Plutonic Rainbows

Gianni Versace's Spring 1991 show has a strange advantage over many louder fashion moments from the same period: you can understand it from a distance. The Marilyn faces, the James Dean faces, the colour, the hard outlines, the almost impolite clarity of the image. It doesn't need an explanatory caption before it starts working on you.

That was not the same as being simple. Vogue dates the collection to October 7, 1990, in Milan, and its archive notes how wide Versace's art net was that season: Sonia Delaunay, Victor Vasarely, Russian Constructivists, Vogue covers, Pop culture, all dragged into the same bright room. He gave Vogue the useful sentence himself: "To use art in a flat way, without creative intervention, is in bad taste." I like the defensiveness of that line. It admits the danger. It also tells you he knew exactly where the charge was.

The Warhol-derived Marilyn Monroe and James Dean prints could easily have been a stunt, the kind of museum-shop cleverness that ages before the hanger cools. Instead they became one of the clearest images of the supermodel decade, not because the references were rarefied, but because they were already public. Versace did not borrow Pop Art to look clever. He used it because fame had become material.

That is where the collection sits beside the house's other early-nineties arguments. I wrote about the March 1991 finale as a runway event that made the models feel inseparable from the collection, and about the backstage engineering of a later Versace season in Eight Hands to Get Dressed. Spring 1991 is the print version of the same instinct. The runway isn't merely showing clothes. It is laundering mass imagery through the body until the image looks newly expensive.

The Met's record for a Gianni Versace evening dress from Spring/Summer 1991 is almost comically dry by comparison: silk, glass, gift of Gianni Versace, 1993, Costume Institute object number 1993.52.4. Museum language does that. It turns a screaming dress into catalogue grammar. However, the dryness helps. It reminds me that this was not just a memorable runway photograph drifting around Pinterest; it was an object with weight, material, donor history, and a place in an institutional archive.

Christie's gives the other afterlife. A Gianni Versace Couture suit from the same Spring/Summer 1991 moment, allover Marilyn Monroe and James Dean imagery, with rhinestone and silk embroidery trimming the jacket pockets, sold in the Elizabeth Taylor sale in 2011 for $20,000. That detail feels exactly right. The thing moved from runway to celebrity wardrobe to auction lot, and each stage made the original idea more literal. Fame wearing fame, then fame sold as provenance.

I am wary of calling the collection a collaboration with Warhol, since the interesting part is not a neat artist-designer partnership anyway. The interesting part is how Versace understood repetition. Warhol had already made the famous face into a mechanically repeatable surface. Versace put that surface back onto a moving famous body, and the loop became almost too perfect: Marilyn, Dean, Naomi, Gianni, Taylor, Donatella reissuing the prints in 2018 at the Milan Triennale, the archive learning to sell its own shock back to itself.

Some clothes become historical because they solve a construction problem. Others because they catch a social one before it has proper language. Spring 1991 did the second. It recognised that glamour no longer needed to pretend it was separate from media saturation. The dress could be the magazine, the model, the artwork, the souvenir, and the advertisement in one go. Too much, probably. That was the point.

LaserDisc did not make films interactive. It made them addressable. That is a smaller claim, and probably a more interesting one, because so much of modern viewing now depends on the idea that a moving image is not only watched but indexed, searched, paused, sampled, resumed, and dragged around by its spine.

The format arrived in the United States as MCA DiscoVision in 1978, then moved into the LaserVision and LaserDisc names around 1980. It looked absurdly physical by later standards: a 30cm optical platter, roughly the size of an LP, with a video signal laid onto a surface that had to be handled with the wary ceremony of something expensive. Yet its strangest legacy is not the disc itself. It is the numbering.

LaserDisc had two basic personalities. CLV, or Constant Linear Velocity, gave you about an hour per side on a 12-inch disc. The player slowed the rotation as the laser moved outward, from 1800 rpm near the inside to around 600 rpm near the edge, keeping the read speed steady. CAV, or Constant Angular Velocity, kept the disc spinning at 1800 rpm and gave you only about 30 minutes per side, but in return it exposed the film as a sequence of reachable images. Still frame, slow motion, forward and backward stepping, repeat playback, fast track: these were not decorative features. They were a different contract with time.

