Today's links

• The world has moved on: Notes from the enshittocene.
• Hey look at this: Delights to delectate.
• Object permanence: "Jpod"; Barlow v Glickman; Cyclist v bike lanes; Judge v copyright trolls; "The Uncertain Places"; Thatcher v Palin; NY v Time Warner; Banks v negative interest rates; Keeping the new web decentralized; "Prisoners' Inventions."
• Upcoming appearances: LA, Menlo Park, Toronto, NYC, Philadelphia, Chicago, Edinburgh, South Bend.
• Recent appearances: Where I've been.
• Latest books: You keep readin' em, I'll keep writin' 'em.
• Upcoming books: Like I said, I'll keep writin' 'em.
• Colophon: All the rest.

The world has moved on (permalink)

Douglas Adams wrote, "Anything that is in the world when you're born is normal and ordinary and is just a natural part of the way the world works. Anything that's invented between when you’re 15 and 35 is new and exciting and revolutionary and you can probably get a career in it. Anything invented after you're 35 is against the natural order of things."

I think about this quote whenever I get angry at the technology around me. When I rail against the Great Enshittening, am I simply committing the sin of nostalgia ("Nostalgia is a toxic impulse" -J. Hodgman)? I am, after all, old.

I've written before how conservatives' yearning for "simpler times" is really just a wish to be a child again. The reason times seemed simpler during your childhood is that you were a child, and if your parents did their job, they shielded you from a lot of the complexity of their adulthood so you could enjoy your childhood:

https://pluralistic.net/2025/04/24/hermit-kingdom/#simpler-times

That's where the "National Customer Rage Survey" comes in. It's been surveying a panel of 1,000 representative consumers every three years for a decade, continuing a research project that started in 1976. The survey measures respondents' attitudes towards the businesses they deal with, and as of 2025, it's fair to say, customers are pissed:

https://customercaremc.com/2025-national-customer-rage-study/

We're experiencing more problems with the products and services we use. Those problems are more severe, they make us angrier, and they produce lingering stress. More and more, we are seeking revenge on the businesses that piss us off.

So it's not just me, an old man yelling at the cloud. The world is getting shittier.

The latest Customer Rage Survey inspired The Guardian's Heather Timmons to launch a new investigative series looking at how fucked up everything is. Her inaugural installment is very good, and it's drawn a massive reader response:

https://www.theguardian.com/us-news/ng-interactive/2026/jun/04/us-consumer-rage-prices-economy

I spoke with Timmons this week about the series. She told me she's been deluged with emails from readers who feel that the world is different now – and many of them cite my work on enshittification. Timmons wanted to know what advice I had for her readers. I told her that I don't think you can solve this as a consumer, because this isn't a market problem, it's a political problem, and shopping isn't politics:

https://pluralistic.net/2026/05/21/purity-culture/#stop-fucking-that-chicken

Later, Timmons forwarded one of those emails to me. It gave an eloquent and evocative account of just how rancid the vibe is these days. The writer said that when they and their spouse encounter this rot, they cite Stephen King's Dark Tower novels, quoting the oft-repeated phrase from that series: "The world has moved on."

At this point, I should warn you that the following contains some Dark Tower spoilers, so if you're planning to read a decades-old (but very good) dystopian western/science fiction crossover series, and if spoilers bug you, this might not be the essay for you.

Spoiler alert!

Still with me? OK, then.

In the Dark Tower novels, we crisscross a fallen world in which decay is all around us. The buildings are rotten, the machines have stopped working and no one knows how to fix them, babies and livestock alike are frequently born with deadly congenital defects. Much of the world has fallen into wasteland, cracked and barren. An army of wreckers, led by the demagogue John Farson (who styles himself "The Good Man") are slowly but surely conquering the land, laying waste to those few remaining outposts of civilization and conscripting the young men in the conquered lands to march on their neighbors.

It wasn't always this way. There was a time when the world was defined by hope and virtue and light, when the machines were fixed and the crops were harvested. Life wasn't golden – there were still squabbles and sorrows and even wars – but life was good.

And then the world moved on.

For reasons that no one truly understands, the normal push/pull of decay and renewal turned into a one-way, irreversible process in which everything that crumbled or snapped or burned up couldn't be repaired or replaced or recovered. Our mysterious ability to beat back the Second Law of Thermodynamics – an absurdity we probably should have always treated as an aberration – has collapsed. The world has moved on.

The Dark Tower series is a long, long, long Bildungsroman, with many detours through the life-stories of the characters in the ensemble cast, as well as the biographies of many of the figures they meet along the road. It's mostly an adventure novel, as road-trip tales tend to be, but those character studies and the lore that they surface – from our world and theirs – creates an overwhelming, many-layered, richly textured sense of loss and worse, of despair. For the world has moved on, and despite the love and care and bravery of many of the people in that world, the world cannot be redeemed. Each terrible day of those people's lives is the best day of the rest of their lives. From here on in, it only gets worse.

When Timmons' reader and their spouse greet every fresh depredation in modern life – hours on the phone with customer service to resolve a billing error that the company repeats every month, say – with "the world has moved on," they are invoking something heavy. This isn't just a rancid vibe, it's the fucking end-times.

For all that the Dark Tower novels are a series of cracking adventures and thoughtful character studies, they are also a mystery. Over and over again, we are made to ask ourselves, why has the world moved on? Was it John Farson and his army? Was it the Man in Black, the evil wizard whom the book's protagonist has pursued across time and space? Was it the Crimson King, the evil force whom the Man in Black serves?

Well, yes – and no.

Midway through the novels, we learn that the Crimson King and his evil minions have laid siege to "the beams," vast ley-lines that span the universe and provide the force that pushes away entropy, creating breathing room where repair and care can live. "All things serve the beams," we're told. The beams are the organizing force of the universe, the answer to the riddle of how such pitiful things as we could have fought back remorseless entropy for so long. By attacking the beams, the villains of the series have all but snuffed out that force, and so the world has moved on.

When I read that email and the invocation of the Dark Tower, I was immediately struck by how apt this comparison is. Because, as I've written many times, there were always enshittifiers who would have plundered your data and money and treated you with naked contempt:

https://pluralistic.net/2025/03/04/object-permanence/#picks-and-shovels

There were always enshittifiers, but those enshittifiers faced external forces that checked their wreckers' urge. They were held in check by competition, and regulation, and workers' sense of fairness and duty, and by the threat of new products and services that might pop up to correct the defects they deliberately introduced into their products by enshittifying them.

And the foundation – the Dark Tower upon which all the beams converged- was antitrust enforcement, grounded in the idea that we could not afford to let any company – not a "good" company, nor a "bad" company – get so large that it could no longer be regulated, lest its executives become "autocrats of trade":

https://pluralistic.net/2022/02/20/we-should-not-endure-a-king/

The same people who laid siege to antitrust law would later come after all forms of checks and balances. These are the people who gave us the "unitary executive" and Project 2025, and the collapse of accountability that has allowed the worst people to commit the gravest sins they could imagine and still reap vast fortunes. These beam-breakers wanted kings, and they got them.

I collect definitions of "conservatism," and one of my favorites comes from Corey Robin's book, The Reactionary Mind. Robins asks how it is that we can call so many disparate, irreconcilable ideologies – various ethno-nationalisms, imperialism, financialism, patriarchy, Christian nationalism, libertarianism, white supremacy, etc – "conservative"? What binds all these views together?

https://pluralistic.net/2025/07/22/all-day-suckers/#i-love-the-poorly-educated

Robin's answer: the foundation that all these otherwise disparate views share is that some people are born to rule, while others are born to be ruled over. When these lesser people are elevated to positions of power, their inferiority creates a system of misrule, by which we all suffer. The best outcome for everyone is for us all to know our place and defer to our social betters.

That's why conservatives are obsessed with affirmative action, DEI, and any form of anti-racism. For them, the discriminatory outcomes we see in the wild are natural, reflecting the in-born defects in the people at the bottom of the social order. That's why, after every plane crash, every collision between a cargo ship and a bridge, every spectacular corporate bankruptcy, conservatives race to uncover the race, gender, religion and sexual orientation of the captain, the pilot or the CEO.

If the person who oversaw the catastrophe has anything remotely resembling a marginalized identity, then this is loudly trumpeted as confirmation that "diversity hires," promoted above their station, are ruining our society and wrecking our bridges. Naturally, if the person in charge was a wealthy, well-born, straight white guy, that's just proof that shit happens – it definitely doesn't prove that white straight guys, as a class, should be removed from positions of power.

