Plutonic Rainbows

Somebody finally checked the exam paper. Researchers re-annotated 5,700 questions across all 57 subjects of MMLU, the general-knowledge test that anchored nearly every model launch for four years, and found that around 6.49% of it contains errors: wrong answer keys, ambiguous phrasing, questions with no correct option at all. In the virology subset, 57% of the questions they examined were flawed. Correcting the mistakes changed the model rankings, which means part of the ordering we had been reading as capability was models agreeing with a marker who was wrong. Alex Williams collected that study and several like it for Communications of the ACM last week, and a smaller detail in his piece is worse. BIG-bench, built by hundreds of researchers, shipped with a canary string: a unique token embedded in the dataset so anyone training a model could filter the benchmark out, and anyone auditing one could check whether they had. When OpenAI ran contamination checks for the GPT-4 report, BIG-bench had been swallowed by the crawler anyway. The model can produce the canary on request.

Nobody decided to cheat there. The pipeline did it by default, because a held-out test set that has sat on GitHub for three years is not held out in any sense that matters. Instrument noise is a third failure of the same sort: the Leaderboard Illusion authors submitted two identical checkpoints of one model to Chatbot Arena under different names and the scores landed 17 points apart, about the size of gap that gets written up as a generational leap. None of this is subtle, and all of it is fixable in principle. Rotate the questions, proofread the keys, publish confidence intervals, stop reporting single runs.

The objection that doesn't dissolve under better hygiene was made by Raji, Bender, Paullada, Denton and Hanna at NeurIPS in 2021, and I think it's correct and mostly ignored. Treating any benchmark as a measure of general ability is a category error, not a calibration problem. A benchmark is a specific, finite, contextual set of tasks. You can make it bigger, cleaner and fresher, and it will still be specific, finite and contextual. No amount of engineering converts a sample into a faculty. So a cleaned-up leaderboard buys you a more honest number about a narrower thing, and the narrowing is the whole content of the result.

Which is why the measurement work I find worth reading isn't the work that claims to have built a better exam. It's the work that says out loud what smaller quantity it is actually reporting.

François Chollet's version, running since 2019, is that intelligence is not a stock of solved problems but the efficiency with which you acquire skill at problems you've never seen. ARC-AGI is built on the distinction: it tests fluid intelligence rather than crystallized, and restricts itself to a small set of Core Knowledge priors so a system can't win by having read more than the person it's compared against. A model that arrives holding task-specific knowledge the human lacks is scoring the cleverness of whoever encoded it. ARC-AGI-3, released in March, pushes the idea about as far as it goes. Agents are dropped into turn-based environments with no instructions, no stated goal and no reward signal, and have to work out what the game is before they can play it. Scoring compares the actions an agent burns against a human baseline rather than counting right answers. Humans solve 100% of the environments; frontier systems, as of March, score below 1%. The report states the scope plainly, fluid adaptive efficiency on novel tasks and nothing else, and maintaining it is manual work: each version has been rebuilt to resist the optimisation that ate the last one, so what ARC-AGI offers is a gap its authors keep re-opening by hand.

METR changes the unit rather than the questions. Instead of asking what fraction of a fixed set a model gets right, it asks how long a job can be before the model stops finishing it. The 50% time horizon is the human-expert completion time at which an agent succeeds half the time, fitted across a couple of hundred software tasks with real people timed on the same work. January's update grew the suite from 170 tasks to 228 and moved it to new infrastructure. Measured over the full history the horizon doubles roughly every six and a half months; measured since 2023, about every four; since 2024, under three. The doubling time is itself halving, and that is the finding, not the third significant figure METR attaches to each estimate. I like this number better than any accuracy percentage, partly because hours of human work is a unit a non-specialist can hold, and partly because it fails visibly: a saturating suite runs out of long tasks, a shortage you can see in the task list rather than a ceiling hidden in a percentage.

