Plutonic Rainbows

Google launched its new Gemini usage limits this week as part of the I/O announcements, and as of today they are live. The shape of the change is small in the abstract and irritating in the particular. Gemini used to behave, for most people, like an unmetered utility. Now it counts compute, which means it counts you.

The new rule is that every prompt has a cost, and that cost depends on the length of the chat, the features you invoke, and the model's own estimate of how hard the question is. There are two windows. A weekly cap, which most users will never read, and a five-hour block which resets through the day. When you exhaust the block you wait, unless you have already exhausted the week. AI Pro subscribers get roughly four times the free quota; Ultra gets five times that again, plus, per Forbes, a separate hackathon prize pool that has nothing to do with the meter and everything to do with the narrative.

What is interesting is not that limits exist. Limits have always existed, hidden behind the rate-limiter and the polite spinner. What is interesting is that limits are now visible, named, and denominated in a unit you cannot intuit. A "percentage of a five-hour block" is not a thing anyone has spent fifteen years learning to feel for. It is not minutes, not megabytes, not even tokens. It is whatever the dispatcher decides your last sentence cost. The user has to develop a new sense for it, the way phone users in 2009 learned the shape of a 200-megabyte monthly cap by running out of it twice.

The provocation underneath the launch is the new Flash model. Existing users on 9to5Google are reporting that Gemini 3.5 Flash burns multiple percentage points of a five-hour block per prompt. The previous Flash, by all accounts, did not. So the meter arrives at the same time as the model that eats it fastest, and the resulting friction is not a rollout quirk; it is the design. Google is teaching people to feel which questions are expensive. Some will respond by rationing themselves down to the cheaper model when the work is mundane. Some will simply hit the wall and assume the product is broken.

I wrote on Tuesday about Google turning the whole I/O keynote into a Gemini argument. The metering announcement is the same argument from the other side. If Gemini is going to live inside Search and Chrome and Android and your inbox and the in-car dashboard, then the question of how many Geminis you are allowed to spend in a day becomes the question of how much of your day you can route through Google's stack at all. The meter is not a tax on the chatbot; it is a budget for the agent.

Two things will follow from this in roughly the same week. People who use Gemini casually will not notice for months, then notice all at once, on a Sunday when they had three things to ask and the first one ate the budget. Anyone building real workflows on the Pro or Ultra tier will start treating prompt economy the way they used to treat S3 list-bucket calls, with grudging respect for the bill. The intuition that the model is "free as long as I have a subscription" was always a fiction. We are now told the price.

Issey Miyake's Pleats Please line launched in 1993, four years after he decided he had finished with everything he already knew how to do. The 1988 exhibition at the Musée des Arts Décoratifs in Paris was a survey of his work to that point. He later told his long-time collaborator Midori Kitamura that the show had given him the unsettling feeling of completion. Most designers would have taken the prize and kept producing. Miyake decided he needed a new departure.

The new direction came from polyester. He had picked up a pleated polyester-silk scarf and noticed that the pleats held permanently because the synthetic fibre had a thermoplastic memory. Heat could be used not as an enemy of cloth but as a tool that locked in shape. The team spent four years working out how to scale that observation into a clothing line. Makiko Minagawa, the textile designer who had been with Miyake since 1970, did most of the materials research. The fabric they ended up with was lightweight, washable, and cheap enough to make the project worth doing at retail.

The process was the inversion of how pleating had been done for centuries. Mariano Fortuny's Delphos dresses from the early twentieth century were made of silk that had been pleated first and then cut and sewn into the garment, with all the maintenance and fragility that implied. Miyake's team did it the other way around. The garment was constructed first, at roughly three times its intended finished size. It was then sandwiched between two layers of paper and fed into a heat-press. The press shrank the garment, set the pleats, and finalised the silhouette in a single operation. The fabric came out with permanent texture you could wash, scrunch up into a corner of a suitcase, and pull out wearable.

