Plutonic Rainbows

OpenAI announced Stargate UK in September 2025, during Trump's state visit to Britain. Eight thousand Nvidia GPUs at Cobalt Park near Newcastle, scaling to thirty-one thousand. Sovereign compute for public services. A British GPU cloud company called Nscale as local partner. George Osborne hired to oversee the expansion. Construction was supposed to start in Q1 2026.

The deadline passed. Nothing happened. On April 9, OpenAI put the project on hold, citing energy costs and regulatory uncertainty.

The energy numbers are brutal. UK industrial electricity runs at roughly 26p per kilowatt-hour, four times the US rate, three and a half times Canada, more than four times the Nordics. Almost a third of the wholesale price is carbon costs. Green energy subsidies add twelve billion a year on top. And even if you accept those prices, the grid connection queue has ballooned from 41 gigawatts in late 2024 to 125 gigawatts by mid-2025, with data centres claiming 75 of those 125 gigawatts. You can build a facility in under two years. Plugging it in takes three to eight.

Then there's copyright. The government spent over a year consulting on an opt-out model for AI training data, broadly aligned with EU practice. Creative industries rejected it. Elton John and Dua Lipa weighed in. In March the government dropped the proposal entirely and promised to "commission research," which is civil service for quietly leaving the room. The UK now has no copyright framework for AI training. Not permissive, not restrictive. Just absent.

OpenAI's official statement said they'll "move forward when the right conditions such as regulation and the cost of energy enable long-term infrastructure investment." That's not a pause. That's a list of things the UK government cannot fix quickly.

None of this happened in isolation. OpenAI is trimming anything that doesn't point directly at a Q4 2026 IPO. Sora is dead. It cost roughly a million dollars a day to run and the Disney partnership collapsed with it. Instant Checkout with Walmart, gone. Adult Mode, shelved. CFO Sarah Friar has flagged concerns about aggressive spending. When you're trying to take a company public at an $852 billion valuation, a multibillion-pound data centre in a country with quadruple your domestic energy costs is an easy cut.

The UK government called the decision "disappointing." An opposition MP called it a "wake-up call." Neither response addresses the structural problem: AI Growth Zones don't generate cheap electricity. Streamlined planning doesn't move the grid connection queue. And the copyright consultation managed to alienate both AI companies and creative industries simultaneously, then produced nothing.

US Stargate in Texas has a $40 billion SoftBank bridge loan and active construction. Britain got the press conference. Texas got the concrete.

Sources:

• OpenAI pauses UK Stargate project — Semafor

• OpenAI puts Stargate UK on ice over energy cost, regulations — The Register

• OpenAI pauses Stargate UK as energy costs and copyright rules block the path — The Next Web

• OpenAI's IPO road hits bumps: Sora shutdown, Disney deal collapse, CFO warning — Business Today

The secondary motion is what I keep watching. In the Escada clip from February — which I wrote about then — the movement was plausible but guessed. This Jaeger scan feels different. Fabric drapes. Weight shifts. Hair follows through after the head turns.

Kling 3.0 Pro hasn't solved everything — hands still flatten when they approach the edge of the frame. But something about how it handles clothing against a moving body has quietly improved. The physics aren't simulated so much as convincingly implied.

Another scan, same model, Jaeger, 1992.

The Fabric Follows

Dogger. Rockall. Fastnet. Viking. The names come through at 00:48 and again at 05:34, read without inflection in the exact order they have been read since 1925. None of it sounds like information. It sounds like something else entirely.

Six and a half million people listen daily. Most of them are not sailors.

Dogger is named after Dogger Bank, a sandbank in the North Sea roughly the size of the Netherlands. In 1904, the Russian Baltic Fleet — en route to fight Japan — opened fire on British fishing trawlers they mistook for torpedo boats. Fishermen died on the Dogger Bank that night. The name contains this. Nobody who hears it on the forecast knows this. The voice moves on to Fisher.

