Plutonic Rainbows

Yesterday at least 1,000 staff at Google DeepMind's London office wrote to Debbie Weinstein, the head of Google UK, asking her to recognise the Communication Workers Union and Unite the Union as their joint representatives. The CWU says 98 per cent of its members at DeepMind backed the move. If Google recognises the ask, DeepMind becomes the first frontier AI lab anywhere in the world to formally unionise. Google's spokesperson, in a careful sentence reported by Research Professional News, confirmed receipt of the letter and added that "at this stage in the process, there has been no vote to unionise." That phrasing tells you which fight Google plans to have.

The trigger is the Pentagon contract I wrote about three weeks ago, the deal that lets the US Department of Defense run Gemini on classified networks. At the time, more than 600 Google employees, including directors and vice presidents, signed an open letter to Sundar Pichai begging him not to do it. Google signed it anyway, last week. The day after the news broke publicly, the union request was on Weinstein's desk. The two events are not separated by any amount of decorum.

The escalation curve is worth noticing. The CWU's published demand list asks Google to "reinstate" its pledge against developing weapons or surveillance technology, the language of a commitment the workers feel was rolled back rather than absent. The recognition request, the 98 per cent vote, and the explicit threat of research strikes are not the opening move; they are what comes after the open letter, the petition, and the internal ethics review have been tried and absorbed. A workforce that organised by letter once is now organising by union, because the letter route stopped delivering.

Project Nimbus is in the demand list too, alongside the Pentagon deal. Nimbus is the cloud-and-AI contract Google and Amazon hold with the Israeli government, and the case against it has been running internally for years without traction. Bundling Nimbus into the recognition push is a tell. The workers are not asking for higher wages or better RSUs. They are asking for the historical right to refuse work, which is exactly the kind of right a union, rather than a letter, is built to enforce.

The CWU's John Chadfield called this collectivising against "circling the ethical drain of military-industrial contracts." The phrasing is union rhetoric, but the underlying claim is real. Frontier labs have spent the last two years arguing their work is too consequential to be governed by ordinary commercial logic. That argument cuts both ways. If the technology is too consequential to be a normal product, it is also too consequential to be a normal employment relationship, where the employer unilaterally decides who the customer is. DeepMind is the first lab where the workers most empowered to refuse, the researchers themselves, are now using the mechanism that comes next after the letter.

Google can choose to recognise voluntarily, refuse and go to the Central Arbitration Committee, or try to drag this out. The first option is unlikely. The second is the obvious play and almost certainly what the "no vote has yet taken place" line is preparing the ground for. The third buys time but burns researcher goodwill in a market where DeepMind already loses people to Anthropic and OpenAI every month. None of these are clean. The cleanest path closed last week, when the contract was signed.

Open a terminal emulator on a 5K display. By default, the window is eighty characters wide. Open PEP 8, the Python style guide, and you will find that lines should be capped at seventy-nine. Open the Linux kernel coding style document and the soft limit is again eighty. The GitHub diff view, the man page, the email patch convention, the README that wraps because anything else looks wrong — all of them hold to a width that nobody in the room remembers being chosen.

It was chosen in 1928, by an IBM engineer called Clair Lake, and it was a piece of cardboard.

Until that year, the dominant punched card was Herman Hollerith's, which had run at twenty-four columns and then forty-five. IBM was working on something denser. Lake's design squeezed eighty narrow rectangular holes across the same physical card, in ten rows for numerical coding, with twelve rows added two years later for alphanumeric extensions. The card itself was 7⅜ inches by 3¼ inches. That was the unit of data for nearly half a century. By the 1960s, when programmers wrote FORTRAN or COBOL on coding sheets and then had keypunch operators turn them into stacks, the eighty-column card was so standard that the languages themselves were structured around it. COBOL programs used column 7 as a continuation indicator and the last eight columns as an identifier you could re-sort by, in case the deck got dropped (which apparently happened a lot). The card was the line, and the line was the card.