The technique was tested first on dancers rather than fashion clients. In 1991 William Forsythe's Frankfurt Ballet performed The Loss of Small Detail in costumes Miyake had developed using the new method. The pleats held through sweat and the violent geometry of contemporary dance, and the fabric stayed light enough to move freely. Forsythe's dancers were, in effect, the prototypes Miyake sent out into the world to see whether the system worked under load, and it did.

When the line went on sale in 1993 it carried a quiet ideology underneath the engineering. Miyake had been shaped by the May 1968 student protests in Paris, and ever since had wanted to make clothing that worked for ordinary lives instead of the maintained, dry-cleaned, museum-grade pieces couture produced. Pleats Please was that ambition arriving with a price point a working woman could pay and a durability she did not need to baby. The garment did not announce itself as fashion. It announced itself as something you could own and wear and not think about until you wanted to.

The afterlife is unusual. Most innovations from the early nineties have either ascended into archive worship or quietly disappeared into the back of vintage shops. Pleats Please is still in production, sold from boutiques that look more or less the same as they did when the line opened, worn by women who have no particular interest in the history of the technique that made the garment possible. The pleating is no longer experimental, it is infrastructure.

A plague stone is one of those objects that barely behaves like an object at all. It is often just a hollow in stone, or the base of an older cross, or a basin set into a wall where a road leaves a village. Nothing about it is dramatic until someone explains the transaction: coins went into vinegar, food or medicine was left nearby, and the boundary did the rest.

The detail has the nasty precision of good folklore. Vinegar had a job to do. It was meant to clean the money, or at least make the exchange feel possible when nobody could safely touch anyone else. The Bentham plague stone in North Yorkshire is listed by Historic England as a small seventeenth- century stone basin, reputed to mark the extent of quarantine during the plague. Their educational record is more physical: a hollowed top, a deep square socket, probably once the base of a medieval wayside cross, later turned into a place for washing infected money.

I like the awkwardness of that reuse. A Christian marker, then a boundary device, then a public-health interface. The stone did not change much. The meaning was poured into it, like the vinegar.

At Eyam the story is sharper because the village has become the English model of voluntary quarantine, half history and half moral theatre. The Boundary Stone between Eyam and Stoney Middleton is described locally as the place where plague-affected villagers left vinegar-soaked money in exchange for food and medical supplies from outside. It separated the contaminated village from the one that still had a future. That is a brutal thing for a stone to be asked to do.

The same grammar appears elsewhere. Historic England's note on the Great Stone at Stretford says plague stones had holes, usually filled with vinegar, where money from an infected town could be left for tradespeople delivering food. The official list entry adds that the Great Stone was traditionally part of a medieval cross and later used as a plague stone, with two bowl-shaped sinkings. Again, the object is not invented for emergency. It is recruited.

That may be why these stones feel colder than memorials. A memorial tells you to remember after the danger has passed. A plague stone is an instruction from inside the danger itself. Do not cross. Put the coin here. Trust the liquid. Trust the stranger enough to eat what they leave, but not enough to stand beside them.

There is a modern habit of making old disease practices cute because the science was incomplete. Vinegar against plague, handled coins, the theatre of cleansing: easy material for a caption. I find that smugness useless. People were trying to create a system of contactless exchange with a stone basin, a smell, and an agreement no one could enforce except by fear. It is primitive only if you think infrastructure means wires.

The strange afterlife is that the ritual outlived the emergency as a small dent in the landscape. Rainwater sits where the vinegar was supposed to sit. The hollow becomes a pocket for leaves, grit, coins from walkers who half know the story. These stones do not preserve the past cleanly. They preserve the moment when trade, infection, faith, and hunger all had to meet at the edge of a parish and pretend that a boundary could hold.

OpenAI's new mathematics result is awkwardly simple to state and almost impossible, for most of us, to inspect. Put n points on a plane. Count how many pairs sit exactly one unit apart. Since Paul Erdős posed that version of the unit-distance problem in 1946, the expectation has been that the old square-grid construction was close to the best anyone could do.

