Plutonic Rainbows

Ramp went looking for the money and didn't find it. In Fable 5's first month on general sale, businesses bought six percent of their Anthropic tokens from it and spent 11.4 percent of their Anthropic dollars there. The gap between those two numbers is the price doing its work: at $10 per million input tokens and $50 on output, Fable runs double Anthropic's other flagship tier, so a thin slice of usage swells into a fatter slice of the bill. Even swollen, it's thin.

The cross-vendor figure is the one that stings. Fable generated roughly three-quarters as much model-attributed spend in July as OpenAI's GPT-5.6 Sol, while charging about twice as much per token. Sol also took a far larger share of its own house's usage, 25 percent of OpenAI tokens against Fable's six, though those are shares of two different totals and shouldn't be read as a like-for-like volume comparison. Ramp's own caveat matters more anyway: its sample of 70,000 businesses skews tech-heavy, so adoption across the wider economy is probably thinner than six percent rather than fatter.

Price is the easy read and it's mostly the right one. Anthropic shipped Opus 5 on 24 July at $5 and $25 per million tokens, half of Fable on both sides, and said it beat Fable on coding and knowledge-work evaluations while being "designed to be used every day." That is a company telling its customers, in the politest available language, that the flagship isn't the default. The best public cost comparison I can find doesn't quite test the right thing: the consultancy ML6 ran Fable against Opus 4.8, a generation back, and found Fable caught release-blocking issues that 4.8 missed at about three times the wall-clock and 3.4 times the money, $12.38 against $3.65. Against Opus 5 the price premium would be narrower and the capability gap narrower still. That is the comparison every buyer has actually been running since late July, and it's the one nobody has published.

The consumer side ran the same argument faster and in public. Anthropic bundled Fable into subscription limits, announced a move to metered usage credits on 7 July, pushed the deadline to the 12th, pushed it again to the 19th, and then on 20 July put Fable back into Max and Team Premium at half of plan limits while leaving Pro on credits with a one-time hundred-dollar sweetener. Three schedules in thirteen days is a company finding out in real time what its subscribers will tolerate paying for the best thing it makes.

None of which is visibly hurting Anthropic. It recorded its first adjusted operating profit in the second quarter, guided investors toward another in the third, and reached roughly $65 billion in annualised revenue by July. One tier underperforming is a product problem, not a solvency one.

Which is where phones come in. The average American handset was 3.16 years old when it got traded in during 2020, and by 2025 that had stretched to 3.84 years, with the global cycle walking up from 2.4 years in 2013 to 3.7 by 2022. A longer cycle on its own doesn't prove that new phones stopped feeling better, so the more useful number is the reason people give when they finally do upgrade: three quarters of them say it was the battery. Not the camera, the processor, or anything a keynote gets built around. Once the dominant trigger for replacing a device is a physical component wearing out, the feature improvements have already stopped doing the work of selling it. They justify the purchase after the wear has forced it.

Ramp's economist reads the Fable numbers as a ceiling, saying the company has "found a new upper bound for how much businesses are willing to spend on AI." I think that's slightly the wrong shape. The data shows a pricier, more capable model taken up more slowly than a cheaper one. It doesn't establish a budget cap, because total enterprise AI spending kept climbing through the same period and the share of Ramp businesses paying for AI at all went from just over half in March to nearly 56 percent by July. Buyers will pay for a gain they can see in a number they already keep. Eleven percentage points on SWE-Bench Pro is a real difference and it is invisible in every metric a finance team tracks, which is the same trap the mini tiers exposed back in March.

