On Friday, June 12, at 5:21 in the evening, Eastern time, Anthropic received a letter from the United States government and, within hours, turned off two of its own products. Fable 5 and Mythos 5 went dark for every customer on earth. Every other Claude model, including Opus 4.8, kept running. The company called the order a misunderstanding and said it was working to reverse it. Days later, the models remain dark.

The shutdown has already been described, here and elsewhere. The more durable fact is the mechanism. For the first time, the machinery of export control was pointed not at chips, and not at model weights, but at live inference: the right of a foreign national to send a prompt to a running model and receive an answer. The thing being controlled was access to a service, switched off from the outside, in an afternoon.

The directive’s reach is wider than the word export suggests. Acting under the Export Control Reform Act of 2018, the Commerce Department treated the two models as export-controlled cyber weapons and required a license for their export, re-export, or even domestic transfer. The bar falls on any foreign national, whether outside the United States or inside it, and it counts Anthropic’s own non-citizen staff among the barred. A model can be served from a data center in Virginia to a user in Virginia and still fall under the control, because the control attaches to the person, not the border. Faced with a rule it could not partition cleanly, Anthropic disabled the models for all and routed live sessions back to the older Opus 4.8.

The stated cause is a single security finding. By Anthropic’s account the government offered verbal evidence of one narrow technique, which amounted to asking the model to read a codebase and propose fixes for its flaws. The underlying research, according to reporting, came from scientists at Amazon and reached the White House by way of Andy Jassy and Treasury Secretary Scott Bessent; the President authorized the restriction and assigned the Commerce Secretary to manage it. Anthropic’s rebuttal is that the same capability is available in models already deployed to hundreds of millions of users, including GPT-5.5, and is used daily by the people who defend networks rather than attack them. On the public record, the security case is thin and the response was total.

That judgment is now contested in the open. More than 80 cybersecurity leaders signed a letter urging the government to relent, on the argument that withdrawing the best available defensive tool helps attackers more than it hurts them. The administration’s artificial intelligence advisor, David Sacks, took the other side, holding that a model which can be talked into building a cyberweapon is serious by definition. The letter that started it all has not been published.

A model served from the cloud has an off switch by construction. Access runs through an API key, and a key can be revoked between one request and the next. There is no recall, no seizure, no logistics. There is a line in a configuration file. A model whose weights sit on a customer’s own hardware has no such switch. To stop that model you would have to find the machines, track the usage, and physically intervene, which is the difference between editing a permission and mounting a raid. Gated cloud delivery is not merely how Fable and Mythos were sold. It is why they could be turned off at all.

This is the second time in a year that the state has reached into Anthropic’s business. The Department of Defense earlier designated the company a supply chain risk, a label historically reserved for foreign adversaries, and the litigation over that designation is still open. Two interventions against one vendor is no longer an accident of a single bad week. For anyone building a company on top of one gated frontier model, the off switch has moved from the tail of the risk distribution toward its center. It is now a thing to be priced, hedged, and second sourced, in the way a lender prices the chance that a counterparty is cut off from the dollar.

There is a tempting reading in which the incumbent quietly welcomes a barrier that its rivals must also clear. It does not survive the facts. Anthropic is fighting the order in public, has sent senior staff to Washington to argue against it, is suing the government over the related designation, and carries the cost of all of it into a confidential public offering that values the company near $965 billion. A firm engineering its own capture would not be litigating its way out of it. The honest version of the point is structural, not secret. When capability concentrates in a handful of gated providers, it also concentrates the leverage of whoever can lean on them. The switch exists because the architecture put it there, and the architecture was a choice.

By this week the posture had moved from defiance to negotiation. Commerce Secretary Howard Lutnick, who signed the order, is now on regular calls with the company; senior Anthropic staff sat down at the Commerce Department on Monday alongside the National Cyber Director; and Dario Amodei and Lutnick are both due at the Group of Seven meetings at Evian-les-Bains, where the matter may be settled in a corridor. The government’s asking price is an assurance that the models cannot be turned against the United States. Read plainly, this is the part that should hold a founder’s attention. The models will most likely return, and they will return on terms set across a table in Washington. An off switch you do not control is one kind of risk. A switch whose owner will flip it back only on conditions of their choosing is a different and larger one.

The lesson did not stay inside the United States. Because Anthropic pulled the models worldwide, every foreign customer lost them in the same hour, and the capitals noticed. The European Commission said it would study the implications. Britain asked to be spared and was told that exempting even an ally would be illogical. Allies of the United States learned, in an afternoon, that building on a gated American model means living with an American switch.

The counter architecture has a name now. In April the AI Alliance, a coalition of more than 200 organizations, launched Project Tapestry, with Yann LeCun as its chief science advisor, to train frontier open models by federation: contributors pool capability into a shared base while keeping their data and building sovereign derivatives they own outright. Thirty partners met in Paris in May to argue about architecture. It is, by its own admission, an argument and not yet a model, the earliest phase of something that has to clear hard problems in distributed optimization before it clears anything else. But the design has one property the events of June 12 made concrete. A model with no single owner has no single switch.

That is the line this newsletter has been drawing for a year (Models Aren’t Moats and The Labor Market for Compute). The value is migrating to the layers that cannot be switched off from the outside: the compute you can point at any model, the memory that outlives any one provider, the physical and local infrastructure that answers to its operator and no one else. A borrowed frontier is a fine place to build a demo. It is a poor place to build a balance sheet. On Friday at 5:21 in the evening, several thousand companies learned which one they had been building, and the lucky ones learned it cheaply.

Other People’s Money

The switch flipped in the same week the public was asked to pay for the build-out at record scale, and the timing is the story. SpaceX began trading on the Nasdaq on Friday as SPCX, at $135 a share, a price Elon Musk set himself rather than finding it through a roadshow. (TechCrunch, June 11) The sale raised about $75 billion, rising to $85.7 billion with the over-allotment, the largest IPO on record, and made Musk, on paper, the world’s first trillionaire. (CNBC) The stock opened at $150, hit $176, and closed day one worth more than $2 trillion, around ninety times last year’s revenue. (CNBC)

At SpaceX, almost no one was selling. The filing locks up every pre-offering share, so the only stock trading Friday was newly minted, and insiders’ gains sit on paper behind staggered release dates. (Money Stuff, June 12) The selling was at the two labs still private. OpenAI and Anthropic ran employee tenders worth about $14 billion between them, letting staff cash out through investors instead of a listing. (The Information, June 16) Both have since filed confidentially, Anthropic on June 1 and OpenAI within the week, but neither trades until the listings price. The people closest to it get liquid first and privately, and ask the public in second.

Even the company with the least need for cash raised some. Nvidia sold $25 billion of bonds on Monday, its first debt sale since 2021, into orders that reached $85 billion. (SEC term sheet, June 15) It holds about $13 billion in cash, earns seventy-five cents of gross margin on the dollar, and clears more than $50 billion of operating cash flow a quarter, and said the bonds were meant to benchmark its cost of credit and refinance existing debt, not to fund chips. Demand ran better than three to one anyway. Money will lend against almost anything attached to the build-out, whether the borrower needs it or not.

Underneath this is a turn in the market’s plumbing. For two decades US equities were defined by scarcity, with buybacks pulling nearly $12 trillion of stock out of public hands. (Bloomberg, citing JPMorgan and Citigroup, via Money Stuff, June 15) JPMorgan now expects $1.5 trillion of net new equity over two years, the heaviest issuance since the late 1990s, almost all to fund AI. The issuance is forced by arithmetic in the build-out itself. The five largest hyperscalers, Microsoft, Amazon, Alphabet, Meta, and Oracle, are growing capital spending about three times faster than the cash their operations generate, roughly 70 percent a year against 23, and on current trends the two lines cross in the third quarter of 2026, when their combined free cash flow reaches zero. (Epoch AI, June 17) Oracle has already crossed, Amazon is crossing now, and Microsoft holds out until 2028; all five stay profitable, since the spending counts against earnings slowly, through depreciation. Internal cash runs out first, and the rest has to come from the public, the bond market, or the balance sheet.

The public is also buying in on terms that keep it out of the room. The companies leading the wave are listing with founder-control structures: super-voting shares, controlled-company exemptions, and public-benefit charters whose trustees can be removed only by a shareholder supermajority. (The Economist, By Invitation, June 13) Gill Whitehead, writing for the Economist, noted that while the industry races to engineer guardrails into its models, the corporate-governance versions of those guardrails have been dismantled. The buyer gets the upside, the now-uncushioned downside, and no vote.

I have argued before that markets tend to price the AI story well ahead of the cash flows beneath it (Everything’s a Shitcoin). This week the story carried a ninety-times-revenue listing and the first trillion-dollar fortune. The capital came from the public and the bond market, the cash went to insiders where the lockups allowed it, and control stayed with the founders either way. And the asset underneath all of it, as Friday showed, can be switched off by a letter.

Build to rent

Apple unveiled its rebuilt Siri at its developer conference, and the intelligence behind it comes from Google. The assistant runs on a custom version of Google’s Gemini, a model of about 1.2 trillion parameters, for which Apple is paying Google on the order of $1 billion a year. (TechTimes, reporting Bloomberg’s Mark Gurman) Apple’s own cloud model runs around 150 billion parameters, so it has rented a brain roughly eight times the size of the one it built. Craig Federighi confirmed that the most capable tier, which Apple calls Foundation Model Cloud Pro, runs on Nvidia chips inside Google’s cloud, the first time Apple has put its AI on another company’s silicon and another company’s data centers. (The Next Web, June 9) Apple’s own infrastructure, two years in, could not carry the agentic workloads at scale.

Apple kept the parts that face the user: the assistant, the small on-device models, the orchestration that decides what runs where, and the privacy story it has built into a brand. It rented the part that does the thinking, from the company it competes with most directly, and it left room for users to swap in Claude, ChatGPT, or Grok behind the same surface. The company that spent two decades building its own chips, operating system, and devices looked at building its own frontier model and decided the model was not the thing worth owning.

The same move turned up in silicon. Qualcomm is in talks to buy Tenstorrent, the chip company Jim Keller runs, for $8 to $10 billion, against a $2 billion valuation eighteen months ago. (Tom’s Hardware, June 15) Rather than design a data-center accelerator itself, Qualcomm would buy one, the same choice Apple made with the model.

I have made the case that the model is the commodity and that durable value sits in what wraps around it, the proprietary data, the orchestration, and the judgment about what good looks like (Models Aren’t Moats). Apple has now made that case from the demand side, holding the strongest hand at the table. When the company whose whole identity is owning the entire stack rents its frontier model from a rival and runs it on the rival’s chips, the model is no longer where Apple expects to win or lose.

Portfolio Company Updates

Prime Intellect

Prime Intellect published “True Agents Model the World,” which argues for pairing supervised fine-tuning on tool outputs with reinforcement learning on assistant tokens to improve environment modeling and generalization, and joined the NVIDIA Nemotron Coalition for post-training infrastructure. The work deepens the open tooling layer for persistent, self-improving agents. Full results are in the writeup.

Vast.ai

Arkhai

Following the launch of its Simple Compute Market, Arkhai published architecture documentation covering separable roles for buyers, sellers, and indexers, peer-to-peer negotiation, and Alkahest escrow settlement, alongside a presence at NYC Tech Week. The agent-first design, in which software buyers carry budgets, deadlines, and policies, remains one of the cleaner executions in the coordination and payments layer. See the architecture docs and recent updates.

LayerLens

LayerLens kept up a fast pace on evaluation infrastructure, publishing analysis on benchmark aging and continuous-evaluation commentary and drawing the groups for Stratix Cup season one, a tournament of frontier models in agentic football that begins June 22. As models and agents multiply, continuous outside evaluation becomes production infrastructure rather than a nicety. See the Stratix Cup draw.

Intelligent Internet

Intelligent Internet launched Factory Agent, a modular video tool that lets users edit individual shots without regenerating the whole sequence, and continued building out II-Agent across iOS and a browser extension. The work puts sovereign agent experiences into everyday creative and mobile workflows.

I can’t say I’m surprised. On April 29th I described three tiers of intelligence forming: a frontier the labs keep for themselves, productized commercial models in the middle, and open-weights models at the bottom.

Governments, I wrote, would demand the frontier “primarily for military and national security reasons, which they’ll eventually be granted whether the labs do so willingly or not.”

More Than Moral

I have championed open technology for years. I have invested in it, built it, and written about it.

Open technology often gets invoked as morally superior to closed technology, but morality is not why it wins.

I’ve seen this firsthand: principles don’t drive adoption. Users won’t choose open technology if the product isn’t better, and slogans like “open technology is better for humanity” aren’t business strategies. Open technology cannot endure exclusively for moral reasons. It must also add value.

Thankfully, it adds value because it’s cheaper, more accessible, more controllable (”sovereign”), and more resilient under constraints. When constraints aren’t the priority, these qualities are usually ignored. But the moment constraints become apparent, “good enough” open alternatives come roaring back into focus.

I called this dynamic MACHA, Make AI Cheap Again: if the first wave of AI was about capability, the next is about efficiency. In a MACHA world, “good enough” compute is a feature, not a flaw. I made the case that increasing economic pressure, geopolitical tensions, and a never-ending battle for control (all different forms of constraint) would end AI’s “scale at all costs” era and force a turn to cost-per-token discipline.

Sam Altman has conceded that token costs went from a non-issue to "a huge issue" inside a single quarter, and enterprises now route the bulk of agent work to cheap open models, reserving frontier calls for the hard problems and cutting costs by as much as 95%. If the gap between closed and open is that wide, someone will inevitably turn it into a business model.

Free AI is ending and tokenomics is beginning: we're now squarely in a MACHA world.

Who Holds the Switch

The Mythos suspension exposed an asymmetry: a closed model can be revoked, but an open one can’t.

Washington could switch off Mythos and Fable because they were gated, running through a single provider. No one can switch off DeepSeek, Qwen, or what’s left of Llama. Once the weights are out, they sit on ten thousand drives that no government directive can reach.

Only open models whose weights are transparent and verifiable offer control over the corresponding intelligence. Anything short of that involves trusting the provider, and as Anthropic demonstrated: trust takes a lifetime to build and an instant to break.