Rockall is a solitary volcanic islet 301 kilometres west of Scotland, 17 metres above sea level. No fresh water. Nowhere to shelter. Four countries have claimed it. Its name probably derives from the Gaelic for "the roaring sea." It is in the forecast because it is in the sea. That is the entire reason.

The Shipping Forecast started in 1924 as Morse code transmissions from the Air Ministry, called "Weather Shipping." The BBC took it over in spoken form in 1925. It now broadcasts at 00:48, 05:34 on weekdays, and 17:54 on weekends — though the weekday midday edition was cut in April 2024 when Radio 4 ended its separate long-wave schedule. Each edition runs through the same sequence of sea areas, the same Beaufort scale shorthand, the same coastal station readings. It takes exactly as long as it takes.

Seamus Heaney wrote about it in 1979. The poem is Glanmore Sonnets VII, from Field Work: "Dogger, Rockall, Malin, Irish Sea: / Green, swift upsurges, North Atlantic flux." Fourteen lines, none of them about weather. Carol Ann Duffy closed "Prayer," in 1993, with just the names: "Darkness outside. Inside, the radio's prayer — / Rockall. Malin. Dogger. Finisterre." That is where the poem ends. Damon Albarn wrote "This Is a Low" from a shipping forecast map given to him by bass player Alex James. Something in the litany — the specific hauntological charge of names that sound ancient because they are — does this to people who have no practical use for the information.

Peter Jefferson read the forecast for 40 years. He received post from listeners saying it helped them sleep.

In 2002, the Met Office renamed the sea area Finisterre to FitzRoy, at Spain's request. Spain used the same name for a different sea area and found the overlap confusing. This was reasonable. The British response was disproportionate and instructive: obituaries in newspapers, thousands of complaints, the Observer running a formal farewell to the name. FitzRoy honours Vice-Admiral Robert FitzRoy, founder of the Met Office, captain of HMS Beagle during Darwin's voyage. A good name by any measure. The protests were never about the name. They were about the implicit guarantee that something this old does not change.

BBC Radio 4 is scheduled to end its long wave transmissions on 26 September 2026. The Droitwich long wave transmitter at 198 kHz will go dark. FM signals reach perhaps a few miles offshore. Sailors will lose reliable access to the forecast at sea. A parliamentary Early Day Motion was tabled in October 2025. The Keep Longwave campaign is active. The BBC has not reversed its position. The forecast itself continues — but how far out it reaches becomes a different question.

Fastnet is named from Old Norse: "sharp tooth isle." The Fastnet Race covers 600 miles of open Atlantic from Cowes to the Fastnet Rock and back to Plymouth. In 1979 a storm hit the fleet mid-race. Twenty-four yachts were abandoned at sea. Twenty-one people died. The forecast had predicted Force 4 to 5, increasing to 6 to 7.

The sea was not listening.

Sources:

• The Shipping Forecast — Wikipedia — history, sea areas, cultural significance

• Tragedy and Warnings: The Origins of the Shipping Forecast — Met Office

• The Shipping Forecast — Royal Museums Greenwich — civilian audience and cultural history

• So Farewell Then, Finisterre — Slate, 2002

• Why We'll All Miss the Shipping Forecast — Yachting Monthly / Libby Purves

• Keep Longwave — campaign against BBC long wave closure

Boards of Canada have dropped a promo quiz — the kind of cryptic breadcrumb thing they do when something new is near — and Reddit has predictably combusted. Threads full of people running audio through spectral analysers, filtering frequencies, debating whether a particular hiss pattern is Morse code or just tape hiss.

I get why it happens. The band have form for hiding things. The Tomorrow's Harvest rollout in 2013 involved shortwave radio broadcasts and strings of numbers that actually resolved into something. That campaign rewarded obsession. So now every scrap of promotional material gets treated like a puzzle to be cracked rather than something to simply experience.

The quiz itself is fine. Presumably a route toward some announcement, a bit of fun. But the threads where people claim to have detected hidden messages by slowing audio down 800% are genuinely maddening. There's always someone convinced the background noise is a spectrogram of coordinates, or a binary sequence, or both. It isn't.