When teletypes and dot-matrix printers arrived, they were built to print eighty columns because that was the width of the data they were going to render. The DEC LA30, introduced in 1970, did exactly that. When dumb terminals replaced teletypes, the screens were sized to print one card line per screen line, conventionally at 8 pixels per character on a 640-pixel screen, which is the same arithmetic worked the other way. By the time the IBM PC shipped its 80×25 text mode, no card had been used to enter program code in years, but the ratio was load-bearing.

PEP 8 caps Python at 79, not 80, because some terminals reserve the last column for a wrap indicator. That is the punch card asking for one column back to flag a continuation it can no longer make itself. The persistence of COBOL in banks and government back offices is the obvious fossil. The eighty-column window is the quieter one, embedded in muscle memory, diff tools, and review etiquette.

You can argue with it. People do, every few years, on the Python forums and in kernel threads. The thing that is genuinely strange is how rarely the argument wins. Black formats to 88, ruff defaults near there, individual teams pick 100 or 120, and yet the cultural gravity remains 80. The card is gone, the keypunch operator is gone, the green-screen VT100 is in a museum, and the line still breaks at the same place a piece of stiff card used to end.

Rei Kawakubo showed Comme des Garçons in Paris on 6 March 1994 under the title Metamorphosis. The collection ran for autumn– winter 1994–95. Cecilia Chancellor opened. Linda Evangelista closed. Christy Turlington, Kate Moss, Stella Tennant, Shalom Harlow, Amber Valletta, Nadja Auermann and Eve Salvail were in between. By the cast list alone you can read what the show was not, which is a quiet studio exercise. It was a major Paris ready-to-wear at the loudest moment of the supermodel decade, and what it put on those bodies was a series of garments built to look wrong.

The technique was boiled wool. The fabric was knitted or woven to size, then deliberately shrunk after construction. What came back from the wash was a class of garment that no longer fitted the body it had been cut for. Sleeves rode short. Shoulders sat high. Greatcoats lost their length in odd places, kept it in others. Duster coats came out of the process with frayed raw edges and crinkled cotton linings hanging below the wool. Sweaters bobbled in patches and not in others. The Met's later notes called it abject; the National Gallery of Victoria, which holds a top-and-trousers set from the show as part of the Takamasa Takahashi gift, files it under reframing fashion. Both phrases are reaching for the same thing, which is that the garment had been put through something the wearer's body could not undo.

This matters because of where it sits in the timeline. Three years later Kawakubo did the Body Meets Dress, Dress Meets Body show for spring 1997, the one with the duck-down padding and the bulges and the press conviction that the project had finally tipped into pure provocation. The shrunken-wool collection is the obvious precursor and is rarely cited as one. Metamorphosis is the same argument made with subtraction rather than addition. Kawakubo had been heating the fabric until the garment stopped behaving like a garment. The 1997 show heated nothing and added wadding. The conclusion in both cases is that the body fashion exists for is not the body inside the clothes, and the gap between the two is where the work happens.

There is a second thing the show did that is easier to miss. Boiled wool is a folk technique. It is what the Tyrolean jacket is made of, the loden coat, the heavy military greatcoat that keeps its shape because the felt has already decided what shape it will be. Kawakubo was using a craft method already coded as European, rural, and protective, and turning it on the wearer. The result reads less as deconstruction in the architectural sense, that word she has always disliked, and more as a kind of counter-tailoring, a way to make a coat that has refused the shoulder it was sewn for.

Vintage market still places these pieces. A black boiled-wool tunic dress from the show comes up at Lithe Curation; the grey- lined duster coat surfaces through JHROP; the Homme Plus suit appears at dot COMME with the original lining still hanging out. What you can't reconstruct from the surviving garments is the walk. You have to reach for the Getty image bank and the Yohji aftermath that the same Paris season was still working through to put the show in motion again. The clothes alone tell you everything is wrong. The bodies in them, in March 1994, were the most famous in the world, and the dissonance was the point.

Until the 1970s, when you handed money to a sales assistant in Selfridges, the assistant did not give you change. They could not. There was no till at the counter. There was instead a small brass aperture on the wall behind the counter, and the assistant rolled your banknotes and the docket into a wooden or metal canister, screwed it into the tube, pulled a handle, and your money flew off through the building's walls to a centralised cash office somewhere out of sight, where a clerk processed the transaction, signed the receipt, screwed the change back into the canister, and fired it back. You waited at the counter. The whoosh of returning canisters was a constant retail sound, and the tubes that carried them were called Lamsons.