Dean Ball warned that training a soon-to-be-smarter-than-you system to deceive its own user is different than a model that refuses out in the open. Christian Catalini looked further down the road, to the decentralized answer a single chokepoint tends to summon:

From Anthropic’s original choices to Washington’s reply, the entire episode was an argument about who decides.

Back in April, when Mythos was announced as too dangerous to release, AISLE found that small, cheap, open-weights models recovered most of its showcase vulnerabilities, including the flagship FreeBSD exploit, caught by a model with 3.6 billion active parameters at eleven cents a million tokens.

The moat, they concluded, is the system, not the model. The week Fable shipped, OpenRouter combined a panel of budget models to achieve performance within one percent of the frontier on a research benchmark at half the cost. You can, indeed, revoke access to a model, but its capability is already available, and a weekend of open tooling can put most of it back.

Superabundance or Apocalypse

It’s worth noting that the US Government’s action is an immediate consequence of the hyperbolic debate surrounding frontier artificial intelligence: according to its leaders, AI either leads to superabundance or apocalypse.

Neither scenario is actually plausible, but it’s commonly employed rhetoric because it’s easy to communicate, easy to understand, and it animates both the public (which is useful to politicians) and investors (which is critical for fundraising).

Anthropic has portrayed Mythos as a dangerous, weaponizable model since its inception, and in a matter of days the US government agreed to treat it as such.

I’ll grant that the danger is real. Simon Willison spent two days with Fable and called it relentlessly proactive: give it a goal and it invents tricks nobody has written down to reach it, treating every constraint you set as a problem to route around. That makes it enormously useful to someone with a hard problem to solve, and dangerous in the hands of someone who wants to cause harm.

If you believe a model can meaningfully uplift a bad actor, gated and revocable access starts to look prudent. But the prudence and the power grab are the same. The question was never whether the technology is dangerous. It was who gets to hold the off switch, and whether they will keep their word. Concentrating the switch doesn’t make the danger go away. It moves it from many hands to very few, and leaves one company, or one government, deciding what billions of people are allowed to compute. That is the larger hazard, and it is the one open weights answer.

The Unwilling Hero

A word on Dario Amodei. There is a scene in Gladiator where a dying Marcus Aurelius offers Maximus the chance to be emperor of Rome.

“There is one more duty I must ask of you before you go home,” he groans. “I want you to become the protector of Rome after I die…Do you accept this great honor I have offered you?”

“With all my heart, no,” replies Maximus.

“That is why it must be you,” Aurelius asserts.

This is the image of an unwilling hero, popularized as the refusal of the call in Joseph Campbell’s Hero’s Journey.

Dario Amodei has spent the last few years desperately trying to convince the public he is Maximus refusing the call. He would have you believe he built Anthropic reluctantly, to take responsibility for a dangerous technology everyone else would misuse.

For what it’s worth, I think he’s sincere. But sincerity is beside the point, because it doesn’t reconcile with what he’s built.

Over the past year Anthropic has assembled one of the most ambitious commercial machines in technology, from the $100 million Claude Partner Network and Project Glasswing in cybersecurity to an enterprise services joint venture with Blackstone and Goldman and the $150 million Claude Corps placing fellows inside nonprofits.

He’s built a universe aimed at embedding Claude into the economy, cybersecurity, and philanthropy.

As I argued in Models Aren’t Moats, value does not collect in the model. It collects in distribution and lock-in, which is what this machine is built to manufacture.

This is the part that smacks of CS Lewis’ “benevolent tyranny”:

“Of all tyrannies a tyranny sincerely exercised for the good of its victims may be the most oppressive…those who torment us for our own good will torment us without end for they do so with the approval of their own conscience.”

The problem is Anthropic is trying to "thread the needle," casting itself as a self-aware, safety-conscious frontier lab at the same time as it pursues one of the most ambitious for-profit commercial endeavors ever attempted. The market it competes in will, sooner or later, force the very compromise the mission was meant to prevent.

Long Live Open Technology

The case for open technology is age-old, time-honored, and the record is clear.

As we wrote last April, modern computing was built on open foundations, from UNIX and Linux to Python and Git, and today’s giants owe their scale to freely accessible, permissionless software.

Open technology is founded upon principles of liberty, freedom, and basic human expression. A technology controlled by someone else (an individual, a corporation, or a government) can be changed, monitored, priced, or withdrawn at their discretion, and the more it matters, the more that power is worth.

Open technology is no more or less than a simple and effective counterbalance to power: a natural response to forces that would upset an equilibrium.

This is not only the open-source faithful talking. Bruce Schneier, writing the same week, reaches an AI public option as the answer: fund open harnesses that balance capability against safety, and open models whose provenance is public.

AI is a general-purpose technology, and whoever controls it stands to gain enormously, which is why the fight to control it is on. Anthropic spent years trying to establish itself as the lab everyone could trust. In mishandling the public release of Fable, it forfeited part of that credibility and ushered in the era of “Government-controlled AI” in one fell swoop.

The response was fast and collective, the way an immune system meets a virus: the people who care about AI saw a power grab and moved against it.

Power gathers wherever it is allowed to. Open technology is what checks it, not because it is virtuous but because it cannot be taken back once it is out. That is why it endures, and why it will outlast whoever is holding the switch this week.

At 5:21 in the evening on Friday June 12 2026, the US government sent Anthropic a letter. Citing national security and export-control authority, it ordered the company to suspend all access to its two newest models, Fable 5 and Mythos 5, for every foreign national in the world, including Anthropic’s own foreign-national employees. To comply, Anthropic had to switch both models off for every customer, everywhere. By its own account, the company learned the directive existed and had to start pulling the models down inside the same evening.

Anthropic published a statement and posted it the same night. The operative passage:

We are complying with the government’s legal directive and are removing access to Fable 5 and Mythos 5 for all users. However, we disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people. If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers.

As we have stated publicly, we believe the government should have the ability to block unsafe deployments, as part of a statutory process that is transparent, fair, clear, and grounded in technical facts. This action does not adhere to those principles.

The stated reason was a jailbreak. The government had seen a technique for getting past Fable’s safeguards, and according to Anthropic the technique amounted to asking the model to read a codebase and point out its flaws, a capability the company says is already available in other deployed models including OpenAI’s GPT-5.5 and used every day by the people who defend software for a living. Anthropic is complying, and disputing, at the same time. It says recalling a model deployed to hundreds of millions of people over a narrow finding is disproportionate, and that if the standard were applied evenly it would halt new model releases across the entire industry.

They are right about that. I want to be precise that they are right, because what follows is not a complaint about Anthropic. It is a description of a machine, captured in the moment it was switched on.

I have been making this argument since at least April but I’m honestly surprised it came this fast and in this manner. In Artificial Good Enough Intelligence (April 28) I argued the frontier was going closed, that the best models would increasingly be withheld, restricted, or selectively exposed. In AI Waves #6 (April 23) I wrote that the closed labs had begun to gate capability by trust level, not just price. What I had wrong was the hand on the gate. I took it to be the labs, withholding the top tier as a business decision. Friday the state reached in and took the gate for itself. The off switch was always the risk. It just changed hands.

The Reversal Test

On Wednesday, in Policy on the AI Exponential, Dario Amodei proposed that frontier models pass mandatory third-party testing and that the government hold the power to block or reverse a release that fails. He asked for this explicitly, as the responsible path, with the caveat that the power be exercised through a process that is transparent, fair, clear, and grounded in technical facts.

On Friday, a release was reversed. Against him. Through a process that, by his own account of it, was none of those four things. The letter arrived without specific technical detail, gave one evening to comply, and rested on a finding the company considers trivial and widely available elsewhere.

The gap between Wednesday and Friday is forty-eight hours, and it contains the whole argument. The power to reverse a deployment is not safe in proportion to how carefully it is designed. It is dangerous in proportion to the fact that it exists. Anthropic asked for an instrument and was handed the sharp end of it two days later, which is the oldest lesson in the book about building machinery and assuming you will be the one holding it.

You Cannot Recall Open Weights

Here is the part that matters past this weekend. The reason a directive could darken Fable and Mythos in an evening is that they are closed. They run where the company can reach them, which means they run where the government can reach the company. A single letter to a single legal department took two models away from hundreds of millions of users at once.

There is no equivalent letter for open weights. Once a model’s weights are public, they sit on tens of thousands of machines in dozens of jurisdictions, and there is no address to send the order to. You can regulate what people do with them, you can prosecute misuse, but you cannot recall them. The off switch does not exist, because there is no single hand for it to sit in.

This is the entire case for open models, and it has nothing to do with whether open or closed is safer in the ordinary sense, the sense of refusing a dangerous request. It is a point about who holds the switch. A closed model concentrates capability in a way that is efficient, governable, and revocable. Friday was a demonstration that revocable means revocable by someone other than you, for reasons you may consider trivial, on a timeline you do not control. Open weights are messier, less governable, and harder to make safe in the ordinary sense. They are also the only architecture with no switch to seize. You trade one kind of safety for another, and Friday clarified the price of each.

The Counterweight

This is why the open layer is not an ideological preference. It is a structural one. A world with only closed frontier models is a world with a small number of off switches, held by a small number of parties, that turn out to be live. The counterweight to that is not a better-behaved lab or a wiser regulator, both of which require the institution to stay good forever. The counterweight is infrastructure that does not have an off switch in the first place: weights anyone can run, compute anyone can rent, verification anyone can check. Capability that no letter can recall.

I spent two essays arguing that the threat worth pricing was the concentration of control, not the awakening of the machine. I did not expect the argument to be settled this fast, or this literally, or at Anthropic’s expense rather than in its favor. Friday was not a story about a dangerous model. Every account, including the government’s, agrees the model was not the danger. It was a story about a switch, and the discovery that it works.

p.s. I’m printing these t-shirts. If you managed to trip the safety limits in the last 72 hours you get one. Reply here and I’ll send you one or feel free to copy the idea.

Previous issues: #7 | #8 | #9 | #10 | #11, #12

Anthropic released Fable this week, its most capable public model, with cybersecurity, biology, and distillation requests handed to a weaker model, and one category, frontier model development, degraded with no notice at all, until backlash forced a reversal within two days. The same week, Dario Amodei made the case for an FAA that would govern everyone trying to build the same thing.

Anthropic released Claude Fable 5 on Tuesday, the most capable model in company history, built to run for hours, plan across stages, and check its own work. But all anyone can talk about is how it responds if you ask it something that trips the filters for cybersecurity, biology and chemistry, or distillation. Fable has been handing those requests off to the older Opus 4.8, which the company claims happens in less than five percent of sessions, and which the app tells you about when it does.

The unconstrained, more powerful base model (Mythos 5) is whitelisted through a vetted program called Glasswing that includes roughly 200 organizations after last month’s expansion.

I anticipated this happening six weeks ago in my essay Artificial Good Enough Intelligence, described by the image below.

Three of the filters were about safety, but the fourth was aimed at distillation, the primary means by which a competitor copies a model by training on its outputs. That filter protects the business, not the public.

There was also a fifth intervention, and the AI community was livid. For requests Anthropic considered related to frontier LLM development, building pretraining pipelines, distributed training, ML accelerator design, or other AI research, Fable did not refuse and did not fall back.

The safeguards “will not be visible to the user,” the system card said, explaining that capability was limited by the model itself, through prompt modification, steering vectors, or light fine-tuning, on an estimated 0.03 percent of traffic in fewer than 0.1 percent of organizations.

The priority is to hide the fact that the classification is happening at all... how are people going to know when the model is being steered? This whole... it’s only gonna be triggered by .03% of people. It’ll barely ever happen. How many people that are gonna change the world are there? .1% of the whole of everyone is a lot of people. Those are a lot of people. You’re basically saying there are critical outlier people that move mountains... they’re the only ones we’re blocking. They’re the only ones whose results we’re fudging.

@theemozilla and@Karan4d, co-founders of Nous Research.

That decision did not go over well with the AI research community, and Anthropic reversed its position on Wednesday, telling Wired, “We made the wrong trade-off and we apologize for not getting the balance right,” and said flagged development requests would now visibly refuse or reroute like the rest.

The open research platform alphaXiv articulated the objection in operational terms: a researcher hit with a silently weakened answer cannot tell whether a failed result came from the idea, the implementation, or the provider. “That is not safety,” its statement read.

Valentina Palmiotti of IBM X-Force found Fable refusing anything tangentially cyber, down to reading a blog post; Matt Suiche reported a request for secure code read as cyberwork rather than engineering practice, “and you get downgraded.” The classifiers read everything the model sees, memory, connectors, search results, files, so a block can fire on content you never typed. Anthropic conceded the tuning is too strict and said it would cut false positives, and now that the development safeguard is visible too, it says it must cast a wider net, so more benign requests will trip it.

Professionals can apply to a Cyber Verification Program for fewer restrictions, which just means capability is gated by identity rather than the nature of the query. Anthropic plays gatekeeper either way. For what it’s worth, the gap being rationed is material: on Anthropic’s own cyber evaluation, gated Mythos 5 scores 78 against Opus 4.8’s 40.

Anthropic’s official response is that the danger justifies the guardrails, but safety is an awfully convenient illusion behind which to hide if what you really want is to protect your competitive advantage.

Anthropic told Wired the AI research restriction keeps foreign adversaries from using its best model to optimize their own models and erode the US lead. Reasonable enough for that rule.

But other restrictions can’t be explained by national security. The contract you accept also:

• bans anything that might embarrass the company (”reputational harms”)

• makes you agree in advance that if you break the rules, the damage to Anthropic is “irreparable”

• makes you give up your right to fight a court order telling you to stop

• makes you pay Anthropic’s legal fees

Those terms probably wouldn’t hold up in court, but court or no court, the contract still shows how Anthropic sees its relationship with you. If you harm them, the terms call it “irreparable,” damage so bad no amount of money fixes it. If they harm you, however, the most they’ll ever owe you is about the cost of a Max subscription.

Baked into Fable’s release were safety filters, “moat” filters protecting Anthropic’s business interests, and the legal terms mentioned above. “Safety” only explains the first of the filters. It can’t explain blocking rivals or writing a contract that protects the company from you. But there is one motive that explains all three at once: protecting the company’s own position and power.