OpenAI's GDPval asks a third thing, whether the model can produce the actual deliverable. It draws 1,320 tasks from 44 occupations across nine sectors, based on real work products, and has experienced professionals from the matching occupation blind-compare model output against human output. It is explicitly positioned against the exam format, which is a fair criticism arriving from a company that spent years publishing exam scores. Its limit is economic rather than conceptual: expert human grading costs money per item forever, so the property that makes it credible is the property that stops it scaling, and the lab funding it is a lab it evaluates. We've already seen how carefully a scoreboard can be arranged when the same party sets the test and reports the result.

These are not competing answers to one question, and reading them as a leaderboard of leaderboards is the mistake. A system can extend its METR horizon by sustaining longer software tasks while staying useless on GDPval's deliverables, and ARC-AGI has nothing to say about either. Adaptation efficiency, autonomous task duration and occupational output quality are three quantities, not three estimates of one. The argument about what the milestone even is persists partly because people keep expecting one of them to settle it.

What I do when I'm choosing a model for real work is duller than any of this. I keep a small set of tasks drawn from work I actually have: verify nine URLs and report honestly which ones are dead, read a thousand-word draft and find the paragraph that sags, take a photograph and say where the faces are. They never get published, so they can't be trained on, and they measure the only thing I need to know, which is whether this model does my job. Public scores are close to useless for the first and silent about the second. That's a purchasing procedure rather than a theory of intelligence, and I've stopped waiting for anyone to hand me the second one.

Blue, and then more blue, and then the gloves. Powder-blue leather with a green inlay running up the back of each hand, worn indoors, held up at the throat, in a studio where there was nothing for a hand to need protecting from. They match the jacket exactly. The green in them answers the green in the scarf, the scarf answers the gold in the earrings, and the earrings come back round to blue in their cabochon stones. Five colours are working at once, blue, white, gold, orange and green, which ought to be chaos and isn't, because each one has been given somewhere else to be.

January 1993 is a peculiar moment for a photograph like this to be sitting in American Vogue. The appetite for excess is all still here, gold, silk, saturated colour, height in the hair, but the staging has been stripped to almost nothing: a plain near-white sweep, no set, no props, no gradient behind her head. Put the same clothes in 1987 and you'd expect a room, or at least a lit backdrop doing some work. That's one frame and one photographer's choice rather than proof of a movement, so take it as a reading. Still, the ornament stays and the environment goes, which is the direction the whole decade was travelling.

Helena Barquilla's calendar sits on the same line. She is Spanish, and her spring 1993 season ran through Lacroix, Byblos and Hervé Léger alongside the Madrid houses, among them Purificación García, whose best work of that period went almost entirely unwatched. By autumn she was walking Mugler and Krizia and doing couture for Balmain and Dior. She also walked Claude Montana's own-label spring show that year, by which point Lanvin had already replaced him, two Golden Thimbles notwithstanding. That's the constructed, declarative end of the trade, and within a couple of years a good deal of it would read as period rather than present tense.

The makeup is doing the same job as the tailoring. Count the decisions in the eyes alone: a dark brow taken to a point, black liner along the upper lash line, lashes loaded, a shaded socket, a warm sculpted cheek and a terracotta-brown lip drawn rather than smudged. The skin underneath isn't chasing the glassy, half-wet finish the contemporary version of this face would insist on. It's smooth and matte and expensive, and the objective isn't natural beauty at all, it's idealised sophistication, a different product entirely. The hair settles it. Swept back off the face, controlled, with real height at the crown, it supplies the authority the shoulders and the earrings then confirm.

So the result reads less like a woman who happened to be photographed and more like a manufactured image of cosmopolitan adulthood. I mean manufactured as a compliment. Contemporary styling spends most of its effort concealing the fact that styling occurred: the undone hair that took ninety minutes, the no-makeup makeup, the borrowed-from-a-boyfriend shirt fitted to the millimetre. This photograph does the reverse. It announces that a person has been dressed, coiffed, made up, lit and shot, and the announcement is most of the pleasure.

