The summer between the end of high school and the start of college, I wrote a ridiculous play about pirates and put on a staged reading with some friends at an amphitheatre at a local park before a small audience of friends and family. It was never published or staged again. But I just got a message from an old high school friend I haven’t seen in years. He accidentally quoted the play in a conversation with friends, was asked what he was quoting, he couldn’t remember either, and wracked his brain until he finally remembered it was that silly play reading that we did one day in the park over 10 years ago. It made me happy. (The line was, “Huzzah for mercantilism!” by the way.)
A very tiny percentage of creators go on to be famous, but that doesn’t mean that people don’t remember little things you did for years and years. Who came up with most of the world’s most famous jump rope rhymes? Who coined some of the famous idioms we use in daily speech? Who made up ‘Jingle Bells, Batman Smells?” Somehow, all of these things stuck and spread around.
When I was a small child, I saw a high school put on a production of the musical HONK. In one song, the mother duck describes various dangers that her baby should avoid in the water, including fishing line, which could strangle him. A member of the ensemble played the role of fishing line, doing a maniacal laugh and over-the-top strangling motions, and I found it hilarious– and to this day, that’s an example I often think of when talking about how ensemble members can still stand out in theatre. The guy who played the role might not even remember that he did that, but I do.
I took Suzuki violin lessons as a kid. The teacher made up lyrics to some of the songs, and she let her students make some up, too. Now whenever I hear the instrumental of one of those pieces, I always remember these ridiculous lyrics about a skunk that we sang in violin class. I don’t even know which student invented them!
In middle school, I found a video about atoms parodying Bill Nye made by some kids for a school product. It probably had less than 1,000 views, but I think of quotes from that video all the time. They had a parody of “We Will Rock You” with the chorus, “Protons, neutrons, electrons” that I think about a lot.
I just love that this is part of human life. Our memories don’t just pick up quotes from great art, literature, and music, but little things, too.
L'innocente élève du Conservatoire. / No 12.
Têtes de femmes. Lithographs, 1828 – 1829, Paris. Designed by Charles Philipon. Printed by Joséphine-Clémence Formentin. Published by Charles Tilt, London. Sold by François Pierre Janet. Musée Carnavalet, Histoire de Paris
Tbh I think the "but data centers are important infrastructure, not just AI" talking point misses that like
Ok so roads are important infrastructure. A lot of stuff that's important happens on roads. Now, let's imagine that quadrillionaire Matt Stench has decided that the next big tech innovation is the Wide Car. It's a car that takes up six lanes despite seating only one passenger.
The Wide Car is supposed to be the future, and everyone's going to be driving Wide Cars, even though nobody who makes Wide Cars is turning a profit. Employers are offering Wide Cars as an employee benefit, and getting "nah." Some employers are going as far as demanding their employees drive Wide Cars, and the result is that people take time out of their workdays to get in the mandatory gas usage for their Wide Car before driving home in a regular car.
In spite of the fact that the Wide Car is clearly set to fail, there's an enormous push to expand to twelve-lane roads to accommodate a bunch of Wide Cars that simply will not materialize. This is not an organic response to demand, but a speculative investment that amplifies the existing issues with road development for no good reason.
If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
Neoclassical economics assumes rationality. The corollary of, "If you're so smart, why aren't you rich?" is "you're rich, so you must be very smart!" Thus it is that many people assume that if powerful, well-compensated CEOs insist that "AI is changing everything," well then, AI must be changing everything.
But the evidence for this "changing everything" thesis is thin on the ground. Despite a global mania that has reduced the real, pressing need for digital sovereignty to the imaginary need to create "sovereign AI," no one can really articulate the case for "sovereign AI." If Donald Trump ordered Big Tech to turn off all of your country's chatbots tomorrow, nothing would change. Every one of your country's ministries and corporations would chug on with nary a hitch. Households, too, though perhaps a few of the younger members of those families would have to do their own homework again.
(Contrast this with what would transpire if Trump directed his tech giants to switch off your country's Office 365 access, or to brick your Android and iOS phones, or to killswitch your John Deere tractors. Your country would effectively cease to exist. If "digital sovereignty" means anything, it means doing something about this urgent fact):
The world is full of people who insist that "AI is changing everything" but who – when pressed – have to admit that what they mean is that they're pretty sure that AI will change everything. Eventually. After we allow it to consume all the planet's energy, carbon, water and financial resources.
Maybe.
(They're pretty sure.)