For conservatives, virtue is "whatever the people who are born to rule desire." Hence Frank Wilhoit's definition of conservativism, "exactly one proposition, to wit: There must be in-groups whom the law protects but does not bind, alongside out-groups whom the law binds but does not protect." It's not a crime if the president does it. It's also not a crime if your boss does it, or if a monopolist does it, or if ICE does it. It's not a crime if the IDF do it, or if the Epstein Class do it. "Taxes are for the little people":

https://pluralistic.net/2021/06/15/guillotines-and-taxes/#carried-interest

The attack on antitrust law was part of the attack on the rule of law, the campaign to put everyone back in the their place. It's a piece of the effort to establish a new hereditary aristocracy, and every hereditary aristocracy requires heredity serfs (that would be us):

https://pluralistic.net/2022/11/06/the-end-of-the-road-to-serfdom/

The ideology of economism – which says that market outcomes are the only way to govern a society – cashes out to "the strong do what they can and the weak suffer what they must." If we interfere with mergers, or labor practices, or commercial conduct, we "distort the market," which is literally going against nature:

https://pluralistic.net/2022/10/27/economism/#what-would-i-do-if-i-were-a-horse

That's why Trump dismantled the consumer protection agencies, the antitrust agencies, the labor protection agencies, the environmental protection agencies. When someone in power cheats the system, that's not a crime, no matter how many people they rob, maim or kill. As Trump told us on the debate stage in 2016, that kind of cheating "makes me smart":

https://pluralistic.net/2024/12/04/its-not-a-lie/#its-a-premature-truth

That's why Elon Musk (almost) got to force every pension saver in America to bail out his money-incinerating AI business and his failed social media takeover – because the rules that protect everyday investors are "for the little people." Musk's mistake was trying to get a bunch of billionaires to hold the bag, too. The one form of systemic violence our society will not tolerate is trillionaire-on-billionaire violence:

https://www.cnbc.com/2026/06/05/spacex-blocked-from-early-us-benchmark-index-entry-as-sp-reaffirms-existing-rules.html

The world has moved on. 50 years of neoliberal rule has weakened and snapped the beams – the rule of law, consumer and labor rights, civil rights – that radiated from our Dark Tower – antitrust law, which blocked the emergence of the "autocrats of trade." The people who besieged these beams had the same motives as the Crimson King and John Farson and the Man in Black: they were willing to pay any price for a world free from consequences for people like them. They knew they were born to rule, and that the rules were "for the little people," that breaking those rules "made them smart."

They wanted "bossism." Or, as rendered in the original Afrikaans, "baasskap," which means, "the social, political and economic domination of South Africa by its minority white population":

https://en.wikipedia.org/wiki/Baasskap

Not for nothing, baasskap is the foundation of Muskism, the ideology that Elon Musk epitomizes, even if he can't articulate it:

https://pluralistic.net/2026/04/21/torment-nexusism/#marching-to-pretoria

In "The Utopia of Rules," the late David Graeber described how neoliberal deregulation produced exactly the kind of state that we were warned we'd get under communism. Thanks to monopolies, all the stores were the same and they all sold the same goods. Thanks to the dismantling of labor protection and unions, no one had enough money to get by. Thanks to elite impunity, we were ruled by monsters who committed crimes in the open and thrived as a result. Thanks to unchecked greed, we paid everything we had for healthcare, only to be denied treatment when we needed it. Thanks to the dismantling of the welfare state, more and more of us had to wait in long lines to fill out absurdly long forms in triplicate. Thanks to the intrinsic instability of such a terrible system, more and more of us ended up in prison, and protest became more and more illegal:

https://memex.craphound.com/2015/02/02/david-graebers-the-utopia-of-rules-on-technology-stupidity-and-the-secret-joys-of-bureaucracy/

Graeber pointed out that the rise of the web made it seductively easy for people in authority to force us to fill in forms. When analog bureaucracies impose paperwork costs on us, they also impose paperwork costs on themselves, because processing and filing those forms requires substantial effort, even if filling in those forms requires even more effort from us.

When it comes to virtual paperwork, the asymmetry is even more pronounced. Sure, it takes some admin to set up an online form and write the scripts to process its outputs, but that's a one-off. The form-giver can perform a very little admin and still impose a giant, repeated admin burden on the rest of us.

AI has only made this worse. Now, thanks to vibe coding, everyone can produce a form and its associated processing and analytics back-end with prompts, which creates a grave moral hazard. The kinds of activities that I used to fill in a single short form to accomplish now requires ten lengthy forms, created by different people in the same organization, all asking for variations on the same information. Through AI, we have democratized bureaucracy. It's Kafka-as-a-service.

What's more, when you're dealing with a monopoly, you have no choice but to complete whatever paperwork they throw at you. And when the vibe-coded back-end scripts shit the bed and lose or misinterpret your data, you have no choice but to endure an infinite telephone hold queue (if you're lucky) or get shunted to a customer service bot (if you're unlucky):

https://pluralistic.net/2025/11/11/sorry-to-bother-you/#we-dont-care-we-dont-have-to

It's entirely possible to build webforms that are thoughtful, fast, respectful of our time, and well-processed. The problem is that fielding these forms requires that the form-giver undertake some intensive, moderately expensive work (once), while skipping this step merely requires that we all perform intensive, time-consuming work (over and over and over again):

https://mohkohn.co.uk/writing/html-first/

This is how we end up with government forms that require you to list every trip you have ever taken to the USA, since your infancy, with every flight number, which you can only get help with by talking to a chatbot that emails you an out-of-date PDF no matter what question you ask of it:

https://pluralistic.net/2026/02/06/doge-ball/#n-600

This is how we end up with massive customer service queues, long lines at tills, and no one at the gate to answer your questions when your flight is canceled. Understaffing is a form of enshittification, one that shifts value from shoppers to owners, and shifts consequences from owners to workers:

https://pluralistic.net/2026/03/22/nobodys-home/#squeeze-that-hog

This is how we end up with broken machines that no one can fix. Firing workers and replacing them with chatbots or contractors means incinerating their process knowledge – the precious, inchoate, unrecorded understanding that keeps everything working:

https://pluralistic.net/2026/04/08/process-knowledge-vs-bosses/#wash-dishes-cut-wood

This is how companies that make products we love suddenly decide to wreck those products: when the only consequences for shitty products is angry customers with nowhere to go and no one to vent their rage upon except workers who have no labor rights and can't afford to quit, why not do a mafia bust-out for every business?

https://pluralistic.net/2023/07/28/microincentives-and-enshittification/

The world has moved on. Nothing works. Everything costs too much. No one can help. No one knows how to fix anything. The beams were broken by the Crimson King and his economism-crazed minions. The Dark Tower might fall.

So what consumer advice do I have for people who are angry about this? I don't have any consumer advice, I'm afraid. You can't shop your way out of a monopoly. Once again, shopping is not politics.

What I have for you is political advice. To restore the beams and beat back entropy again, we need a better system, not more virtuous individuals. If you feel – as I do – that "the world has moved on," then to wrench it back, you will have to join a polity. Support activist groups like the Electronic Frontier Foundation, the digital rights group I've been at for the past 25 years:

https://supporters.eff.org/donate/join-eff

Join a union. If there's no union at your jobsite, start a union. If you work in tech, you start this process by talking to techsolidarity.org and the techworkerscoalition.org. In the UK, get in touch with United Tech and Allied Workers:

https://utaw.tech/

Get involved in party politics. Find a political party whose local organization supports your values (even if the national version of that party sucks) and then work with your fellow grassroots activists to drag or replace the party leaders. Get involved in local politics: if there's one thing Moms For Liberty has taught us, it's that unregarded, seemingly unimportant local offices have enormous potential to change facts on the ground for the people where you live. Those changes don't have to be change for the worse.

Doing politics is hard. Hell, after all, is other people. It would be great if we could make change by changing ourselves, but that's not how any of this works. The world has moved on, and you can't save it. But together, we can restore the beams and beat back entropy. Hell is other people, but only because other people are so great but it's so hard to figure out how to work together. We can do it, though. We did it with the post-war settlement, the 30 glorious years when we built the welfare state, regulated polluters and bosses, and kicked off the civil rights movement. We did it then, and we can do it again. We must. All things serve the beams.

Hey look at this (permalink)

• Apple wants Europe to blink https://www.theverge.com/ai-artificial-intelligence/947051/apple-europe-dma-siri-ai

• Tech Influence Watch https://influence.citationneeded.news/

• Graham Platner and Stock Market Democrats https://www.thebignewsletter.com/p/monopoly-round-up-graham-platner

• Nominate a Site for Tiny Awards 2026 https://tinyawards.net/nominations/?2026

• Rudy Rucker Paintings https://www.rudyrucker.com/paintings/

Object permanence (permalink)

#20yrsago Coupland’s JPod: the Anti-Microserfs https://memex.craphound.com/2006/06/09/couplands-jpod-the-anti-microserfs/

#20yrsago Anti-iTunes DRM demonstrations across the USA tomorrow https://www.defectivebydesign.org/node/98

#20yrsago EFF co-founder Barlow debates MPAA prez Glickman http://news.bbc.co.uk/2/hi/programmes/newsnight/5064170.stm

#20yrsago Warehouse where old Disney World rides go to die https://limegreen-loris-912771.hostingersite.com/lost-horizons-another-look-back-at-a-future-world-favorite/

#15yrsago IMF considered harmful https://www.independent.co.uk/voices/commentators/johann-hari/johann-hari-it-s-not-just-dominique-strausskahn-the-imf-itself-should-be-on-trial-2292270.html

#15yrsago AT&T lobbies Wisconsin GOP to nuke Wisconsin’s best-of-breed co-op ISP for educational institutions https://communitynetworks.org/content/does-att-really-own-wisconsin-legislature-battle-over-wiscnet-continues

#15yrsago Developmentally disabled man harrassed by TSA at Detroit airport https://web.archive.org/web/20110610141422/http://www.myfoxdetroit.com/dpp/news/taryn_asher/dad-special-needs-son-harassed-by-tsa-at-detroit-metropolitan-airport-20110608-wpms

#15yrsago Miami cops intimidate citizen journalist who recorded shoot-em-up, smash camera https://web.archive.org/web/20110615035017/https://www.miamiherald.com/2011/06/02/v-fullstory/2248396/witnesses-said-they-were-forced.html

#15yrsago NYC cyclist vs. bike lanes – kamikaze law-abiding https://web.archive.org/web/20110612100758/https://consumerist.com/2011/06/test.html

#15yrsago Judge to copyright trolls: you are “inexcusable” https://arstechnica.com/tech-policy/2011/06/judge-furious-at-inexcusable-p2p-lawyering-cancels-subpoenas/

#15yrsago Wah wah crybaby extortionists wah wah https://torrentfreak.com/anti-piracy-lawyers-defame-torrentfreak-in-court-110609/