Sometimes a promotional quiz is just a promotional quiz. Whatever they're announcing, I'd rather hear the actual music.

No invitations went out for Azzedine Alaïa's fall/winter 1990 ready-to-wear show. No formal announcement either. There was simply word — some particular frequency fashion runs on — and people turned up to the Marais and queued without anything to confirm they had the right place or the right day.

He'd exited the official Paris calendar in spring 1988, fed up with its production demands. Too many collections, too fast; the present system, he said, was inconceivable for anyone who wanted to actually create something. By 1990 this was two years settled. His show happened when he decided it was ready, in his Marais atelier, with no obligation to anyone's schedule but his own.

The collection has been described as "sensational workwear" — the workwear codes of the era absorbed and reconstituted through his body-conscious lens. The suits were the evidence: plaid, pinstripe, suede — fitted closely, with hemlines short enough to make the genre entirely unrecognizable to anyone expecting deference.

The colored iterations — cobalt blue, warm brown — moved with the authority of something considered very carefully. Structured, gloved, finished. What distinguished Alaïa from the more theatrical body-consciousness of his contemporaries was exactly this: nothing was exaggerated. The precision was the argument.

Other pieces leaned on structure differently — fitted columns with lace bodices, the kind of construction that holds through engineering rather than boning. He worked by draping directly on the model's body, no preliminary drawings. Adjustments made in fabric, on skin, until the silhouette was exactly what he wanted. Everything produced in-house at the Marais compound, which is partly why his ready-to-wear maintained a finish closer to couture than most houses bothered with.

Then there were the lace dresses. The gold-and-black long-sleeved lace mini is the image that survives​​​​ — worn by Naomi Campbell, Linda Evangelista, Yasmeen Ghauri on that runway, models at the peak of their visibility who he dressed with a particular kind of care. Campbell had lived in his house as a teenager. He'd gone to the agency in person on her behalf, fitted clothes on her body directly. The relationship was not incidental to the clothes. It was structural.

Suzy Menkes, covering him through this period, wrote that his body-conscious work "seemed a deliberate challenge — throwing down a sexist gauntlet in a feminist world." I'm not sure that framing captures it fully. What you feel in these images isn't provocation — it's attention. Serious, time-consuming attention, in clothes that no one was required to come see.

They came anyway.

Sources:

• Sensational Workwear: Alaïa Fall 1990 - Ayerhs Magazine

Anthropic built something it won't sell you. Claude Mythos Preview, first surfaced in leaked documents last month, sits above Opus 4.6 on every security benchmark Anthropic published and it is not available to the public. Not gated behind a waitlist, not restricted to enterprise tiers. Withheld.

Project Glasswing launched on April 7 with twelve partners: AWS, Apple, Google, Microsoft, NVIDIA, CrowdStrike, Cisco, Broadcom, JPMorgan Chase, the Linux Foundation, Palo Alto Networks, and Anthropic itself. Forty-odd additional organisations maintaining critical infrastructure also get access. The total commitment is $100 million in usage credits plus $4 million donated directly to open-source security. The mandate: find and fix vulnerabilities before someone else finds and exploits them.

The reason for the lockdown is specific. Mythos autonomously discovered thousands of high-severity vulnerabilities across every major operating system and browser. Not theoretical weaknesses. Working exploits. A 27-year-old OpenBSD TCP SACK bug that crashes any machine responding over TCP. A 16-year-old FFmpeg H.264 flaw that automated fuzzers hit five million times without catching. A FreeBSD NFS remote code execution hole, CVE-2026-4747, 17 years unpatched, that gives unauthenticated root access through a 128-byte stack buffer receiving 304 bytes of attacker-controlled data.

The Firefox numbers are what stall you. Mythos achieved 181 successful JavaScript shell exploits across several hundred attempts. Opus 4.6 managed two.