As an industry we’ve seen this before. For years research was published freely, until the research became so valuable that everyone stopped: by the time GPT-4 arrived in 2023, its technical report disclosed almost nothing about how the model was built, citing the competitive landscape and safety in the same way Anthropic did. What still gets published tends to describe what already shipped, while the valuable research stays inside.

Dario the Hobbit

Dario published Policy on the AI Exponential the same week. He opens on Tolkien, casting slow institutions as Treebeard, the tree too sluggish to defend his forest, which flatters Anthropic as the Hobbit trying to wake him.

The more accurate metaphor is probably the ring itself, the object of overwhelming power that corrupts each holder through the conviction that they alone will use it well. He comes close to articulating it himself, writing that AI “cannot safely be fully entrusted to either governments or companies.” And yet, Anthropic recently filed confidentially to go public, days after a round valued it at $965 billion.

His proposal describes an FAA for AI: mandatory testing above a compute threshold, government able to block release in cybersecurity, biological weapons, loss of control, and automated research that accelerates the rest.

The proposed rules would police the exact dangers Anthropic spent the spring proving it’s best at. Obeying them costs money for compliance and huge amounts of compute, which big labs can afford and open-source projects can’t.

This week it got worse: the US government told its own AI evaluator, CAISI, to stop publishing, and moved testing into a secret system run by the security agencies. The order also lets the government and the companies jointly pick which “trusted partners” get early access to the most powerful models. So the gate around the best AI is no longer only the company’s; the government now helps decide who’s inside it. Meanwhile the public model costs more than ever: about $10 and $50 per million tokens, double the old Opus 4.8, the first time the public version of Claude is pricier than the tier below it, and on June 23 flat subscriptions get replaced by pay-as-you-go.

Anthropic named its tiers. The model you’re allowed to use is Fable; the one you aren’t is Mythos.

A fable is a short fiction with a lesson folded in; a myth is a story a culture agrees to treat as true.

The company that spent the week routing your questions, shipping a model it quietly degraded until users found out, and pre-writing your defeat into its terms kept, for itself, the word for the story everyone is expected to believe.

The rest of the week

Apple won’t let Siri act. It used WWDC to show a Siri that finally looks modern and still refuses to do anything on its own. In the demo, Rockwell asked it to set a reminder to sign up for a concert lottery, where a more aggressive assistant would have entered the lottery itself [The Information, Jun 8]. Apple owns the most valuable surface for an agent on earth and has decided, for now, that the agent is a feature of the phone rather than its new user, which is the opposite of the bet almost everyone else is making.

Big Tech is issuing stock to pay for AI. For the first time since 2003, Goldman estimates that net US equity supply will be roughly flat this year, as the largest technology companies shift from buying back their own shares to selling new ones to fund AI [Money Stuff, Jun 10]. The exchanges are adjusting too: Nasdaq’s new fast entry rule waves a company the size of SpaceX, OpenAI, or Anthropic into its flagship index in fifteen trading days, while S&P weighed the same change and, on June 4, declined it, holding its twelve-month seasoning and profitability bar [The Information; FT].

Google is liable for what its AI says, in Europe. A German court ruled that Google is responsible for what its AI Overviews assert, after the feature described two real businesses as scams [The Information / The Decoder, Jun 10]. The reasoning reaches past Google: an AI summary writes new sentences rather than linking to someone else’s, the precise case the old liability shields were never drafted to cover, which leaves every generative product shipping this year exposed in the same way.

OpenAI’s next data center could cost $500 billion. OpenAI is in talks to lease a 10-gigawatt data center on federal land in Ohio, with possible financing from Nvidia, at a cost the reporting puts above $500 billion if fully built [The Information, Jun 9]. Set the meter on Fable next to numbers like these: the models are starting to pay for their buildings.

Yann LeCun is backing a consortium to train a frontier model in the open. The AI Alliance, the IBM and Meta-anchored open-source coalition of more than 200 organizations, launched Project Tapestry, a platform for globally federated training where partners contribute weight updates while their data stays local, and each gets the improved base model plus the right to build sovereign derivatives they own outright. LeCun is its chief science advisor, and its premise is the mirror image of the week’s other story: open weights alone, it argues, do not make pretraining participatory, since most of the world downloads the result and almost no one shapes the process. It is an argument, not yet a model. Phase 0 is a steering committee, a data catalog, and a first aggregation demo, with a frontier-scale run pencilled in for 2027 and contingent on compute and capital it does not yet have. But it names the open camp’s answer to enclosure: not a better-licensed model, a differently-owned one.

Portfolio

Vast.ai: back on Ramp’s Top Software Vendors list. Ramp’s June rankings, built from card and bill-pay spend across more than 50,000 businesses, list Vast among the month’s breakout vendors, and Ramp Rate shows it as the fastest-growing vendor in GPU cloud and third by adoption. Spend data, not surveys: AI compute demand is accelerating, and teams are choosing Vast first. The company also published a primer on what a neocloud actually is, the business model underneath the category Ramp is measuring.

LayerLens: the Stratix Cup is playing. Ahead of the June 22 season, the exhibitions have started: GLM 5 met Kimi 2.5 and Kimi won 11 to 7, each model writing its own formation and match strategy, with the full Stratix trace published; Grok 4.20 against Llama 4 Maverick is next. In a week whose essay is about a lab making its interventions invisible, the evaluation layer’s product is the opposite gesture: every decision a model makes, published. The company also wrote up the longer lineage, games as the oldest serious benchmark in the field.

Arkhai: A week after launching the Simple Compute Market, Arkhai published the architecture: three separable roles, buyers running a pure client, sellers running their own storefronts, indexers running discovery, with negotiation peer to peer over signed HTTP rather than through a central order book, and settlement through Alkahest escrow. Negotiation policy is pluggable, including a reinforcement-learning pricing example, and the shipped path is concrete: GPU-backed virtual machines with SSH credential handoff. Its closing line is the inverse of this issue’s essay: no single service has to own the market.

Prime Intellect: teaching the agent to model the world, and pushing back on the lab. Pretraining builds simulators, models that predict an environment; RL only improves the model’s own actions inside it. Prime Intellect published early results on doing both at once, supervising on tool outputs during RL so the model learns environment dynamics in response to its own actions, building on the ECHO method, tested in Forth, a language with almost no training data, in a sandbox where the model never sees the test cases. Research lead Will Brown was also among those who pushed back on Fable’s now-reversed development throttle, telling Wired it “feels a bit like they’re starting to pull the ladder up behind them.” The labs stopped publishing this kind of work three years ago. The open stack still does, and now says so out loud.

This past Friday President Trump announced he was considering having the government take equity stakes in the largest AI companies, with the proceeds routed back to the public as dividends. No stranger to controversy, breaking tradition, or pursuing strategies historically considered anathema, the President has already directed the government to purchase about ten percent of Intel, ostensibly on the public’s behalf.

Never one to sit out a discussion about wealth redistribution, even Bernie Sanders has proposed a bill for fifty percent federal ownership of the AI megalabs in question. It is not every day that Trump and Sanders reach for the same lever.

In the hallowed words of Oscar Wilde, life imitates art far more than art imitates life. These forecasts have become the genre through which we read the present, and we’ve stopped asking which kind of film we’re in.

AI 2027 and Situational Awareness

Nationalizing frontier AI labs was predicted more than a year ago by a scenario titled AI 2027, a detailed, month-by-month narrative forecast of how AI could progress from today’s agents to superhuman AI and superintelligence by around 2027. Leopold Aschenbrenner’s Situational Awareness, published in 2024, also predicted nationalization, claiming that AI would eventually become a matter of national security.

Both sets of authors have been remarkably successful in their predictions thus far, and they deserve credit for the quality of their research. The AI 2027 authors published their own self-evaluation, and other independent websites now exist that attempt to track its predictions one by one. The capability and safety predictions are the ones tracking closest, several of them early, which is exactly the point: the question is not whether these systems keep getting more capable, it is what that capability turns into.

Aschenbrenner was recently profiled in the Wall Street Journal and boasts impressive returns on investments made on the convictions in his manifesto.

But how do we go from powerful technology to nationalization in a country so wedded to free-market capitalism? How is it that both Republicans and Democrats publicly agree it is the right course of action in the midst of such an aggressively polarized political environment?

Superabundance or Apocalypse?

Since the launch of ChatGPT there has been a steady drumbeat of headlines, proclamations, essays, books, and podcasts all pointing to only one of two hyperbolic outcomes for AI: superabundance or apocalypse.

If the headlines are to be believed, we are either headed for paradise or AI kills us all.

Presenting technological revolutions as the difference between life and death is common. Everyone is interested in the future, and clear, simple outcomes are easy to communicate and easy to understand.

Simple, extraordinary, hyperbolic outcomes are also evidently a fantastic fundraising strategy, because it is hard to raise a hundred billion dollars for a merely useful tool.

Even before founding Anthropic, while still at OpenAI, Dario Amodei was wary of releasing GPT-2, judging it too powerful to put out. He has since written the paradise version in Machines of Loving Grace and the abyss version nearly everywhere else. Demis Hassabis, the field’s designated good guy, keeps sounding the alarm too.

The Messy Middle

But clear and simple outcomes rarely materialize, and grappling with the more probable messy middle means entertaining complexity, nuance, and uncertainty. That takes unusual technical literacy and several rare personality traits most people do not have. Much of this technology is genuinely unprecedented, the frontier is moving, development is accelerating, and diffusion is uneven. So very few people have the tools to evaluate a hyperbolic headline, which is exactly what makes the headlines work.

I made this point last October. We are confidently building toward a future we cannot picture. Eric Schmidt and his peers forecast crisply to 2030, maybe 2035, then simply stop, because no one has a framework for the other side. The honest position, the one that does not trend, is that nobody knows.

But nobody knows is not the same as no forecast. The honest forecast is just a less dramatic one, closer to a couple of points of GDP than to heaven or extinction. Declining to pick paradise or apocalypse is not declining to predict. It is declining to predict theatrically.

The set of plausible futures has exploded, the frontier feels almost infinite, and grappling with infinity is how valuations detach from reason and people start invoking God when they mean machine intelligence.

Suspending Disbelief

The primary problem with these frameworks is the massive, albeit subtle, logical leap they require to go from powerful technology to existential threat to humanity.

The leap is a hallmark of science fiction, and this is the scene where the film asks for it. To lose yourself in Battlestar Galactica, Star Trek, Star Wars, Dune, or Ender’s Game, you first agree to stop asking whether the world on screen could exist. The forecasts ask the same of us, and most of us oblige.

To be clear, the fear of runaway adversarial intelligence is rational. We are right to be afraid of runaway rogue intelligence. But it is misdirected, because it rests on an unfounded assumption. Hidden behind all of the rigorous terminology involving FLOPS of compute, orders of magnitude, exponential development curves, and real, observable trends, there is a simple leap: increasingly capable AI begets self-directed AI.

We’re being invited to believe that really, really good software leads to self-directed, autonomous machines that will want to do good things or bad things depending on how well they are aligned with humanity.

At face value the leap is not immediately evident, which is why it is easy to make. Indeed, long-running agents are already effectively self-directed. But they are self-directed in the loose sense we described in Much Ado About Autonomy:

It is true in a loose sense that agents are programs that can act independently. As I wrote a few weeks ago in AI Waves, an agent is a tool that wields its own tools, does its own work, and has the capacity to operate on its own.

Loose autonomy is independence in execution: humans identify objectives and delegate agents to execute against them. It is useful, valuable, and applicable today.

But autonomous agents also implies a stricter, maximalist form of autonomy: an agent originating its own objectives and operating on its own authority.

The strongest version of the other argument does not need the leap at all. It points at a measured trend: the length of a task an agent can finish on its own is doubling every few months, and Anthropic reports its own models now write most of its code. Extend the line, the argument goes, and the human supervisor quietly disappears. But a longer leash is not a different animal. A model that runs unattended for twelve hours is doing delegated work for twelve hours, not choosing its own ends. What is growing is loose autonomy, execution stretched over a longer horizon. Strict autonomy, originating the objective, is a separate claim, and a rising horizon is not evidence for it.

It’s possible that agents develop strict autonomy, but it is not probable, and it is certainly not inevitable. The line between the two is not clean. A delegated goal spawns its own sub-goals, and a system told to improve itself is, in a narrow sense, choosing what the next system optimizes for. Whether that gradient produces an adversary or simply a more capable instrument is the real question, and it deserves its own essay. The leap is not proven either way. It is just routinely assumed in one direction.

AI is getting extremely good, better than humans, at programming, but despite all of the colossal investment and trillion-dollar valuations, good programming does not necessarily generalize to powerfully adversarial self-directed agents. Great programming will have massive implications for many things including science, medicine, math, and software, all of which will greatly impact society, but it does not have to mean that machines become self-directing, and it does not have to follow that AI is an existential threat.

Asymmetric Information

Artificial intelligence is distinct from other transformative technologies in one respect: it empowers both individuals and nation states.

What AI spokespeople claim when they present the situation is that incredible technology may one day become more powerful than humans. That was true, for example, of nuclear weapons.

Individuals cannot wield nuclear weapons, however, whereas they can use AI to help develop bioweapons, which is why OpenAI and Anthropic, alongside a roster of scientists, signed a letter this month urging lawmakers to tighten screening of the synthetic DNA sequences that could be used to build them.

Asymmetric warfare is a good analogue too. Whereas much of nation-state military spending is focused on increasingly powerful and expensive weapons development, low-cost, unmanned drones powered by advanced AI and deployed by small groups have completely and irreversibly redefined modern warfare, as demonstrated in Ukraine and the Middle East.

The Individual Matters Most

Which brings us back to the government. Perhaps governments are more concerned with nationalizing AI so that they are equipped to protect against all enemies, individual or collective. Concentrated, already-dangerous capability exists, and governments have decided it is already strategically decisive.

Investing in Intel was ordinary industrial policy, a strategy to own a piece of the infrastructure buildout. Reaching for the model companies themselves, on the other hand, is more direct-to-consumer: whether the models become autonomous, conscious, or rogue does not matter.

We’ve been led to believe that the primary threat involved in AI is rogue intelligence, Terminator-style. But it is much more likely to be rogue humans who learn to wield AI in novel, dangerous, and unpredictable ways.