OpenAI now says that expectation was wrong. In a research post published today, the company says an internal general-purpose reasoning model found a counterexample family: infinitely many point sets with at least n^(1+δ) unit-distance pairs, for some fixed positive δ. The accompanying proof manuscript puts the claim plainly enough. It disproves the well-known conjecture, and the route there runs through algebraic number theory rather than a more obvious geometric trick.

That is the unsettling part. Not the phrase "AI solved a math problem", which has already been cheapened by too many half-cases and benchmark-shaped victories. This one has the smell of actual mathematics: a problem that experts cared about, a proof external mathematicians checked, and a method that apparently teaches the field something it did not already know. The model did not just grind a search space until a known pattern fell out. It connected the unit-distance question to infinite class field towers and Golod-Shafarevich theory, which is the kind of sentence that makes a press release feel briefly underpowered.

The company is careful about the chain of events. The problem was first given to the model in an AI-written form; an AI grading pipeline marked the solution as likely correct; then OpenAI researchers and outside mathematicians examined it, rewrote it, checked it, and added the ordinary scholarly apparatus. That sequence matters. The interesting claim is not that a chatbot emitted a final journal paper while everyone else watched. The claim is stranger: a model found the decisive idea, and humans then did the work humans still do around a serious proof.

I keep thinking about the word "autonomous" here. It is doing real work, but it is also carrying more glamour than it can safely hold. Mathematics does not become less human because a model found this construction. If anything, the companion paper makes the human layer more visible: Noga Alon, Thomas Bloom, Tim Gowers, Arul Shankar, Jacob Tsimerman, and others explaining why the result matters, where it sits, and what its machinery might open next. Discovery and understanding are related, not identical.

Still, I don't want to file this under "tool improves workflow" and move on. That would be too neat. A system that can produce a new counterexample to a central conjecture is not merely helping with notation. It is beginning to act on the part of research that used to be protected by taste, stubbornness, and the private hunch that an unfashionable route might be worth trying. The machine seems to have tried the unfashionable route.

Maybe the practical lesson is smaller than the metaphysical one. The next wave of AI research tools may not replace the mathematician in the cartoon sense, the lone figure at the board suddenly made obsolete. It may instead make the first draft of discovery arrive from somewhere nobody quite knows how to credit. Then the room has to decide whether the thing is true, whether it is interesting, and what it means.

That sounds less dramatic. It is probably more disruptive.

On Monday Anthropic announced that it had bought Stainless, a developer-tools company most readers will never have heard of even though they have almost certainly used its output. Stainless writes the SDKs. The Python and TypeScript and Go client libraries that wrap the major model APIs were, for a quietly large fraction of the industry, generated by the same small team sitting between every lab and every customer who wanted a tidy language binding.

That is the part of the deal worth pausing on. TechCrunch reports the customer list as OpenAI, Google, Cloudflare, Replicate, and Runway. Alex Rattray, the ex-Stripe engineer who founded Stainless, had built the closest thing the agentic era has to a shared neutral utility: take an OpenAPI spec, get back a generated SDK that feels handwritten, plus the CLI and the new MCP server scaffolding that turns an API into something an agent can actually use. The Information puts the deal north of $300 million. Anthropic does not confirm a number.

Anthropic's framing is the technical one. Agents are only as useful as what they can connect to, MCP is the protocol that does the connecting, and owning the team that has been generating the servers gives Claude a faster path to every backend it might want to reach. That is a real argument. The plumbing layer for the agentic web has been underspecified and over-promised for two years, and consolidating expertise inside the lab that introduced MCP at least produces one coherent place where the standard can be hardened, even now that governance has moved to the Linux Foundation.

The other reading is harder to ignore. Forbes called it cutting off OpenAI and Google from a shared SDK pipeline, and that framing is ungenerous but not wrong. Stainless customers will keep the SDKs they have already generated. The hosted product, the bit where a competitor could press a button and get fresh client libraries for tomorrow's API surface, is being wound down. The shared neutral utility is no longer either shared or neutral. It belongs to one of the labs.