Which is why images like this feel haunting and not merely dated. Old clothes aren't haunting. The thing that has actually gone is the assumption sitting underneath the collar and the gloves, that an adult woman should look completed, and that looking completed was a reasonable public ambition rather than a slightly embarrassing one. Fashion swapped it for looking unbothered. You can date the swap roughly and everybody does, but the more useful measure is that nothing in this frame is apologising for the effort.

The gloves are where I'd start if I had to defend that. Both hands are up at the collar, wrists bent, fingers curled rather than gripping, fingertips resting on the white shirt without pulling at it. Nobody adjusts a collar that way. It's the gesture of someone arriving somewhere or about to leave, borrowed from film rather than from fashion, and it commits the picture to a narrative it never explains. She is not wearing gloves because her hands are cold. She is wearing them because the woman in this photograph is the kind of woman who has gloves.

Writing in the Artificial Intelligence Review in 1989, Jordan Pollack got the big thing right and the important thing wrong. He said the connectionist revival would keep growing, and it did, beyond anything he set out. What he expected it to grow into was a field he called connectionist fractal semantics, in which the distributed pattern would become a new kind of symbol, one with internal structure you could reason about, capable of pointing back to the larger structure it came from. He had a name ready for the object: a supersymbol. Nothing of that sort was ever built. The prediction failed in the strangest possible direction, by being fulfilled in volume and refused in kind.

Between 1988 and 1992 artificial intelligence occupied a strange interval between promise and afterlife. One system was fading and the other returning, and for a few years both were audible at once. Symbolic AI still carried the authority of the old dream: intelligence made explicit, filed, named, formalised, replayed. A bureaucracy of mind, in which every concept has its proper label and every conclusion can be traced back along a chain of reason to where it came from. The machine would not learn the world so much as be instructed in it, and for a while this seemed less like one option among several than like the only description that could possibly be true.

By the end of the decade the dream had gone hollow. Expert systems, sold as the industrial future of the field, turned out to be brittle contraptions, impressive inside narrow corridors of competence and expensive to keep upright outside them. They worked when the world behaved like the system's map of the world, and ordinary reality refused that containment, going on producing exceptions, ambiguities, tacit meanings and half-known contexts that no rule base absorbed.

Symbolic AI didn't disappear, though. It lingered the way an institution lingers after its purpose has become uncertain, and the great knowledge-engineering projects of the period have a melancholy grandeur about them. If machines lacked common sense, then common sense could be entered by hand, one assertion at a time, as though the entire background of human life were a document awaiting transcription. Douglas Lenat began Cyc in July 1984 at MCC in Austin on roughly that premise. By the end of its first six years the project had entered over a million assertions, and Lenat's own estimate of what remained was about two person-centuries of further work to reach the hundred million he thought necessary before the system could begin learning usefully on its own. Two centuries of clerical labour, budgeted, to arrive at the starting line. The more the project encoded, the more clearly it showed the abyss underneath knowledge: the residue of habit, embodiment, memory and practical familiarity that people draw on constantly without knowing they are doing it.

Connectionism was coming back at the same time, out of an earlier obscurity. The two Parallel Distributed Processing volumes landed in 1986 and offered a different picture, cognition as pattern emerging across many small adjustments rather than the manipulation of clean symbols. Meaning spread across weights instead of filed in a drawer. The claim that mattered for what followed was not that this worked better. It was that the distributed pattern was supposed to remain readable: microfeatures standing for something, a representation you could open.

The revival had a spectral quality, because none of it was new. Pollack described it plainly as the rebirth of a programme that thrived from the forties through the sixties and was severely retrenched in the seventies. The ideas had been there near the beginning and were pushed aside by the prestige of symbolic reasoning; the key training algorithm had already been written down in 1974 and left to sit. What surfaced in the late eighties was a path not taken, resurfacing precisely as the official future began to decay.

The symbolic order got one more authoritative statement, and it was a good one. Jerry Fodor and Zenon Pylyshyn published their critique in Cognition in 1988, opening with the observation that connectionist models were catching on, that there were conferences and new books nearly every day, and that the fan club included the most unlikely collection of people. Their argument was systematicity: anyone who understands "John loves the girl" understands "the girl loves John", and a classical architecture explains that symmetry for free. What looks like a concession in their paper isn't one. You can reconcile the two, they write, all that's required is that you use your network to implement a Turing machine, which is to say a network only gets systematicity by becoming the thing it claimed to replace.