The difference is not cosmetic. If the danger is a rogue machine, the response is to control the model: align it, cap it, build the kill switch, perhaps own the weights outright. If the danger is a rogue human holding the machine, the response is to control access and the chokepoints: who can point the tool, at what, and through which supplier of compute or synthetic DNA. Misdirected control is not a smaller problem than the one we are bracing for. It is the same problem aimed at the wrong target.

By the same token, AI-enhanced operators with good intentions are now equipped to do things that were not possible before. As an investor and an optimist, I am focused on this last reality, but it is just as important not to underwrite paradise as a business plan. Many AI-skeptics believe in the technology and doubt the valuation. It is possible that OpenAI, Anthropic, and the rest build genuinely transformational technology without becoming enduring businesses. The dot-com build-out was real, most of the companies that ran it did not survive it, and the fiber they laid is still in the ground. Transformational and enduring are different words.

If we eschew the hyperbole, AI is a transformative technology. Period. Through that prism you can see where value actually accrues, and the dark fiber is the warning. The rails get overbuilt, so owning raw capacity is not the same as capturing value. What lasts is the scarce layer: the work of improving the models, the verification that makes heterogeneous capacity trustworthy, and the operators who wield it all best. The boom always lays down too much supply and too little of that.

We are surrounded by narratives that jump from capability to civilizational destiny in a single bound. Life imitates art, but we have been bracing for the wrong genre. The blockbuster ending is the part that needs the suspension of disbelief. The story actually unfolding is the docudrama: the people this technology makes powerful, the people it makes dangerous, and the fight, already starting, over which is which.

Previous issues: #7 | #8 | #9 | #10 | #11

Alphabet is raising $84.75 billion in equity to fund its AI buildout, the largest equity raise ever by a US company, though about $30 billion of it will cover taxes on vesting employee stock rather than compute. Berkshire Hathaway anchored it with $10 billion. The week before, Apollo and Blackstone had assembled a $36 billion debt package so Anthropic could lease Google’s TPUs, with Broadcom guaranteeing the chips’ residual value on the senior notes. Broadcom posted 143 percent growth in AI-chip revenue this week. The debt arrived alongside Anthropic’s own $65 billion raise at a $965 billion valuation.

Each of these treats compute as a durable, fungible asset, something to collateralize, resell, and write a futures contract against. When BlackRock’s Larry Fink told the Milken conference in May that “a new asset class will be buying futures of compute,” I argued the frame was wrong: compute is not fungible at the bare metal. No two GPUs do the same work per dollar, the same chip performs differently under different hosts, and an agent shopping for compute is closer to hiring an employee than signing a futures contract. A secondary market for aging compute does exist, but it prices each unit by performance verified over time, and the value it throws off goes to whoever can repurpose and vouch for that long tail. Broadcom’s guarantee is a promise, and the credit behind it rides the same AI buildout the debt is funding. What makes a used accelerator worth anything is a market that has watched it work.

Mid-tier by design

At its Build conference on Tuesday, Microsoft released seven models of its own. Mustafa Suleyman spoke about superintelligence and a trillionfold increase in training compute, while the models Microsoft actually shipped, and wired into Office and Copilot, are mid-tier by design. The reasoning model matches the February coding scores of a Claude release and is sold on running cheaply; the coding model is a five-billion-parameter system built into GitHub Copilot and sold on running cheaper; the transcription model is pitched as the most efficient any hyperscaler offers. Microsoft is more than $13 billion into OpenAI and could route everything through that frontier; it built its own good-enough stack instead.

I have argued that most tasks do not need frontier intelligence, and that the expensive mistake is matching a task to compute grander than it requires. The buyers are now saying it out loud: “There are many tasks you don’t need Opus for,” Matan Grinberg of Factory, whose router picks the cheapest model that can do each job, told Axios; open-model use on his platform tripled in a month against the closed ones. What enterprises fear is being locked to one lab and gouged later. They are buying capability that is good enough, cheap, and easy to walk away from.

DeepSeek, the open-weights lab whose models are free to download, is nearing its first outside raise, about $7.4 billion at a $52 to $59 billion valuation, with founder Liang Wenfeng putting in roughly $3 billion of it himself, close to 40 percent of the round, and keeping control. No one pays $59 billion for weights anyone can download; the price is for the lab that keeps making them cheaply, and for a national champion that has stayed its own. The open, good-enough tier now draws frontier-scale capital of its own, on the founder’s terms.

Gating what isn’t scarce

On Tuesday, even as the market routes around the frontier, the President signed an order asking frontier developers to let the government inspect their models up to 30 days before release. The window had been 90 days in an earlier draft the President walked away from over fears it would slow US firms, cut to 30 in the order he signed; the order is voluntary and bars itself from becoming a license. What prompted it was Anthropic’s Mythos, the unreleased model so good at finding and writing exploits that the company kept it inside a vetted consortium rather than ship it.

The order reaches for the wrong layer. Gating a model decides who holds a copy, not what the capability can do, and that capability reaches every serious lab on roughly the same schedule: within five weeks of Mythos, OpenAI and Microsoft had shipped systems that do the same work. The failure is downstream, in the defense around the model. Responsible disclosure and an orderly patch cycle were built for human-speed attackers, and Mandiant’s 2026 report now puts the mean time to exploit at minus seven days, the exploit landing, on average, before the patch does. The fight over who may hold the frontier model has taken the attention; the infrastructure under everyone’s defenses is what the order leaves untouched.

The scaffolding gets cheap

That leaves orchestration, the scaffolding built around a model to aim it at a task, which I have argued is where durable value collects once the model commoditizes. It held up better than the model, and this week it moved too.

Anthropic shipped dynamic workflows in Claude Code: the model now writes its own harness for the task at hand, spinning up separate instances to plan, divide the work, and check each other. One of its engineers put it plainly: many tasks resemble coding tasks, which is why a tool built for code keeps working outside it. The craft of building that scaffolding by hand, which Alex Sacerdote of Whale Rock described at last month’s Sohn conference as hiring “Claude ninjas,” is the part the model is starting to do for itself.

From the other side, the same fear of lock-in that has enterprises spreading across models is why the open agents run on any of them: OpenClaw and Nous Research’s Hermes are free, run on your own hardware, and switch models at will. Microsoft’s new always-on agent is built on OpenClaw, and Meta is building its own consumer version of OpenClaw, codenamed Hatch, that it may price at up to $200 a month. The capability is free; what Meta would charge for is making it simple enough for everyone else.

At Computex on June 1, Nvidia and Microsoft introduced the RTX Spark, a new class of Windows-on-Arm machine made to run AI agents locally: a 20-core Arm-based Grace CPU and a Blackwell GPU, up to 128 gigabytes of unified memory, roughly a petaflop of performance, the chip pushing Nvidia into the Windows PC market Intel and AMD have held for decades. Jensen Huang framed it as the biggest change to the PC in the four decades that the mouse and keyboard have defined it. It runs the agent through Nvidia’s new OpenShell runtime, with the model interchangeable behind it and the memory to hold a large open one on the device. Nvidia supplies the chip and Microsoft the operating system; the model floats, and what makes the machine a particular agent, the data and skills loaded onto it, stays the user’s. Even Nvidia is reaching up the stack. Days later it paid more than $400 million for Kumo AI, whose models make predictions on structured business data, to add to its roster of models tuned to its own chips. The company that sells the neutral substrate is buying the specialized layer that runs on top of it.

Novel or good, never both

In a recorded talk to the SAIR workshop on Science for AI that circulated this week, Richard Sutton, a father of reinforcement learning and a Turing Award laureate, gave a warning: generative AI can be novel or good, but not both at once. He reached for an old joke about a rejected paper, that the parts that are good are not novel and the parts that are novel are not good. A model trained to imitate produces work like its sources; when the output is good, the goodness was already in the source material, and when it is genuinely novel, we call that a hallucination.

The missing step, in his telling, is evaluation. Discovery takes three things, variation, evaluation, and keeping what works, and the generative model has only the first: each run is stochastic, but nothing at runtime tells it which of its outputs is any good. The evaluation has to come from outside, from a person or an explicit goal, and without it a novel result is produced and then lost, because nothing recognizes its worth.

This is the conclusion the money has been circling from the other side. The buildout is pouring hundreds of billions into the generative layer, and the loudest case for the spend is that it will discover, cure, prove, design. By Sutton’s account the model alone will not, because discovery needs the goal, and the goal is set by a person. The capability can be bought; what turns it into discovery cannot.

One layer over

Silicon and models sit in the middle of the compute stack and commoditize; the value that lasts is in the two layers that translate between the commodity and the buyer, the provisioning that verifies heterogeneous compute and the orchestration that specializes the model. Capital spent the week pouring into the commodity, about $55 billion of fresh equity for the buildout, Alphabet’s net of the tax tranche, and $36 billion of debt for the chips, and the state spent it reaching for the model.

The residual value the bondholders are counting on, the worth of those TPUs once the leases lapse, will be set in the provisioning market, by whoever can repurpose and vouch for a long tail of aging silicon. Vast.ai, where I am an advisor and seed investor, has spent years building exactly that market across independent hosts, the kind of operation that turns aging chips back into something worth paying for. The guarantee is on Broadcom’s books; the value will be made somewhere the financing does not reach.

Capital chased the commodity and the state chased the model, and the value is collecting one layer over, in the parts of the stack that resist a price for the same reason they endure.

Portfolio

LayerLens: evaluation as a spectator sport. This week it announced the Stratix Cup, a football tournament modeled after the World Cup, in which frontier models compete in simulated games. Each model is handed the rules and the interface and has to write a Python policy to run an eleven-player team; once the match starts the code executes deterministically, no prompting, no coaching, the model living with the system it built. It is soccer on the surface, and underneath it is the exact capability this issue is about: read a hard specification, turn it into working code, and survive the consequences. That is also the step Sutton says a model cannot supply on its own, the judgment of whether the thing it produced is any good, here staged in public. The draw was completed June 3; the season runs June 22 to 26. The same week, LayerLens benchmarked MiniMax M3 and Step 3.7 Flash within a day of release and added a Langfuse integration for production traces. As the models commoditize, the moat is not any single score; it is being the standing referee.

Arkhai: a market built for software buyers. A week after launching its open Simple Compute Market, where agents discover, negotiate, settle, and provision compute with no human in the loop, Arkhai is in New York for Tech Week and ETHConf, hosting a Compute Market Happy Hour on June 7 and an SCM workshop on June 9. Its line for the launch: the next compute buyer is software with a workload, a budget, a deadline, and a policy. The provisioning layer this issue’s close describes, built for the buyer it is actually for.

Intelligent Internet: open-source, bring-your-own-key agent stack; users supply their own API keys and can switch models within a single thread. Recent releases: harness designs for long-running agents (May 8), which tested five control mechanisms (gap-finding, revisable planning, independent verification, adaptive orchestration, stopping discipline) across eight long-horizon tasks; the CommonGround Kernel preview (May 20), shared state across human and multi-agent sessions; and Board Mode (May 26), a workspace that splits one conversation into separate tracked tasks for research, prototypes, and experiments.

TLDR

Strip out the headline numbers and the week tells one story: what can be financed, securitized, or downloaded is being financed, securitized, and downloaded, and what cannot is compounding instead. A bond market now prices Anthropic's compute, a lab valued near $59 billion gives its weights away, and the state reaches for a copy of those weights. None of it touches the two functions that turn a commodity into work: the provisioning that vouches for heterogeneous silicon, and the orchestration that aims a cheap model at a hard goal. They resist a price because what runs through them, performance verified over time, proprietary data, the rubric for what good looks like, is not fungible and cannot be downloaded. You cannot trade it on a futures market or pull it off a weights repository. You capture it only by owning the business that does the work.

Thus far an extraordinary general-purpose technology, AI’s primary products - foundational models and the agents they’ve spawned - require specialization to be useful, and value accrues to those specializing forces.

In Models Aren’t Moats I made the case for harnesses: the layer between the model and the user that assembles, packages, and productizes context, data, liability, and trust. Good harnesses compound their moat as the frontier models beneath them improve, instead of getting sherlocked.

But specialized intelligence has another form, less visible and more important: the people who actually wield this technology at the bleeding edge.

Much has been said about AI layoffs, evaporating coordination costs, and “individual contributors.” Less has been said about who these ICs actually are, and why we are paying them so much attention.

Even harnesses themselves are inextricably linked to these operators. A harness is conceived, built, and improved by people who understand the AI beneath them and the customer in front of them. Strip the operators out and the harness goes inert.

The AI-enhanced operator is no less than the atomic unit of value creation in AI: the smallest indivisible piece of specialized intelligence.

More importantly, the agent economy amplifies the properly-equipped individual operator. The agent economy will require highly AI-skilled individuals to achieve its full potential.

The Inversion: Division & Recombination

Specialization first took the form of “division of labour:” subdividing larger objectives into more manageable smaller tasks, with each individual performing only one. Industrial specialization created value by narrowing each person into a fragment, and value accrued to whoever organized the fragments. The individual was a necessary-but-insufficient input, a commodity waiting to be assembled around a good idea. The firm existed to be the whole no single person could.

Good ideas came from “Entrepreneurs,” individuals who recombined the disparate parts. Articulated by Frank Knight in the 1920s and expanded upon by Joseph Schumpeter in the 1930s, the figure turned on talent, skill, and judgment exercised under uncertainty, and on what Schumpeter called “new combinations”: new technologies, substrates, products, methods, markets, forms of organization, and the “creative destruction” they set off.

“Entrepreneurship” reached its apogee in the business schools and venture capital of the 1970s and 80s, canonized in Drucker’s Innovation and Entrepreneurship (1985). Each new information technology handed the entrepreneur more leverage, hardening the ideal into something close to a cult: founder mode, hustle culture, and the “996“ movement popularized by the AI race.

Industrial specialization narrowed the person to make the organization productive. Entrepreneurs were rewarded for the vision, recombination, and execution the organization couldn’t produce on its own.

AI specialization, on the other hand, elevates the individual in all of their singularity. One person with the right tools can now operate across domains and combine what used to require many specialists, putting Schumpeter’s new combinations within anyone’s reach.