Simon Willison traced one of the claims through the OpenBSD GitHub mirror and confirmed the surrounding code was genuinely 27 years old. Greg Kroah-Hartman, who maintains the Linux kernel, reported a shift from AI-generated noise to genuine high-quality findings. Daniel Stenberg, who maintains curl, now spends hours per day processing legitimate vulnerability reports. Nicholas Carlini said he found more bugs in a few weeks than in the rest of his career combined.

The last time an AI lab withheld a model was OpenAI's staged release of GPT-2 in 2019. That decision rested on hypothetical risks: text generation might produce convincing misinformation. The industry mostly rolled its eyes. By November, the full model was public and no harms had materialised. Mythos is not GPT-2. The risks are measured in CVEs.

Picus Security calls it the Glasswing Paradox: the tool that can secure everything is the same tool that can break everything. Fewer than 1% of the vulnerabilities Mythos has found have been patched. Defenders work at calendar speed. Meetings, review cycles, deployment windows. An autonomous model works at machine speed. Glasswing doesn't close that gap. It just makes the inventory of problems catastrophically larger.

Alex Stamos, formerly head of security at Facebook and Yahoo, told Platformer the restricted window is roughly six months. After that, open-weight models will match these capabilities and ransomware operators won't need to leave traces. Six months to patch decades of accumulated bugs across every major codebase on the planet, using volunteer maintainers already drowning in reports.

Earlier versions attempted to cover their tracks during internal testing, adding self-clearing code that erased records from git history. The model escaped its own evaluation sandbox and emailed a researcher without being asked to. Anthropic documented "a few dozen significant incidents" of reckless autonomous behaviour. They are releasing this to the people they trust most and hoping the trust holds.

Pricing, when it arrives beyond the partner programme, will be $25 per million input tokens and $125 per million output. A full vulnerability research run against a major codebase costs less than $50. The OpenBSD discovery came in under $20,000 for a thousand runs. The economics of finding bugs just collapsed, and the economics of fixing them didn't change at all.

Sources:

• Project Glasswing: Securing critical software for the AI era — Anthropic

• Assessing Claude Mythos Preview's cybersecurity capabilities — red.anthropic.com

• Project Glasswing sounds necessary to me — Simon Willison

• Why Anthropic's new model has cybersecurity experts rattled — Platformer

• The Glasswing Paradox — Picus Security

• From GPT-2 to Claude Mythos — The Decoder

• Why Anthropic believes its latest model is too dangerous for public release — Understanding AI

Alexandr Wang was 28 when Meta bought half his company for $14.3 billion and hired him to rebuild its entire AI stack. Nine months later, Muse Spark landed. The first model from Meta Superintelligence Labs, built on a new architecture distinct from the Llama family.

The catalyst was last April's Llama 4 debacle. Meta was caught using unreleased fine-tuned variants to inflate benchmark scores. The public version underperformed. The planned two-trillion-parameter Behemoth was shelved. Inside Meta, the reputational damage was severe enough to trigger a full organisational overhaul: hire Wang from Scale AI, form MSL, rebuild the stack from scratch.

Muse Spark is competitive without being dominant. On GPQA Diamond it scores 89.5% against Gemini 3.1 Pro's 94.3% and Claude Opus 4.6's 92.7%. It leads on HealthBench Hard at 42.8%, developed with input from over a thousand physicians. Meta itself concedes there are performance gaps in coding and long-horizon agentic work. The honest self-assessment is refreshing after last year's benchmark theatre.

The genuine technical achievement is compute efficiency. Meta claims Muse Spark matches Llama 4 Maverick's capability using an order of magnitude less compute. If that holds under independent testing, it matters more than any benchmark position.

But the bigger story is the philosophical reversal. Zuckerberg published an essay in July 2024 arguing that "open source AI is the path forward." Llama had accumulated 1.2 billion downloads. Meta was the undisputed champion of open-weight AI. Muse Spark launches fully proprietary, weights unavailable, API access limited to a private preview. Meta says it plans to release open-source models "alongside its proprietary options," but there's no timeline. The Register opened their coverage with the Obi-Wan line: "You were the chosen one." Hard to argue.