David Waltz, a year earlier, had doubted you could take a large randomly wired network, show it enough raw sensory input and desired output, and get intelligence out the other end. The learning space for vision and audio was astronomically large, he said, and learning to perceive by feedback seemed cognitively and technically unrealistic. On the narrow question of whether the method scales, he was wrong, and the last fifteen years are the refutation.

The asymmetry between those two objections is the thing I'd point at. Waltz was answered. Fodor and Pylyshyn were not answered; they were outrun. Nobody demonstrated that distributed representations achieve systematicity without implementing a classical architecture underneath. The models simply got large enough that the question stopped being asked, which is a different outcome from being settled, and it left the philosophical objection intact and unattended somewhere behind the industry.

So the lost future of that interval isn't symbolic AI. Symbolic AI failed legibly, and a legible failure can at least be mourned on schedule. The loss that goes unmarked is the connectionism imagined in 1989: the readable microfeature, the supersymbol with inspectable internal structure, the representation you could open and reason about. That research line didn't die so much as get overtaken by systems whose representations nobody can read. We have interpretability now as a field, staffed and funded, which is itself the admission. It exists because the thing Pollack expected to come built in has to be excavated after the fact, from the outside, with uncertain results.

Cyc, meanwhile, never stopped. The transcription continued for decades, and by 2017 the knowledge base held something like 24.5 million assertions, roughly a quarter of the way to the line Lenat had named as the beginning. The project outlived its own future, which is a quieter fate than collapse and much harder to put a date on.

By 1989, Digital Equipment Corporation had fifty-nine technical staff assigned to maintaining the infrastructure and rule base behind its internal expert systems, at that point the most widely publicised application of AI anywhere.

The obvious objection is that fifty-nine people was a bargain. XCON, the configurator most of those rules served, was reckoned to be saving DEC around $25 million a year, and against a number like that a headcount is rounding error. Fair enough. The trouble was never the size of the bill, it was the shape of it: recurring, rising with the rule base, and quoted to nobody at the point of purchase. A system sold on the promise of bottling up scarce expertise turned out to need a permanent staff to keep the bottle from going off.

The winters get told as a story about capability. The machines couldn't do what was claimed, so the money left. Thomas Haigh's reading of the record is less tidy: the famous first winter of the 1970s largely didn't happen, and the real slump was the two-decade one following the 1980s bubble. What collapsed in 1987 was a hardware market rather than a technology. Cheap Unix workstations from Sun ran the same software the specialised Lisp machines ran, and the dedicated machines stopped making sense. Plenty of the expert systems kept running for years afterwards on ordinary computers. The field concluded the idea had failed, which was a larger conclusion than the evidence supported.

Nobody can claim they weren't told. Drew McDermott used the word at an AAAI panel in 1984, at the top of the boom, on a bill called The Dark Ages of AI, borrowing it from the nuclear winter argument then going on. He described a deep unease that the expectations being set would end badly, and he was four years early.

So the lesson everybody agrees on is don't overpromise. Ted Senator puts it plainly in his AAAI paper on what the bust should teach this boom: be measured about strengths and limitations even when the excitement is genuine. That's the cheap lesson, though. No one has ever been talked out of a funding round by their own caution.

The expensive lesson is the fifty-nine. The hardware business died in 1987 for reasons of its own, but what stopped companies replacing their expert systems was never the cost of building them. It was the cost of keeping them correct while the world they described moved underneath. That problem hasn't gone away, it has been renamed. Engineers air it constantly, as complaints about evaluation suites going stale and retrieval indexes rotting quietly. Where it doesn't appear is the investment case, which is still written in training runs and inference margins, as though correctness were a fixed cost you pay once.