William Stickney Lamson, a Civil War veteran who ran a five-and-dime in Lowell, Massachusetts, patented the first cash-carrier system in 1881. The original was almost comically simple: hollow wooden balls rolling along gently sloping wood-and-leather rails, propelled by gravity from the sales counter to a cashier's loft above. He founded the Lamson Cash Carrier Company in Boston the next year. By 1884 an Irish-American agent, John Magrath Kelly, had set up the British arm in London and secured the European, African, Australian and Middle Eastern rights to the patents. By 1888 the Lamson Store Service Company Ltd was capitalised at £85,000, the equivalent of nearly ten million today.

The technology evolved fast. Wire systems came next, suspended pulleys that fired carriages between counter and office on tensioned cables. Then, in 1899, Lamson absorbed an American rival, the Bostedo Package and Cash Carrier Company, and renamed it the Lamson Pneumatic Tube Company. That was the form the technology took for the next seven decades. By 1911 there was a purpose-built factory at Hythe Road, Willesden Junction, in northwest London, and the tubes were going into Selfridges, Harrods, John Lewis, Whiteley's and the Army & Navy.

What is hauntological about Lamson is not the equipment, which is well-documented and unambiguous. It is the spatial logic. The till did not live where the sale happened. The till was a room. Money was a thing in motion through walls. The cashier was an institution rather than a piece of equipment, and the act of selling something to a customer involved temporarily losing physical possession of their payment to a separate department of the building. This required trust between assistant and customer that has no modern analogue, the till being now the thing that confirms the sale rather than the thing the sale waits on. It also required an architecture. Every counter piped or wired to a central node. Every store designed around the geometry of cash movement. Walk into a flagship interwar department store with the original Lamson layout in mind, and the floor plan suddenly makes sense in a way it cannot if you assume the till has always been a box on a shelf.

The British systems lasted longer than they should have. Lamson Engineering Ltd, formed by merger in 1937, only ceased independent operation in 1976, when it was acquired. By that point most stores had moved to electronic point-of-sale terminals, but a number of installations stayed running well into the post-war decades, sometimes for cash, sometimes downgraded to internal mail. A few survive as restored curiosities. The Up-to-Date Store at Coolamon, in rural New South Wales, still has its original ball-and-rail system in working order, the only such installation known anywhere.

There is a particular lesson here for anyone who has worked in modern retail and assumes the till is a kind of natural fact, the place where money meets transaction at the point of contact. It isn't. There was a longer era when the building counted the money for itself, in a single secret room, and you waited politely for the canister to come back.

OpenAI replaced GPT-5.3 Instant with GPT-5.5 Instant as the default ChatGPT model today, and the rollout pairs two things that should probably be considered separately. The model hallucinates less, by OpenAI's own measurement: 52.5% fewer hallucinated claims on high-stakes prompts in medicine, law, and finance, and a 37.3% reduction on the conversations users have explicitly flagged as factually wrong. It also draws on much more of your context by default, pulling answers from past chats, uploaded files, and Gmail for paid users on the web.

The accuracy improvement is the easier story. GPT-5.3 Instant already shaved 26.8% off the previous baseline, which I covered in an earlier post about OpenAI's release cadence, so 52.5% on top of that is a real engineering result rather than a marketing one. AIME 2025 climbs from 65.4 to 81.2. MMMU-Pro goes from 69.2 to 76.0. These are the unglamorous benchmarks that actually correlate with whether a model can be trusted to draft a discharge summary or pre-read a contract.

The personalization side is the part I keep turning over. The default ChatGPT now treats your archive as retrieval material. Ask a question, and the answer can pull from a chat you had two weeks ago, a PDF you uploaded last quarter, or a thread in your Gmail. There is a memory-source list attached to each response so you can see what was used and remove what you do not want quoted. The control surface is real and deliberately exposed. Memory sources are not visible to anyone you share a chat with, which closes the obvious leak.