That distinction matters because CLV did not contain image numbers in the same way. It was the sensible format for watching a feature without getting up quite so often. CAV was wasteful, fussy, and wonderful. It treated the film as a numbered object. A remote control with a keypad could become a little index machine, and the film stopped being a tape you dragged through by friction. It became a thing you could summon by address.

I don't want to overstate it. LaserDisc menus were not DVD menus in waiting, fully formed and merely waiting for the plastics to shrink. LaserDisc was cruder and, in places, freer. The number went in. The player went there.

There is something bracing about that directness. We tend to remember the DVD era as the moment home video became navigable, but LaserDisc had already taught collectors, schools, museums, and people with too much patience that video could be handled as an archive. Not metaphorically, either. A CAV side was a set of frames with addresses. The tradeoff was visible in the object: half the running time, more sides, more interruptions, a machine asking you to care about its internal method.

That is the bit that survived. Not LaserDisc as a consumer format, because it never became cheap or convenient enough to win the room. What survived was the expectation that images should answer to us. Click a chapter. Scrub a bar. Pause a stream and expect the frame to wait without tearing itself into noise. The floppy save icon kept the outline of a dead storage medium on every toolbar. LaserDisc left a less visible fossil: the assumption that moving pictures have coordinates.

Streaming has made that assumption feel natural, almost beneath notice. The interface is cleaner now, which means the machinery has become harder to see. No one has to choose CAV and lose half a side of capacity just to get a clean still. No one listens for the player to hunt across a vast silver disc. The frame arrives because of buffers, codecs, manifests, caches, and a progress bar that pretends all of this is just a line.

Maybe the old nuisance was useful. A LaserDisc player made the compromise legible. If you wanted proper access to the image, you paid in minutes, sides, shelf space, and the ridiculous dignity of standing up halfway through a film to turn over the future.

The Trump administration has found the narrowest possible verb for AI oversight: ask. Not require, not license, not hold. Ask. The executive order signed today creates a voluntary route for developers of covered frontier models to provide access before release, for up to 30 days, so trusted partners can test for advanced cyber capabilities and the government can work out where the next class of risk begins.

That sounds small until you remember what the model release calendar now looks like. A lab can spend months on a capability, then decide who sees it, which customers are safe enough, which governments should be briefed, and whether a red-team finding is a reason to pause or a reason to add another filter. I wrote earlier about Anthropic's Mythos expansion into infrastructure, where the same question appeared in operational clothes: if a system can find serious vulnerabilities at scale, keeping it in a vault is not obviously safer than putting it in the hands of people who run the pipes.

The order is trying to stand in that uncomfortable place without admitting it is standing there. The White House text sets up classified benchmarking for advanced cyber capabilities, tells agencies to define thresholds for covered frontier models, and creates an AI cybersecurity clearinghouse within 30 days. It also says the review channel must not be read as a mandatory licensing, preclearance, or permitting system. That sentence is doing a lot of political work.

NBC describes the mechanics plainly: the government wants early access to the most powerful systems before public release, but the testing depends on voluntary collaboration from companies such as Anthropic, OpenAI, and Google. NPR's version adds the useful bit of recent history, that an earlier draft gave the government up to 90 days and the final order cut that to 30. TechCrunch puts the narrowing down to industry objections and late-May pushback, including David Sacks' concern that the draft could slow American firms against China. None of this is surprising. It is the entire problem in miniature.

A review that is mandatory becomes a licensing regime, even if nobody uses that phrase. A review that is voluntary becomes a norm, and norms are only as strong as the incentives around them. If the biggest labs participate, the smaller ones will be judged by their absence. If the biggest labs refuse, the order becomes a laminated request on federal stationery.

The strange thing is that both outcomes move power without quite moving law. The government is asking for a look inside the machine before the rest of us meet it, while insisting the request is not a permit. The labs keep the release calendar, the model weights, the product messaging, and most of the practical knowledge about what the thing can do. Somewhere between those two facts sits the public, learning about dangerous capability after the paperwork has already found a softer name for it.

Maybe that is the only workable first step. I don't mean that as praise. The alternatives are ugly in different ways: a licensing state that freezes the field around incumbents, a market that treats cyber capability as another launch-week metric, or a voluntary club whose rules harden quietly because no one in Congress can write the real ones fast enough.