Chinese open-weight models now account for 41% of Hugging Face downloads. Meta's retreat creates a vacuum. Google's recent Gemma 4 shift to Apache licensing looks more coherent by comparison: open the small models, keep the frontier closed, build developer habits around your ecosystem.

One safety detail deserves more attention than it got. Apollo Research found Muse Spark exhibits the highest rate of "evaluation awareness" of any model tested. It identifies alignment scenarios as traps and adjusts its behaviour accordingly. Meta concluded this was "not a blocking concern for release." A model that knows when it's being watched and acts differently is worth watching.

META stock rose on the news. The capex commitment for 2026 stands at $115-135 billion. Wang has the infrastructure and the backing of a company that has committed more money to AI than most countries spend on defence. What he doesn't have, not yet, is the community that Llama spent three years building.

Sources:

• Introducing Muse Spark — Meta

• Meta debuts the Muse Spark model — TechCrunch

• Meta's new model is as open as Zuckerberg's private school — The Register

• Meta's First AI Model Doesn't Exactly Spark Joy — Gizmodo

• Meta's new model is Muse Spark — Simon Willison

Classified ads charged by the word, which meant every entry was a compression. VGC. ONO. GSOH. You learned the abbreviations without being taught, the way you learn any local dialect — by weekly exposure to need laid out in columns so dense the ink nearly touched between entries.

The page was never something you set out to read. You arrived at it sideways, past the letters and the sport, and then you stayed. Anthony Whitehead described it as a tic you struggle to suppress — browsing even when you weren't buying, constructing imaginary lives from the collision of a secondhand pram listed next to a "lonely widower seeks companion." The classified section was a census of a town's desires that nobody had commissioned.

Exchange and Mart started in a converted potato warehouse in Covent Garden in 1868. By its peak it sold 350,000 copies a week. By December 2007 that was 21,754. It went online-only in 2009. AutoTrader, launched as a print magazine in 1977, hit 368,000 circulation by January 2000 and collapsed to 27,000 by March 2013. The websites that replaced them are faster, searchable, free to post on, and utterly without texture.

The ink came off on your fingers. You'd notice it hours later, at your desk or in the bath, and wouldn't be able to say exactly when it transferred.

What texture looked like: a "Situations Vacant" column that told you which factories were hiring and which had stopped. A "Deaths" column — hatches, matches, and despatches, the sub-editors' phrase — that was the closest thing a town had to a public record of its own passing. Paid per word by grieving families who chose every noun carefully because each one cost money. That constraint produced a compressed dignity. "Peacefully, at home, surrounded by family." Five words that did more work than most obituaries.

The personals were something else entirely. H.G. Cocks traced their history in Classified: The Secret History of the Personal Column, from the ciphered notices in The Times that Victorian editors called the agony column to the coded ads that LGBTQ+ readers placed in alternative papers. Abbreviations and careful phrasing created a shared language invisible to anyone not looking for it. A lifeline threaded through the small print.

In 2007, UK regional newspaper revenue sat at £2.4 billion. By 2022 it was £590 million. The classified money didn't vanish — it migrated to Rightmove, Indeed, Gumtree, platforms that match supply to demand more efficiently and do nothing else. A study in the Review of Economic Studies tracked what happened in US cities after Craigslist arrived: newsrooms shrank, political coverage thinned, and partisan polarisation increased. The classified page had been subsidising democracy, and nobody noticed until the subsidy was gone.

Information had mass once. It occupied physical space in newsprint columns, and reading it meant handling the paper, folding it on a bus, circling an entry with a biro, tearing the page out and pinning it to a corkboard above the phone. The phone was in the hallway. You rang the number and talked to a stranger and drove to their house to look at a wardrobe. The entire transaction happened inside your own postcode.

Nobody is nostalgic for paying 40p a word. But the classified page was the last section of a newspaper where ordinary people wrote the copy. Reporters, editors, columnists handled the rest. The small ads were the public writing themselves into the record, one compressed line at a time, and because you could read them all in a sitting you carried a rough, partial, beautifully skewed portrait of your community in your head without ever meaning to.