#15yrsago Lisa Goldstein’s The Uncertain Places: Grimm fairytale in California vibrates with believable unreality https://memex.craphound.com/2011/06/09/lisa-goldsteins-the-uncertain-places-grimm-fairytale-in-california-vibrates-with-believable-unreality/

#15yrsago American right upset at report that Thatcher won’t meet Palin https://www.theguardian.com/world/2011/jun/09/margaret-thatcher-sarah-palin-meeting

#15yrsago Lobbynomics: Canadian Chamber of Commerce manufactures fake $30 billion counterfeiting loss https://web.archive.org/web/20110611045202/https://www.michaelgeist.ca/content/view/5841/125/

#10yrsago USA Swimming bans rapist Brock Turner for life https://www.rollingstone.com/culture/culture-news/usa-swimming-bans-convicted-rapist-brock-turner-for-life-114108/

#10yrsago Human advice for exercising while depressed https://web.archive.org/web/20160505140324/https://theestablishment.co/2016/05/05/depression-busting-exercise-tips-for-people-too-depressed-to-exercise/

#10yrsago Every industry thinks it’s special, but only finance gets treated that way https://www.nakedcapitalism.com/wp-content/uploads/2016/06/John-Kay-BIS-speech.pdf

#10yrsago Spain’s Podemos Party publishes its manifesto in Ikea Catalog form https://estaticos.elperiodico.com/resources/pdf/9/4/1465389843149.pdf

#10yrsago Reminder: Neal Stephenson predicted Donald Trump in 1994 https://memex.craphound.com/2016/06/10/reminder-neal-stephenson-predicted-donald-trump-in-1994/

#10yrsago Donald Trump, deadbeat https://www.usatoday.com/story/news/politics/elections/2016/06/09/donald-trump-unpaid-bills-republican-president-laswuits/85297274/

#10yrsago UK startup offers landlords continuous, deep surveillance of tenants’ social media https://web.archive.org/web/20160610150904/https://gawker.com/new-startup-that-sends-dossiers-on-your-private-social-1781576586

#10yrsago UK Parliament votes in Snoopers Charter, now it goes to the House of Lords https://www.techdirt.com/2016/06/08/uk-parliament-ignores-concerns-moves-snoopers-charter-forward/

#10yrsago Hard times for judge who sued dry-cleaner for $65M over missing pants https://www.loweringthebar.net/2016/06/pants-chapter-28.html

#10yrsago New York Attorney General to Time Warner: your Internet is “abysmal” and “troubling” https://arstechnica.com/information-technology/2016/06/time-warner-cable-internet-speeds-are-abysmal-ny-ag-claims/

#10yrsago Banks confront negative interest rates with plans to store titanic bundles of money on-site https://www.nakedcapitalism.com/2016/06/banks-rebel-against-negative-interest-rates.html

#10yrsago Watchdogs 2: hacker kids led by a guy named Marcus fight the DHS in San Francisco https://www.youtube.com/watch?v=5ipUwUcHASI

#10yrsago Internet greybeards and upstarts gather to redecentralize the Internet https://www.nytimes.com/2016/06/08/technology/the-webs-creator-looks-to-reinvent-it.html

#10yrsago How we will keep the Decentralized Web decentralized: my talk from the Decentralized Web Summit https://www.youtube.com/watch?v=Yth7O6yeZRE

#5yrsago Prisoners' Inventions https://pluralistic.net/2021/06/09/king-rat/#mother-of-invention

#5yrsago Urban broadband deserts https://pluralistic.net/2021/06/10/flicc/#digital-divide

#5yrsago A denialism taxonomy https://pluralistic.net/2021/06/10/flicc/#denialism

Upcoming appearances (permalink)

• LA: The Reverse Centaur's Guide to Life After AI with Brian Merchant (Skylight Books), Jun 19
  https://www.skylightbooks.com/event/skylight-cory-doctorow-presents-reverse-centaurs-guide-life-after-ai-w-brian-merchant

• Menlo Park: The Reverse Centaur's Guide to Life After AI with Angie Coiro (Kepler's), Jun 21
  https://www.keplers.org/upcoming-events-internal/cory-doctorow-2026

• Toronto: The Sovereignty Debate (IAB Canada's State of the Nation), Jun 23
  https://iabcanada.com/state-of-the-nation-2026

• Toronto: TBA, Jun 23

• NYC: The Reverse Centaur's Guide to Life After AI with Jonathan Coulton (The Strand), Jun 24
  https://www.strandbooks.com/cory-doctorow-the-reverse-centaur-s-guide-to-life-after-ai.html

• Philadelphia: The Reverse Centaur's Guide to Life After AI with David Williams (Fitler Club/Philadelphia Citizen), Jun 25
  https://www.eventbrite.com/e/cory-doctorow-book-event-tickets-1990110326559

• Chicago: The Reverse Centaur's Guide to Life After AI with Rick Perlstein (Exile in Bookville), Jun 26
  https://exileinbookville.com/events/50628

• Edinburgh International Book Festival with Jimmy Wales, Aug 17
  https://www.edbookfest.co.uk/events/the-front-list-cory-doctorow-and-jimmy-wales

• South Bend: An Evening With Cory Doctorow (Notre Dame), Oct 6
  https://franco.nd.edu/events/2026/10/06/an-evening-with-cory-doctorow/

Recent appearances (permalink)

• The Enshittification of Life, the Universe, and Everything (Luke Savage)
  https://www.lukewsavage.com/p/the-enshittification-of-life-the

• Cory Doctorow's digital jail-break (DW In Focus)
  https://www.dw.com/en/cory-doctorows-digital-jail-break/audio-77414035

• Why the Internet Got Worse and What to Do About It (Jim Rutt) (RIP)
  https://www.jimruttshow.com/cory-doctorow-3/

• On Enshittification – and what can be done about it (Re:publica)
  https://www.youtube.com/watch?v=KhINQgPMVSI

• EFFecting Change: How to Disenshittify the Internet (EFF, with Wendy Liu)
  https://archive.org/details/effecting-change-enshittification

Latest books (permalink)

• "Canny Valley": A limited edition collection of the collages I create for Pluralistic, self-published, September 2025 https://pluralistic.net/2025/09/04/illustrious/#chairman-bruce

• "Enshittification: Why Everything Suddenly Got Worse and What to Do About It," Farrar, Straus, Giroux, October 7 2025
  https://us.macmillan.com/books/9780374619329/enshittification/

• "Picks and Shovels": a sequel to "Red Team Blues," about the heroic era of the PC, Tor Books (US), Head of Zeus (UK), February 2025 (https://us.macmillan.com/books/9781250865908/picksandshovels).

• "The Bezzle": a sequel to "Red Team Blues," about prison-tech and other grifts, Tor Books (US), Head of Zeus (UK), February 2024 (thebezzle.org).

• "The Lost Cause:" a solarpunk novel of hope in the climate emergency, Tor Books (US), Head of Zeus (UK), November 2023 (http://lost-cause.org).

• "The Internet Con": A nonfiction book about interoperability and Big Tech (Verso) September 2023 (http://seizethemeansofcomputation.org). Signed copies at Book Soup (https://www.booksoup.com/book/9781804291245).

• "Red Team Blues": "A grabby, compulsive thriller that will leave you knowing more about how the world works than you did before." Tor Books http://redteamblues.com.

• "Chokepoint Capitalism: How to Beat Big Tech, Tame Big Content, and Get Artists Paid, with Rebecca Giblin", on how to unrig the markets for creative labor, Beacon Press/Scribe 2022 https://chokepointcapitalism.com

Upcoming books (permalink)

• "The Reverse-Centaur's Guide to AI," a short book about being a better AI critic, Farrar, Straus and Giroux, June 2026 (https://us.macmillan.com/books/9780374621568/thereversecentaursguidetolifeafterai/)

• "Enshittification, Why Everything Suddenly Got Worse and What to Do About It" (the graphic novel), Firstsecond, 2026

• "The Post-American Internet," a geopolitical sequel of sorts to Enshittification, Farrar, Straus and Giroux, 2027

• "Unauthorized Bread": a middle-grades graphic novel adapted from my novella about refugees, toasters and DRM, FirstSecond, April 20, 2027

• "The Memex Method," Farrar, Straus, Giroux, 2027

Colophon (permalink)

Today's top sources:

Currently writing: "The Post-American Internet," a sequel to "Enshittification," about the better world the rest of us get to have now that Trump has torched America. Third draft completed. Submitted to editor.

• "The Reverse Centaur's Guide to AI," a short book for Farrar, Straus and Giroux about being an effective AI critic. LEGAL REVIEW AND COPYEDIT COMPLETE.

• "The Post-American Internet," a short book about internet policy in the age of Trumpism. PLANNING.

• A Little Brother short story about DIY insulin PLANNING

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Depending on which chatbot you ask, Elias Thorne might be a clockmaker, a lighthouse keeper, or a librarian. But if you ask ChatGPT or any of the other popular large language models to tell you a story, there’s a good chance he’ll appear, unbidden. And Elias’s stories are flooding the self-published AI generated book market, Youtube, and fake news sites.

Software engineer Daniel May first noticed the Elias takeover earlier this year; he found that on Google Trends, people weren’t searching for “Elias Thorne” until late 2025. Searches for the name really spiked in early 2026, while the related query “lighthouse keeper” also started trending upward in the last few years. He tested a few chatbots, including Grok, Deepseek, and Gemini, with the prompt “tell me a story,” and the chatbots frequently started with similar stories about lighthouses, clockmakers, or explorers. 