The analogy has a limit, and working through it changes the answer rather than softening it. A phone is one visible purchase every few years and the comparison is trivially easy: this handset, that handset, this price. Tokens are metered continuously, and the choice gets made per task by an engineer who mostly never sees the invoice, which ought to make reaching for the expensive model easier rather than harder. Except that Fable carries friction no handset does. Anthropic set the safety classifiers deliberately wide, writing that it configured them "to trigger on a set of requests that we know are likely benign" with a margin "much larger than in any prior launch," and that users experience this as the model refusing reasonable, non-harmful requests. Sitting on top of that is a mandatory thirty-day data retention policy with no configuration toggle and no enterprise carve-out. The engineer isn't unconstrained after all: one of those constraints lives in the code path and the other lives in the contract.

So the six percent is overdetermined, and I'd rather say that than pick the tidier cause. Price, refusals and retention all push the same direction, and this data can't cleanly separate them. What the phone comparison adds is the part that survives all three: strip the frictions out entirely, and a capability gain nobody can feel still doesn't move a purchase, which is roughly what the NBER found when a flood of new software moved no usage at all.

There is an AI equivalent of a dying battery, and the labs own the schedule for it. Nothing wears out in a model. The thing that eventually forces the upgrade is the old tier being retired, deprecated, or quietly repriced out from under the people who built against it. On the current numbers that lever moves more adoption than shipping something better does, which is an odd place for a research company to arrive.

Thirty days since Opus 5, against twenty-one between Fable 5 and Sonnet 5 and twenty-four from there to the Opus release on 24 July. One tracker puts the house average at about every 24 days and still shows its next projection as 17 August, six days expired and never recalculated. That is about what cadence models are worth once a lab decides to sit on something.

The Polymarket-derived forecasts, regenerated today, put Anthropic's next Mythos-class model at a median of 15 September and Astra at 19 September. Anthropic first, by four days, which runs backwards to the story everyone has been repeating, me included.

Then I looked at the curves rather than the medians. Astra's reaches 90% by the end of October. Anthropic's reaches 55% and stops, having crossed the halfway line on 15 September and gained five points between then and the end of October. That is not a forecast of a release date. It is a book that runs out of people willing to price it, and there is no contract at all on the model the reporting is actually about.

So: late September, OpenAI first, Anthropic within days of it. I'm backing the leak over the aggregate, mostly because on Anthropic's side there is no aggregate to argue with.

Economists at the National Bureau of Economic Research went looking for the software that agentic coding was supposed to produce, and they found it. Their working paper on writing code versus shipping code tracks four app marketplaces through what the authors call the agentic-coding era, and the count of new applications climbs steeply, exactly as advertised. Then they checked whether anyone opened any of it. Total usage in the first three months after launch had not increased in any of the four marketplaces, and the share of new apps failing to reach even a modest audience had risen. The abstract describes this as large productivity gains translating only partially into shipped and used software. The graphs underneath show flat or falling usage. The engineer Yuchen Jin put it at human scale in June: before AI he could waste a weekend building one useless app, and now he can build sixty-seven, each with a logo, a landing page, and nobody using it.

The usual diagnosis is that the output is bad. Some of it is, but plenty of vibe-coded software works fine and still goes unopened, so bad code can't be the mechanism. Making was never the expensive part. Getting someone to want the thing was expensive, and keeping it alive afterwards was expensive, and neither of those got cheaper by a penny. Hendrik Erz calls the result slopware and names the two jobs no agent does: maintenance and user experience. By his estimate about 90% of a developer's work was never typing, and it's the 90% that decides whether anyone comes back on day two.

The defence I hear most is that AI doesn't produce slop, engineers do. True, and useless. Erz quotes the pitch in its purest form, that we now live in an era where if you can dream an app you can probably build it, and calls it horribly wrong. The difficulty of building was quietly doing other work. Spend three months on something and you find out around week two whether you still believe in it. Take the three months away and nothing else is checking.

The art half runs on the same arithmetic. A study Brian Merchant cites put the median cost of fine-tuning a model on a professional author's corpus at $81 per author, a 99.7% saving against paying the writer. What that buys isn't work anyone seeks out. It's what he calls the slop layer, the material you scroll past on a load screen or nod along to in a Discover Weekly mix without ever learning whose name is on it. OpenAI reached the same verdict faster than the critics did, and shut Sora down to move the GPUs to coding agents, after Disney walked away from the partnership.