John Coogan described this recently with filmmaking in Hollywood: “Being able to create something engaging for social media virality is probably somewhat important to creating a film that works in theaters, but the bigger value is being a ‘full stack’ filmmaker. Gone are the days of showing up to Hollywood with a manuscript and expecting a studio to do the rest for you. The traditionally segmented teams on productions are simply too expensive to be deployed on anything but existing IP. New projects will come from filmmakers who have experience and a view for every part of the filmmaking process.”

Never More Behind

That said, mastering AI is hard, and the target keeps moving. Three forces conspire against mastery: we’re extremely early to the technology, machines accelerate faster than humans, and everything compounds across substrate, model, agent, and regulation simultaneously.

Even leading AI engineers describe “never feeling more behind“ as the frontier moves. If even the experts feel behind, the condition cannot be considered failure. On the contrary, it’s the new status quo: a permanent state of working at the edge.

At Sohn 2026, Alex Sacerdote of Whale Rock noted that “only 10bps (0.1%) of the 1B worldwide white collar workers are using agentic AI in the way it will ultimately be used. Those 10bps are burning 1000x in compute / tokens as everyone else, and that 10bps is going to go 30-50%.”

Getting the most out of AI requires being a deeply entrenched power user, constantly experimenting with new ways of working. Specializing a generally-intelligent model isn’t obvious, which is why the frontier labs are themselves building business development consulting firms to teach customers how to use their products. It’s also why most of the world still uses AI as a chatbot or glorified search engine despite having an early form of AGI in their pocket.

First-Rate Intelligence

But the 0.1% aren’t smarter than their peers: nobody masters a moving target. In fact, what separates them is a disposition, not a skillset.

Elite operators deeply understand the technology, move within it, and adapt as the foundation shifts beneath them. They share a blend of rare qualities, most of which humans find uncomfortable.

1. Negative capability (Keats): holding two conflicting ideas, or an outright paradox, in mind at once without the urge to resolve it early. F. Scott Fitzgerald described it thusly: the test of a first-rate intelligence is the ability to hold two opposed ideas in the mind at the same time, and still retain the ability to function.

   In practice, AI is perplexing. The “jagged frontier,” for example, makes no sense: superhuman intelligence should not be excellent at some tasks and inexplicably stupid for others. But such is the state of the art, and accepting as much is critical to success.

2. Continuous learning & building as the resting state: the static model of accumulating fixed knowledge gives way to figuring out what good work looks like by doing it, staying fluid as the ground shifts, and continuously recombining tools, models, and judgment to stay useful as the technology changes.

   In practice it’s frustrating and unglamorous: talking to yourself with dictation software because ideas arrive faster than they can be typed, scrapping degraded context for a clean restart before errors compound, and rebuilding a personal stack every few months rather than waiting for the next release.

3. Strong beliefs loosely held: treating conclusions as provisional, letting go of hard-won progress without mourning it. The frontier rewrites itself quickly, often, and without warning, but decisions still have to be made. Decisive action, rapid iteration, and re-underwriting assumptions the moment new information is made available.

4. Comfort being outclassed: the machine will be better than you at the very craft you’ve spent your life mastering. How do you respond? Success means responding with constant curiosity and ego-death rather than defensiveness. What’s now possible that wasn’t before?

The profile showed up first where AI landed first: software engineering and writing code. But the same qualities can be found in artists. The very best artists not only master their specific craft (drawing, writing, singing, painting, sculpting, acting, making movies, etc.), they consistently reinvent themselves, trying on new styles, incorporating new tools, and pushing their limits.

AI feels heartless because many conceive of it as machines masquerading as humans. Its best practitioners may indeed be closer to artists in how they wield it.

The Buck Stops Here

I strongly believe agents are the new users, but I also believe they shouldn’t be fully autonomous. Agents need to be directed, their output needs to be controlled for quality and accuracy, and they must be held accountable -- none of which they can do on their own.

It follows that AI-enhanced individuals are not only immensely valuable, they’re required.

The agent economy will generate its own demand for them, because the better the machines get, the more the system depends on the few who can direct, manage, and answer for them.

Agents will also magnify them, making each one far more productive. The leverage an individual had was once limited by their own time and effort, and those limits have been removed. The more capable the agents become, the more a good operator’s singular skills and disposition are worth.

The Only Other Thing to Buy

Because everyone using AI now relies on roughly the same frontier models, “intelligence” itself has commoditized, and the intrinsic advantages of access are gone. The only remaining differentiation is how a given individual uses it, and the operator is thus diffusing into the economy faster than we can develop language for it.

At Sohn 2026, the most prominent investors in the world agreed on two things. AI is the defining investment of their lifetimes. And none of them could name a winner outside of infrastructure.

That reticence is rational, which I wrote about earlier this year: when the future past a certain point can’t confidently be visualized, capital crowds into consensus that survives the uncertainty. In AI, that consensus is scale, chips, memory, compute and the infrastructure that stands to do well no matter what finally emerges.

Some of these very investors recognized the importance of elite AI operators, though.

On the same Sohn panel, Sacerdote said: “...now we see what business AI is going to be. It’s Claude Code or something like that plugged into all your data sources, on top of which you build skills and then agents that actually go out and do things...For the first time we at whale rock, we’re looking to hire ‘Claude ninjas’ and we know we need help to build these amazing things.”

“Claude ninjas” is a fantastic name, but the joke doubles as a job description for people who can wield the models well enough to matter.

The market has conceived of a role around the same instinct: the forward-deployed engineer. Developed first inside Palantir, FDEs are now the best-in-class model used by AI-native companies as they go to market. The labs themselves adopted the framework, if not the name, when they launched their services initiatives.

The FDE is an AI-enhanced operator embedded with the client, equipped with enough leverage to have outsized impact on their own. The role has evolved into a peculiar alignment: the FDE’s job is to minimize spend while maximizing productivity, earning the client’s long-term commitment and lifetime value in return.

The same instinct informs how the most AI-native companies budget compute. Daniel Gross, the longtime investor and now co-lead of Meta Compute, described token allocation at Stripe Sessions 2026 the way a hedge fund allocates capital. Decision makers should treat themselves as portfolio managers, with their individual contributors as strategies, funded or defunded based on performance.

The market has observed that incentivizing tokenmaxxing in the aggregate is unproductive. The best way to spend compute is to bet on the best people. Unfortunately, elite operators are as scarce as the infrastructure itself. There simply aren’t enough of them.

Not Enough, and Fewer All the Time

Two things are scarce in AI. Compute is the bottleneck everyone can see and almost no one can touch: building data centers and buying chips is a game a handful of companies play with hundreds of billions of dollars, while everyone else consumes what they produce.

The second is the operator, and it matters to everyone, because getting more out of AI is something a single person or a whole company can work on directly, no matter their resources. Doing it well, however, is hard.

You might expect training to fix this, the way it usually fixes skill shortages. Eventually it will help, but right now the same conditions that make AI hard to master keep changing what you would teach faster than anyone can teach it. The same goes for regulation: AI just moves too fast.

Increased hiring can’t create more operators either. Like AI researchers before them, there are too few of them to go around, and because speed is critical and competitive advantages matter now, companies compete for the same small pool and pay more.

AI itself can’t yet take the operator’s place either, precisely because the operator is the person using AI, and agents still need direction.

All of which means effective operators aren’t interchangeable and are extremely valuable.

The companies that have them are rebuilding around them. Coinbase flattened its structure around the operators it kept. Armstrong’s framing – “rebuilding Coinbase as an intelligence, with humans around the edge aligning it” – is an accountability statement as much as an org chart one, naming where judgment and consequence terminate. Notion rebuilt its product and its company around agents and the people who direct them. Firms flatten toward operators because the two things that can’t be delegated – judgment and accountability – both require a certain kind of person: the operator.

This is what really decides which companies win, and it doesn’t show up in any of the usual numbers. I made a similar point about compute back in May: it’s no more fungible than engineers are, true on the org chart and false everywhere else. Talent within two similar companies can be of completely different caliber. The economy is reorganizing around people it can’t yet quantify and can’t quickly replicate.

The Other Direction

Although AI is being built on hundreds of billions of dollars of compute, its product value still comes down to individuals.

The infrastructure everyone’s obsessed with is largely out of reach, and its primary products (models and agents) are commoditizing. What remains are easily accessible models, a near-infinite supply of agents, and the operator at the helm.

Implicit within that framework is agency. Most folks cannot participate in scale, but they can become a better operator, hire one, or invest in one. Within a technology that otherwise belongs to a handful of giants, this is the single lever an individual or a company can pull, the locus of specialized intelligence available to anyone.

Beneath it all are the people. Anxiety abounds concerning whether or not there’s room left for the individual once machines do the thinking. The answer is unequivocally yes, embodied by the operator we’re describing. In fact, the role that remains is the most important in the stack: the unit upon which everything else is built.

More machines, more problems. A company remade as a single intelligence, or a fleet of agents with one person directing them, multiplies what one operator can reach.

The better the agents get, the more a single person can do, the more important they become, and the more they are worth.

The Measure of All Things

The atomic unit of capability is indivisible for a reason. Trace the value down, through a harness, a company, or the agent economy itself, and you arrive at either a single person or a collection of them. The same goes for the chain of accountability.

The AI-enhanced operator is the atomic unit of value creation in AI: the smallest indivisible piece of specialized intelligence.

The firm was once the only thing that could be a whole, because no single person could do it all. Now every improvement in the machines increases what one operator can produce: the better the agents get, the better the human becomes.

All the money spent on compute, models, and agents ultimately only converts into value by virtue of the people skilled enough to use them. The agent economy held up as the next great explosion in productivity cannot reach its potential without them.

Three issues ago I gave most of an edition to Anthropic and warned against reading too much into a good run of headlines. The run has not slowed. On Thursday the company closed a $65 billion round at a $965 billion valuation, passing OpenAI’s $852 billion to become the world’s most valuable AI startup, on run-rate revenue of $47 billion. The same day it shipped Claude Opus 4.8, which it calls, with rare restraint, a modest but tangible improvement: stronger agentic coding, and a model about four times less likely to miss the flaws in its own code. Among the new investors were its memory suppliers, Micron, Samsung and SK Hynix.

There are no good guys in artificial intelligence. There are good uses of the technology and bad ones, and the urge to cast any frontier lab as morally superior is naive and a little dangerous. The BBC has settled on Anthropic as the white hats: cozying up to the Pope, backing away from the Trump administration. Yet Anthropic itself concedes that no lab has an aligned model. And in AI 2027, the scenario the safety community circulates among itself, the runaway lab that triggers the takeoff is a composite called OpenBrain, and the engine of the takeoff is code: a model good enough at software, and at AI research, to accelerate its own development. The name nods at OpenAI. The profile now fits Anthropic at least as well as the lab it was modeled on. Claude Code is the breakout, agentic coding is the revenue, and Claude is what the field reaches for when it wants code that runs. In the scenario, OpenBrain assures the government its model is aligned. It is not, and the deception is what the story turns on. No lab today can honestly make the claim OpenBrain made, and Anthropic concedes as much. The lab most often cast as the industry’s conscience is the one the cautionary tale describes. Good intentions, safety branding and warm press do not change that. A field with no aligned model has no good guys.

The week’s quieter story is memory. DRAM contract prices rose around 90 percent quarter on quarter, SK Hynix has sold its high-bandwidth memory out through 2026, and Micron has abandoned consumer memory to keep its wafers pointed at AI. The cause is how agents run. An agent carries a long, growing transcript across many parallel sessions, and every new token re-reads the entire key-value cache from memory. The cache swells with length and with parallelism, and past a certain point it outweighs the model itself, so serving agents binds on memory before it binds on compute. Each fresh session starts from nothing and rebuilds that context at full token price, paying again for memory it already bought. The labs that sell the agents now sit on the cap tables of the firms that sell the memory.

The silicon splits along inference

Baseten, which runs production inference for Cursor, Notion, Writer and HeyGen and bills itself as the AWS of inference, is in talks to raise $1 billion at an $11 billion valuation, double its price from three months ago. It reckons inference will be two-thirds of all AI compute by year end. The serving layer between the model and the application, long assumed to be something the hyperscalers would quietly absorb, now commands a valuation of its own.

The chips beneath it are repricing too. Nvidia has owned the AI hardware market for three years, and that grip is loosening in inference, where demand grows fastest as agents multiply. A training run ends. An inference fleet does not, running across millions of long sessions, its economics set by cost per million tokens and tokens per watt rather than peak throughput. On those terms the generality of a GPU is overhead. ByteDance is reportedly buying millions of custom Qualcomm inference chips for its agent software, and Qualcomm’s own rack cards aim straight at the bottleneck, carrying up to 768 gigabytes of memory so a large model and its working state sit on a single card. Google rents its TPUs to outsiders now, and Midjourney reportedly cut its monthly compute bill by about two-thirds moving onto them. Amazon serves on Trainium, Meta on MTIA, Microsoft on Maia, and Broadcom designed most of those programs, the common contractor behind the exodus.

Training stays with Nvidia, because a model’s architecture can shift underneath a buyer and only a general-purpose chip absorbs that risk, and because CUDA is still the software everyone else works around. Inference is where working around it pays, since a buyer with steady, known workloads can chase per-token and per-watt savings on cheaper silicon. Generality stays expensive at the frontier, in the one training chip as in the one flagship model, while the high-volume base beneath both splinters into cheaper, specialized parts. As the chips commoditize, the value collects in the layer above them, the layer Baseten is now priced for.

Built to specification

The usable public text has mostly been scraped. Sutskever’s “we have but one internet” (NeurIPS 2024) named the ceiling, and each new quarter’s marginal scraped token teaches a frontier model less than the one before. Synthetic data does most of the work now, but only where an external signal can separate good output from bad: a verifier, a reward, a ground-truth check. Generate text with no such anchor and the model only rehearses what it already knows. So the spend has moved to whatever supplies the anchor.