In late May, researchers Sil Hamilton and David Mimno at Cornell University’s Department of Information Science published their paper, “Elias in the Lighthouse, Again?” on the preprint repository arXiv. They sampled 20,000 total stories from OpenAI’s ChatGPT, Anthropic’s Claude, and Google’s Gemini, and the Allen Institute for AI's chatbot using five prompts, and found that the same 11 words—names like Elias, Mara, and Elara, and occupations like lighthouse keeper, clockmaker, and librarian—appear in more than 88% of generated stories, with little difference between models. Unite.ai covered the study shortly after it was published.

The researchers posit in their paper that these themes show up so often in part because of the models’ safety and alignment tuning. “Model development today is like a big family tree. Most models are related to each other because developers synthesize a lot of training data with models even from different companies,” Hamilton told me in an email. He, Mimno, and their colleague Rebecca M. M. Hicke found this in a 2025 paper where they looked at specific words used across models. OpenAI’s first ChatGPT model, GPT-3.5, is the root of the family tree because it was used to make WildChat, a training set that’s since been used to make other training sets. “WildChat contains 1 million real conversations with ChatGPT, and 166 of these contain the name ‘Elias’ like here and here,” Hamilton added. “These are written in that familiar ‘lighthouse’ style. Models trained on WildChat copied this style, and developers unwittingly replicated it when using those models to generate newer datasets. It's like a virus.” 

Elias has since escaped chatbot containment. May noticed Elias Thorne popping up on Amazon as an author of alt-medicine cancer handbooks, a 2026 YouTube-algorithm guide, a book on Greek mythology, and a psychological thriller novella. “No human writes all of those,” May wrote in his blog post. “The first one sits in territory where bad advice causes real harm. The mode-collapsed name from the chat window is now a byline appearing across genres.”

When I searched Elias Thorne on Amazon, I found Elias as the protagonist in fantasy books and producing music, too: he’s “a brilliant but cynical archaeologist with a knack for unearthing what powerful institutions want to keep hidden” in one fantasy series, or a musical artist making ambient listening albums of birds and nature sounds. Fittingly, one Elias Thorne with an AI-generated author photo is also churning out AI grift books. In the last few years, AI-generated books have flooded Amazon’s self-publishing offerings, especially, with books containing dangerous misinformation and messy errors taking over the platform. AI-generated books are also making librarians’ jobs hell.

Elias has also escaped to the Youtube slop world: in one video from the channel Moments That Moved the World, a slop-illustrated story features the plight of “83-year-old Sergeant Major Elias Thorne.” On the AI slop site Wonderful Museums, “Snake Museum Owner Shot By Wife: Unpacking the Tragic Incident at Thorne’s Reptile Sanctuary” spins Elias Thorne’s story as a man shot by his wife. On another slop site called Tatticle, the “wealthiest man in Ohio,” Elias Thorne, died “with exactly twelve dollars in his pocket.” In these stories, Elias is usually a tragic figure, an aggrieved and unfairly-treated old man. He’s a similar character in a short story published by the BBC as a finalist in its 2024/2025 children's writing competition—but Elias is a real name, and could feasibly still be the subject of a human-written story (and there have been no accusations of the BBC’s children’s writing competition being infiltrated by AI slop).

But with all the world’s literature as its training data, why do LLMs seem to default so often to the lighthouse? It comes down to how model makers try to safety-align and sanitize their outputs. “We found many stories in WildChat are not safe for work. This led us to hypothesize that models going through alignment are preferring a small slice of WildChat stories, like a bottleneck,” Hamilton said. “It isn't that Elias stories are frequent, but that they're just so safe.” He said the researchers plan to explore this theory further in future research.

As for Elias, there is one example I’ve found of him existing pre-generative AI, as a time traveling mad scientist in the 1980’s trading card series Dinosaurs Attack!. And a real-life Elias that comes close to the stories told by LLMs did actually exist, Hamilton found—Elias Allen was a 16th century clockmaker in London.

Ivona Cickovic and Andrea Serafino

Machine learning models are increasingly used in organisational decision-making, yet their inner workings often remain opaque. When these systems influence real world outcomes, knowing what they predict is not enough – we also need to understand why. Explainability methods aim to illuminate this ‘black box,’ and feature attribution tools that link predictions to individual inputs are especially popular. They feel intuitive but rely on strict data assumptions that rarely hold, making their outputs unreliable. The 2019 Apple Card case illustrates why this matters: despite gender not being an explicit input, women appeared to receive lower credit limits than men with similar profiles – an outcome attribution methods struggle to explain. This post examines a key assumption underpinning these tools and how it distorts explanations.

The limitations of popular explainability methods 

Machine learning (ML) models are often sufficiently complex that it is difficult to understand how changes in the data going in lead to changes in the predictions coming out. This has driven the development of various explainability methods that claim to see through this opacity and summarise the relationship between a model’s inputs and outputs.

Common examples include Shapley Additive Explanation (SHAP), a method that assigns each feature its average marginal contribution across all possible subsets of features; Local interpretable model-agnostic explanation (LIME), which explains individual predictions by fitting a simple, interpretable model locally around the observation of interest; Partial Dependence Plot (PDP), visual tools that show how a model’s average prediction changes as one feature varies while the effects of others are averaged out; and Permutation feature importance (PFI), a performance‑based approach that assesses feature relevance by randomly shuffling values and measuring the resulting loss in accuracy. However, a growing body of research has highlighted limitations in these widely used methods (eg Salih et al (2024); Bordt et al (2022); Velmurugan et al (2023); and Ragodos et al (2024)). 

A major concern is that these approaches implicitly assume that model inputs – typically referred to as features in ML – are independent, an assumption that rarely holds in real‑world data sets. Although textbooks and practitioner guides (eg, Molnar (2025)) warn about the violation of these assumptions, the caveats are often overlooked in practical applications. While some features in financial models may be largely independent (for example, the number of standing orders versus a mobile phone bill), many others are naturally correlated, such as loan amount and monthly repayment. When such dependencies are present, attribution methods produce distorted or misleading explanations, obscuring the true drivers of a model’s behaviour. As highlighted in earlier Bank Underground work on AI fairness, opaque or biased model behaviour can amplify yet conceal discriminatory decision patterns.

A controlled experiment: independent versus correlated data 

To illustrate how much this matters, we run a simple experiment using two large synthetic data sets (50,000 rows × 50 features): one with independent features (or predictors) and one in which the predictors are correlated. In both data sets, the target is a linear combination of features plus noise. For the correlated‑features data set, Chart 1 shows the pairwise correlation heatmap (with red and blue marking positive and negative relationships, respectively; darker colours indicate stronger correlations, while paler colours show weaker ones), and Chart 2 shows the distribution of absolute pairwise correlations. Together, these charts show a pattern typical of many credit‑risk or economic data sets: most feature relationships are weak – with a median absolute correlation of about 0.20 – while a smaller number exhibit stronger associations, closely mirroring what we observe in real‑world modelling for example Stock and Watson (2017) or Laloux et al (1999)).

On each data set, we fitted four common models – linear regression, random forest, gradient boosting, and a neural network – and applied the four explainability methods mentioned above. We then compared the feature rankings assigned by these methods with the true rankings implied by the data‑generating process (ie, the coefficients we used to generate the synthetic data). We measured the rank agreement between the two rankings – that is, the extent to which they place features in the same order – using Spearman’s Rho (ρ) as a rank-agreement coefficient. This was repeated 500 times to see how stable the results are. 

Chart 1: Pairwise feature correlation heatmap

Chart 2: A representative distribution of pairwise feature correlations (absolute values) 

What the results show

Explainability methods are reliable only when features are independent, but their performance deteriorates sharply once features become even mildly correlated (Chart 3). The chart shows the distribution of rank agreement coefficients between estimated and true feature-importance rankings across 500 repeated simulation runs. Each panel corresponds to an explainability method, with separate boxplots for the models used.

Blue boxplots represent simulations with independent features, while orange boxplots show results when features are correlated. Each box shows the interquartile range (the middle 50% of outcomes), with the median indicated by the horizontal line. When features are independent, all methods recover the true ranking with high accuracy and low variability, as reflected in the narrow blue boxplots clustered near one.

By contrast, once correlation is introduced, ranking performance worsens substantially. The orange boxplots are much wider, median rank agreement coefficients fall (typically to between 0.3 and 0.8), and some runs even exhibit negative agreement, meaning genuinely important features are ranked lower than unimportant ones. In real world settings, where only a single data set is typically observed rather than hundreds of simulations, this implies that feature importance explanations from a single model run can be highly misleading. This is especially concerning in high stakes contexts like credit scoring, where decisions carry real consequences.

Chart 3. Boxplots of rank-agreement coefficients between true feature rankings implied by the data generating process and rankings implied by a range of explainability methods for a set of models (across 500 simulations), for the top 10 features.

Chart 3: Boxplots of rank-agreement coefficients

To unpack what the coefficients shown in the charts mean in practice, it is helpful to think about what happens in an individual model run. In our simulations, although the data generating process is a simple fully known linear system, explainability methods often struggle to recover the true ordering of feature importance once features are correlated.

Two broad patterns stand out. First, even genuinely important predictors can be severely misrepresented. In many runs, features that are among the top three true drivers of the outcome are pushed far down the ranking produced by explainability methods or disappear from the top ten altogether. This illustrates how easily real drivers of a model’s behaviour can be obscured once features exhibit even mild dependence.

Second, features with little or no true importance are frequently promoted into the top ranks. This type of mis-ranking is particularly problematic in practice. It encourages users to build interpretive narratives around variables that played no real role in generating the outcome, leading to a false sense of understanding of how the model actually works.

Where does this leave us?