Still, the cumulative effect is a chatbot that is harder to use casually. You now have to think about what you have told it across months, what is sitting in your Drive, and which of your archived emails it might surface in a quick reply. The Axios writeup made the tradeoff plain: lower hallucination rates can make people trust answers more even when the model is still capable of being wrong, and a personalization layer increases the cost of any wrong answer because you assumed the system had read your situation correctly.

The model is also trying to feel less like a chatbot. OpenAI says it has cut "gratuitous emojis" and reduced unnecessary follow-up questions, so the tone defaults closer to a colleague than to a customer service avatar. After the GPT-4o backlash earlier this year, when users campaigned to keep the model that "affirmed" them, this change is interesting. The new default is calmer and more concise, which is the opposite of what the loudest user segment demanded.

Developers get GPT-5.5 as chat-latest. Paid users keep GPT-5.3 Instant for three months before it is retired. There is no router toggle this time, no two-day rollback, no public scramble. OpenAI appears to have learned at least that part of the lesson.

The same Monday Anthropic announced its Wall Street joint venture, OpenAI announced one of its own. Different consortium, same shape. OpenAI's vehicle is called The Deployment Company, valued at $10 billion, with around $4 billion raised from TPG, Brookfield, Advent, and Bain. OpenAI keeps majority control. The partners between them carry access to more than 2,000 portfolio companies. The point of the structure is to push GPT into the operating layer of those companies, not to sell them seats.

Yesterday's post was about Anthropic doing the same thing with Blackstone, Hellman & Friedman, and Goldman, at a smaller $1.5 billion. I read that as a one-off, a clever move from the lab that has been the more enterprise-flavoured of the two. The fact that OpenAI was running the identical play in parallel changes the reading. This is not Anthropic being unusual. This is the new shape of frontier-lab commercial strategy, and both labs arrived at it at the same time.

What both companies seem to have decided is that API revenue, however large, is not enough to justify what comes next. Capex commitments at this scale need a different kind of revenue. They need integration deals, multi-year transformation contracts, the sort of thing that gets paid for out of operating budgets rather than software budgets. That is consulting work. Business Insider reported on Monday that one insider called the Anthropic vehicle "the McKinsey of AI", which is honest enough to be useful. McKinsey, BCG, Bain and Accenture have spent decades building the infrastructure for this kind of relationship. The labs do not want to spend decades.

So they have rented it. The PE firms are not really investors here, or not only investors. They are introduction layers. Blackstone alone runs about 275 portfolio companies. The four firms behind OpenAI's vehicle collectively touch thousands. None of those companies is going to call up OpenAI cold and ask for a deployment template. They will, however, accept a phone call from their own owner suggesting they try one.

There is a quieter detail underneath. Both labs are heading toward IPOs this year. PitchBook is already warning that OpenAI's might slip into 2027, but the direction is clear. A frontier lab going public needs a story about how its enterprise revenue compounds without requiring every customer to hire prompt engineers. A McKinsey-shaped attachment, with templates and reusable engagements, is exactly that story. The S-1 will look better with it than without.

What I keep noticing is how short the path was. Eighteen months ago the consensus was that the labs would compete for distribution: which one gets into Office, which one gets into Google Workspace, which one wins the chatbot. That is still happening, but it has stopped being the interesting question. The interesting question is which one gets quietly embedded in the close-the-books process of a mid-sized industrial holding in Ohio, and who got paid to put it there.

In 1982, France Télécom began handing out small beige terminals for free to anyone with a phone line. The terminal had a keyboard, a CRT, a modem, and no microprocessor. It dialled into a national videotex network using a standard called V23 bis, and on the other end of the line sat thousands of services that could be reached by typing short codes. The system was called Minitel, and within a decade it covered nine million households. By the peak in 1993, somewhere around 25 million French citizens were logging more than 90 million hours a month across roughly 26,000 services, more than a decade before most Americans had heard the word "internet".

The thing that gets forgotten is how deliberate the policy was. President Valéry Giscard d'Estaing's government rolled Minitel out during a period when French elites felt that the dominance of US firms in telephone equipment, computers, databases, and information networks was a threat to national sovereignty, or at least to cultural pride. The terminal was free because the state wanted volume. Usage was billed by the minute, the network paid out to service providers, and nobody needed a credit card or an account. It was a closed garden run by the post office, and for roughly fifteen years it worked better than anything else on earth.