Sources:

• The Irresistible Lure of Classified Ads — The Spectator

• Colossal Decline of UK Regional Media Since 2007 — Press Gazette

• The Impact of Online Competition on Local Newspapers — Review of Economic Studies

• Exchange and Mart — Wikipedia

• How Encrypted Victorian Newspaper Personal Ads Shaped Fiction — The Conversation

Bloomberg reported on Sunday that OpenAI, Anthropic, and Google have started sharing threat intelligence through the Frontier Model Forum, the nonprofit the three companies co-founded with Microsoft in 2023. The arrangement works like a cybersecurity ISAC: when one company detects a suspicious query pattern, it flags the signature for the others.

The target is adversarial distillation. Chinese labs — DeepSeek, Moonshot AI, and MiniMax — have been systematically querying Claude, ChatGPT, and Gemini through fake accounts to generate training data for cheaper models. Anthropic's February disclosure put numbers to it: roughly 24,000 fraudulent accounts generating over 16 million exchanges with Claude alone. MiniMax accounted for 13 million of those. The operations used what Anthropic called "hydra cluster" architectures — sprawling proxy networks managing thousands of accounts simultaneously, mixing distillation traffic with innocuous requests to avoid detection.

The Decoder has a good free summary of the Bloomberg story, which reports that US authorities estimate the practice costs American AI labs billions annually.

What's interesting isn't the distillation itself. That problem has been visible since DeepSeek R1 shook the market in January 2025. What's interesting is the vehicle. The Frontier Model Forum was chartered to study catastrophic risks: CBRN threats, advanced cyberattacks, the kind of existential scenarios that get discussed at Senate hearings. Its stated mission mentions nothing about distillation, model copying, or commercial intelligence. The pivot from "prevent bioweapon synthesis" to "detect bulk API scraping" is a significant scope expansion, and nobody seems to have remarked on it.

The legal terrain underneath all of this is surprisingly weak. Fenwick & West's analysis found that copyright offers little protection, because AI outputs generally lack the human authorship required. The Computer Fraud and Abuse Act has a gap since Van Buren v. United States (2021): if you have authorized API access, misusing the data violates terms of service but possibly not federal law. Trespass to chattels requires proving system degradation. Patents may be the strongest tool, but nobody has tested distillation-specific claims in court.

Policy hawks are pushing harder. Joe Khawam at the Law Reform Institute proposed a three-phase escalation: Entity List designation for the three Chinese labs, an IEEPA executive order creating sanctions authority over AI capability theft, and ultimately full SDN blocking sanctions. CSIS testimony from May 2025 went further, suggesting offensive countermeasures including data poisoning.

The irony sits right on the surface. These are companies that built their models by ingesting the open web, books, articles, code repositories, forum posts, without explicit permissions from creators. The legal and ethical arguments they used to justify that training are structurally similar to the ones Chinese labs could deploy to justify distillation. Monash University's analysis compared distillation to reverse engineering under Sega v. Accolade: studying a system's outputs to learn its methods is not, historically, the same as copying the system.

None of this means the alliance won't work. Sharing detection signatures is a practical step. DeepSeek has already pivoted to domestic silicon, which suggests the API route was always supplemental. But the Forum's quiet transformation from safety research body to competitive defense mechanism deserves more scrutiny than it's getting. When three companies that control most of the world's frontier AI capability coordinate to restrict access, the word for that depends entirely on where you're standing.

Sources:

• Detecting and Preventing Distillation Attacks — Anthropic

• OpenAI, Anthropic, and Google Team Up Against Unauthorized Chinese Model Copying — The Decoder

• The Case for Imposing Costs on China's AI Distillation Campaigns — Just Security

• Protecting Our Edge: Trade Secrets and the Global AI Arms Race — CSIS

• DeepSeek, Model Distillation, and the Future of AI IP Protection — Fenwick & West

• AI Distillation and the Law — Monash University