The cold periods, on whichever count you accept, arrived when the money behind AI was overwhelmingly governmental, including much of what looked from outside like a commercial hardware market. A handful of decisions could switch it off: the Lighthill report in Britain, the Strategic Computing Initiative cancelling new AI spending in 1988. Henry Kautz argues a third winter is unlikely, and he may be right that the floor sits higher now. Commercial money tied to renewals isn't obviously safer, though. It fails differently, as erosion rather than a freeze, and erosion has no announcement date anyone can point at afterwards.

In March 1991 the Baltimore Sun ran a piece about a widow from Camden, New Jersey, who had somehow become the Queen of Camp. Edith Fore had fallen over in a low-budget commercial for a medical alert pendant and cried out, in a voice nobody had coached, "I've fallen, and I can't get up!" The line went everywhere. T-shirts in shops around Baltimore and across the country. Two rap songs. Comedians, radio DJs and chat show hosts repeating it until it wore smooth. Life magazine called it the statement of the year. For a while you could not trip over in public in America without hearing your own accident narrated back at you.

The line is still in circulation thirty-five years later, detached from anybody. I can find the commercial in about four seconds. What I cannot find, and have no particular right to demand, is the shape of the life that carried on around it. An archive is very good at keeping the artefact and hopeless at keeping the continuity, which means the fragment it hands back is always the loudest part of somebody rather than the largest.

Mark Fisher is usually read as arguing the reverse. In Ghosts of My Life he writes that digital archiving destroyed the fugitive evanescence that used to characterise watching television, seen once and afterwards only remembered. Things we assumed were gone turn out to be recoverable and endlessly repeatable. "What we have lost, it can often seem, is the very possibility of loss." He is describing the fate of artefacts, though, not of people, and set beside Edith Fore the two accounts stop competing. The clip is perfectly preserved. Preserving it did nothing whatsoever for her.

The seam runs somewhere through the early nineties. Visibility then was broadcast rather than accumulated. You could be seen by tens of millions on a Saturday evening and leave behind almost nothing that would still be indexed in 2026, because the indexing did not exist and nobody had begun to assume it should. Change your surname, move abroad, take an ordinary job, and the thread simply stopped. Today even modest exposure lays down sediment: accounts, interviews, databases, a search engine quietly stitching your twenties to your fifties whether or not you consented to the join.

I treat that disappearance as a failure, as though the past were sitting intact somewhere and my access to it were merely poor. It was not a failure. It was the normal operating condition, and it was industrial in scale. A paper given at the American Culture Association in April 1993 collects the damage with a certain weariness: of the 21,000 films made in America before 1951, by the American Film Institute's reckoning, only half survive, and for the silent period the loss ratio may climb above 75 percent. The missing prints include work by Griffith, Garbo and Laurel and Hardy, so obscurity was never the qualifying condition.

That paper cuts against me, though, and I should say so. Gary Burns was writing about music magazines and industry trade journals disappearing in real time while he watched, and the only reason I can read his warning is that somebody scanned it into exactly the kind of database he was arguing did not exist. The machinery I am being wistful about rescued him. It rescued the commercial too. Neither rescue reached Edith Fore, and I cannot honestly tell whether that is a limit of the archive or simply a job nobody was ever assigned.

Photographs are the part I find hardest to hold steady. A picture from 1991 runs two clocks on one surface. Inside it nothing has finished: the clothes are current, the lighting is current, the person is mid-sentence in a career that still had somewhere to go. Outside it, thirty-five years. Fiction handles this better than archives do: Mark Jenkin drops two fishermen into a harbour that is still 1993 and spends the film watching what it costs them. The trouble is that the early nineties refuse to look historical. The colour is too clean, the rooms are too much like rooms, the faces carry none of the formal distance that lets a Victorian portrait announce itself as finished business.

What disappeared alongside the individuals was the machinery that made them legible, and that machinery was extraordinarily narrow. A line from a pendant advert could reach most of a country inside a year because there was almost nowhere else for attention to be, and the route into the room was narrower still. Saturation was cheap then in a way it has not been since.