Then the web arrived, and France kept its garden walled. Service providers were making real money on the existing system. Users were comfortable. The government had no political appetite to subsidise a transition to an English-speaking American protocol when the French-speaking national one was still doing what most French people wanted it to do. The country that had been a decade ahead of everyone else on consumer networking spent the back half of the 1990s coasting on the system it already had, while broadband matured elsewhere. By the time it became obvious which side of that bet had aged better, the gap was already wide.

The shutdown came on 30 June 2012. The Orange subsidiary of France Télécom, by then managing what was left of the network, said it had reached its natural death. Around 670,000 terminals were still in circulation when the plug was pulled, mostly used by farmers exchanging cattle data, doctors transmitting patient details to the national health service, and small tradespeople placing orders with suppliers who had never bothered moving online. Janine Galey, an 85-year-old mother of seven in Paris, told the Guardian she had used her Minitel until around 2000 and then gone straight to an iPad, skipping the desktop web entirely. There is a thirty-year window of French daily life in which a meaningful slice of the country transacted online without ever touching a browser.

What persists is the policy instinct. The same logic that built Minitel, that French communications infrastructure should be French and that the state has a legitimate role in shaping it, runs underneath a great deal of contemporary EU digital policy. GDPR, the Digital Markets Act, the AI Act, the recurring French enthusiasm for the phrase "souveraineté numérique" in cabinet briefings: none of that is causally downstream of Minitel in any clean way, but the intellectual furniture is the same. A country that once built its own network and ran it for thirty years is not going to be constitutionally relaxed about Mountain View running the next one.

The terminals themselves are kitsch now. They turn up in flea markets in the 11th arrondissement for thirty euros, beige plastic with the slide-out keyboards that supposedly inspired Steve Jobs's first Macintosh. Most of them still work if you can find a phone line that will carry the V23 bis signal, which is harder every year. The ghost is not the hardware. The ghost is the assumption, baked into a generation of French civil servants and now their successors, that the network is a thing the state can have an opinion about. The web, by contrast, has always insisted that it is weather. France was the last country to fully concede the point, and arguably has not conceded it yet.

A year before the famous hip-hop show, Lagerfeld put bike shorts on a Chanel runway. The Spring-Summer 1991 ready-to-wear was presented in Paris in October 1990, with a beach theme. Cycling shorts turned up under sequined tops, under little structured jackets, under cropped pieces in the saturated colours Chanel had not really worn since Coco was alive. The leggings-and-leotard logic that aerobics had pushed into ordinary wardrobes by the late eighties got promoted, on that runway, into the most expensive ready-to-wear in Paris.

The cast read like an inventory of the moment. Claudia Schiffer, Karen Mulder, Linda Evangelista, Tatjana Patitz, Yasmeen Ghauri, Naomi Campbell. Tim Blanks, looking back at the show twenty-odd years later for Style.com, used it as the marker for when backstage stopped being a back room. He remembered four or five camera crews at the start of the season and four or five hundred by the end of it. Whatever you call the supermodel era, this collection sits inside the moment it became a media phenomenon rather than an industry one.

What the show actually did, on the level of clothes, was harder to read at the time. Chanel in 1990 still meant something specific to the women who bought it: a quilted bag, a chain belt, a tweed suit, a particular kind of older clientele the house had spent the previous decade trying not to lose while also trying not to ossify around. Lagerfeld's job, by then, had been to keep both audiences in the room. The beach collection was an attempt at the second part. Bike shorts under a tweed jacket are not a concession to an existing customer; they are a bet that there is another customer arriving.

The reference points were sport and sportswear, not couture. An American context kept showing through, the cyclist on Venice Beach, the aerobics studio, the pop video. Lagerfeld liked to say his life was based on change, also change of mind. What was right for the next ten minutes might not be right after that. The Spring 1991 show looks now like the moment that change-of- mind became a method rather than a quip, the moment where the house's commercial heritage started getting pushed through a filter from somewhere outside Paris and let back out as something else.

