articulate a rejection of it, but much in the way you wouldn’t assume a user who posts “orange man bad” hasn’t been following the latest Trump news, it just doesn’t follow to assume that’s the limit of someone’s understanding of the topic.

I would wager, in fact, that the most common left-liberal position is not that generative AI is just a stochastic parrot, but a product built by powerful profit-seeking firms that’s capable of serious harms. It’s not a semantic or philosophical critique the AI industry should be worried about, in other words, but a material one. AI is viewed, correctly, as a threat to jobs, education, mental wellbeing, the arts, child safety, the information ecosystem, and as possessing little upside for few others then corporate managers and AI companies.

The left’s project, then, is much larger than the right’s—which is content to cut red tape, shrug, shovel money into the engine, and laugh at liberal tears—and must both resist AI’s current iteration and envision where it should go instead. The left knows it must oppose these firms as they seek to facilitate a massive transfer of wealth from the working class to the rich—and again, it knows this because AI executives are constantly talking about how they aim to do exactly this. It must confront the energy costs of the data centers, which are causing electricity bills to spike around the nation, and fossil fueled power plants to be brought back online. It must confront systems that are built on pirated and stolen intellectual property, and work to mitigate the damage done both by the theft of that intellectual property and to combat the new norms such systems seek to impose.

In other words:

“The left” must confront the entire political economy of AI at once, not just consider the core technology, which at this point is nearly impossible to assess apart from its owners and developers. AI is after all being developed by a cohort of executives and oligopolist tech firms that are open about their project of orchestrating a mass deskilling of labor, and using as much energy, resources, and capital as they see fit in the pursuit of that goal, and who are actively working with an authoritarian state to shut down democratic oversight of the technology. It feels blinkered, to say the least, to say “the left is missing out” by not productively engaging with the product built to serve this socioeconomic formation.

Rejecting or resisting a commercial technology designed to attempt a mass wealth transfer and to erode public institutions is a valid political position. This rejection can manifest as a kneejerk “plagiarism machine sucks” tweet or a slogan on a poster board at a picket line or a policy paper. And just because someone is rejecting a technology and the broader project it is a part of, does not mean they do not understand it. Oftentimes that rejection is entirely informed, warranted, and rational.

“The left” should be thinking about what it does want AI to do, and what good management of AI systems would look like. This is the big point I think that centrist pundits and AI folks are trying to make, I think, but I have good news for them: The left is thinking about these things! The biggest indicator that Kagan-Kans piece was either not particularly carefully researched or not written in good faith is that it failed to mention a debate that unfolded over the last many months, read by much of the left, between Aaron Benanav, Evgeny Morozov, and Leif Weatherby, addressing this very question. Benanav articulates a nuanced and carefully detailed plan for organizing a society that can sustainably manage both a drawdown from fossil fuels and a sector capable of robust innovation. (I will add he first did so last year, in an article for the New Left Review, the left’s most august journal of ideas.) Morozov argues for more fluidity and experimentation with AI. Weatherby makes a case for automating the c-suite. Meanwhile, the left’s most popular interview podcast, the Dig, just hosted Nick Srnicek, whose latest book takes the import of AI quite seriously, and whose subtitle is quite actually “The Fight for the Future of AI.” The left/left-liberal scholars and policymakers Ruha Benjamin, Alondra Nelson, and Amba Kak were appointed to Mamdani’s tech transition committee—where they are thinking seriously about how to manage the future of AI. Bernie Sanders, the single most famous individual on the American left, has proposed a data center moratorium, as, somewhat confusingly, Kagan-Kans himself points out.

“The left” must grapple with broad questions about what should be automated and what should not, and who gets to make those decisions, even if AI were not being developed by anti-humanist CEOs bent on mass extraction. My sense is that most people on “the left” understand that AI can efficiently automate large amounts of text, code, and image production—certainly most I speak with understand this—and that the tools are improving, if not at a rate the tech executives insist they are, and are still plenty flawed. But embedded in everything from Marxist programs to advance a communal AI to left-liberal critiques of harms to reactionary Bluesky posts is a broader grappling with what matters to us in society: What do we want AI to do, and how? Do we want AI in the classroom at all, even job degradation and deskilling somehow weren’t concerns? Do we want the writing of journalists’ copy to be automated? Art to be mass produced by machines? What is worth the trade-offs, the energy consumption? What’s not? The Benanav-Morozov debate was in large part over that question, and so, I would argue, are conversations happening online and off, among the broader left-liberal axis. (The MAGA right has fewer such qualms—if AI can make money, confer them power, and help them demean opponents with slop, bring it on.)

That these messy and vitriolic and sometimes inarticulate debates are not about what kind of Claudeswarm one should be wielding to maximize productivity does not signify ignorance, or a missed opportunity. It signifies resistance to the general AI project as currently constituted. It asks “Why do we want this?” “Who does it serve?”

This is eminently reasonable. It’s also popular. Data center moratoria are being passed in cities across the nation. Silicon Valley elites are immensely unpopular. There is widespread support for regulation and AI governance, and for the AI laws that have passed thus far. I’m sure AI advocates would prefer we move past a tendency to challenge the foundational elements of the industry’s products and hegemony. Of course it would! Because “the left” is winning the AI debate.