I could have found out what happened to Edith Fore. When I checked the 1991 piece I specifically did not go looking, and I want to be straight that this was a decision rather than a limit. The essay does not need it, she never volunteered for any of this, and going after the rest of her life to round off an argument about the indignity of fragments would be its own small demonstration of the problem. A culture that records this much has stopped producing the conditions under which anything gets missed properly, and it turns out that not looking is now something you have to choose on purpose.

In 1983 a seventeen-year-old competitive horse rider from Skanör entered an Elite model contest in Stockholm and came third. The prize was a trip to Paris and a contract with a short shelf life. Then nothing happened. Vogue, writing about her five years later, put it without decoration: a star was not quickly born, and Tatjana found no work for a year.

She had been born in Hamburg to an Estonian dancer and a German travel writer, raised on the Swedish coast, riding since she was seven, and she had trained as an actor rather than as a mannequin. For a year Paris had no use for any of it.

Almost nothing is happening in this photograph and it is still difficult to look away from. No styling to speak of, hair parted in the middle and left alone, a plain wall behind her. The face does all of it: the long oval, the set of the eyes, a mouth that isn't quite smiling and isn't withholding either. She told Vogue in 1988 that people had always said she looked special, that she didn't look like anyone else, and that she was going to make it because of that. Read cold it sounds like bravado. Look at the picture and it reads as a technical assessment. What separates her isn't a feature you could list, though. She isn't doing anything for the camera, and that turns out to be the whole career.

Peter Lindbergh worked it out first. In 1988 he took six models to a beach at Malibu, put them in white shirts, and let them behave like people rather than mannequins. Patitz stood out in a frame designed to level everyone. Her obituary in the Guardian named the mechanism: among all those wind-mussed, sun-smudged women having a good time, she seemed to be inhabiting happiness rather than performing it. The others are doing delight at the lens; she is in a good mood and the lens happens to be there, which no photographer can direct. Lindbergh went on shooting her, on and off, for close to thirty years, and he liked her best when she wasn't smiling. I've written before about the day those shirts were shot, and Patitz is the reason that image keeps working.

Then January 1990, the British Vogue cover, five women in a row, and George Michael seeing it and deciding those were the faces he wanted lip-syncing "Freedom! '90." Jil Sander then kept her for years as the face of a house built on removing things while Chanel had her on the runway doing the opposite, a range most of that generation never got asked for. Anna Wintour called her the European symbol of chic, Romy Schneider meets Monica Vitti, and added the part that stings slightly: far less visible than her peers, more grown-up, more unattainable.

Which brings me to the problem with this post. Six American Vogue covers alone, two of them in consecutive months, which was a rare thing then, plus three decades of editorial and a run of photographers, Ritts, Penn, Demarchelier, Meisel, Elgort, who each found a different woman in the same face. Five images is not a selection from that, it's a rounding error, and I spent longer discarding than choosing. One I didn't have to agonise over, because an Escada page from 1991 already has a post of its own. Of the rest, I picked the gold and the sand because the jewellery is absurd and she carries it as though it weighs nothing, and the Vogue Paris cover with Elaine Irwin because two blondes at that wattage should cancel each other out and instead the page seems to run hotter. Both were straight swaps for something else an hour earlier.

The polka-dot silk on the velvet chaise is the one I'd defend hardest, and the least to do with her face. She isn't posing so much as occupying, one hand laid over the other, the whole thing arranged and slightly bored, and it works because she looks like a woman who has somewhere else to be. That wasn't only a look. At the height of it she was flying forty times in a month, and she responded by moving to California to be near horses and open country rather than staying where the work was. Decades later she was still turning the word supermodel over, sounding it out syllable by syllable, saying she'd never really understood it, and I don't think that was modesty. She was describing a category she'd been filed into by other people.

Tatjana Patitz died in January 2023, at fifty-six. A couple of years before that she said she had never sold her soul, and it's a claim almost nobody in that industry gets to make without an argument following it.

Which leaves the pictures, and this one is the whole argument in a grey dress: cropped hair, pearls, a blank studio, and nothing offered to the lens at all.