This is not a general debate about "AI safety" floating above the industry. It is a bargain being shaped around named systems, named labs, and named agencies, with critical infrastructure as the moral pressure point. The phrase voluntary review sounds almost polite. The thing underneath it is more basic: who gets to see the dangerous part first, and what happens if they don't like what they see.

Anthropic's dynamic workflows let Claude Code write a script that fans a task out to dozens or hundreds of subagents, then runs it in the background while your session stays free. Turn on ultracode with /effort ultracode and Claude stops waiting for you to ask. It plans a workflow for every substantial task, often several in a row: one to understand the code, one to make the change, one to verify it. This is the most powerful mode the tool has, and the one most likely to flatter you into believing scale is the same thing as progress.

The pricing is honest if you actually read it. Each subagent burns its own tokens, so a hundred agents working a hundred files costs roughly a hundred times what one agent on one file does. Ultracode compounds that by turning a single request into a chain of workflows. Anthropic caps a run at 1,000 agents with 16 running at once, which stops a runaway loop while doing nothing about the cost of a run you fully meant to launch.

The orchestration is real engineering. The plan lives in a JavaScript script the runtime executes on its own, away from the conversation, so intermediate results stay in variables instead of clogging Claude's context. Agents editing files in parallel can each be handed their own git worktree, a private copy of the repo, so two of them never write over the same line. Nothing improvises; the script decides what runs next.

That is the gap the caps don't cover: they protect you from accidents, not from intent. The machinery guarantees the agents won't collide, but it has nothing to say about whether you needed a hundred of them. A weak plan doesn't get better when you run it in parallel; it just gets more expensive to be wrong.

Anthropic has decided the safest place for its most restricted model is not a vault. It is a larger, supervised room full of people who run things that break badly. On Tuesday, the company said Project Glasswing will expand from roughly 50 early partners to about 150 additional organisations in more than 15 countries, with Claude Mythos Preview pointed at code that sits under power, water, healthcare, communications, and hardware.

That sounds like a rollout, but the word is too clean. This is a controlled spread of capability that Anthropic itself treats as dangerous enough to gate. Each new organisation has to meet its security requirements before access. The point is not that Mythos can write better incident reports or draft friendlier Jira tickets. The point is that it can find vulnerabilities at a scale human teams can't comfortably absorb.

The numbers have the unpleasant brightness of a successful stress test. Anthropic says the initial partners have found more than 10,000 high- or critical-severity flaws since the early April launch. In a separate open-source scan, the company reported 23,019 potential vulnerabilities, with 6,202 estimated as high or critical. Of 1,752 high- or critical-rated findings that were independently reviewed, 90.6 percent were true positives.

That is impressive, obviously, and also a workload generator with a safety label on it. CyberScoop quoted Anthropic's own description of the new bottleneck: the "human capacity to triage, report, and design and deploy patches." I don't think that is a footnote. It is the story. Security has spent years saying that defenders are outnumbered by attackers, by legacy systems, by badly maintained dependencies, by the sheer amount of code that modern life has made ordinary. Now a lab has built a machine that can make the backlog visible faster than the institutions can fix it.

There is a strange moral inversion here. If a powerful vulnerability-finding model stays inside the lab, the defenders remain underpowered. If it leaves, even under a vetted programme, the circle of people who can operate it gets larger. I wrote in April about the earlier Glasswing fight, when the politics around Mythos looked like a contest between private access, national-security anxiety, and a government that wanted the tool close while worrying about everyone else using it. Today's announcement doesn't dissolve that tension. It formalises it.

The partner list is also doing political work. Anthropic's launch page named AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks among the launch partners, with up to $100 million in Mythos Preview usage credits and $4 million in donations to open-source security groups. SecurityWeek, citing the Financial Times, says the new wave includes organisations such as Okta, Samsung, ENISA, and NATO. Dark Reading reported on ENISA's likely access a day earlier, framing it as the result of cooperation between the European Commission and Anthropic. Put less politely: this is becoming infrastructure policy by customer list.

That may be the only practical route. Nobody wants the model handed around casually. Nobody serious wants critical software maintainers to keep finding flaws at human speed if attackers are going to get similar tools. So the answer becomes a club: enough members to matter, few enough to audit, with security requirements standing in for public law.