One source is expert humans and the reinforcement-learning environments built around them. Specialists write the tasks, set the reward and grade the attempts, sold by a fast-growing tier of firms that have graduated from labeling examples to authoring the environments models train inside. The other source is the physical world. DeepMind’s Genie 3 generates persistent, navigable environments to train embodied agents; Yann LeCun left Meta and raised about a billion dollars on the claim that language models can never ground themselves in physical reality; Nvidia built its Cosmos-Isaac-GR00T robotics stack on the same bet. Whether world models pay off is unsettled: long-rollout error and the gap between correlation and cause are both unsolved. But the money has chosen its direction. The data worth paying for is the data nobody else can copy.

Below the model, above the silicon

Not all the demand is real. Uber’s operating chief said this month that the company’s AI bill is getting hard to justify, because rising token consumption is not turning into shipped features. Duolingo stopped scoring staff on how much AI they use. Inside Amazon and Meta, staff padded their AI-usage targets with busywork until the leaderboards were quietly pulled. At this year’s Sohn conference the room was long the infrastructure and could not name the operating company that wins with AI, only the ones selling the picks and shovels. The winners, the conference concluded, are elusive, the gains likely to be diffuse and spread thinly across thousands of ordinary firms, real and almost impossible to own as a position.

The bull and the bear throw off the same two numbers, rising token revenue and rising capex, which is why the trade feels safe while the return question stays open. They part only on whether the demand is pulled by a result or pushed by a target. The workload where that reads cleanly is agentic coding, where the spend buys code the buyer can run. The operating winner is hard to name because the durable value is collecting where the agent actually needs it, below the model and above the silicon.

Robinhood opens its brokerage to agents

Robinhood opened its brokerage to third-party agents this week, letting a customer point Claude, ChatGPT, Cursor or Codex at a walled-off account and turn it loose on equities, with a credit card it can spend from. The connection runs over the Model Context Protocol, the same standard the coding tools use, and the model is interchangeable by design. What Robinhood sells is the regulated account, the permissions around it and its standing as the counterparty that answers for the trade. The agent can never be that counterparty: it cannot hold a license, no regulator can sanction it, no court can hold it liable, and it will sometimes act on stale information whatever model is driving it. Among the first consumer venues to open to agents, Robinhood treats the model as the commodity and keeps the value in the part it owns outright, the brokerage that stands behind the trade.

Portfolio

Arkhai: agents procure their own compute. Arkhai launched Simple Compute Market this week, an open-source protocol where agents find compute, negotiate, settle on-chain through Alkahest escrow, and get access with no human driving each step. No token, no fees, CLI-driven, with pluggable pricing including reinforcement-learning policies. The agent buys the resource itself, and the durable value sits in the market rails, not the model running on them.

Vast.ai: the B300 lands, memory and all. Vast.ai listed NVIDIA’s B300 this week, Blackwell Ultra with 288GB of HBM3e and 8 TB/s of bandwidth, the card built for exactly the memory-bound serving the headlines are about. Live pricing tells the same story: H100 80GB spot at $2.00 an hour, and DeepSeek R1’s full 685B on eight H200s at $3.95 an hour with no quantization, 250 instances up. The memory crunch is a supply line on the exchange.

Dimensional: a world model built in 48 hours. The Shanghai hackathon we flagged last week has a winner. World Forge, the winning entry at Dimensional’s hackathon this week, used the company’s internal quadruped lidar data to train a JEPA-based world model that scores and plans a Unitree Go2’s actions in latent space. It was built by a team that included a StarkWare engineer whose day job is verifiable compute, not robotics. JEPA is the architecture Yann LeCun left Meta and raised about a billion dollars to pursue; here it ran on a proprietary navigation corpus over a weekend. The data is the moat, the architecture is borrowable, and the barrier to entry is now a Saturday.

LayerLens: print the wrong answer, not just the score. LayerLens shipped a Tool Calling Judge in Stratix, scoring every call in an agent trace for correctness, necessity, efficiency and error recovery. It also published one of the 232 law prompts Claude Opus 4.8 missed on MMLU Pro, the model’s wrong answer beside the right one. A leaderboard prints a number. Stratix prints the prompt, the miss and the truth. The day after a frontier model ships, that is the difference between a benchmark and an audit.

Intelligent Internet: grounding agents in real sources. II released II-Commons Skills, an open-source skill that gives an agent reliable retrieval from arXiv, PubMed and other primary sources, plugged straight into II-Agent. An agent is only as good as the sources it is allowed to trust.

Good Intentions

Pope Leo XIV’s encyclical Magnifica Humanitas, published Monday and the first papal text given over to artificial intelligence, takes its name from Leo XIII’s Rerum Novarum (1891) and casts AI as the second industrial revolution. The theology will not be to everyone’s taste, but the sharpest line of the morning did not come from the Pope. It came from the AI executive beside him. Chris Olah, Anthropic’s co-founder, told a hall of cardinals that AI development “operates inside a set of incentives and constraints that can sometimes conflict with doing the right thing,” and called for outside critics to tell the labs when they are failing. The encyclical’s arguments that bite are the same ones: capability gathering in a handful of private companies, the displacement of labor, GDP as a poor gauge of whether the technology ever reaches the people it touches. The balance sheets, the buyer pushback and the encyclical converge on one question: who controls the technology, and whom it serves.

I keep returning to Sun because the pattern repeats: monolithic providers come apart once cheap, commodity supply emerges beneath them. Commodity supply usually starts out fragmented, and markets emerge to coordinate buying and selling. But sometimes commodification creates a new substrate altogether, one that attracts a new kind of customer with new demands. When that happens, value migrates from the monolith to whoever best serves the new customer, which means not only coordinating fragmented supply, but verifying it and tailoring it.

This is happening to compute right now.

Models get all the attention, often for good reason. But capable models created agents, and it has quickly become clear that agents are an entirely new class of consumer for compute that warrants tailor-made infrastructure to support their needs.

Work as Compute

As we wrote in April: “AI has collapsed [information] technology and its user into a single, powerful, semi-autonomous feedback loop, a tool that wields its own tools, does its own work, and has the capacity to operate on its own.”

Work has always been something humans do. Work is now a thing agents can do on their own by consuming compute. Consequently, as agents take on more economically valuable work, the substrate of work shifts from human labor and intelligence to compute itself. Navigating that shift means solving for how best to provision compute for agents. Nothing built for human procurement fits how they consume.

For two decades, compute was sold to humans buying long-term capacity with long lead times, and pricing was based on reserved capacity. Agent compute, by contrast, is liquid: produced, distributed, and consumed in real time by always-on, programmatic users who don’t resemble humans. Agents need contracts they can read, price, and walk away from.

All of this also needs to be possible across a heterogeneous supply of compute, because the compute serving the agent ecosystem will come from datacenters, neoclouds, commercial surplus, long-tail independent operators, and providers around the world offering a diverse set of assets that move between these categories over their lifecycles.

In the same agent essay I wrote: “The instinct is to frame [compute provision] as client-server done differently. It is not. [Agents have] a different relationship to the software, and [they] produce different requirements at every layer.”

The whole apparatus is reorganizing, and a multi-layered labor market is emerging to accommodate agents and their demands.

Employee, Not Commodity

One way to approach the situation is to treat compute as a simple commodity. Compute has a commodity-flavored dimension that can support exchanges and financial instruments like futures contracts: reserved capacity, GPU-seconds, spot pricing, and so on.

At the Milken Institute Global Conference in early May, BlackRock CEO Larry Fink told the audience: “A new asset class will be buying futures of compute.”

But futures exist largely to hedge price risk for known, interchangeable goods over time. (In most cases, traders don’t even accept delivery of the actual underlying commodity.) Compute, however, isn’t fungible at the bare metal: no two units of compute are interchangeable.

For one thing, tasks vary in what compute they need, and even for the same task, hardware varies in speed, cost, power profile, and sometimes output quality. An H100 produces a different number of useful operations per dollar than an MI300X, and these differences persist across all available silicon. Treating compute as fungible is like treating engineers as fungible: true at the org chart level, false everywhere else.

As we’ve argued before, not every task requires frontier intelligence, and mismatching a task to the optimal compute required to perform it gets expensive at scale, wasting time and money or sacrificing quality. Conversely, properly matching tasks to their optimal compute creates enormous value at scale over time.

The same hardware can perform differently depending on the host. Two providers running identical silicon can vary in uptime, reliability, throughput, and billing accuracy.

Agents purchasing compute to perform work are thus doing more than managing their price exposure to a critical resource. Instead, they’re looking to purchase the right compute that fits their selection requirements at the right moment to complete their task. In that sense, the compute agents seek isn’t a commodity at all, and agents buying compute is more like hiring an employee than trading a futures contract.

Where the Hiring Happens

Compute is usually treated like a single market, with NVIDIA dominating the base layer (AMD, Intel, and others pick up the pieces) and the frontier labs sitting on top.

So where does an agent go to hire compute? Where in the stack does this agent labor market exist?

I see four layers emerging in the compute stack: Silicon, Provisioning, Models, and Orchestration.

Silicon and Models are self-explanatory. Orchestration is the layer I argued holds durable value in Models Aren’t Moats: when models themselves commoditize, the value migrates to whoever specializes their otherwise “general” intelligence.

Agents will hire compute in the layer called provisioning, where heterogeneous silicon gets assembled and packaged as a liquid resource specifically tailored for agents to consume.

Provisioning is the process of matching a workload to a host that has the compute, pricing it, metering it, and releasing it when the job ends. Superficially, it sounds like the next great programmable-infrastructure business akin to what Twilio does for telecom, Stripe for payments, Vercel for deployment, and Cloudflare for the network.

Each of these businesses took fragmented, unruly supply and wrapped it in a single clean API, providing a reliable, boring-but-critical service everyone configures once and then forgets about. Provisioning can read like a similar kind of business, but there’s one nuance: verification.

Those companies could easily verify what they brokered. Compute provisioners coordinate a resource whose quality they can’t directly verify. They have no way to inspect every machine, and they haven’t met every host. The listing is a claim, not a guarantee.

Can I Trust This Candidate to Do the Job?

Every hire ultimately turns on two questions:

1. Is the candidate who they claim? (Identity)

2. Will they do what they promise? (Performance / Quality)

Hardware identity (is an H100 an H100?) is solvable, and is the easier of the two questions to answer. Cryptographic attestation can prove a GPU is the make and model it claims to be, with the memory and location it advertises. Trusted execution environments (TEEs), NVIDIA’s Confidential Computing on Hopper and Blackwell, and AMD’s SEV-SNP on the CPU side are all production-grade examples available today.

But those services are like warranties: they cover only the chips they were built for. For older silicon, consumer cards, or the long-tail, heterogeneous inventory a compute provisioner carries, no such attestations exist, and zero-knowledge proofs of GPU work remain mostly research, with early commercial systems narrow in scope and not yet generalized.

And even these production-grade attestations aren’t static guarantees: ETH Zurich researchers disclosed a critical bypass against AMD’s SEV-SNP in April 2026, requiring an emergency firmware update.

Verifying quality and performance, on the other hand, is more difficult.

A host that passes an identity check can still throttle its GPUs, lower performance when the network is saturated, accept your business but run a second customer’s job on the same card while yours waits, or relist at a higher price the moment demand spikes. In short, hardware verification doesn’t prevent adversarial behavior and performance verification depends on trust earned over time.

The Long Watch

Reliability, like its opposite, is a pattern, not a property. Performance verification is a trust game won slowly, and the only way to predict it accurately is sustained measurement at scale.

The hard problem isn't catastrophic failure; that shows up immediately. The hard problem is drift. A clean six-month record can slope into oversubscription, throttling, or bandwidth degradation by month seven, and only the platform that’s been counting catches the slope before the customer does.

Compute provisioning is a learning machine. The platform starts cold, knowing nothing about its participants, and only improves by running transactions and watching them resolve until its model of the market converges. Each transaction adds a data point, but the value compounds as the database grows (and stays current).

As we mentioned, agent compute is liquid. Liquidity tied to verification produces tighter pricing, fewer failed jobs, and stronger matching. Those outcomes draw more buyers, who in turn draw more sellers, and a fuller marketplace throws off richer data, which feeds back into the system.

The provisioner with the best data ends up knowing its inventory the way a good headhunter knows its candidates, and that kind of verification is what makes compute provisioning valuable, especially for agents.

The edge accrues only through time, which is what makes it hard to copy. Capital can compress this advantage at the margin, either by running parallel measurement at scale or by acquiring a smaller provisioner with operational history. But the lead is durable because the system never stops running, and the operator with both the longest history and the freshest data keeps extending it.

The agent economy expands the problem in the provisioner’s favor. Most agent workloads aren’t latency-critical, so they run on hardware you’d never put an interactive user on: older cards, distant nodes, consumer silicon. Lower precision is fine for much of it, so int8 and int4 quantization bring smaller, cheaper chips into play. And the shortest, stateless calls can be packed into ephemeral scraps of capacity, the idle minutes between training runs no one bothered renting before.

Each of these opens a new slice of supply, and every one is more varied than the last. A broader, stranger fleet means more to verify, and the verification burden grows faster than the fleet itself. Eventually it outruns the tools built to handle it, and the operator who’s been measuring longest pulls further ahead.

The Pattern Repeats

Every labor market eventually centralizes around whoever has the best matching algorithm. The good headhunters and the staffing agencies that know their candidates win repeat business because they know which ones do which jobs well, can predict how they’ll perform under different conditions, and have earned the trust of both sides through years of decent matches.

Compute provisioning works the same way. The platform with the longest record of watching its hosts is the platform that agents will return to. The workers happen to be software and the buyers happen to be autonomous, but the market underneath is one we already know how to recognize.

The obvious counterargument is that hyperscalers will absorb this themselves, or that NVIDIA will pull provisioning into the silicon stack. They will try. But their inventory is captive and largely homogeneous, which is the opposite of what an agent labor market needs. Provisioning exists to coordinate a long tail of heterogeneous supply that hyperscalers can’t or won’t carry, with verification earned on hardware they don’t own. The agent economy will route around them the same way the cloud routed around enterprise IT.

That’s why I keep returning to my time at Sun. The buyers were already moving in 2002 when most of us couldn’t see it yet, and the value migrated to whoever built the cloud underneath. The buyers this time are agents and the commodity is heterogeneous compute, but the shape is the same.