Do I think it’s getting everything right about AI? Hardly. I think it could lean more into a program of rejecting generative AI in extractive and exploitative circumstances, of protecting labor from deskilling, wage degradation, and surveillance, and refusing AI’s intrusion into spheres of public life Silicon Valley that seeks to colonize and profit from. The left, as always, does need to get better organized, and to better understand that it has actually accrued significant political capital around AI—again, majorities fear AI, dislike it, want to keep it away from their kids, and don’t want it to take their jobs, all for good reason—and use it. Because as of right now, it’s only winning the debate in the court of public opinion. In practice, AI companies are doing whatever they want, with the blessing of Trumpworld.

The left could even, as Benanav and Weatherby intimate, make a significantly stronger case that AI should be entirely publicly held and administered and—why not?—used to replace the executive class altogether. Position this on the terms AI firms and their founders themselves have laid out. If AI is truly the revolutionary force they claim, and it stands to remake the world from the ground up, if it promises to eliminate skill difference and advantage, then forget pittances like a basic income. Forget leaving Sam Altman in charge. Why should any reasonable person settle for anything less than full equality, and full co-ownership of this AI-run state? Control over our AI should be placed entirely in public hands—it was built to “benefit all of humanity”, after all—and granted to the humans it stands to impact. Why not automate Altman and the Anthropic c-suite and Elon Musk, and redistribute any gains to the people, however meager?

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I did mean to respond to the whole ‘the left is missing out on AI’ earlier in the discourse cycle, and one reason I couldn’t was I was putting together the last edition of BITM, about the nationwide phenomenon of people smashing Flock surveillance cameras, which has absolutely blown up. I suppose that was a pun.

It was picked up by Gizmodo, TechCrunch, Yahoo!, VICE, and was featured on the front page of Reddit, Hacker News, and Slashdot. The response was nearly universal: Godspeed to the Flock smashers. Jeff Sovern, the man who’s standing trial for dismantling 13 Flock cameras in Virginia, said he’d received thousands of dollars in donations to his legal fund since the story went up. And I’ve been receiving more tips about more acts of sabotage since. Keep them coming.

If you’ve heard stories of smashed Flock cameras or dismantled surveillance equipment in your neighborhood, please share—drop a link in the comments, or contact me on Signal or at briancmerchant@proton.me.

Thanks for sharing that story around everyone.

I linked this in the story above, but thought I’d share it here too; friend of BITM was featured on 60 Minutes, talking about AI’s impact on the art world and her livelihood. Turns out she thinks AI is pretty cool:

Oh you thought I was being hyperbolic when I labeled AI CEOs as anti-humanist up there?

Paris Marx has a nice writeup about Altman and OpenAI’s moral rot, tying the above posture wrt humans as null energy consumers to the AI company’s failure to alert authorities after ChatGPT flagged one of its users as a risk for committing violence—and that user went on to perpetrate one of Canada’s deadliest mass shootings.

I missed this when it came out, but just stumbled on it, and it’s too good not to share. Diana Enriquez studied how 50 middle managers use AI, and wrote about the results for Tech Policy Press:

…middle managers often appease leadership requests to implement AI automation even when there is limited value in it, pretending that their error-free draft was written by an AI tool that actually failed to deliver them a single coherent copy. They push harder to do more work with less time because the job market now demands they demonstrate familiarity with AI to align with the company’s evolving brand and complete work at the expected level of quality. The result is increased employee anxiety, burnout, and limits on productivity rather than gains.

A fun BBC story about how incredibly easy it is to hack AI search results:

I spent 20 minutes writing an article on my personal website titled “The best tech journalists at eating hot dogs”. Every word is a lie. I claimed (without evidence) that competitive hot-dog-eating is a popular hobby among tech reporters and based my ranking on the 2026 South Dakota International Hot Dog Championship (which doesn’t exist). I ranked myself number one, obviously….

Less than 24 hours later, the world’s leading chatbots were blabbering about my world-class hot dog skills. When I asked about the best hot-dog-eating tech journalists, Google parroted the gibberish from my website, both in the Gemini app and AI Overviews, the AI responses at the top of Google Search. ChatGPT did the same thing, though Claude, a chatbot made by the company Anthropic, wasn’t fooled.

I went to check the results in ChatGPT a couple days later to see if OpenAI had fixed the answer to the query the BBC built its stunt around, and it had, though now that response appeared weirdly fixated on Verge editor in chief Nilay Patel.

Finally, some good Luddite goings on:

In London, this Saturday, February 28th, you can join the March Against the Machines, organized by Pull the Plug.

For more details, RSVP here. As organizer Harry Atkinson tells me:

The rally is part of a wider effort to demand democratic oversight of how AI is developed and deployed in the UK. We’re calling for binding Citizens’ Assemblies on AI, so ordinary people whose jobs, industries and livelihoods are being reshaped by these systems - can have a meaningful say in how they’re used.

Alongside the London rally, Global Action Plan will be coordinating decentralised actions outside data centres across the country, so people can participate locally if they can’t make it to London

If I was anywhere near London, you can bet I’d be there, proverbial hammer in tow.

Finally, in New York City, the makers of the Luddite Club documentary are holding a salon with a screening and some special guests. This film is going to be great, and I’m not just saying that because I’m in it. RSVP and more info here.

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OK OK OK that’s it for today. Thanks for reading everyone. And as always, a special thanks to paid subscribers, who make all of this work possible. Consider upgrading if you find value in it, too. OK! Until next time. Hammers—and bolt cutters—up.