The awkward timing is that Anthropic is also trying to become legible to public markets. Yesterday's confidential S-1 filing made the company look less like a private research lab and more like a systemically important vendor preparing for quarterly scrutiny. Glasswing makes the same argument in operational form. If Claude is now part of how governments, banks, cloud providers, and infrastructure operators think about software risk, then the old category of "AI company" starts to feel too small.

I don't have a neat objection to the expansion. The alternative is not purity. It is slower discovery, quieter defects, and the hope that adversaries remain less capable than the people patching the pipes. Hope is a poor patching strategy. Still, this is a hard thing to watch without noticing how much authority is moving into private coordination: who gets access, which vulnerabilities get prioritised, whose infrastructure counts as globally important, and how quickly the rest of us hear about the bugs already found.

Prada's Spring 1996 show looks almost deliberately difficult in archive photographs: avocado greens, muddy browns, awkward sandals, prints with the suburban cheer drained out of them. It doesn't have the instant legibility of Versace sex or Chanel's logo theatre. Even the title, Banal Eccentricity, sounds like a private joke told by someone who expects you to work a little.

That was part of the force. Prada's own archive lists the show as SS 1996 womenswear, with the runway broken into exits like evidence. Vogue dates the collection to 1 October 1995 and describes the palette around avocado greens, sludge browns, mixed clashing prints, and sandals that seemed to resist prettiness on principle. I like that phrase, resist prettiness, although it risks making the clothes sound pious. They weren't pious. They were odd, dry, and sometimes funny.

Miuccia Prada had already made luxury behave strangely. She took over the family company in 1978, and the nylon backpack of 1984 had done something quietly rude to the old leather-goods hierarchy. Nylon was not supposed to be the material of desire. It was supposed to carry gym shoes, rain, airport irritation. By the mid-90s, that same intelligence had moved onto the body: ordinary fabrics, sour colours, deconstructed shapes, the kind of domestic pattern that looked as if it had escaped from a kitchen curtain and found itself in Milan.

AnOther's account of mid-90s Prada gets the shock of it right. The show has become folklore as the moment avocado, ochre, chartreuse, turquoise, lilac, and brown turned from bad taste into a proposition. Kristen McMenamy opened; Amber Valletta, Kate Moss, Shalom Harlow, Kirsty Hume, and Carolyn Murphy followed. Marcos Valle's "Rio Boogie" played underneath, which is a detail I enjoy because it gives the whole thing a lightness the clothes sometimes refuse. There was music, movement, models with faces fashion already knew, and still the collection would not do the expected glossy thing.

The easy reading is that Prada made ugliness chic. True enough, but too neat. Ugly chic was not just a reversal, as if she had taken an ugly object and stamped beauty on it. The better trick was making taste feel unstable. A brown could be ugly in one room and exact in another. A Mary Jane could read schoolgirl, dowdy, perverse, clever, or all four by the time it reached the end of the runway. The collection didn't ask to be liked. It made liking look like the least interesting response.

nss magazine's history of ugly chic notes the 1950s patterns, Mary Janes, dreadful purples with avocado greens, and muddy browns, and also repeats the strange show-day detail of a bomb scare at Prada headquarters. That almost sounds too symbolic, the literal threat arriving before the aesthetic one, but fashion history is full of these impolite coincidences. Editors still went. The show mattered enough to risk being late, or frightened, or both.

Alexander Fury later called Miuccia Prada the master of "ugly", arguing that she makes the undesirable desirable. That is close, though I think the verb "desirable" smooths the texture too much. Desire is part of it, yes. So is embarrassment. So is the tiny social panic of noticing that the thing you dismissed as wrong has started to organise the room.

What survives from Spring 1996 is not merely a colour story or a shoe shape. It is a permission slip for fashion to mistrust its own good manners. The collection made taste look less like an instinct than a learned reflex, and once you see that, the whole polished surface gets less convincing. A bad green starts doing philosophy. A clunky sandal becomes an argument with the mirror.

The television rental shop was one of those places where the future arrived with a payment book. Not the bright future, exactly. A heavier one, with a service counter, a van round the back, and a showroom window full of sets that looked less like furniture than weather systems. You didn't buy the future. You agreed to have it in the house for another month.

