Plutonic Rainbows

The BBC Radiophonic Workshop opened in a corner of Maida Vale Studios in 1958 and stayed open for forty years. Most of what came out of Room 13 was incidental: a sting between continuity announcements, the throb beneath an Open University programme on continental drift, a sound for the moment a presenter said "and now, the news from Africa." None of it was meant to outlive the broadcast it sat under. Almost all of it has.

Delia Derbyshire arrived in 1962, having been told three years earlier by Decca that they did not employ women in studios. Her toolkit at Maida Vale was unforgiving in a way that is hard to picture now. Sine, square, and white noise generators. Reel-to-reel machines that needed to be slowed by hand or sped up against their will. Razor blades, splice tape, a wax pencil for marking the cut. Recordings of her own voice, of doors, of the metal lampshade she loved most, which rang like a bell when struck. To make the 1963 Doctor Who theme she built the parts manually from oscillator tones and tape loops, splicing them by hand into a continuous line. The thing took weeks. It sounds, even now, like a transmission slipping out of its proper decade.

What I keep returning to is the texture rather than the technique. There is a particular quality to Workshop sound, dry, slightly metallic, suspended between musical and merely structural, that shows up almost nowhere else. You hear it in the public information films about crossing the road. You hear it under the title cards of schools programmes. You hear it in the long open of The World About Us, which Derbyshire's Blue Veils and Golden Sands scored in 1968 using only her own re-pitched voice and that lampshade. The sound carried the BBC's institutional weight in the same way the Reithian announcement carried it, which is to say it was supposed to be neutral and ended up, by accident, deeply strange.

This is where the hauntology argument becomes hard to dismiss. Mark Fisher kept circling back to the Workshop in his writing on lost futures because the sounds came from a moment when British public broadcasting believed it was building something durable. Education would expand. Programming would improve. Children watching schools TV in 1971 were, in some quiet sense, being addressed by the future. The future arrived and dismantled the apparatus that had been addressing them. The Workshop closed in 1998. The cues survived because tape is patient and digitisation is cheap, but they survived without the institution they were made to serve.

A friend once said that Workshop music sounds like memory leaking out of a wall. I think she was right. It's the residue of a broadcasting culture that genuinely believed in its own purpose, played back inside a culture that no longer believes in much of anything collectively. When the Doctor Who theme appears in a streaming-platform reboot, smoothed and orchestrated, what is missing is not the melody. It's the room. The unmarked studio, the splice tape, the woman with a Cambridge maths degree cutting tones out of oxide and glue because nothing else existed to make them with.

I'm not nostalgic for the technical limitations. I'd take a DAW over a razor blade any day. What I notice is that we've lost the institutional permission those limitations sat inside, the idea that a public broadcaster might fund a small unmarked room for forty years to make peculiar sounds for documentaries about the Tuareg. You don't get Blue Veils and Golden Sands out of a procurement spreadsheet. You get it out of a place that was, briefly, willing to pay people to be strange in service of something larger than the quarter.

At Anthropic's developer conference in San Francisco this morning, head of product Ami Vora announced that the company has signed a deal with SpaceX to use "all the capacity" of the Colossus One data center. The Anthropic statement put a number on it: more than three hundred megawatts of new capacity, over two hundred and twenty thousand Nvidia GPUs, available within the month. In the same breath Vora also announced that Anthropic was doubling the five-hour rate limit on Pro, Max, Team and Enterprise plans, removing the peak-hours limit on Claude Code for Pro and Max users, and raising API rate limits for Opus.

It's the second announcement that explains the first. The five-hour rate cap and the peak-hours throttle were not a pricing experiment. They were a supply problem dressed up as a plan, the artefact of a company shipping a coding tool that ate compute faster than the company could buy it. Anthropic shifted to usage-based pricing earlier this year because the flat tiers had become a way of subsidising the developers who happened to hit refresh fastest. The Colossus One deal is the supply-side half of the same fix. Two hundred and twenty thousand GPUs arriving inside thirty days is not a strategic partnership. It is a fire extinguisher.

Where it came from is the strange part. Colossus One is xAI's flagship data center. SpaceX absorbed xAI in January, and the corporate diagram now reads SpaceX → xAI → Colossus, with Cursor reportedly under option for sixty billion. The same Elon Musk who, a week ago in an Oakland federal courtroom, was on the stand arguing that OpenAI's for-profit conversion betrayed the field's founding mission, is now landlord to the lab whose research lineage runs straight out of OpenAI itself. The moral lien Musk was litigating in the morning is, in the afternoon, leasing megawatts to his rivals. This is what the post-2024 compute economy looks like up close. The arguments are about humanity's long-term future. The transactions are about the next quarter's GPU allocation.

It is also, narrowly, a coup for Anthropic. The Amazon expansion deal last month gave it geographic spread for regulated customers; the SpaceX deal gives it raw headroom in time for the IPO window. The company has been losing developer goodwill in small, accumulating ways, the throttles, the peak hours, the muttering on forums about whether Claude Code had been quietly nerfed. Doubling the cap is the kind of thing that resets a narrative. Whether the GPUs land on schedule will determine whether the reset holds.

What sits oddly under all of this is the absence of any pretense that the transaction needed a story. Anthropic did not frame the SpaceX deal as alignment-aligned, or safety-compatible, or even neutral on Musk. It framed it as capacity. The compute is there, the customers are waiting, and the company that has spent the last three years branding itself as the careful one has decided that "all the capacity of Colossus One" is too much capacity to turn down on principle. Principles, in the foundation-model business, turn out to be priced per gigawatt.

You can stand outside an old building and feel something close to insult. The classroom is still there. The family house has different curtains in the windows. A perfume bottle from 1996 sits on a shelf, half-full, the liquid darker by a few shades. Memory frames these objects in permanent weather, a particular autumn afternoon, a lamp glow, the emotional climate of one specific evening. The objects themselves have been ageing in silence the whole time, accumulating dust while entire phases of life disappeared elsewhere.

Mark Fisher used to write about hauntology as the sensation of being haunted by lost futures and unrealised possibility, and old places do this almost too well. They aren't ruins. They're abandoned timelines still faintly active beneath the present, and walking back into one feels wrong because the emotional world has gone but the material shell hasn't. The place continues. The version of you that belonged in it does not. That asymmetry can register as something close to hostility.

Old objects develop a similar autonomy with age, not literally malevolent, only charged with the eerie suggestion that they have outlived us emotionally. A childhood cassette tape on a shelf, a theatre programme that survived two house moves, an amplifier still warming quietly in a dark room because nobody bothered turning it off. The café still opens every morning while someone else walks through rooms that were once metaphysically important.

And the disturbing part is that we eventually become the absent presence on the other side of someone else's reckoning. Bookshelves will stand. Old routers will keep blinking in corners. Permanence belongs more reliably to matter than to experience, and the world has a habit of carrying on with terrible calm once the moment has passed.

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.