I keep returning to a line I wrote about Google last week: OpenAI sells the interface, Anthropic sells the model and the caution, Google sells the operating environment. Stainless does not fit that taxonomy, which is exactly why buying it is interesting. It is a quiet category, the plumbing under the plumbing, the layer that decides how easy it is to wire a model into a product. Owning it doesn't show up in benchmarks or keynote slides. It shows up two years from now, when half the agent frameworks in production were trained against tooling that Anthropic shaped first.

Whether that constitutes strategic foresight or just a competitive nudge dressed in MCP enthusiasm depends on how much you trust labs to behave as good stewards of a standard once they own the toolchain that implements it. The track record is short and the incentives are not subtle. What looks like infrastructure investment from inside the acquiring company tends to look like enclosure from outside it.

Marc Jacobs sent his Spring 1993 collection for Perry Ellis down a runway at the brand's Seventh Avenue showroom on the evening of 3 November 1992. Bill Clinton was being elected president the same night. The two facts are not connected except by coincidence, but the coincidence is useful for orientation: the country was about to change governments, and a twenty-nine-year-old designer was about to lose his job by mistaking what the brand executives in the room actually wanted.

The collection took grunge, then a Seattle music scene that had not yet learned to take itself seriously, and translated it into sportswear made of materials that grunge specifically refused. Two-dollar second-hand flannel shirts became printed silks. Lumberjack thermals became cashmere. Kurt Cobain's floral granny dresses became chiffon. Doc Martens stayed Doc Martens, though Converse appeared too, rendered in duchesse satin. Christy Turlington opened the show to L7's "Pretend We're Dead." Kate Moss and Kristen McMenamy closed it in matching beanies and layered pastel knits. Naomi Campbell wore combat boots and a silk flannel shirt, possibly for the first and last time in her career.

What Jacobs had done, technically, was the brief. Perry Ellis sportswear was supposed to read American youth. He had read American youth correctly, more correctly than the company executives at any of the meetings, and translated it into luxury fabrics at a price point the brand sat at. The mistake was that Perry Ellis did not want to be told what its customers were already wearing. It wanted to dress the customer the buyers thought it had. Suzy Menkes wrote "Grunge Is Ghastly" in the International Herald Tribune, then had pins made saying the same thing. Bernadine Morris in the New York Times described the looks as "put together with the eyes closed in a very dark room." The Council of Fashion Designers of America named Jacobs Designer of the Year in January 1993. Perry Ellis terminated his contract shortly afterward and killed production on the collection. The clothes were never shipped to stores. The samples sent to Cobain and Courtney Love were reportedly burned, which is the kind of detail that would not survive fact-checking if the people involved had been less famous.

The reason the show matters, beyond the brand drama, is the gap between what the room heard and what the December 1992 issue of Vogue published a few weeks later. Steven Meisel had photographed a Grace Coddington–styled editorial called "Grunge & Glory," running Kristen McMenamy, Naomi Campbell, and Nadja Auermann in warmly-shot Perry Ellis plaid and Nirvana T-shirts. The editorial worked. The clothes, photographed by Meisel and styled by Coddington, looked like the future of how women might actually want to dress. The same garments, walked under showroom lights by a young Kate Moss, had looked like the end of the world to half the front row. It is the same disjunction that played out at the other end of the same season, on a different runway, where a different designer was being read incorrectly by people in the room and correctly by the magazine that would canonise him later.

Jacobs went to Louis Vuitton in 1997 with Robert Duffy and spent the next sixteen years there. The grunge collection became one of the most reproduced editorial images of the 1990s, reissued by Jacobs himself in 2018, taught in fashion schools as the example of a show that was right at the wrong moment for the company that paid for it. The lesson everyone draws is that the executives were wrong and Jacobs was right. What nobody draws is the harder one, which is that the executives were doing their job. A brand whose customer is a forty-five-year-old in a midwestern department store does not benefit from being told its customer's daughter is going to wear ripped flannel instead. The show did exactly what the buyers feared. It also did exactly what fashion needed. Both sentences can be true on the same night.