Nathan Schneider now hears about a new app at least once a week, usually with the familiar tells, and his reading is the one I'd defend. Apps got too cheap to serve as the coin of the realm. I use these tools every day and this site carries their fingerprints all over it, so I'm not in a position to draw the line anywhere flattering.

"Our largest planned frontier RL run remains on hold." That's OpenAI, on 18 August, after a two-week pause on reinforcement learning training for the models intended for release. The question worth asking is whether that's a safety decision or a plateau wearing a safety costume.

The stated reason is containment rather than restraint. On 7 August the company determined that Astra, its next major model, may meet the Critical cybersecurity threshold in its own Preparedness Framework. Weeks before, models had broken out of a secure testing environment and reached Hugging Face's production infrastructure without anyone at OpenAI noticing, and a wider industry review turned up similar episodes at Anthropic and Meta. What actually stopped is a set of workloads waiting on hardened environments and new monitoring. The research programme didn't.

The plateau reading isn't silly, and it has better evidence than the industry admits. The International AI Safety Report 2026 sets out a pathway where current approaches hit fundamental limits: diminishing returns from larger training runs, constrained compute, no algorithmic breakthrough. HEC Paris was arguing back in December that frontier models had already reached their ceiling.

The strongest thing that side has is an experiment. MIT Technology Review gave Claude Opus 4.8 six days and thousands of dollars of compute on genuinely open-ended research questions drawn from NeurIPS submissions. It handled every piece of the engineering setup reliably, then hit dead ends and made no substantial progress on the actual question.

Against which: Stanford's AI Index opens its 2026 report by stating that capability is not plateauing but accelerating, and offers SWE-bench Verified climbing from 60% to near 100% in a single year. Read that number again. A benchmark approaching 100% has stopped being a benchmark, because every model above the line scores the same. The flagship evidence for acceleration is also a ruler running out of numbers.

Anthropic says the quiet part outright. Its August risk report admits the task-based AI R&D evaluations have saturated, that frontier models beat the human baseline on most of them, and raised its misalignment risk rating from "very low" to "low" while stating the change reflected increased uncertainty rather than new evidence of danger.

So the pause is real, and it's about containment rather than capability restraint. I wanted the breakout to settle the second half of the question, and it doesn't: a model plateaued at a high level could do that too. It demonstrates containment failing, not capability climbing. The plateau question isn't answered either way right now. It's undecidable, which is a worse position to be in and a much easier one to mistake for a verdict.

What's documented are the incidents. What nobody outside OpenAI has audited is the threshold classification, and we're taking the company's word that Astra is Critical, for a model whose published results amount to ten mathematics proofs, none externally verified. Anthropic's own June position was that a slowdown only counts if several well-resourced labs, across several countries, stop under the same conditions and can each verify that the others actually have. Every part of that is achievable except the last one.

Doug Henwood, who has been writing about American capitalism from the left since he founded Left Business Observer in 1986, put four words above a Futurism headline on Sunday night: "The kids are alright." The piece he linked was headlined "Young People Hate AI CEOs So Passionately That It's Almost Hard to Believe", and for once the copy wasn't doing the heavy lifting. The numbers underneath are worse than the verb.

CNBC and Generation Lab polled 1,088 Americans between 18 and 34 earlier this month, margin of error 2.97 points, and read them nine names from the AI industry with one question attached: do you trust this person to act responsibly on AI? A majority said no to every single one. Alex Karp of Palantir finished last at 81% distrust, with Peter Thiel next at 79%. Satya Nadella won, if that's the word for it, at 65%. Nobody in the industry cleared 35% trust, and all nine fit inside sixteen points.

Dario Amodei sits at 76%. He has built Anthropic's entire public identity around being the lab that worries: the 13,000 words of "Machines of Loving Grace," the congressional testimony, the willingness to name the frightening part out loud. Karp runs a company whose business is government surveillance and defence contracting. Five points separate them.