The Fall 1991 show, six months later, ran the same trick at a higher temperature, piled-on chains and the line about Christmas trees. That collection took the headlines, partly because the press had caught up with what was being attempted, partly because hip-hop codes inside Chanel were a more legible provocation than bike shorts inside Chanel. The beach show stayed quieter in the record, even though it was the one that established the frame.

Looking at the Getty stills, the thing that stands out is how unforced the styling reads. The supermodels are not trying to sell you the bike shorts. They are wearing them as if cycling shorts under a jacket were already an ordinary thing for a woman with money to wear in October 1990, which it was not. By the time the next decade arrived, the experiment had hardened into a costume cliché, leggings under everything, athleticwear codes on the high street. The thing the runway did first does not always survive into the version that becomes the rule, but it leaves the imprint.

Lytham St Annes is a quiet seaside town between Blackpool and the Ribble estuary. In June 1957 a machine the size of a delivery van was switched on inside a Post Office building there, and Ernest Marples, the postmaster general, pressed a button. ERNIE, the Electronic Random Number Indicator Equipment, generated the first nine-digit Premium Bond numbers in history. Two thousand numbers an hour, drawn from the thermal noise of neon gas tubes, fed to a teleprinter, matched against bond serials that were not yet on a computer because no computer existed at the right scale to hold them.

ERNIE was built at the Post Office Research Station at Dollis Hill, in north London, by the same engineers who had built Colossus during the war. Tommy Flowers oversaw the project. Harry Fensom, who had worked under Flowers on Colossus, was chief engineer. Sidney Broadhurst led the build team. None of them could speak about Colossus, the wartime machine was classified for thirty more years, but the techniques transferred sideways into a different national project. The state had decided to encourage saving without raising taxes. Premium Bonds were the answer. The lottery needed numbers no human could fix, and the men who had broken Lorenz cipher knew how to make them.

The Science Museum description puts it plainly: for many people, ERNIE was the first electronic brain they had ever heard of. Not the first they had used. The first they had heard of. The press anthropomorphised the machine immediately. Christmas cards arrived in Lytham St Annes addressed to ERNIE personally, and millions of people who had never used a typewriter, let alone a computer, took for granted that a steel cabinet in Lancashire was deciding their luck once a month.

The line of succession is long. ERNIE 2 arrived in 1973, sixty-five thousand numbers an hour. ERNIE 3 in 1988 ran at three hundred thousand, and produced in April 1994 the first Premium Bonds millionaire: a man from Surrey, ten thousand pounds invested, bond number 29JZ644125. ERNIE 4 in 2004 generated a million numbers an hour, weighed ten kilograms rather than fifteen hundred, and was small enough to retire to the National Museum of Computing at Bletchley Park when ERNIE 5 took over in 2019. ERNIE 5 is a chip the size of a grain of rice, built by a Geneva firm called ID Quantique. It uses the quantum behaviour of light rather than thermal noise. It produces nine million numbers in twelve minutes, replacing a machine that needed nine hours, replacing a machine that needed near enough three days for the first draw.

What persists across all five generations is the name. The technology is unrecognisable; the physical object has shrunk by something like a factor of a hundred thousand; the source of randomness has migrated from the thermal jitter of valves to the irreducible weirdness of photons. ERNIE is still ERNIE. NS&I uses the same nickname they used in 1957. The brand is older than nearly every computing system in continuous use anywhere on earth.

There is a hauntology in this, and it is the inverse of the usual one. The usual hauntological object is a thing whose function has died and whose body remains, leaving a husk. ERNIE is a name whose function has survived through five complete bodily reincarnations. The ghost is the handle, not the cabinet. The cabinet from 1957 sits in the Science Museum collection. ERNIE 4 sits at Bletchley Park, a few rooms from the Colossus rebuild, where it can keep its grandfather company. ERNIE 5 sits inside a server rack in Lancashire and is invisible at the scale of a glance.

Once a month at the start of every month, somewhere on that server, the descendant of a wartime code-breaking machine still picks the numbers. The teleprinter has been replaced, the neon tubes have been replaced, the engineers who built the original machine are long dead, and yet the press release that goes out from NS&I still credits the result to ERNIE's draw. Whatever is doing the work, the name does the explaining.