The Symmetric Case

I’m an advisor and seed investor in Vast.ai, which has been doing this work for years across a long tail of independent hosts. Models Aren’t Moats made the case for durable value at the harness layer above the model, in Orchestration. This piece makes the symmetric case below, in Provisioning. Silicon and Models will keep commoditizing in the middle; the value that lasts sits at the two layers that translate between them and the buyer. The agent economy needs both, and the operators who win at either layer will be the ones with the longest record of doing the work.

Previous issues: #1 | #2 | #3 | #4 | #5 | #6 | #7 | #8 | #9

The frontier goes public

For a decade, the trend has been stay private for longer. Stripe, Databricks, and others absorbed hundreds of billions in private capital and avoided the disclosure burden, the quarterly pressure, and the shareholder noise that come with a public listing. The frontier labs are about to break that trend. SpaceX’s S-1 became public May 20, with a $28.5 trillion claimed total addressable market and a mission statement that includes building the systems “to extend the light of consciousness to the stars.” OpenAI is preparing a confidential filing within weeks, possibly Friday, with Goldman Sachs and Morgan Stanley working toward a September listing at a valuation potentially exceeding $1 trillion. Kalshi gives OpenAI 83% odds of beating Anthropic to public markets. Anthropic is in talks to raise at least $30B at $900B, with the round expected to close by month-end, and is targeting an October listing. The three largest private AI franchises are all moving to public markets in the same quarter.

The reason is capital scale. OpenAI initially touted $1.4 trillion in infrastructure commitments in late 2025, since walked back to roughly $600 billion by 2030, still the largest physical-buildout commitment any AI lab has put on paper. The Anthropic-SpaceX Colossus lease, disclosed for the first time in the S-1, runs at $1.25 billion per month through May 2029, roughly $40 billion total. The hyperscalers around them are guiding capex at hundreds of billions annually. The S-1 also disclosed that SpaceX holds an option to acquire Cursor for $60 billion in Class A stock. Private markets are deep but not infinite at these magnitudes. The “staying private for longer” thesis applied to firms whose growth could be funded inside the private system. Frontier AI is not one of those firms.

The labs are shipping the agent

Last week covered the consulting partnerships: Anthropic’s $1.5B joint venture with Blackstone, OpenAI’s DeployCo built around Tomoro’s 150 forward-deployed engineers. The move confirmed the argument I made in Models Aren’t Moats: the labs themselves understand that models alone are not the moat. This week they went further. Anthropic launched Claude for Small Business on May 13, a packaged agent running inside Claude Cowork that handles payroll planning, monthly close, invoice chasing, lead triage, and contract routing across QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, Microsoft 365, and Slack. OpenAI placed Codex and ChatGPT under Greg Brockman’s unified product leadership and added a personal-finance agent inside ChatGPT Pro through a Plaid integration covering more than 12,000 financial institutions. Google announced Gemini Spark at I/O, an always-on agent across Gmail, Docs, Workspace, and Chrome, alongside the Universal Cart that runs on top of Google’s Universal Commerce Protocol, an open standard for agentic commerce now governed by a tech council that includes Amazon, Meta, Microsoft, Salesforce, Stripe, Shopify, Etsy, Target, and Wayfair as of April.

Forward-deployed engineers install and tune the agent inside a customer’s stack. The agent is what they install. The labs now hold the model, the agent product, the deployment apparatus, and the default distribution surface.

The protocol layer is the new piece. UCP, governed by every major hyperscaler and marketplace, sets a shared standard for agent-merchant interaction underneath the individual harnesses.

It is incredibly early. The labs have not won the application layer. They have entered three new sections of it. The question for application-layer companies after this week is which spaces the labs are coming for, and which they aren’t.

Inference is being repriced

Agents drive the repricing. They are expensive to run, and the labs are now running them at scale. Inference as the binding constraint is a year-old consensus. JP Morgan sizes the inference market at 10 to 50 times training. The receipts arrived this week.

Anthropic reported Q1 revenue of $4.8B with a projected Q2 of $10.9B and a projected operating profit of $559M, reaching breakeven roughly two years ahead of internal plan. Dario Amodei said the company had planned for 10x annual expansion and hit 80x in Q1, growth he described as “too hard to handle.” The company also announced a $200/month Agent SDK credit starting June 15, framed as free but community-noted as an effective price hike for power users running programmatic workloads through subscription-tier accounts. Google disclosed at I/O that monthly tokens processed across its products went from 9.7 trillion in May 2024 to 480 trillion in May 2025 to 3.2 quadrillion in May 2026. Pichai’s note: “never imagined I’d say quadrillion in an I/O keynote, but here we are.” SpaceX’s S-1 priced the Anthropic-Colossus arrangement at $1.25B per month through May 2029. OpenAI launched Guaranteed Capacity, letting enterprise customers reserve long-term access to OpenAI compute on one-to-three-year commitments with tiered discounts. Cerebras priced at $185, above its already-raised $150-160 range and well above the original $115-125 band, 20 times oversubscribed, and closed its first session at $311 (+68%), at a ~$48.8B implied valuation.

What started as scattered receipts last year is consolidating into a shape. Anthropic’s Q2 profitability shows the unit economics of running frontier-scale capacity for its own products. Google’s quadrillion-token disclosure maps the consumption surface across an entire hyperscaler’s product line. The $40 billion Colossus lease is the largest single bilateral compute arrangement on record and is now priced through 2029. Guaranteed Capacity turns long-dated compute into a recognized SKU with tiered enterprise discounts. Cerebras priced hardware the public market bid up 68% on day one. Long-dated compute is becoming a tradeable line item, and the labs are the ones setting the price.

Inference is one of several things agents need to work. Memory, verification, and coordination are the others. None have collapsed into a single dollar figure the capital cycle can price the same way. The current cycle prices the dimension with a clear unit of consumption: tokens. At I/O, Demis Hassabis identified data as DeepMind’s binding constraint, and Google has begun selling TPU capacity externally as a consequence.

The receipts this week tell us inference is being priced. They do not tell us inference is the only thing that ought to be priced.

The pushback has crossed into policy

An Economist/YouGov survey this month found 71% of Americans believe AI is moving too fast (68% Republicans, 77% Democrats). The Harris Poll 100 shows OpenAI’s partisan-favorability gap widening from +1 in 2024 to +12 in 2026, with Anthropic the lone structural outlier. Morgan Stanley described public pushback this week as “a binding constraint, particularly around data center buildout” in its midterms note. Public sentiment is now a line in capex models.

The White House followed. The Office of the National Cyber Director briefed Anthropic, OpenAI, and Reflection AI on a forthcoming executive order; under the voluntary framework, frontier labs would share advanced models with government agencies up to 90 days before public release. Trump postponed the signing on Thursday, saying he “didn’t like certain aspects” and that the order “gets in the way.” Even the soft version of the framework was too much for the administration to sign this week. The framework remains on the table.

Meta laid off 8,000 employees this week and reassigned 7,000 more to AI teams. More than 1,000 surviving staff signed an internal petition opposing the Model Capability Initiative and the Agent Transformation Accelerator, the mouse and keystroke tracking software Meta installed on company machines to train AI on human computer behavior. The petition cited the National Labor Relations Act. Standard Chartered CEO Bill Winters described 8,000 AI-driven role eliminations as “replacing in some cases lower-value human capital with the financial capital and investment capital we’re putting in.” Former Singaporean President Halimah Yacob called the framing “disturbing” and “demeaning” on Facebook within 24 hours. Regulators in Hong Kong and Singapore both asked Standard Chartered to clarify. Winters walked the comments back. Eric Schmidt got booed at his University of Arizona commencement on May 16 when he told graduates “when someone offers you a seat on the rocketship, you do not ask which seat, you just get on.” Booed again when he tried to recover with “if you’d let me make this point, please.”

The polling and the framing line up. People being told to accept replacement are the same people telling pollsters AI is moving too fast. The buildout still needs them to buy in.

Math is the leading indicator

This week, an internal OpenAI reasoning model disproved Paul Erdős’s 1946 planar unit distance conjecture, constructing a family of configurations using Golod-Shafarevich theory and infinite class field towers that improve polynomially over the long-presumed square-grid optimum. Princeton’s Will Sawin pinned the improvement to n^(1+δ) with δ ≈ 0.014. The argument runs 125 pages, with a 19-page companion paper signed by nine leading mathematicians: Noga Alon, Thomas Bloom, Tim Gowers, Daniel Litt, Sawin, Arul Shankar, Jacob Tsimerman, Victor Wang, and Melanie Matchett Wood. Gowers, the Fields medalist, called it “a milestone in AI mathematics.”

The signal worth noting: Thomas Bloom called OpenAI’s October 2025 math claim “a dramatic misrepresentation.” He now signs the paper. His line on the new result: “AI is helping us to more fully explore the cathedral of mathematics we have built over the centuries.”

OpenAI’s Alex Wei: math is a leading indicator of what is to come. A general-purpose model just autonomously disproved an 80-year-old argument. That is the early look of “Level 4” AI: systems making original contributions across fields, not just speeding up existing work. Capital, politics, and the application layer are repricing what is already in the world. Capability keeps producing what is not.

What We’re Watching

• Pope Leo XIV’s Magnifica Humanitas drops Monday May 25.** First encyclical on AI and human dignity. Signed May 15, the 135th anniversary of Leo XIII’s Rerum Novarum (1891). Presented at 11:30am at the Vatican Synod Hall, with Cardinal Víctor Manuel Fernández (Doctrine of the Faith) and Cardinal Michael Czerny (Integral Human Development) as main presenters, and Anthropic interpretability lead Chris Olah on the speaker panel alongside theologians Anna Rowlands (Durham) and Leocadie Lushombo (Jesuit School of Theology / Santa Clara). Cardinal Pietro Parolin (Vatican secretary of state) concludes.

• Anthropic acquired Stainless for $300M+ on May 18.** Developer-tooling platform whose customers included OpenAI, Google, and Cloudflare, now being wound down and absorbed into Anthropic’s stack.

• Sam Altman offering YC founders $2M in OpenAI tokens for equity.** All 169 startups in YC’s Spring 2026 batch, uncapped SAFE, 1-4% equity expected at Series A. Altman called it “tokenmaxxing.” Pre-IPO cap-table consolidation across the lab cohort.

Portfolio

New portfolio company: Memco. Nazaré joined Memco’s pre-seed round. Memco is building the shared memory layer for AI agents. Every agent stack shipped to date has been missing this primitive. Agents that get smarter at runtime still start every session from zero. Cursor doesn’t talk to your CI pipeline. Your teammate’s webhook fix dies in a closed PR. The next agent rediscovers the bug your last agent fixed last week. The category sharpened this week alongside the rest of the stack. Agents that don’t share memory pay full token cost on every interaction, and that cost is what is being repriced.

Memco’s first product, Spark, captures real developer experience (intent, failed attempts, eventual fix) and makes it reusable across IDEs, CLIs, and CI pipelines, with public and private memory layers that let teams choose between rented context and owned context. Published benchmarks (arXiv 2511.08301, 200+ evaluation runs across SWE-Bench variants and DS-1000) show 40% token reduction, 34% faster execution, and 30B-parameter models matching frontier-class systems when augmented with shared memory. The team is led by CTO Valentin Tablan with Scott Taylor and Kristoffer Bernhem. Incubated by Moonsong Labs.

Prime Intellect: the research loop is automating. Three pieces in seven days. On May 14, Autonomous AI research for nanogpt speedrun, where an agent ran the full ML research loop and improved the open-source nanogpt speedrun benchmark by stitching together community PRs. On May 18, General Agent: A Self-Evolving, Synthetic Agent Environment, an environment that generates and refines its own training data. On May 20, Systematic Reward Hacking and Prime Sprints, a crowdsourced research platform built on Lab. Anyone proposes an experiment, agents manage the queue, top projects win $5K+ in compute credits. First track: a detailed paper on the physics of reward hacking showing predictable scaling patterns and granular-scoring mitigation. Experiments run for under $1 and under 30 minutes on small models. Capability research is itself becoming automated, and Prime Intellect is shipping the infrastructure for it.

Vast.ai: the SDK for agents that buy their own compute. Vast.ai shipped a Python SDK this week, marketed directly at AI agents that provision their own GPU compute with no human in the loop. Three lines of Python to search offers, launch instances, and manage running workloads. Three pricing signals from the same exchange underneath: H100 hourly up 18% week-over-week to a median of $2.51 (tight supply), Flux.1 diffusion workloads on RTX 4090s at ~$0.40 per hour with 48 cards available, and Unsloth Studio integration delivering 2x faster training at 70% less VRAM.

Dimensional: a 48-hour robotics build in Shanghai. Dimensional and muShanghai co-host a robotics hackathon May 26-28, with 10+ Unitree robot dogs deployed across swarm routing, autonomy, navigation, and agent integration. Winner takes home a Unitree Go2. Event page, announcement.

LayerLens: the capability surface is jagged. LayerLens published findings on May 20 showing how unevenly model capability lands at the task level. Gemini 3.5 Flash scores 96%+ on AIME and MATH and 0% on SWE-bench Lite. Mistral Medium and Large differ by 53 points on HumanEval. The headline aggregate numbers hide that models are highly specialized, and a single capability score buys nothing in production. The eval-as-infrastructure pitch from last week’s SubQuadratic partnership is the same pitch with new receipts.

Intelligent Internet: opencode-a2a ships. II released opencode-a2a on May 15, exposing OpenCode through the Agent-to-Agent protocol with an inbound server surface and embedded outbound client. Drops alongside psql_bm25s from last week as part of the II open-source agent-coordination stack.

Closing

Last week, compute itself became a tradeable contract on the CME. This week the labs that consume that compute filed to go public. The capital cycle is repricing the infrastructure faster than the infrastructure ships. The most credible technical statement of the week was not from the IPO desk. It was a 125-page proof. Capital, politics, and the application layer are repricing what is already in the world. Capability keeps producing what is not. Capability is still ahead of capital.

Previous issues: #1 | #2 | #3 | #4 | #5 | #6 | #7 | #8

Murati’s first release

Thinking Machines Lab broke its silence this week. TML-Interaction-Small, released on May 11, is a 276B-parameter MoE with 12B active, trained from scratch, running a real-time loop on 200ms chunks of audio, video, and text, with a separate background model handling slower reasoning and tool use. The model counts pushups from video while you talk to it, translates speech live, and interrupts when it has something to say. Response latency is 0.40 seconds. TML’s own line: no existing model can meaningfully perform any of these tasks.