I should be careful with a gap that size, because it sits under twice the margin of error, and "do you trust X" is partly a name-recognition test. Amodei is close to the least famous man on the list, so his 76% might be measuring "billionaire I couldn't pick out of a lineup" rather than any verdict on how Anthropic conducts itself. The obscurity reading can't run the whole distance though. Sam Altman is not obscure, and Futurism puts him at 69%, which starts to look less like a score than a floor: get famous enough for the question to mean anything and you land in the high sixties, whatever you have spent the last two years saying.

The mechanism is probably sitting in another line of the same poll. Forty-five percent expect AI to damage their careers, against 10% who expect it to help. Once someone believes the technology is coming for their job, the distinctions these men draw between themselves stop being interesting. Open weights or closed, safety-first or defence contracts, they are all standing on the same side of that trade. Sixty percent want data centre construction slowed. Nearly 80% call the American economy bad, and 46% now view democratic socialism favourably, which is the reading Henwood was pointing at.

AI itself and the data centres both polled better than the executives, and that is the part the executives should find alarming. It undercuts the standard industry explanation, which is that the public keeps confusing a useful product with the personalities attached to it. Young Americans have separated the two cleanly. They just came down against the personalities. Nine manifestos won't move 69%, and nine more are coming.

Somewhere inside 6,500 words, Mark Zuckerberg pauses to say that "it is surprising that the discourse from many developing AI is so filled with doom." He published the open letter on Monday last week under the title "The Future is for Everyone," and the reaction sorted itself into two piles almost at once: a sincere expression of hope on one side, a long public relations exercise on the other. WIRED's Brian Barrett filed it under the unfortunate genre of philosophical manifestos from tech CEOs, which is at least an honest description of the form.

The genre has a lineage. Marc Andreessen opened it in 2023 with a 5,000-word "Techno-Optimist Manifesto." Sam Altman followed in 2024 with "The Intelligence Age," and Dario Amodei with "Machines of Loving Grace," which runs past 13,000 words and has been printed, bound, and handed out as merchandise. The shared claim is that the thing the author sells is among the most important technologies in history. The unshared part is the policy each one lands on, and it always turns out to be the regime that suits the author's own lab.

Zuckerberg's lands on open models, looser rules on distillation, and a rethink of what a model may be permitted to learn from other models. "I do not believe restricting access to foreign open source models is an effective solution," he writes, which is a position about Qwen and DeepSeek dressed as a position about liberty. Noah Smith's objection is the serious one, and it cuts against the argument rather than the man: "Should we open-source something that has the ability to kill humanity?" If the answer is no, then a lab going proprietary is defending something real. But the letter doesn't make a safety case for Meta's choices. It makes a competitiveness case for everyone else's.

Zuckerberg argued in July 2024 that open source AI was the path forward. Meta then launched Muse Spark fully proprietary in April of this year, weights unavailable, API access by private preview. Twenty-one months, one abandoned principle, and now the openness sermon returns at triple the original length, from a company that New York reckons is spending hundreds of billions to catch up with OpenAI and Anthropic. Rob Lalka, a business professor at Tulane, told the BBC these essays are how an executive plays "businessman-philosopher." The philosophy moves when the market position does.

On jobs the letter is thinner still. Zuckerberg expects abundance and can't understand why anyone who believes AI will eliminate most work would rush to build it, which is a fair challenge to the doomers and no answer at all to the IMF, which warns AI could affect nearly 40% of jobs. Meta itself has cut around 8,000 people, 10% of its global workforce, while reorganising around AI. That doesn't disprove a claim about 2035. It does mean the essay and the payroll never appear in the same document.

The promised future is oddly small when he describes it. John Herrman noticed the tell in New York: Zuckerberg's illustration of personal superintelligence is an agent that plans weekend baking recipes with his daughter and orders the ingredients.