One person who's had a lot of opportunity to observe the shear between the stated business/AI situation and the real business AI situation is Nikhil Suresh from Hermit Tech, a consulting firm of "radically ethical data wizards" (that is, tech consultants). Suresh reports on his experience talking with hundreds of executives (and, more importantly, their subordinates) about what (if anything) AI is doing for business in an essay entitled "AI Mania Is Eviscerating Global Decisionmaking":
Suresh has a good track record of writing trenchant, frank criticism of AI. You may know him from his 2024 essay, "I Will Fucking Piledrive You If You Mention AI Again":
Or possibly from his "Contra Ptacek's Terrible Article On AI," a stinging rebuttal to Thomas Ptacek's widely read "My AI Skeptic Friends Are All Nuts":
While those are important pieces of critical AI realpolitik, none of them have the heft or urgency of "AI Mania Is Eviscerating Global Decisionmaking," whose thesis can be summed up with this passage from halfway through this 6,000-word article:
[W]e’re facing a coordination problem around executives being honest around the AI gains they’ve witnessed – if they co-operate, they keep their jobs. If they defect, they will possibly be fired by their embarrassed peers (who have now been implicitly called liars, cowards, or incompetents) and then replaced with someone that will toe the line anyway. If they could all admit the truth at once there might be some hope, but there is no way to coordinate that event.
In other words, corporate leadership is starting from the premise that AI has (or will) radically change the business, and they're working backwards from that premise to find the evidence to support this article of faith.
In support of this thesis, Suresh cites "hundreds" of conversations with execs and employees who spoke to him on the condition that he would "file the serial numbers" off their stories. These, combined with his own experience consulting for large, multi-billion-dollar companies make it clear that "AI mania" is an absolutely justifiable label for the state of AI in corporate circles.
Here are a few highlights from this morning's read – moments where I had to look away from my screen and read out a passage to my wife so that we could share a "holy shit" moment.
A person worked for a division that "pivoted" to re-engineer its software to create interfaces that support AI agents. When it became apparent that only ten users had touched this expensive new technology, they "pivoted" again to support "agentic workflows." Why did they double down on AI agents after discovering such yawning market indifference for "agentic"? "Because every company has to do something agentic now."
Suresh describes this as a literal religious mania. In the 500+ employee businesses Suresh studied, the only people who were promoted – or even spared from being fired – were people who professed "religious declarations of faith" about "the transformative power of AI." Employees who voiced honest, informed objections to AI in the workplace were passed over for promotions or targeted for layoffs.
This has created a situation in which everyone – "boards, executives, employees, vendors, consultants" – has a strong incentive to lie about how much AI is delivering for their companies. Suresh says he's seen announcements from publicly traded companies about their AI triumphs that he knows for a fact never took place.
Suresh says he's never seen a successful enterprise AI project: "Every single one – we have seen 0% success in a year and a half." Not one of their clients would face a business challenge if OpenAI went out of business tomorrow. The problem most companies struggle with is that they're "terminally bad at running software projects effectively." Adding AI to the mix doesn't solve this problem – it just adds a whole new range of ways that software deployment can fail.
Chatbots don't help. The internally facing chatbot that's supposed to help employees figure out how to navigate the business sucks because it is only as good as its training data – the business's documentation of its own processes. Businesses suck at documenting their processes. Customer-facing chatbots also suck. They either can't solve your problem, or, when they seem to solve your problem, the "solution" goes nowhere.
Suresh recounts his sole positive customer service chatbot experience: a Mitsubishi chatbot with a natural sounding, responsive voice politely took all the details of an automotive failure and promised him a callback. That callback never came, but Suresh is certain that Mitsubishi has logged this as a chatbot success story, even though the experience convinced him not to buy a Mitsubishi car.
Suresh and his team at Hermit Tech now have a policy of not even asking about ongoing AI projects. They've learned that by the time an AI project has begun, no one will discuss it honestly until it reaches a crisis point.
Suresh says he frequently encounters people who reflexively utter the AI catechism: "AI is changing everything." But when he presses these people for details, they admit that their organization "does not currently use LLMs for anything, and indeed, that they cannot name a single thing that has changed other than they get some use out of ChatGPT."
This shear ("AI is changing everything"/"Well, OK, we're not using AI for anything") is so extreme that Suresh once met an exec who confessed to crafting an AI-centered AI strategy for a $2b/year business, even though that exec "had never even used ChatGPT or any AI tool in their life."
Some people have privately admitted to Suresh that they've embraced AI in order to earn a career-boosting corporate reputation for "thought leadership." But many other people (especially nontechnical people) sincerely believe that AI is about to "change everything." As Suresh says, if you're in business with a liar, you might be able to reason with them in private – but you can't reason with a true believer.