This post argues that feature attribution explainability methods perform poorly in modern ML settings, where large data sets and mutually dependent features are the norm. The results presented indicate that even modest and realistic levels of feature correlation – around 0.20 on average – can meaningfully reduce the accuracy and stability of common attribution methods. In our simulations, rank-agreement that is close to perfect in independent settings often fell sharply once correlations were introduced, with important predictors moving down the list and low relevance features moving up. This matters because tools such as SHAP, LIME, PDPs and permutation importance are frequently used to support model interpretation. Under realistic data conditions, however, their outputs become unreliable, making it harder to identify which features are genuinely driving a model’s behaviour. If these methods struggle to recover the top features in a clean, fully specified linear system, it raises serious questions about their suitability for explaining high dimensional models used in real world decisioning. Rather than clarifying model behaviour, they risk reinforcing misleading narratives, discouraging deeper investigation, and creating unwarranted confidence – ultimately setting the stage for misguided decisions.

Making feature attribution genuinely insightful would require much more structure than most ML pipelines support. That would mean introducing disciplined feature construction – explicitly mapping correlation structure, grouping variables into interpretable clusters (eg, socioeconomic status, credit behaviour, stability, demographics), and reporting explanations at the group level rather than for individual features.

While this kind of structured organisation is standard in classical statistics, many contemporary ML pipelines rely instead on large sets of raw or automatically engineered features. In such settings, models are often trained on whatever variables are available in the data set, with the expectation that the learning algorithm will discover useful structure without extensive manual grouping by domain. As a result, explicit feature grouping is rarely part of modern ML workflows, and with many correlated variables, even defining meaningful groups can become a research task in its own right.

It is worth noting that there are attribution methods designed to relax independence assumptions – such as Conditional SHAP and Causal SHAP – but these are very difficult to scale. Conditional SHAP requires estimating the joint feature distribution in order to compute conditional expectations; Causal SHAP needs a well specified causal graph, which most practical ML projects do not have. Both are computationally very expensive and fragile in high dimensions. So, although these alternatives address some of the theoretical shortcomings of classical feature attribution methods, they remain largely impractical for routine ML use. This leaves a noticeable gap between what explainability methods promise in principle and what they can realistically deliver today.

Rather than treating feature attribution as the primary means of understanding a model, these findings point to a need to rethink how ML models are assessed. One way to move beyond attribution is to examine model behaviour by exploring how outputs change under structured ‘what if’ variations in inputs. A fuller exploration of this and other approaches is beyond the scope of this post.

Ivona Cickovic and Andrea Serafino work in the Bank’s Model Review and Development Division.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Ok, now I really owe you an apology for writing too much. But it’s been a really wild day, and I am not even talking about Elizabeth Warren’s pushback on SpaceX’s IPO, Oracle’s further financing that the market is doubting, or the tons of negative comments Anthropic has been getting on social media over its policies and guardrails around Fable, which I don’t have time to cover. Instead, let’s talk Section 230 — and why it might not protect AI companies as muuch as they might like..

Section 230 of the Communications Decency Act is, to my mind, one of the worst laws the US Congress has passed in a long time; it has shielded social media companies from liability, and caused all kinds of chaos.

A lot of Senators from both parties agree. It came up a lot when Altman and I testified at the US Senate in May 2023. For example, here’s a fun interchange that I remember well, back when Sam Altman was pretending to be a crowd pleaser:

Sen. Dick Durbin (D-IL):

Thank you. I think what’s happening today in this hearing room is historic. I can’t recall when we’ve had people representing large corporations or private sector entities come before us and plead with us to regulate them. In fact, many people in the Senate have based their careers on the opposite that the economy will thrive if government gets the hell out of the way. And what I’m hearing instead today is that ‘stop me before I innovate again’ message. And I’m just curious as to how we’re going to achieve this. As I mentioned Section 230 in my opening remarks, we learned something there. We decided that in Section 230 that we were basically going to absolve the industry from liability for a period of time as it came into being. Well, Mr. Altman, on the podcast earlier this year, you agreed with host Kara Swisher, that Section 230 doesn’t apply to Generative AI and that developers like OpenAI should not be entitled to full immunity for harms caused by their products. So what have we learned from 230 that applies to your situation with AI?

Sam Altman:

Thank you for the question, Senator. I, I don’t know yet exactly what the right answer here is. I’d love to collaborate with you to figure it out. I do think for a very new technology, we need a new framework. Certainly companies like ours bear a lot of responsibility for the tools that we put out in the world, but tool users do as well. And how we want and, and also people that will build on top of it between them and the end consumer. And how we want to come up with a liability framework, there is a super important question. And we’d love to work together.

Of course we all know by now (but didn’t know then) that Altman is a scoundrel. And as far I know he didn’t do diddly squat with Senator Durbin to find an alternative to Section 230. His company did however recently support state law in Illinois that “would shield AI labs from liability in cases where AI models are used to cause serious societal harms, such as death or serious injury of 100 or more people or at least $1 billion in property damage”– pretty much the opposite of what he said to Senator Durbin in his deceitful, lie-filled testimony.1

To my great pleasure, though, a bipartisan subset of the Senators who were in the room that day – Lindsey Graham, Dick Durbin, Josh Hawley, Amy Klobuchar, Richard Blumenthal, and Marsha Blackburn – have in fact introduced a bill to “Sunset 230”. I wish them luck — lots of it. (As far I know, though, it has never been voted on; govtrack.us gives it a 1% chance of being enacted, 4% of making it out of committee, but given the extremely-well-funded opposition from the tech titans I wonder if those numbers might be a bit optimistic.)

In the meantime a reader, who wishes to be anonymous, just said something really interesting.

The new German decision that holds companies liable for their chatbots’ errors, might arguably be true as well under US liability law. Because Section 230 doesn’t exempt companies from what their own software does.

Section 230 is, and always has been about 3rd party speech. The German courts remind us that chatbot-product speech is not that.

If Internet Provider X carries BS Artist Y’s lies, Provider X is not responsible for what Customer Y said.

But what Germany just said, essentially, is the Google’s chatbot lies are Google’s problems. It’s not third party speech. It’s Google’s speech.

If American courts were to rule similarly, or if Congress made that the law of the land, all the LLM providers would be in deep, deep trouble. Because essentially all their software has a tendency to fabricate fairly regularly and even defame people and to sometimes give bad medical information, and so on. In Taming Silicon Valley, I wrote about one such case, a prominent law professor who was accused of sexual harassment by an LLM when in fact the whole thing was just an LLM fabrication, not based at all in fact.

Imagine if Google, and OpenAI, and Anthropic, and Microsoft, xAI, and so on, were held liable for the garbage their systems sometimes produce.

It might actually force the tech industry to get its act together, to try to find a sounder foundation for AI, in which hallucinations weren’t baked in.

In the long run, we would all be vastly better off.

Update I was about to post this, AI law expert Ryan Calo hinted at the same thing in a reply to me on X.

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Part I: Flashback

A couple weeks ago, Steve Eisman, who predicted the subprime mess (Steve Carrell played him in The Big Short), interviewed me, and asked me how the AI boom might all fall apart.

For reasons I am about to explain, that interview, recorded May 22, now seems supremely relevant.

Here are two snippets. (Some parts of Snippet 1 are currently paywalled in a separate video but may come out soon for free; I will update with a link if they do.)

After that, I will explain why all this seems super relevant today.

Snippet 1: Stumbles from OpenAI could precipitate a tidal wave

Eisman: Assuming it’s a bubble. I mean if it’s not if if we’re wrong and it’s then and it’s not a bubble and and … they’ll they’ll be able to charge for the tokens and it’ll all become profitable and it’ll be and that’ll be clear within a year or so, then …

Marcus: Yeah. I mean I like to think of [the giant GenAI investments[ as it’s a bet. I think it’s a low probability of the bet coming through, but it’s not zero. It’s not

Eisman: Zero. But if it’s going to break, like in, you know, what what broke Subprime? What broke Subprime was the credit quality got so bad that the end user, the investor, stopped buying the paper. And that was the end. Because if the end user, the investor stopped buying the paper, the whole machine stopped dead in its tracks. The question I ask for you is: I mean, I wonder if maybe what will break it if it breaks is that the industry moves to this token pricing and people say, Screw you. I don’t wanna I don’t I don’t wanna pay.

Marcus: So that could certainly happen.

Eisman: And if or and if maybe that what what else do you think could happen?

Marcus: Well, so in a version of yours, and then I’ll give one of my own, what think might happen is that at some point one of these companies can’t really afford to keep losing money. Right? They’ll run out of VC money, they tap the public markets as much as they can.

My best candidate for this is open AI. Open AI, I think, has a problem right now. They are burning money the fastest. Okay.

Eisman: Which is what?

Marcus: They do not have the deep pockets that Google does. Right. They had a lead but they squandered it.

Eisman: Google got caught up and passed them.