Radio Rentals began in Brighton in 1930, renting radio sets before the company grew into television and video recorders. By the colour-TV years, chains such as Radio Rentals, DER, Rediffusion, Granada, Visionhire and Martin Dawes had become ordinary names on the British high street. A Historic UK account of the colour-TV rental boom puts the appeal bluntly: sets were expensive, valve and cathode-ray-tube technology could be temperamental, and monthly rental made the machine feel possible. If it failed, somebody came out.

That last part matters. Renting a television was not just a credit arrangement, although it was certainly that. It was a relationship with an infrastructure of maintenance. The shop window promised novelty, but the real product was continuity: the engineer, the replacement set, the sense that this big humming box in the corner belonged partly to a company whose sticker was still on the back. Domestic technology had not yet become disposable enough to pretend it was weightless.

The figures are slightly absurd now. Radio Rentals claimed more than two million customers, 500 shops, and more than 6,000 technicians and skilled installers. That is not a niche. That is a parallel utility system, threaded through sitting rooms and shopping precincts, doing for entertainment what the gas board did for heat. A television came with a man in a branded van, which is a sentence that already feels older than the object it describes.

There is a useful little ache in that model. The rented set made modernity feel conditional. You could watch the same coronations, Cup Finals, sitcoms, news disasters and Saturday night light entertainment as everyone else, but the apparatus was on loan. The image belonged to the nation; the box belonged to Thorn, Granada, Visionhire, whoever had the paperwork. It was mass culture with a standing order attached.

The end came with the usual mixture of consolidation and cheapness. Radio Rentals was acquired by Thorn in 1968; Granada's rental line grew out of Robinson Rentals, incorporated in 1954 and renamed around 1969. By 2000, the Radio Rentals and Granada names had been folded into Boxclever, and a retrospective account of the lost TV rental shop records the bricks-and-mortar side ending in 2003. Boxclever still exists online, its own company history describing an amalgam of Radio Rentals, Visionhire, Rediffusion, DVR and DER. The ghost survives, but without the shop window.

I like the vanished shop more than I probably should. Not because renting was romantic. It could be expensive, paternalistic, and mildly humiliating in the way all household credit can be humiliating. However, it left behind a clear shape of dependency. You knew the machine had a chain of responsibility behind it. Somebody carried it in. Somebody could take it away.

Flat screens killed that theatre. So did supermarkets, online finance, consumer credit, landfill thinking, the whole clean nonsense of frictionless ownership. The old rental shop asked you to walk into town and negotiate with modernity over a counter. It made the future local, brown-carpeted, slightly warm from the backs of twenty display sets. Somewhere in that warmth was a truth we now hide inside checkout buttons.

Anthropic has moved from private-market rumour to SEC machinery. On Monday, the company said it had confidentially submitted a draft Form S-1 for a proposed IPO of common stock. The offering has no share count yet, no price, and no public prospectus. It is still conditional on SEC review, market conditions, and the usual list of factors that mean, in plainer English, this can be paused if the weather turns.

However, the signal is not cautious. Anthropic has put its hand on the door first. CNBC reports that OpenAI is preparing its own confidential filing, while Axios frames the year as a race between SpaceX, Anthropic, and OpenAI for public-market attention at trillion-dollar scale. That sounds absurd until you look at the numbers Anthropic is already asking investors to swallow.

Last week the company announced a $65 billion Series H at a $965 billion post-money valuation, led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital. Its own Series H note says run-rate revenue crossed $47 billion earlier in May. In February, according to the Guardian's summary of the funding history, Anthropic was valued at $380 billion. The market has not so much repriced the company as yanked it through a trapdoor into another category.

I wrote in April about Anthropic moving past OpenAI in the private valuation story. At the time, the IPO talk sounded aggressive because the company was still being priced in whispers, board meetings, and preemptive term sheets. Now the sequence is visible: raise at almost a trillion, file confidentially, let the public S-1 follow if the regulator's comments and the market both behave. The filing does not guarantee a float, but it changes the tempo.

The public S-1, if Anthropic proceeds, is where the romance gets audited. Axios makes the useful procedural point: a public filing would include financial data, risk factors, and the details that private-market storytelling can blur. Revenue run rate is one thing. Gross margin, compute obligations, customer concentration, stock-based compensation, related-party cloud deals, and the real cost of serving Claude Code are another. A frontier lab can be a religion in private. In public markets it becomes a spreadsheet with a risk section.