The true believers are in charge. Suresh points out that it would be very weird for the CEO of an engineering firm or a hospital to mandate "specific procedures or building techniques without explicit agreement from the professionals on staff." But when it comes to AI, business leaders will confidently demand that the skilled professionals who perform the business's core functions use AI, even if those professionals don't think it will help.
As an aside: I remember the dotcom era, when the business press was full of articles about the conflict between CEOs and a new workforce that demanded the right to use the web on the job. Today, the business press is full of articles about the conflict between the workforce and CEOs who demand that they use AI.
Suresh describes workers who feel they have to "AI wash" their work: "They just do the work, the same way they have for decades, and say Claude did it." To add verisimilitude to this sham, they write circular processes in which one chatbot prompts another, and then the process repeats itself in reverse, for the sole purpose of consuming AI tokens to score a high rank on corporate "token leaderboards."
How to account for this wildly, expensively irrational corporate leadership? Suresh places the blame in the hypnotizing, mesmerizing power of the AI demo. For example: Hermit Tech is often engaged to set up a database product called Snowflake for its customers. Snowflake has a useless, expensive AI bolt-on called Cortex, that Snowflake itself describes as being 92% accurate under ideal circumstances (that is, at least 8% of the time, it will mislead you, perhaps very badly).
Suresh describes sales meetings with execs who were lukewarm on the idea of retooling with Snowflake, but who were very interested in Cortex. Against their better judgment, Suresh and his team provided them with a Cortex demo, carefully explaining that this AI tool could not satisfy their requirements. Without fail, this resulted in the previously lukewarm customers insisting that they be allowed to purchase Cortex immediately. Sales prospects who'd been unmoved by a pitch for new technology that would result in millions in savings were hypnotized by demos of a product that was described as unsuitable and unreliable.
To their credit, Hermit Tech refused to sell these customers Cortex, and stopped doing Cortex demos altogether. Suresh describes the experience of "the total 180°, that shift from ice-cold to red-hot buying frenzy" as "deeply unsettling." What's more, the Cortex demos that Suresh and co performed were, by his account, pretty uninspiring. The thing that these demos had going for them is that they showed AI actually doing something marginally useful, to execs who'd already spent millions on AI without having anything to show for their money. The spectacle of AI that does something galvanizes corporate leaders who feel like they're the only bosses who can't find a revolutionary use for AI in their businesses.
This is the situation up and down the corporate org-chart. Suresh has a reader whose title is "Head of AI" at a billion-dollar firm who tells him "their job is totally fraudulent but it was the only promotion pathway remaining at the organisation." This exec is hardly alone. They're part of a cohort of executives at companies that have publicly announced "100x" productivity gains, but who confessed to Suresh that nothing of the sort has happened.
Why did these companies make these claims? Because their customers were making the claims. How could you hope to sell to a company that had 100x'ed its productivity with AI unless you, too had 100x'ed your productivity? If, as a vendor, you walked into a boardroom and said that this wasn't a plausible claim, you'd be calling your sales prospect a liar, with real consequences: "getting enterprise contracts cancelled because you wanted to opine on something that doesn’t really matter to your organisation’s mission is a great way to get fired."
With the state of the industry dominated by froth, lies and mutual destruction pacts, it's no wonder that companies are deploying "totally gameable metrics such as 'money spent on AI'" as a means of evaluating employees and divisions.
Between true believers and people who must find ways to plausibly tout their AI usage, there is now a gigantic market for "AI solutions." At best these are just traditional tech consulting contracts, like migrating a database from Oracle to Snowflake, with some kind of ornamental AI usage around the edges so that the person who commissions the work can claim to be "procuring AI-enabled services" for the business.
This isn't a harmless frippery: contracts are delayed and work is put off until the work can be made "sufficiently AI" to attain the minimum degree of buzzword compliance. Worse: every fake AI project that produces real results (because it's not really AI) adds credibility to the AI true believers, who view these projects as proof that AI can do anything, and therefore demand to know why everything isn't being done by AI.
Suresh ends his essay with a long section on how to "navigate AI mania" – advice for how to smile and nod politely when you're confronted with AI bullshit, while steering clear of the worst consequences and avoiding needless fights. This looks like very sound advice for anyone in a corporate environment, but thankfully, that isn't me.
Rather than summarize that advice, I want to reflect a little on two questions that Suresh's essay raises but doesn't answer. The first is why? Why are people in power such easy converts to this religious mania?