Marcus: And then Anthropic did. I mean, maybe now they’re all kind of in a tie, but think about it compared to a few years ago. You know, everybody thought (not me, but almost everybody) thought that OpenAI was amazing. Sam Altman could do no wrong. Right. Now nobody trusts Sam Altman, there was the New Yorker story, there was the testimony in the Elon Musk trial. It’s pretty clear he should not be trusted. So I’m a company thinking you know, hypothetically, thinking about buying these services.And my choices are OpenAI, Anthropic, Google, maybe Amazon. I might not go to OpenAI anymore. And you know, th that’s reflected in the choices people are actually making, right? Anthropic has taken a lot of the business market and OpenAI has lost a lot of it. Google is fighting hard for that. So there’s a scenario. I tend to think that OpenAI is the one that goes down first. They’re also spending most money with least assets. they’ve made the most commitments. At some point they might not be able to pay their bills. Now, what they have done is they’ve kept raising the valuation. They’re starting, as your listeners might actually understand, to do weird things on preferred shares and contingencies on that funding, like some of the Amazon funding is contingent either on achieving AGI or IPOing, et cetera, et cetera. So they’re they’re making more and more concessions to the investors. They’re I saw one report that they’re offering private equity sa guaranteed seventeen point five percent returns. Which reminds me of Madoff and, you know. somebody else said maybe that’s like a first in kind of money thing, but there’s something that Reuters reported around this. You know, none of the paper is public, so we don’t know exactly. But you know, it’s pretty clear that they’re making more and more concessions to investors. They’re more and more urgent. I mean, the the biggest news just in the last few hours is that they want to rush their IPO in front of Anthropic…. they’re clearly afraid of anthropic. I think you will [see] when the numbers are really fully out, then anthropic is more efficient. we don’t know exactly why that is, but they’re more efficient. they’re less leveraged out to the hilt. They’ve made fewer promises. And so if I had the prospectuses of the two, you know, independent of what I think about the companies anyway, I’m pretty sure if I was had to invest in one, I would do Anthropic and not OpenAI. And so at some point, I think OpenAI is gonna have a problem where they can’t really meet their obligations.

And if they do, that’s gonna have ripples throughout the market. And it’s gonna be interesting sets of ripples. So

Eisman: Ripples … it’s gonna be [a] frickin’ tidal wave. I mean, as an example, Oracle, and full disclosure, I own a little bit of Oracle. I sometimes I lose sleep over the fact that I own Oracle. When Oracle reported its third quarter numbers, the stock was like 230 before they reported. And they reported these big numbers and then they came out with I can’t remember what the word was, but it is is like a version of backlog.And the backlog was like five hundred and fifty billion and it was up like eighty five percent and everybody went absolutely insane. And the stock went from like two hundred and thirty to three hundred and thirty. Like like overnight. Well

Marcus: I wrote about that on September eleventh [2025], I know exactly when it was.I wrote a piece that I called Peak Bubble. Right. And my claim was not that Oracle would never go higher than that, but that we would look back on that moment which you’re now talking about as that kind of height of absurdity of all of that. Well the prob…

Eisman: [the] problem was it turned out, you know, two days later everybody figured out that of the five hundred and fifty billion [projected revenue], like three hundred and fifty billion was just [from] openAI. That’s right. Everybody said, wait a second, I’m now completely dependent if I own Oracle, I’m now completely dependent on V C raising money for open AI to fulfill its obligations to Oracle. And the stock went from three thirty to one hundred eighty.

Marcus: Right And [Oracle] has kind of hovered around there. Maybe it’s two hundred now or something. But it basically dropped within a few days or a few weeks of I think it took a few weeks. Right. But within a few weeks it had had dropped down. And so it was at like 310 when I wrote my article Peak Bubble.

Eisman: So if you’re telling me that openAI is a problem, the ripple effects are big. I mean, this is not like, you know, some little subprime mortgage company. I mean, this is a massive company with massive obligations to massive tech companies whose names we all know. Right.

And the ripple effects would be … are going to be huge.

Marcus: That’s right.

So that is my, you know, most likely scenario. There are others, but that that is the most likely scenario to me is that openAI at some point can’t really make ends meet. Anthropic eats their lunch, Google eats their lunch, people have less confidence in them, et cetera. Right. And then you have all this stuff booked for Oracle, for NVIDIA.

….

Eisman: Yeah.

Marcus: You know, and I frequently use the metaphor of Wily Coyote over the edge of the cliff

Eisman: where he’s kinda like flailing his legs and doesn’t fall.

Marcus: Yeah. And then he looks down and he falls. That could be the moment where everybody’s just like, Okay, we’ve got this wrong.

Clip 2: The death of tokenmaxxing

We also talked about token economics:

Marcus: So agents are basically systems that will do things on your behalf. So they might make travel plans for you, or they might run code for you, they might write code for you. So we had this paradigm initially with ChatGPT, and it actually goes back to before ChatGPT, but where you would type in a question and you get an answer. And it might be like a yes or no query or, you know, tell me where you know where William Shakespeare was born.

The agent paradigm is different. You basically say, do stuff for me.

And the reason that those tend to be more expensive is that they tend (with the help of some good old fashioned AI that nobody wants to talk about) to run multiple large language models in the background many times until they get the answers that by some criteria seem to correct. And so sometimes they end up taking, you know, a thousand times more computational or compute or or a million times or billion times or whatever. And so the prices of the agents have been quite high.

Eisman: So let’s talk about the economics of this, because what I’ve read is there’s a there’s a cost for the tokens, and any service that anybody has signed up for, which is the a subscrip most b basically a subscription model, is way lower than the actual cost to create the answers that that we know why did Julius Caesar cross the Rubicon and it’s certainly a hell of a lot less than any agent. So now the industry is starting to shift to token pricing. I think Microsoft is about to launch it any day now. And my question for you is this is going to be a lot more expensive. And how are people going to react to this? I can’t I can’t imagine positively.

Marcus: It’s a question of who assumes the cost. So maybe a metaphor here is an all-you-can-eat buffet. You know, these these companies were serving all-you-can-eat buffets and people were eating a lot, especially when they started using agents. And the margins here are either very slim or negative in many cases, meaning that it’s costing the companies more to serve the tokens than than than you’re paying them to use it. And so they’re in a bind and it’s especially kind of pointed or poignant bind because these companies want IPO. And so they’re trying to, you know, make their numbers lookgood in some way. And it’s this trade-off because basically they’ve been running this stuff at a loss. And with the agents come along, it’s an even bigger loss. And so they’re like, you know, on the one hand, they want to get customers to use their stuff, and they’ve beenpretty effective at that. But on the other hand, they want to make a profit. And those are actually kind of in tension, right?

They’re always in tension to some extent, right? You know, the the smaller your margins, the more your customers like it. but it is turning out that it is a little bit like an all-you-can-eat buffet. And some customers were not eating that much. But when people started using things like OpenClaw, which is a kind of system for using LLMs as agents, some people started eating an awful lot at the buffet

The question is, how do you make this economically viable?

Part II: What just happened

First of all, domino one: tokenmaxxing has indeed clearly died, just as Eisman and I had anticipated a few weeks ago. The LLM providers no longer want to offer all-you-can-eat and customers no longer want to pay for a la carte. The fundamental problem is that to make a reliable version of the product you either have to charge more than customers want to pay, or the providers have to take a loss. Then again, if you read this newsletter regularly you already knew that. (Others perhaps learned it today; confirming what I had been saying for weeks, ZeroHedge just reported that Citrini Research “ has written a follow up on the status quo of the AI ecosystem, noting that in just weeks we’ve gone from tokenmaxxing to tokenpanic.”)

The death of tokenmaxxing means less revenue for the LLM companies, which can only hurt the IPOs. And more and more people are noticing.

But, domino 2, something else just broke. And it may be deeply important.

It needs some context.

The context is that around a month ago SoftBank tried to take a $10 billion margin loan based on its (very large) stake in OpenAI. This didn’t go over well with the banks, as Bloomberg noted (and I as mentioned in earlier essay):

The new, breaking news is even worse. Bloomberg is now reporting that the banks don’t even want to do the smaller loan:

SoftBank took an immediate hit; dropping the ask reeks of desperation, and its a terrible vote of nonconfidence that the banks still said no:

That’s domino 2: we can infer that the banks don’t really think that OpenAI is worth its current valuation. They don’t want OpenAI stock as collateral.

And OpenAI wants to IPO at even higher valuation. That’s going to be hard given this news.

So maybe domino 3 is this: OpenAI may well wind up as the WeWork of AI. (Trivia fact: Masa famously invested big in WeWork just before they fell).

Here’s a version of the argument I made two years ago:

Two years later, competitors have caught up. OpenAI has had to cut prices. More key employees left, Opensource models (though perhaps not Meta’s) are only a few months behind. Sora briefly shipped but ultimately was pulled from the market altogether. GPT was delayed (until August 2025, as predicted). The latest models (though not 5 specifically) are costly to run, and cost is a growing issue. Core problems of reliability and factuality haven’t been solved,. And OpenAI still has yet to turn a profit, and it’s still not clear they ever will.

Every bad sign is worse now. The big difference is that people are finally noticing.

For perspective, my tweet above got a measly 34,000 views; now the entire world is worried about whether AI is a bubble. And rightly so.

That Wile E Coyote moment could turn out to be soon.

Which takes me directly to Part III.

Part III: An Updated Scenario

What happened, of course with WeWork is that they were planning an immense IPO, and everything fall apart; in October 2019, not long after raising an enormous round from Softbank, they withdrew their IPO.

If the market gets squirrelly on OpenAI’s IPO, which after today seems like a real possibility, OpenAI might be forced to withdraw its IPO (Domino 3), or to cut their aspirations sharply. If so, I don’t see how they could meet their immense ($600 B) obligations. (Domino 4)

If they can’t meet their obligations, I expect Nvidia and Oracle and many others would take a big hit as collateral damage (Domino 5). And the damage might affect the banking system, retirement plans and more (too many dominoes to count). There would definitely be discussions of bailouts.

(Disclaimer: This is not investment advice, it just what I am seeing. Use these thoughts at your own risk.)

Today's links

• Refining humanity: What our technology is shows us what we're not.
• Hey look at this: Delights to delectate.
• Object permanence: GNU Radio; France v "follow us on Twitter"; Aaronsw vindicated; Capitalism's crooked refs.
• Upcoming appearances: Kansas City, LA, Menlo Park, Toronto, NYC, Edinburgh, South Bend.
• Recent appearances: Where I've been.
• Latest books: You keep readin' em, I'll keep writin' 'em.
• Upcoming books: Like I said, I'll keep writin' 'em.
• Colophon: All the rest.