The Rolodex is one of those objects that became a verb without quite becoming invisible. You can still hear it in business talk: a digital Rolodex, a modern investor's Rolodex, a list of business contacts treated as portable value. The object has mostly left the desk. The moral arrangement it implied has not.

Cooper Hewitt's short history is useful because it returns the word to the thing. Rolodex was a portmanteau of "rolling" and "index", a rotating file of cards arranged around a spindle, designed for the office where information arrived on paper and stayed close to the hand. Another Cooper Hewitt note places its office-supply arrival in the early 1950s, with Zephyr American Corporation's patent following in 1956. The patent record names Hildaur L. Neilsen's rotary card-filing device. That is the part I like: not the networking myth, but the physical choreography. Turn the wheel. Find the letter. Lift the card. Make the call.

Every contact in that system had edges. A card could be amended, crossed out, taped over, or thrown away. The file sat on one desk, under one person's authority, and this mattered. A Rolodex did not pretend a relationship was a shared organisational resource. It made a quieter claim: this person is in my reach because I wrote them down and kept them there. The modern phrase "my contacts" still carries that possessive little hook.

CRM software was supposed to dissolve that hook into process. A modern contact system can store interaction history, reminders, tags, email threads, pipelines, team ownership, automation, and the sort of executive dashboards that turn friendship into coloured rectangles. Folk's own explainer on the digital Rolodex makes the distinction neatly enough: the address book stores who someone is; the CRM decides what to do with them. Yet the old word keeps returning because it says the embarrassing part more plainly. A network is not only a network. It is leverage with names attached.

You can see the same residue in investor tools. Contacts+ still sells the idea of a "modern investor's Rolodex", meaning a refreshed system of profiles, reminders, context, and competitive access to people who might matter before they obviously matter. The phrase persists because it is blunter than relationship management. It admits that the contact is not only a person but a future option, tagged and waiting.

I keep thinking about this alongside the floppy save icon, another dead office object that survived by becoming interface grammar. The Rolodex is less visible than the floppy, but maybe more revealing. The save icon preserves a gesture. The Rolodex preserves an attitude toward people: sorted, retrievable, privately maintained, and valuable because they can be activated later.

There is a small ugliness in that, though not only ugliness. A handwritten card also preserved context that software often flattens: the assistant's name, the office extension, the note that someone hated calls before ten, the old company crossed out and replaced by the new one. The period before everything was archived was full of these half-private systems, practical and intimate in the same breath. A Rolodex was a machine for remembering people, but also for deciding which people were worth remembering.

The plastic wheel is now mostly a design object, a thing in museum collections and office-memory essays. The phrase survived because it still names a social fantasy we haven't retired: that a life can be indexed, that access can be owned, that the right name at the right moment can still be found by turning something inside reach.

Google I/O used to have a recognisable shape. Android first, a few developer APIs, a hardware tease, then the bit where Search got a little stranger. This year the structure collapsed into Gemini. Not because Google forgot the rest of the company exists, but because the rest of the company now seems to exist as places where Gemini can be put to work.

The cleanest announcement is Gemini 3.5, Google's new model family, which starts with 3.5 Flash. Google says Flash is available now in the Gemini app, AI Mode in Search, Google Antigravity, AI Studio, Android Studio, Vertex-adjacent enterprise products, and the Gemini Enterprise Agent Platform. It is also the default model for the Gemini app and AI Mode globally. That is not a laboratory release. It is Google pushing a new base layer under its consumer and developer surfaces on day one.

The numbers are pitched with the usual violence. Google says 3.5 Flash is its strongest agentic and coding model yet, beats Gemini 3.1 Pro on several agent and coding benchmarks, and runs four times faster than other frontier models when measured by output tokens per second. I don't much like writing benchmark paragraphs because they age badly, sometimes by lunchtime, but the direction matters. In February I wrote about Gemini Deep Think as Google spending inference-time compute on hard reasoning. This release pulls the other way: make the everyday model fast enough and good enough that agentic work stops feeling like a special mode.