I have my own theory. The most important discomfort that powerful people experience is having ego-shattering conflicts with subordinates who know how to do things they do not know how to do. The fact that you're "in charge" is hard to reconcile with the fact that the people you're nominally in charge of tell you that all your ideas are impossible, illegal, immoral, or lethal:
Take that Cortex demo. Sure, Cortex is an expensive, unreliable way to address a Snowflake database. But (unlike Snowflake) Cortex is controlled via conversational, plain-language commands. With Cortex, a boss doesn't need to ask an underling to retrieve information from the company Snowflake system, an interaction that might come with unsolicited feedback about the technical or commercial incoherence of the boss's request. Cortex is the underling, except that unlike a human underling, Cortex never back-sasses you about your foolish questions. The fact that it grossly misleads you 8% of the time is a small price to pay for a life untroubled by uppity pismires who insist that your ideas be connected to base reality as they understand it.
The other question Suresh implicitly raises is, "How can you reconcile the failure of AI in the enterprise with the individual claims of skilled technologists who insist that AI is helping them do great work?" The answer is that these AI users are "centaurs" – experienced workers who are assisted by automation on terms that they set for themselves:
Thanks to their skill and experience, these workers possess discernment, the ability to tell good code from bad, and (more importantly) good uses of code-generation tools from bad. They demonstrate the adage that worker-driven automation improves quality, while capital-driven automation improves throughput:
An automation technique that requires close supervision by skilled and experienced workers isn't going to be a raw productivity powerhouse. You don't "100x" your code this way, at least, not in the sense of firing 99 of your coders and having the remaining programmer pick up all their work. Rather, an automation tool that requires the continuous and conscientious exercise of discernment will let individual practitioners improve their work in extremely satisfying and useful ways. It's a way to spend more on operations in order to produce better outputs. It's not a way to cut your workforce, realize a gigantic savings, and still produce comparable goods and services at a far lower cost.
That is why some individual coders report such delight with their AI tools. They engage with those tools on their own terms, to improve their work in the ways that they, in their expert judgment, consider beneficial. No one ranks them on a "token-maximization" scoreboard. No one tells them they can't do a project if it isn't "sufficiently AI." When they set out to do a project, no one makes them prove that it couldn't be "done by AI."
As ever, the most important fact about a given technology isn't "what it does," but "who it does it for" and "who it does it to."
All the pathologies Suresh observes and documents so well in this piece are hypertrophied versions of the buzzword-compliance dysfunctions from previous bubbles, but at a scale never before seen. Quantity has a quality all its own. These businesses aren't just wasting billions – they're replacing skilled workers with defective chatbots. As I've written before, AI is the asbestos we're shoveling into the walls of our technological society. Our descendants will spend generations digging it out again, and the longer the bubble goes on without popping, the longer it will take to repair the damage.
In other words, corporate leadership is starting from the premise that AI has (or will) radically change the business, and they're working backwards from that premise to find the evidence to support this article of faith.
I've seen this happening and it is terrifying. Being told that the organization you work for has made a huge investment in AI, and that your opportunities for advancement (or even getting to keep the job you have) are contingent on finding a way to use that AI....What are those workers realistically supposed to do? Stand up for accuracy and get fired? Point out the ecological and ethical impacts of increasing AI use and hope the executive who made the AI investment doesn't decide to save money by terminating the squeaky wheels? Try to unionize and get fired? They're too scared to do anything but comply.
And that enhanced tendency toward compliance is a big part of the appeal of AI, as mentioned above. AI doesn't talk back, which business leaders LOVE. So human workers are learning to also not talk back, because they can tell it's dangerous to their jobs to do so. No one will say that the emperor has no clothes when the AI is confidently describing the clothes (and being so complimentary about the emperor's taste in fabric!). If you can't see the clothes, then that's a defect in you; nothing else could possibly explain it.
The funniest thing about the old "emperor's new clothes" story is the part where no one in that story stopped to think about the logical conclusion according to the described properties of that magical fabric.
Some nonzero number of people were going to perceive the emperor as naked.
They were so worried about personally being thought simple, or unsuited for their own job, that no one stopped to think about the people who really were unsuited for their jobs. And anyone who lives in a society knows we've got a fair number of those.
By the description originally given, anyone who was incompetent at their job would get an eyeful of bare emperor, because the cloth would be rendered invisible. Who would choose to dress in something like that? Who would risk parading through the street, appearing to be naked to people who were bad at their jobs?