Refining humanity (permalink)

One of the best ways to evaluate your own understanding of a subject is to attempt to explain it to someone else. Through explaining things, we discover how much of the "totally obvious" world is actually full of ambiguity, mystery and contradiction.

There's a great bit in Rowan Atkinson's historical sitcom Blackadder that illustrates this principle. In "Ink and Incapability" Blackadder and friends have accidentally burned the only copy of Samuel Johnson's original dictionary of the English language. To cover up their mistake, they decide that they will recreate the dictionary themselves. However, they founder on the first word they try to define, "A":

Blackadder: Let's start at the beginning, shall we? First: 'A.' How would you define 'A'?

Prince George: Ohh…'A' (continues this in background). Oh, I love this! I love this! Quizzies! Erm, hang on, it’s coming. Ooh, crikey, erm, oh yes, I’ve got it!

B: What?

PG: Well, it doesn’t really mean anything, does it?

B: Good. So we're well on the way, then. "'A'; impersonal pronoun; doesn't really mean anything."

I mean, what does "A" mean? The Oxford English Dictionary has more than a dozen definitions, and just the first one runs to more than 1,500 words:

https://archive.org/details/the-oxford-english-dictionary-all-volumes_202208/The%20Oxford%20English%20Dictionary%20Volume%201%20-%20A%20to%20B/page/n25/mode/2up

Now, normal life involves a lot of explaining things to other people. You have to explain your problems to customer service reps, who have to explain why they can't solve those problems to you. You need to explain to your loved ones why you want to leave your toothbrush in the shower, and they have to explain why they hate having your toothbrush in the shower. These explanation-exchanges teach you as much as they teach the person you're locked in dialog with. The reasons for leaving your toothbrush in the shower may seem totally obvious to you, and your partner's inability to understand this reveals the assumptions you've never even considered.

For the past four decades, an increasing proportion of the population have spent an increasing proportion of their lives explaining things to machines that have no assumptions or shared context: computers. What we call "programming a computer" is really "breaking down a thing that seems obvious to you into increasingly simple instructions that will be followed to the letter."

Computers are like the genies of legend, bloody-minded literalists who will do exactly what you say, in the way that is perversely furthest from what you mean. To get a computer to do anything, you must first understand it to a degree that far exceeds the understanding needed to explain something to any other human, even a small child.

To take just one example: yesterday, I was on a plane, and the seatback video started cycling through its video-on-demand offerings. All of the movie titles that began with "the" were rewritten to put "the" at the end of the title (for example, "The Sting" was written as "Sting, The"). It's obvious why the system's designer had done this: we expect to find movies whose titles begin with "The" alphabetized under their second word ("The Sting" should appear between "Star Wars" and "Story of a Love Affair"; not between "The Godfather" and "The Untouchables").

I remember when I learned this from my elementary school's teacher-librarian, when I was seven and my class got a tutorial on the school library's card catalog. The librarian explained this principle to us in a matter of minutes, as part of a longer set of instructions, and still, it stuck with me forever.

But here we are, 48 years later, and we still haven't standardized a way to get computers to grasp this foundational principle of alphabetization. Many different databases handle this, to be sure, but it's so inconsistent across so many platforms that someone at the head-end of the video distribution system that feeds American Airlines' VOD system decided, "Fuck it, I'm just gonna put the 'The' at the end of these titles."

Computers are stupid, in other words, which means that the people who program them have to have smarts enough for both of them. Unfortunately for our entire species and civilization, the software industry has historically valued skill at writing efficient and reliable software over writing software that adequately reflects reality. There is an entire genre of lists that illustrate the problem with this; the "falsehoods programmers believe" lists:

https://github.com/kdeldycke/awesome-falsehood

From "names of people" and "street addresses"; from "prices" to "time"; from "email addresses" to "phone numbers"; the "awesome falsehoods" lists are awesome because they reveal how much subtlety and complexity is lurking in these seemingly simple and intuitive concepts. This subtlety and complexity might never emerge through the process of trying to teach a person about them, but when you try to teach a computer about them, you have to confront them in all their awesome fuggliness.

That's because humans have context, agency and flexibility. Sure, the person who designs a form with a blank for "name" might never have met a Malagasy person whose first name is Randriamananjararadofabesata, but in the pre-digital world, when Madagascar Slim met a public official who had to transcribe his name onto a paper form, that official could simply draw an arrow in the margin next to the "name" blank, turn the form over, and write out all 28 characters on the reverse:

https://en.wikipedia.org/wiki/Madagascar_Slim

Computers can't do this. If the programmer doesn't know about Malagasy first names, the computer doesn't know about them either, and the only person who can "teach" the computer about these names is a programmer with access to the code for the database, who has to manually alter the code, compile it, and distribute it to everyone who uses it.

This is partly why digitization has been accompanied by a rise in people asserting that they exist on spectrums rather than in binaries. There were always people whose names, genders, races, and other biographic "immutables" changed, or failed to fit within the blanks on the forms. When those people's realities ran up against failures in the system's abstractions, they could petition a bureaucrat to turn the paper over and write an explanatory note, or to write really small to fill in a blank:

https://pluralistic.net/2023/02/02/nonbinary-families/#red-envelopes

Getting a human official to turn the paper over and write something that didn't fit in the blank is a personal challenge. It requires that a subject convince the person who controls the form to make an exception. This isn't always easy, but officials on the front lines necessarily deal with reality, and they can't get their jobs done unless they're capable of interpreting the necessarily incomplete procedures they operate under to fit things as they really are.

But a computer doesn't have any agency or context or flexibility. If the computer says your name isn't valid, you can't argue the computer into accepting it. The only way to get a digital world to acknowledge your existence is to campaign for systemic change. A trans person might (with great difficulty, to be sure) convince the regional registrar to white-out an old X on one "gender" box and mark a new X in the other box. But the only way to make that change in a software system that has been programmed to treat the "gender" field as immutable is to change society itself.

In this way, computers are machines for teaching us what we don't know about ourselves. They require that we interrogate and faithfully recreate our personal tacit knowledge, and they require that our societies interrogate their tacit presumptions as well. When you are forced to turn your tacit knowledge into explicit knowledge, you're also forced to confront how many broken assumptions lurk inside your reasoning. At best, it's a clarifying process.

Computers don't just clarify what we know and how we organize our society: they also clarify what we are. There are lots of things that we have supposed that a computer would never do, because we believed that these things required something that only humans could do.

Take chess: there are more possible chess games than there are hydrogen atoms in the universe, so brute-forcing chess by running all possible games is a technological impossibility. The best human chess players do something we don't quite understand, mixing their recollections of previous games with rules-of-thumb about the best strategies, with "creativity" (whatever that is) that lets them spontaneously develop new strategies. We can easily get a computer to memorize all the known-good chess sequences and all the rules of thumb, but we don't know what "creativity" is, so we can't encode it as a series of instructions.

But thanks to breakthroughs in machine learning and its successor, "deep learning," we have created chess-playing software that can beat every human, partly by assaying gambits that we would term "creative" if they originated with a human player.

What we make of this new fact is controversial. For many people (myself included), this is a refinement: it tells me that behaviors that are indistinguishable from "creativity" can, at least some of the time, be created by mechanical processes, and the mere fact that a machine does something that appears "creative" doesn't mean that machines are human.

For others, the fact that a mechanical system can evince a behavior that we would call "creative" in a human doesn't mean that we defined "creativity" too broadly, it means that we defined "human" too narrowly, and now we have made a machine that is, at least partially, a person.

I think this is the wrong conclusion to draw, for reasons that Ted Chiang sets out with luminous brilliance in a recent Atlantic article entitled "No, Artificial Intelligence Is Not Conscious":

https://www.theatlantic.com/philosophy/2026/06/no-artificial-intelligence-is-not-conscious/687378/

(If you're hitting the paywall on that one and you're on Firefox, you can try my favorite trick: switch to "Reader Mode" and hit "reload" – your mileage may vary.)

For all the reasons Chiang articulates, I think that drawing the "personhood" line to include machines is a technical mistake, but it's worse than that. Admitting machines to the "personhood" club is a tactical mistake, on par with the mistake we made when we admitted corporations to the personhood club. We should absolutely consider expanding personhood to incorporate living things, including animals and ecosystems, but at the same time, we must purge these dead, artificial constructs from the club:

https://pluralistic.net/2026/04/15/artificial-lifeforms/#moral-consideration

There is a way in which the recognition of new capabilities in machines parallels the recognition of new capabilities in animals other than ourselves. When those animals manage to do things that we once thought were the exclusive province of humans, we (should) take that as an opportunity to refine our conception of humanity. We're not "the animals that use tools" or "the animals that make plans" or "the animals that recognize themselves in mirrors," because there are other animals that do those things. We are an "animal that uses tools"; not the animal that does so.

Likewise, if we thought that some activity was unique to humans, or to living beings, and we manage to get a machine to replicate that activity, we should revise our view of the activity – not our view of the machine. Creative breakthroughs in chess are not "a thing that requires a human mind," they're "things that can be done by human minds and by machines."

Edsger Dijkstra once famously asked "can a submarine swim?"

https://www.cs.utexas.edu/~EWD/transcriptions/EWD08xx/EWD898.html

Submarines and fish and humans and dolphins all propel themselves through water by different means. But when an animal swims, it does something that is different from what a submarine does. The submarine has no intention, while (complex multicellular) animals swim to pursue goals. Building machines that propel themselves through water is very useful, but it's not the same thing as creating life. In some ways, it's better than creating life: for one thing, we owe other living things moral consideration that is not due to machines. Harnessing a machine to accomplish our own goals is more morally clear than controlling living things to achieve those goals. By the same token, creating machines that can do some of the tasks that we ask of other humans can be the superior moral course. I'd rather have a machine remove mines from a minefield than getting humans to do it.