The forecasts stopped converging some time last year. Dario Amodei said at Davos in January that AGI was probably a couple of years out, maybe 2027, on the argument that coding and AI research now accelerate each other. Demis Hassabis, at the same event, put it at roughly 50% by 2030 and named scientific discovery as the unsolved part. Two thousand Metaculus forecasters landed in February on 25% by 2029, 50% by 2033. None of that would be strange if they were estimating the same event. The Atlantic's reporting the same month is the giveaway: two years ago these people broadly agreed on the late 2020s, and what has come apart since is not just the date but the thing being dated.

Helen Toner has the framing that explains it. AGI was never a threshold, it's a fuzzy cloud of adjacent concepts, and for twenty years the fuzziness cost nothing because everything we could build was far outside it. We're inside it now. Serious people have declared AGI achieved three separate times since April 2025, while equally serious people put it a decade out, and they can all be looking at the same models.

I can show you what that looks like without a benchmark. A model reads a screenshot of a stack trace, finds the bug in a codebase it has never seen, writes the patch and runs the tests. In 2015 I'd have called that general intelligence and not thought hard about it. The same model, an hour later, loses track of a five-step plan it wrote itself. Both facts are true of one system, so which one you weight decides your answer, and there's no principled way to choose.

The part that should bother people more than it does is where the working definitions now come from. OpenAI's operational bar, systems that outperform humans at most economically valuable work, is contract language carried over from its Microsoft agreement, and leaked criteria reportedly attached a number to it: $100 billion in profits. That's a commercial trigger, not a claim about cognition, and it wandered into the scientific conversation anyway. So when Sam Altman told Forbes the company had basically built AGI and Satya Nadella said the industry was nowhere near, neither had to be wrong. Gary Marcus and two colleagues spent February arguing in Nature that the arrival claim mistakes benchmark scores for real-world flexibility, against opponents whose definition excluded understanding and agency by construction. That fight can't be settled by better evals, particularly when the labs keep grading their own homework.

Toner's suggestion is the only useful one I've seen: say what you actually mean. Fully automated AI research. Systems that learn as efficiently as a child. Enough labour displacement to break the employment model. Each of those can be argued about with evidence. None of them needs the word.

She is holding it against her chest with both arms, the way you hold something you have just been handed and have not decided about yet. A grey ribbed turtleneck, Linda Evangelista inside it, the 1994 bob falling exactly where it was cut to fall, mouth slightly open, eyes level with the lens. The lighting is doing portrait work. The bag is doing very little, a soft dark envelope of scuffed leather with a buckle at the top, and it takes up a third of the frame because somebody decided it had to.

Capezio began as a shoe shop. Salvatore Capezio was seventeen and newly off the boat from Italy when he set up near the old Metropolitan Opera House in 1887, mending and making shoes for the people performing inside, and for the next century the name meant pointe shoes and rosin. That is a specific kind of authority. It has nothing whatever to do with handbags.

By the autumn of 1994 the name on this bag was a licence. The bags were the business of Holiday Fair, whose president, Steven Hedaya, explained the campaign to Women's Wear Daily in terms that are hard to improve on. What they were trying to convey was "affordable chic." Evangelista had been signed because she "epitomizes fashionable chic." The bag gets affordable, the woman gets fashionable, and the two phrases sit one word apart in the same interview. Before her the ads had used Cindy Crawford posing with the bags in what Hedaya called an all-American, girl-next-door campaign with a casual, weekend feeling. Trading her for the woman who had told the press she didn't wake up for less than ten thousand dollars a day is not an adjustment to the styling. It is a decision about what the reader is being invited to stand next to.

Hedaya laid out the mechanism too. The larger photograph would concentrate on the product; the smaller ones would tell a story, in what he called a spirited soft-sell. So the two halves of the spread are not an accident of layout, they are the brief. The portrait is the story. The other page is the product, and you can watch it work: a bistro table on a pavement, rattan chairs stacked away behind her, coffee and a plate of something at her elbow, a pale ribbed suit with jewelled buttons that is asking to be read as Chanel. The bag sits on the table at exactly her eye height, lit harder than she is.