And we're all just staring in confusion and disbelief at these naked C-suiters, as they proudly slap each other on their (naked) backs, congratulating each other on their masterful implementation of AI.
It feels as if we're having to choose between having a roof over our heads versus using our principles to keep the rain off of us. I can't really blame front line workers for choosing to stay housed.
La Mode illustrée, no. 27, 7 juillet 1878, Paris. Toilettes de Mme Bréant-Castel, r. du 4 Septembre, 19. Ville de Paris / Bibliothèque Forney
Toilette de dîner. — Jupe ronde en faye nuance vieil or, garnie d'un volant en faye grenat clair plissé très-fin, couvert d'un volant de dentelle blanche à tête coquillée; tunique en lampas grenat clair drapée de façon à découvrir le côté gauche de la jupe en faye; cette tunique, très-longue par derrière, est ornée à droite d'une quille en dentelle coquillée et d'une cordelière en soie vieil or remontant jusqu'au corsage; celui-ci, très-long, en lampas grenat, se complète par un gilet recouvert de dentelle blanche; manches en dentelle blanche.
Dinner ensemble. — Round skirt in old-gold faille, trimmed with a finely pleated light-garnet faille flounce overlaid by a white lace flounce with a scalloped heading; tunic in light-garnet lampas, draped to reveal the left side of the faille skirt; this tunic, very long at the back, is adorned on the right with a panel of scalloped lace and an old-gold silk cord extending up to the bodice; the latter, a long garment in garnet lampas, is completed by a vest overlaid with white lace; sleeves of white lace.
—
Toilette de promenade. — Jupe en faye bleu-marine garnie de deux volants plissés; tunique en tissu de laine et soie, fond blanc avec rayure bleu-marine et orange; sur les côtés, quilles en faye bleu-marine avec lisérés orange; corsage très-long; sur le devant, un plastron plissé, avec lisérés orange; en dehors du plastron se trouve une ruche plissée, de même faye, garnissant l'encolure; un demi-revers en même faye plissée garnit le bord inférieur de la manche.
Promenade ensemble. — Navy blue faille skirt trimmed with two pleated ruffles; tunic in a wool-and-silk blend, white background with navy blue and orange stripes; side panels of navy blue faille with orange piping; very long bodice; pleated front panel with orange piping; a pleated ruff of the same faille trims the neckline outside the front panel; a half-cuff of the same pleated faille trims the lower edge of the sleeve.
american blackbirds are icterids but european blackbirds are thrushes but american robins are thrushes but european robins are flycatchers and they are named robin because (checks notes) brits in the 1400s called them "robert" on account of they are just some familiar guy who shows up in your yard. hold on post canceled is that really why they are called that? what the fuck. they did this with jackdaws and magpies too? i can't even be annoyed. how human. "who's that? that's bob." fuck dude it sure is.
In general, Sinners has great cinematography, but I think this tracking shot that follows Lisa Chow across the street from her parents’ Black storefront to their White storefront is one of my favorites:
Look at how it immediately establishes the rules of the movie's setting.
This is Jim Crow Mississippi where Black and White residents essentially live in different worlds. The continuous take forces us (the audience) to experience that segregation in real time as we walk behind Lisa crossing the street. There are no cuts or edits to interrupt the discomfort of having to witness all those visual reminders of racism against Black Americans.
I think it's also significant that it's Lisa, an Asian American woman, who the camera follows. As someone who exists outside the Black-White racial binary, she’s able to traverse these two worlds but the bright red of her shirt still demarcates her as a conspicuous outsider amidst all the blue and brown on both sides, representing the uniquely precarious position of Asians in the U.S.’s racial hierarchy.
i want to do a painting of a tiger taking a bath to put in a bathroom (bathroom-themed bathroom) and to this end i made a little maquette out of clay and i suspect this will scope creep into having both a painting and sculpture of a tiger or perhaps only a sculpture of a tiger. if i do both should they be displayed together or separately
Working on cutting out a large piece of wood to do the painting on, which is a constraint that will either be really fun or really annoying. Maybe both
Wood primed and underpainted and sketch transferred mostly by cutting it out in different chunks and tracing around them. Stripes to be determined. Nobody let me work on this again for at least two weeks
The Fashions
Expressly designed & prepared for
The Englishwoman’s Domestic Magazine
June, 1876
Volume 50, Plate 41
Signed: Jules David; Imp. H. Lefèvre, Paris; Ad Goubaud & Fils Ed Paris; A Bodey sc
Digital Collections of the Los Angeles Public Library