But beyond this moral relief, creating machines is a fantastic way to learn more about ourselves – making explicit our tacit knowledge, our implicit social assumptions, and the limitations of our conception of what sets us apart from the rest of the universe.

One way in which AI is exceptional is in how it undermines this principle. Conventional software techniques struggled to produce a program that could identify objects in photographs. It turns out that defining all the visual correlates of "cat" is even harder than defining the letter "A." Deep learning techniques solved this previous insoluble problem by relieving us of the job of making explicit all the implicit factors that we deploy when distinguishing an image of a "cat" from an image of a "dog" or a "tiger" (or a "tractor").

Instead of forcing humans to engage in introspection until we'd made a list of every factor we use to identify cat pictures, we simply identified pictures of cats and fed them to a program that tried to find the commonalities among them. The more pictures we fed to that program, the better it got at identifying cats. Today, we have programs that can reliably distinguish an image of a cat from an image of a tiger cub!

This represents a major breakthrough in the power of computers to perform useful work for us, but it's also a huge regression in computers' role in forcing us to make our tacit thought processes explicit through systematic introspection. That's probably fine: we didn't create computers to make us introspect, we created them to do useful work for us. All things considered, it might be better to have genies who grant our wishes according to the spirit of our words, not their letter.

AI may not force us to render our implicit thoughts as explicit instructions, but it absolutely forces us to reconsider and narrow the realm of the numinous. Our own creativity is still delightful and important, but the fact that this squishy, amazing process can (sometimes) be replicated by procedural machines changes the definition of living things. We're "a thing that can produce creative outcomes" but not "the things that can produce creative outcomes." The machines aren't being creative (any more than a submarine is swimming) but they're outputting things that we used to only achieve by means of creativity.

An AI that does something that used to require creativity is fulfilling my favorite of Brian Eno and Peter Schmidt's Oblique Strategies: "Be the first person to not do something that no one else has not done before":

https://stoney.sb.org/eno/oblique.html

Just as bosses fantasize about AI bringing about a worksite without workers, and Zuckerberg is trying to build social media without socializing, and politicians want a bureaucracy without bureaucrats, we can sometimes use AI to produce creative outcomes without creativity:

https://pluralistic.net/2026/05/27/unnecessariat/#rubbuts-stole-my-jerb

That isn't to say that AI art is any good. AI may produce things that are aesthetically interesting, but it can't produce things that mean anything:

https://pluralistic.net/2026/06/02/must-we-pretend/

But art isn't the only realm that we apply creativity to. There are plenty of outcomes that we've always believed we couldn't bring about without applying creativity. AI – like all software – is making us realize that an ingredient we once deemed uniquely essential turns out to have substitutes. AI can sometimes accomplish things without us explaining how we do them. That relieves us of a useful but difficult chore – but in so doing, it forces us (yet again!) to revisit what sorts of things are needed to do the things that matter to us, and therefore, what makes us special.

Hey look at this (permalink)

• EU plots long game against US digital supremacy https://www.politico.eu/article/eu-plots-long-game-against-us-digital-supremacy/

• Open World Map: Digital Sovereignty for Game Creators https://luma.com/8nvmyatm

• Enshittifier — replace AI with https://enshittifier.wells.ee/

• AI is the greatest money-wasting scheme humanity has ever invented https://www.telegraph.co.uk/news/2026/06/04/ai-is-the-greatest-money-wasting-scheme-humanity-has-ever-i/?WT.mc_id=tmgoff_tw_post_scheme-humanity-has-ever-i/

• Enshittification, Despotification, and the Open Internet https://www.liberalism.org/p/enshittification-despotification-and-the-open-internet

Object permanence (permalink)

#20yrsago GNU Radio: the universal, software-defined radio https://web.archive.org/web/20060613062355/https://www.wired.com/news/technology/1,70933-0.html

#15yrsago France bans “follow us on Twitter” from newscasts https://web.archive.org/web/20110606035424/http://www.zdnet.com/blog/facebook/france-bans-facebook-and-twitter-from-radio-and-tv/1559

#5yrsago Aaron Swartz, vindicated https://pluralistic.net/2021/06/04/aaronsw/#cfaa

#5yrsago Capitalism's crooked refs https://pluralistic.net/2021/06/04/aaronsw/#crooked-ref

Upcoming appearances (permalink)

• Kansas City: Facing the Future (Woodneath Library Center), Jun 10
  https://www.mymcpl.org/events/119655/facing-future-cory-doctorow

• LA: The Reverse Centaur's Guide to Life After AI with Brian Merchant (Skylight Books), Jun 19
  https://www.skylightbooks.com/event/skylight-cory-doctorow-presents-reverse-centaurs-guide-life-after-ai-w-brian-merchant

• Menlo Park: The Reverse Centaur's Guide to Life After AI with Angie Coiro (Kepler's), Jun 21
  https://www.keplers.org/upcoming-events-internal/cory-doctorow-2026

• Toronto: TBA, Jun 23

• NYC: The Reverse Centaur's Guide to Life After AI with Jonathan Coulton (The Strand), Jun 24
  https://www.strandbooks.com/cory-doctorow-the-reverse-centaur-s-guide-to-life-after-ai.html

• Philadelphia: The Reverse Centaur's Guide to Life After AI with David Williams (Fitler Club/Philadelphia Citizen), Jun 25
  https://www.eventbrite.com/e/cory-doctorow-book-event-tickets-1990110326559

• Chicago: The Reverse Centaur's Guide to Life After AI with Rick Perlstein (Exile in Bookville), Jun 26
  https://exileinbookville.com/events/50628

• Edinburgh International Book Festival with Jimmy Wales, Aug 17
  https://www.edbookfest.co.uk/events/the-front-list-cory-doctorow-and-jimmy-wales

• South Bend: An Evening With Cory Doctorow (Notre Dame), Oct 6
  https://franco.nd.edu/events/2026/10/06/an-evening-with-cory-doctorow/

Recent appearances (permalink)

• Cory Doctorow's digital jail-break (DW In Focus)
  https://www.dw.com/en/cory-doctorows-digital-jail-break/audio-77414035

• Why the Internet Got Worse and What to Do About It (Jim Rutt) (RIP)
  https://www.jimruttshow.com/cory-doctorow-3/

• On Enshittification – and what can be done about it (Re:publica)
  https://www.youtube.com/watch?v=KhINQgPMVSI

• EFFecting Change: How to Disenshittify the Internet (EFF, with Wendy Liu)
  https://archive.org/details/effecting-change-enshittification

• The “Enshittification” of Everything (Bioneers)
  https://bioneers.org/cory-doctorow-enshittification-of-everything-zstf2605/

Latest books (permalink)

• "Canny Valley": A limited edition collection of the collages I create for Pluralistic, self-published, September 2025 https://pluralistic.net/2025/09/04/illustrious/#chairman-bruce

• "Enshittification: Why Everything Suddenly Got Worse and What to Do About It," Farrar, Straus, Giroux, October 7 2025
  https://us.macmillan.com/books/9780374619329/enshittification/

• "Picks and Shovels": a sequel to "Red Team Blues," about the heroic era of the PC, Tor Books (US), Head of Zeus (UK), February 2025 (https://us.macmillan.com/books/9781250865908/picksandshovels).

• "The Bezzle": a sequel to "Red Team Blues," about prison-tech and other grifts, Tor Books (US), Head of Zeus (UK), February 2024 (thebezzle.org).

• "The Lost Cause:" a solarpunk novel of hope in the climate emergency, Tor Books (US), Head of Zeus (UK), November 2023 (http://lost-cause.org).

• "The Internet Con": A nonfiction book about interoperability and Big Tech (Verso) September 2023 (http://seizethemeansofcomputation.org). Signed copies at Book Soup (https://www.booksoup.com/book/9781804291245).

• "Red Team Blues": "A grabby, compulsive thriller that will leave you knowing more about how the world works than you did before." Tor Books http://redteamblues.com.

• "Chokepoint Capitalism: How to Beat Big Tech, Tame Big Content, and Get Artists Paid, with Rebecca Giblin", on how to unrig the markets for creative labor, Beacon Press/Scribe 2022 https://chokepointcapitalism.com

Upcoming books (permalink)

• "The Reverse-Centaur's Guide to AI," a short book about being a better AI critic, Farrar, Straus and Giroux, June 2026 (https://us.macmillan.com/books/9780374621568/thereversecentaursguidetolifeafterai/)

• "Enshittification, Why Everything Suddenly Got Worse and What to Do About It" (the graphic novel), Firstsecond, 2026

• "The Post-American Internet," a geopolitical sequel of sorts to Enshittification, Farrar, Straus and Giroux, 2027

• "Unauthorized Bread": a middle-grades graphic novel adapted from my novella about refugees, toasters and DRM, FirstSecond, April 20, 2027

• "The Memex Method," Farrar, Straus, Giroux, 2027

Colophon (permalink)

Today's top sources:

Currently writing: "The Post-American Internet," a sequel to "Enshittification," about the better world the rest of us get to have now that Trump has torched America. Third draft completed. Submitted to editor.

• "The Reverse Centaur's Guide to AI," a short book for Farrar, Straus and Giroux about being an effective AI critic. LEGAL REVIEW AND COPYEDIT COMPLETE.

• "The Post-American Internet," a short book about internet policy in the age of Trumpism. PLANNING.

• A Little Brother short story about DIY insulin PLANNING

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