This is why the timing matters more than the ceremonial language around going public. Anthropic has spent the spring making itself legible to capital. The company raised at a near-trillion valuation, talked about compute expansion and product partnerships, and kept pushing the enterprise story that makes Claude look less like a chatbot and more like operating infrastructure. Its Wall Street embedding strategy already pointed this way. The model was not merely a product; it was a reason for banks, private equity firms, and portfolio companies to reorganise how work gets done.

Public investors will be less patient with mythology than late-stage private money, at least in theory. They will want to know whether $47 billion of run-rate revenue can hold its shape when discounts, compute costs, and implementation work are all counted honestly. They will also want to know how much of the AI boom is now a queue for the same finite capital. NPR quoted Wedbush calling the moment an opening of the IPO floodgates. Floodgates are a good image, but queues may be better. SpaceX, Anthropic, and OpenAI cannot all be the single impossible listing that defines the year.

The funny part is that confidential filing is meant to reduce drama. It gives the company a private conversation with the SEC before the theatre begins. In this case the privacy is almost beside the point. Anthropic has told everyone where it wants to go, and now the rest of the market has to decide whether a frontier-model lab is a software company, an infrastructure company, a consultancy, a defence-adjacent contractor, or some expensive composite of all four.

Maybe the S-1 will make that cleaner. More likely it will make the argument more interesting.

The awkward thing about an export-control loophole is that it is rarely a hole in the law shaped like a cartoon tunnel. It is usually a place where paperwork, geography, corporate structure, and enforcement patience fail to line up. A chip does not have to travel to Shenzhen to end up in a Chinese AI system. It can go to a subsidiary, a cloud tenant, a reseller, a lab in a friendlier jurisdiction, and still do the same work.

That is why the Commerce Department's weekend guidance matters. CNBC reported that the department moved on 31 May to enforce licence requirements for the most advanced AI chips when the entity is headquartered in China, even if the buyer is physically outside China. The article names Nvidia's Rubin and Blackwell processors and AMD's MI350x as the kind of hardware at stake. The important phrase is not "outside China". It is "headquartered in China", because Washington is trying to make ownership and control matter as much as delivery address.

This is also an admission that the old map was too simple. Last year the Commerce Department said it would not enforce the AI Diffusion rule, which had tried to govern global access to AI chips. According to CNBC, that left a year-long opening in which subsidiaries of Chinese AI firms in places such as Malaysia could potentially buy chips that the broader policy was supposed to keep away from China. Chris McGuire, a former State Department official, told CNBC that overseas subsidiaries could buy Blackwell chips without a licence under the previous posture.

I wrote recently about how Huawei silicon was setting a new floor for Chinese AI pricing. This is the same story from the other side of the table. If Chinese labs can get enough restricted Nvidia or AMD compute through offshore entities, the domestic substitution pressure weakens. If they cannot, Huawei, Cambricon, Moore Threads, and the rest of the local stack become less like patriotic alternatives and more like necessity.

The South China Morning Post's broader account of China's chip redesign makes that pressure visible. The fight is no longer just to build a single Nvidia replacement, but to create a domestic ecosystem that can support leading models reliably. That means GPUs where possible, ASICs where useful, and a lot of unglamorous software work around compilers, interconnects, memory, and serving. Export controls don't create that ecosystem by themselves. They do, however, make the unpleasant engineering trade-offs feel less optional.

Nvidia had a different kind of news cycle running at the same time. The BBC reported from Computex that Jensen Huang announced RTX Spark, a PC chip for personal AI agents, with Windows machines due in the autumn from Asus, Dell, HP, Lenovo, Microsoft Surface, and MSI. Huang called the reinvention of the PC as big as the smartphone shift. That line is keynote-ready, maybe too neat, but the contrast is useful. On one stage Nvidia is selling the future as a domestic appliance: a personal machine that moves from tool to teammate. In Washington, the same company's highest-end silicon is being treated as a strategic material whose customer list has to be read through corporate control.

Those two versions of AI hardware now live together. Consumer abundance at the front of the shop, geopolitical rationing behind the counter. The border between them keeps moving, because every restriction creates a new routing problem and every routing problem creates a new rule. The chip is still just a chip, until someone asks who really owns the company buying it.