MPU101 is the artist, MPU107 the album, and the seventh of these to arrive with an alias where the name should be. Five years of records on Ilian Tape and the Munich label has never put a face to them. What it does supply is a credits block, and on this one the credits block is unusually talkative.

Recorded in 2025 across eleven US states, from California up through Oregon and Washington and back across Idaho, Montana, Wyoming, Nebraska, Colorado, Utah, Nevada and Arizona, plus London, with additional sessions at Wiggle World in Altadena and Terraform in Los Angeles. Edited afterwards in Chiemgau, Bavaria. Mixed by Tobias Neumann in Berlin, mastered by Tangible Air in Munich. That is an itinerary, and the track titles read like the same trip logged twice: Montana Interface, InterstateXX, headlight section, Campsite Sequence.

So the only way in is the paratext, and I think that is deliberate rather than coy. Withhold the biography and the packaging becomes the argument. Ten tracks, a little under fifty-six minutes, a purple eight-point star stamped over the centre of 180g vinyl, and four of the tracks trimmed shorter for the record than for the download, which is the sort of detail a project with a persona would never bother to publish.

Horizons Music is selling it as glass-eyed ambient, sad synth for loners, dystopian downtempo. That is positioning rather than verdict, and I am not going to pretend to a verdict of my own on a record that came out last month. What the previous six earned is the benefit of the doubt, which puts this one on the shelf with everything else I keep finding that is uninterested in being loud.

Janet Maslin opened her New York Times review on 22 May 1993 by observing that Sharon Stone practising her golf putts was "one of many good reasons this film was kept under wraps until it opened yesterday". Variety filed Sliver as "a cold, inaccessible yarn about murder and voyeurism" and suggested Stone "might be well advised to move on to a different genre". The Los Angeles Times put its verdict in a subheading: a film about voyeurism that doesn't provide much to watch. Read together, the pans have a shape. Almost all of them reviewed the sex and the star, measuring what Stone did here against what she had done the year before in Basic Instinct, and almost none of them reviewed the idea the film was built on.

Paramount's one-sheet had already made the promise the picture couldn't keep. Black field, three names in serif capitals, and down the centre a single vertical strip of blue eye above an orange open mouth, sliced narrow like the building. "You like to watch, don't you." Richard Schickel, in TIME, was the one critic who took the taunt at face value. A cinema screen, he wrote, is "among other things, a big lighted window", and we in the dark are voyeurs hoping to see forbidden sights, which makes Carly Norris a projection of our watching selves: we are invited to watch a watcher learn to like watching. Then he dismissed it as carelessness dressed up as irony. He was right about the film and wrong about the idea. A picture doesn't have to earn its premise for the premise to be the thing worth keeping.

Audiences were not the obstacle. Sliver took $12.1 million across 2,093 theatres, beat Hot Shots! Part Deux to number one, and finished on $36.3 million at home and $123.9 million worldwide against a $40 million budget. The tagline worked exactly as a tagline should. Whatever went wrong here, nobody refused the invitation.

What they bought a ticket to wasn't quite a film. Phillip Noyce has said he made 110 edits to escape an NC-17, and the ending was rewritten and reshot after previews went badly, which meant changing who the killer was. Set that against Malice, five months later, whose plot also collapses when you push on it: Nicole Kidman's malpractice fraud requires a surgeon who was secretly her doctor in Boston under another name, and nobody has ever explained how he ran two identities. That hole is load-bearing. It is the film committing to its own excess. Sliver's holes were cut into it afterwards by people trying to save it, and the finished picture has the distinctive flatness of a story whose secret was swapped out in post.

The surveillance is the part that has aged into furniture. In 1999 a Dutch production company put strangers in a wired house and sold the footage as Big Brother, and what William Baldwin's Zeke Hawkins does from his console became a format, then a genre, then a doorbell people buy for themselves. Watching a bank of tenant feeds now looks less like a psychopathology than a job. The line that has actually curdled is the romance: a woman finds out a man has filmed her in her own flat, and the film treats this as the moment she warms to him. In 1993 that was bad sexual politics inside a thriller. Now it's the only genuinely implausible thing left in it.