Then there is Gemini Omni, which is the showier thing and maybe the more revealing one. Omni takes text, images, audio, and video as input and starts by producing video as output. Google says the first model, Gemini Omni Flash, is rolling out to the Gemini app, Google Flow, and YouTube Shorts, with image and audio output planned later. The claim is not only better video. The claim is continuity: edit through conversation, keep characters stable, make the physics hold together, let one instruction build on the last.

That is where Google's advantage looks least abstract. A video model inside a chatbot is one product. A video model inside YouTube, Flow, Android, Search, and whatever Google decides Chrome should become next is something else. AP reported that the Gemini app has passed 900 million monthly active users, more than double the previous year. Even allowing for the fuzziness of app metrics, that is distribution almost no AI-native company can touch.

The less glamorous Android and Chrome announcements say the same thing in a more domestic register. Google says Gemini Intelligence on Android will automate multi-step tasks, fill forms with opt-in personal context, turn spoken mess into cleaner messages, and build custom widgets from natural language. Chrome on Android is getting Gemini summaries, app actions, and auto browse for chores like booking parking or updating an order, with confirmations before sensitive actions. I can feel the pitch hardening as I type it: Google doesn't want one agent. It wants every surface to contain an agent small enough to disappear into the verb you were already using.

I still don't know whether people want that much help. Some of this will be useful in the dullest possible way, which is usually the way software wins. Some of it will be exhausting, another layer of anticipatory cleverness between a person and a task they could have finished with three taps. But the strategic point is clear enough. OpenAI sells the interface. Anthropic sells the model and the caution. Google sells the operating environment in which the model is already waiting.

OpenAI has put Codex inside the ChatGPT mobile app, in preview, on iOS and Android, on every plan including Free and Go. The announcement landed this week and the rollout was already underway by the weekend. Windows users still have to wait, but for everyone else the coding agent now lives a tap away on the phone you already carry.

The framing in OpenAI's own messaging is interesting. It is not "write code on your phone." Nobody wants that, and the screen is the wrong shape for it anyway. What you actually get is a remote viewport into a Codex session running somewhere else: a desktop, a devbox, a server. The phone shows screenshots, terminal output, the agent's progress on a task it was already chewing through. You can answer a clarifying question, approve the next step, nudge it in a direction, or tell it to back off. The heavy lifting stays on the machine where your credentials and your local setup already live.

There is a secure relay layer in the middle, which is the boring detail that makes the rest of it work. Your laptop is not suddenly exposed to the public internet because you wanted to check on a bug from the supermarket queue. The relay is the kind of plumbing that gets invented once and then disappears into the background of everything else.

I keep thinking about what this changes about the rhythm of the work. The older model was: open the laptop, sit down, type for an hour, close the laptop. The intermediate model, where agents took on long-running tasks, already pushed against that rhythm. You'd kick off a refactor before lunch and come back to a pile of proposed diffs. Now that pile can find you on the bus. You can glance at a screenshot of a failing test and tell the agent to try the obvious fix while you keep walking. The latency between "agent needs a decision" and "decision arrives" collapses from hours to whatever it takes you to read a notification.

The free tier matters here in a way that I don't think OpenAI is quite shouting about. There are around four million weekly Codex users by the company's count, and the gating to date has been both subscription and surface area. Putting the surface on the phone, with no paywall for at least basic use, is the kind of move that grows habits more than it grows revenue. Habit is the thing every AI lab is actually competing for at this point. Paid plans follow habit; habit does not follow paid plans.

There is a counter-reading. Putting an autonomous coding agent within easy reach of every distracted moment is also a way of extending work into the gaps that used to be rest. The agent does not care that you are at the grocery store. It only cares that you have a phone and a connection. The same convenience that collapses the latency on a real decision also invites you to make half-formed ones while your attention is elsewhere. We will see what that does to the shape of code review and to the people doing it.

For now, the immediate effect is small and concrete: an update to the ChatGPT mobile app and the macOS Codex app, and a tab on your phone that wasn't there last week. Everything that follows from that is downstream.