Underneath, the wordmark in lower case. At the foot of the page, RICH'S and THE BON MARCHE, the department stores where you could actually buy the thing. Running vertically up the left margin, in type small enough to miss, a toll-free number, sitting there against the café chairs and the jewelled buttons.

Up the same margin, in the same small type, the credit: Laspata/DeCaro Studio. Rocco Laspata and Charles DeCaro had picked up Capezio Bags as one of their early accounts, and that same year they were photographing Evangelista on a field in California with a liger and a ringmaster's whip, for a Kenar campaign built around a circus they assembled daily. Two years before that they had sat her among seven black-clad Sicilian women outside a church in Savoca, and Kenar had bought her outright and nearly buried itself doing it.

DeCaro put the arithmetic plainly years later, talking about Kenar rather than Capezio, though the structure is the same one: when Linda did Kenar, she was doing Chanel and every major designer runway, and Kenar sold triacetate suits.

So the page is an assembly. The bags were another company's business, made and sold under licence. The pictures belong to a studio working for hire, borrowing the visual grammar of couture advertising, the black and white, the pavement café, the tweed. The selling belongs to Rich's and The Bon Marché. Capezio licensed out the name, and the name is the part of this page it actually owns. A company that wants to own more than that has to make something, the way Versace patented a metal fabric and took a prize for the textile rather than the silhouette.

The spread was booked to run again in December, which is how a page like this earns out, not by being looked at once but by being there again when you next turn to it. She stays where they put her, chin on her hand at the little round table, looking off at nothing, the bag in the seat where a companion would be.

Mark Jenkin's camera has to be wound by hand every twenty-seven seconds. He shoots on a 16mm Bolex, records no live sound at all, and brings the actors back months afterwards to lay their dialogue over pictures already cut. On Rose of Nevada he wrote it, shot it, edited it and scored it, sharing the sound design with Ian Wilson. The sprocket holes and the blown-out frames stay in. That is not a quirk of budget, it's the position the film argues from.

I haven't seen it, so what follows is assembled out of other people's accounts and out of a method that is public record. The premise would sit comfortably in a Twilight Zone episode. A fishing boat that vanished with all hands thirty years ago drifts back into a Cornish harbour, empty. Its owner crews it up again: Nick (George MacKay), a father first seen leaving a food bank, with a roof he can't afford to stop leaking into the kitchen, and Liam (Callum Turner), a drifter with a past he won't discuss, under a skipper (Francis Magee) who seems to have mislaid his own name. They fish, they come home, and the harbour is 1993. The village takes them for Luke and Alan, the men who were lost.

Jenkin never explains the mechanism, and the interesting part is what each man does with the accident. Nick spends the film trying to get back to a wife and daughter not yet born. Liam sinks in, takes the dead man's wife and the dead man's daughter, and gets the settled life that was never on offer ashore. The BFI poster puts them side by side in matching yellow oilskins, looking in different directions.

Underneath the ghost story is a harder film about work, and that's the part worth the ticket. The 1993 they land in isn't a lost paradise, it's the moment the bill was run up. When a net tears in a storm, a dozen dock workers turn out to stitch it, and when Nick asks why everyone is so eager, he's told "we're a community" in a register closer to threat than comfort. The skipper offers his own version: for every man at sea, five at home are relying on him. Both lines sound like solidarity and function like a rota. An industry in collapse recruits on the promise of belonging and then treats the men as interchangeable, which is the same trap Jenkin built out of routine and repetition in Enys Men, only with an economy attached to it this time.

If you want the time slip explained, or even gestured at, HeadStuff's reviewer is right that you'll wait two hours for a hint that never lands. That objection assumes the film owes you a mechanism. What it's actually built to do is close a circuit, and reviewers describe a final shot that does it: the two men set out again, arranged in the same pose as the last known photograph of the crew they replaced. Nobody in the village had to lie to them. The work simply needed doing, and there were bodies available.