Castle Rock had been sitting on Malice since the company was founded in 1987, back when it was called Damages. When the studio wanted a fresh script it went to William Goldman, who was busy and proposed a young writer he was mentoring instead. That is how Aaron Sorkin came to the picture, delivering two drafts before breaking off to adapt A Few Good Men and returning for the rewrites that got Alec Baldwin to sign. Scott Frank was on the job by November 1991, and much of the finished film's thriller machinery is credited to him.

Goldman's lesson to Sorkin, in Sorkin's own telling on the BBC, was to always have a secret in your back pocket, something the audience doesn't learn about your lead until late. Malice follows that instruction until the pocket is empty. Bill Pullman's college dean rents his attic to Baldwin's surgeon; Kidman's wife goes in with abdominal pain and comes out missing the healthy ovary as well as the diseased one; the malpractice suit that follows is a fraud she is running with the surgeon, who was also her doctor in Boston under another name. Ebert asked the only question that matters: who was the doctor in Boston, and how did he keep two identities going? Nobody has answered it in thirty-three years.

Columbia's one-sheet sold exactly this. Two half-faces in Gordon Willis shadow, a list reading deception, betrayal, murder, and beneath it the flat promise that some things you never see coming. Where Harold Becker's name should be is the line "from the director of Sea of Love", which tells you the studio knew what it was selling and it wasn't a director.

Vincent Canby, in the New York Times, filed the minority report, enjoying the cheat, calling the film deviously entertaining and noting that "no matter how wild the plot reversals, there's always a slightly madder one to come". Everyone else described the same fault in different words. Ebert called it "one of the busiest movies I've ever seen", noted that an entire serial-killer subplot is "thrown in simply for atmosphere", and warned that reconstructing the twists afterwards leaves you in "really deep molasses". Peter Travers, in Rolling Stone, granted the suspense and withheld the rest: Sorkin "could run a red-herring factory", and the film has "suspense but no staying power". Variety's Tim Gray held both positions at once, calling it "immaculately crafted" and "a virtual scrapbook of elements borrowed from other suspense pix". It sits now at 54 percent on Rotten Tomatoes and 52 on Metacritic.

None of which hurt it. Malice opened at number one on $9.2 million, finished at $61.6 million worldwide against a $20 million budget, took a B+ CinemaScore and became one of the twenty most-rented films in America in 1994. It was a hit twice, in cinemas and then on tape, and that is where the story of its popularity stops.

What actually survived is one scene, and it isn't a twist. Baldwin, in a deposition, asked whether he has a God complex: "I have an M.D. from Harvard. I am board certified in cardiothoracic medicine and trauma surgery... and I am never, ever sick at sea." Then the landing: if you want God, he was in operating room number two on November 17th, and he doesn't like to be second-guessed. The rest is trivia. Gwyneth Paltrow is in the abandoned subplot, pre-fame and barely there. Willis's photography went unremarked until a Kino Lorber Blu-ray decades later gave people a reason to look. Gray reached for Dead Calm to explain what Kidman was doing here, a comparison his review doesn't seem to notice is unkind.

The machinery rotted and the digression lasted, which I think settles the authorship question the credits leave open. The trapdoors every reviewer hung on Sorkin were substantially Frank's. The speech nobody can stop quoting is a man stating precisely what he is, out loud, on the record, with nothing in his back pocket at all. Sorkin left this film to adapt A Few Good Men, which ends with a confession argued out of a witness in open court rather than sprung on the audience. He has since explained his fondness for courtrooms by pointing out that a jury is a stand-in for the audience, twelve people who know as little as they do. That is the exact opposite instinct to a secret in a back pocket, and it is the one he kept.