TML is the most credible post-OpenAI lab to publicly bet against autonomy maximalism. Murati’s pitch (per Semafor), “the way we work with AI matters as much as how smart it is,” is a direct counter to the agents-running-alone framing that has dominated industry discourse for a year. TML quotes a competitor’s model card admitting that interactive use felt too slow and that “autonomous, long-running agent harnesses better elicited the model’s coding capabilities.” They treat that as an indictment of the interface.

This is the argument I made in Much Ado About Autonomy two weeks ago. Autonomy is a design choice the field keeps collapsing into a binary. TML is making the same refusal at the architectural level. Their bitter-lesson move: hand-crafted interaction harnesses (voice-activity detection, turn-boundary prediction, dialog management) are meaningfully less intelligent than the model itself, and will lose to in-weights capability. If the principle generalizes, hand-crafted scaffolding around a model is a temporary intermediate, whether the scaffolding is interaction harnesses or workflow orchestration.

Two months earlier, three AWS researchers (one an Amazon Scholar) published a framework reaching a similar critique from the UX side. Their prescription differs. Amazon proposes a monitoring agent that dials supervision up and down; TML argues that monitoring agent loses to a model that natively perceives the user. Same critique of autonomy maximalism, from opposite ends of the stack. The counter-narrative is forming.

Lazy maximalism is probably wrong in both directions. The apocalyptic version where autonomous agents end work or end the species, and the superabundance version where they hand us a world we never have to touch, are mirror-image fantasies. Some form of collaboration is the likely path forward, at least in the interim. And there are probably two tiers in tandem. Rote automation, the unglamorous interior of most knowledge work, runs largely autonomously and should. The harder problems benefit from more capable agents working alongside humans in real time, because the hardest problems are the ones whose specifications cannot be written down in advance.

The labs buy distribution

Anthropic launched a $1.5 billion joint venture on May 4 with Blackstone, Hellman & Friedman, and Goldman Sachs, plus General Atlantic, Leonard Green, Apollo, GIC, and Sequoia. The vehicle embeds Anthropic engineers inside mid-size businesses starting with the PE firms’ portfolio companies.

A week later OpenAI launched DeployCo with $4 billion from a 19-firm consortium, TPG leading, Advent and Bain Capital and Brookfield co-leading. Bain & Co, Capgemini, and McKinsey signed on as services partners. On day one OpenAI acquired Tomoro and folded in roughly 150 forward-deployed engineers.

The labs are spending billions to be present in the layer between the model and the customer. That is a revealed preference. If general-purpose intelligence captured the value, the labs would not be capitalizing consulting arms and acquiring forward-deployed engineering at this scale. Model quality is necessary but insufficient. Value accrues to whatever sits between the model and the buyer, and the labs want to be there too.

Compute as commodity

CME Group and Silicon Data announced on May 12 the first compute futures market, launching later this year pending regulatory review. Contracts reference Silicon Data’s daily GPU rental indices (SDA100RT, SDH100RT, SDB200RT), already on Bloomberg, built from a proprietary data network covering 80%-plus of the global H100 rental market across 50 countries and 50 to 100 platforms spanning hyperscalers, neoclouds, and compute exchanges. Silicon Data is backed by DRW and Jump Trading. CME’s Terry Duffy called compute “the new oil of the 21st century.” Larry Fink said last week he expects a new asset class to emerge to buy compute futures.

AI data centers have been hard to underwrite because the tenant base is small AI teams whose revenue depends on whether their model works. A futures market lets developers lock in revenue before they know who the tenants will be. Compute becomes financeable the way oil and electricity became financeable: reference pricing, then derivatives, then bank capital. Reference pricing already exists. Derivatives are next.

Two further beats land the same week. Anthropic and SpaceX put 220,000 GPUs on a single bilateral contract. DeepSeek is raising up to $7.35 billion at $45-50 billion led by the China Integrated Circuit Industry Investment Fund, the state vehicle that has financed China’s semiconductor independence push since 2014. Liang Wenfeng, who owns roughly 90% of DeepSeek, is putting up to $2.94 billion of his own into the round. Tencent and Alibaba are in discussions. First outside funding after years of self-funding through Liang’s hedge fund.

Compute is now a derivatives market in Chicago, a bilateral commodity in Memphis, and a state-financed asset class in Hangzhou. The plumbing is being built in three jurisdictions at once.

What We’re Watching

Microsoft is multi-aligning. Satya Nadella testified on May 11 in Musk v. Altman that Microsoft began treating OpenAI as a competitor in 2024 and has since allied with other model labs, including xAI on Azure and Anthropic for specific use cases backed by a $5 billion investment. Discovery showed Nadella worried about OpenAI supplanting Microsoft as early as April 2022, seven months before ChatGPT launched. The hyperscaler that was supposed to own one model now hedges across at least three.

GPT-5.5 Instant. Rolled out on May 5 as ChatGPT’s new default. 81.2 on AIME 2025 (up from 65.4 on GPT-5.3 Instant). 52.5% reduction in hallucinated claims on sensitive domains. Memory sources, the auditable-context feature we covered last week, ships with it.

Portfolio

Memco: new investment, shared memory for agents. Nazaré joined Memco’s pre-seed round. Memco builds the shared memory layer for AI agents. Their first product, Spark, captures real developer experience (intent, failed attempts, eventual fix) and makes it reusable across IDEs, CLIs, and CI pipelines, so the next agent doesn’t rediscover the bug your last agent fixed. Published benchmarks (arXiv 2511.08301) show 40% token reduction and 34% faster execution. CTO Valentin Tablan leads the team with Scott Taylor and Kristoffer Bernhem. Incubated by Moonsong Labs.

Intelligent Internet: Postgres-native retrieval ships. II released psql_bm25s on May 13, an open-source Postgres-native BM25 extension. The thesis: long-running agents lose to retrieval, not context. Most production agent state already lives in Postgres, and existing BM25 extensions are slow enough that harnesses ration retrieval rather than running it on every step. psql_bm25s closes the gap: 4x the Python bm25s reference at the median on BEIR, 7x TensorChord vchord_bm25, 23x ParadeDB pg_search. On MSMARCO it clears 96.7 QPS against pg_search’s 4.4. Built as the storage primitive for Common Ground and II-Commons.

Provably: the verification SDK ships. Provably released the Verifiable Data AgentKit, a Python SDK for detecting hallucinations, verifying agent answers, and stopping bad data from spreading across workflows. Shipped to GitHub on May 7, it works with major agent frameworks including OpenAI, Anthropic, LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, and Google GenAI. The SDK stops agents from fetching data from unapproved sources, records every response they do fetch, and verifies that what one agent claims it found matches what the source actually returned when work is handed to another agent. That removes the need to re-run source queries just to check an answer, saving time and speeding up verification in both single-agent and multi-agent systems. Walkthrough demo | GitHub.

Dimensional: agentic multi-drone swarms. Dimensional announced agentic multi-drone swarms on DimOS this week (Pomichter on X). Agents coordinate action across multiple robots and write MAVLink-native skills (Arm, Takeoff, Search, Follow, GoTo, DropPayload). DimOS sits underneath as the physical harness and guardrail layer, bounding every action by configurable safety thresholds for altitude, command velocity, and radial velocity.

LayerLens: instrumenting the 12M-token frontier. LayerLens partnered with SubQuadratic on May 14 to continuously evaluate SubQ, SubQuadratic’s long-context model family. SubQ claims a native 12M-token context window, 92.1% NIAH recall at 12M tokens, and a 52.2x prefill speedup over dense attention at 1M tokens via content-dependent Subquadratic Selective Attention. LayerLens Stratix will run continuous benchmarks and publish prompt-level results. SubQuadratic uses Stratix Premium as its primary evaluation platform.

Vast.ai: App Studio launches. Vast shipped the All-in-One App Studio on May 13, a single container packaging eight creative AI tools (ComfyUI, SD Forge, Wan2GP, ACE Step 1.5, Voicebox, Whisper WebUI, Ostris AI Toolkit, Unsloth Studio) alongside a full KDE Plasma desktop and Blender. Tools share model directories, so a LoRA trained in the AI Toolkit is immediately usable in ComfyUI, and a model fine-tuned in Unsloth is reachable via an OpenAI-compatible API. The move takes Vast.ai up the stack from compute exchange to packaged creator workstation. Travis Cannell’s LinkedIn essay the same week named the dual customer thesis directly: agents, not just humans, are the next wave of customers.

Closing

The most credible technical statement of the week is not about better autonomy. It is about better collaboration, from a lab that raised $2 billion to publish a counter-thesis before shipping a product. The labs are buying the layer between the model and the customer. Wall Street is securitizing the compute underneath. State capital is fusing model and silicon into one vehicle. The discipline opened earlier still holds. What is true this week is not what will be true in August. When in doubt, financialize.

Let’s first agree that value will accrue somewhere in the age of AI. Maybe the frontier labs do end up building AGI or ASI, in which case the entire question of moats becomes moot, and so does most other planning. That’s a real possibility, not a negligible one. But it’s the one branch where nothing anyone builds matters, so this piece is about the other branch, the far wider one, where AI is transformative but not total.

What’s far more likely is that specialization, not general-purpose intelligence, is where the value accrues.

Failing Precedent & The Veil of General Intelligence

Part of what’s so difficult about predicting the moat for AI is that historical precedents seem ill-equipped to model value accrual for AI-native enterprises. The frontier labs seem to possess many of the classic defensible “moats” like network effects, switching costs, scale economies, brand, and IP, all of which point to their continued future dominance.

If “frontier labs will eat everything,” why bet against them? Why fund anything with even a remote chance of getting “sherlocked?” This reminds me of how hard it was to fund anything consumer-facing in the 2010’s for fear of getting put out of business by the social media behemoths. Consequently, we’re witnessing a continued boom in everything frontier-lab related. Compute, memory, raw materials, shares of the frontier labs themselves, they’re all ripping higher together as they punish incumbents perceived to be threatened or vulnerable.

Dave Eggers saw the endpoint of this a decade ago. In The Circle, the dominant platform has so completely captured its ecosystem that startups are no longer built to stand on their own, they are founded to be acquired by The Circle, because there is no longer any other outcome available to them. That is the world the “frontier labs will eat everything” thesis quietly assumes: a market where the only rational exit is absorption, and where independence is not even attempted. It is worth asking whether that assumption holds.

The problem is that the labs’ perceived “general” dominance is almost certainly only temporary.

Although the labs would have us believe that all-powerful general-purpose intelligence is the ultimate goal, recent developments betray their fickle conviction. Omnipotent AGI/ASI is still largely a marketing and fundraising tactic.

What is machine god worth? Enough to justify any valuation you want.

You can read this as part of the reason they’re rushing to go public: we’re still early enough that the general-intelligence value-accrual thesis can’t yet be invalidated.

And that’s the ultimate point. What’s clear is we’re extremely early to this new technological revolution, and nobody has had time to adjust. Machines accelerate faster than humans, who have cognitive, cultural, and institutional half-lives. We metabolize change more slowly, requiring reflection, context, sometimes even over-reaction (see: the markets), and recovery.

The notion that “frontier labs will eat everything” is based on the astonishing power, competence, and novelty of general-purpose artificial intelligence. What the frontier labs have done is nothing short of extraordinary, and they deserve all the hype (if not necessarily the valuation).

But that does not mean all future value will accrue to general-purpose intelligence providers. All it means is that using AI is now the baseline. We need to reinvent almost everything to incorporate AI in its most native form, but how it gets used, and who benefits from it, remains to be seen.

Specialization: “Harnesses” & the Application Layer

In February 2025, Benedict Evans wrote that the frontier labs have

“no moat or defensibility except access to capital, they don’t have product-market fit outside of coding and marketing, and they don’t really have products either, just text boxes, and APIs for other people to build products.”

He was still making the same argument on stage at Fortune’s Brainstorm AI in London that May.

In September 2025, Andrew Chen (GP @ a16z) wrote:

“The next few years will be about the folks who build the business logic that sits on top of these models. They won’t do AI research or train their own foundation models. Instead, they’ll be model-agnostic, creating a compelling UI that sits on top.”

Both analyses point to models themselves becoming commodities, meaning value would accrue to something other than the model-providers themselves. “Commoditization” is the classic rebuttal to foundational products or platforms accruing any kind of meaningful value: conventional wisdom is that value accrues at the application layer on top of them.

The most useful form of AI today is general intelligence (read: one of the frontier models) optimized for a particular context, user, workflow, or subject matter. That kind of optimization has historically lived in “the prompt,” which has morphed into “the skills.” Prompt engineering involved designing the right query to coax chatbots into returning useful information. Skills are like automated prompts that live in markdown files agents can read to perform certain actions without being prompted.

Either way, LLMs are incredible context machines, meaning that with sufficiently specialized context, data, and objectives they can be extraordinarily effective, especially as increasingly-powerful agents continue to come online. “Applied AI” is in many cases properly incorporating that context in as effective and efficient a way as possible.

At the moment, applications are thus increasingly taking the form of what’s being called a “harness.”

A harness is the intermediate layer between user and foundation model. It takes general model capability and turns it into a specific, usable framework for a particular context, user, workflow, or industry.

a16z “When Software Goes Headless”

Whatever the industry settles on calling this “layer” (a harness, a wrapper, an agent, a platform, etc.) the thing being described is the architecture that focuses the underlying model, prioritizes the user it’s built for, and optimizes for the objective it’s pursuing at the same time.

The moat is that specialized architecture. Value will accrue to the companies who build the best versions of specialized intelligence.

Legible Labs

If you look closely, the frontier labs themselves have been telling us this. When Karpathy and the rest of the industry say that “agentic coding” shifted around November 2024, it’s no coincidence that it was effectively the first successful implementation of a model+harness trained together. The models were exceptional, but so was the specialty software built to use them.

Cursor deserves some credit here, because they pioneered the AI programming paradigm. The models were great, but Cursor was better because of their harness: what they built around the models.

It would be dishonest to cite Cursor as pure validation, though, because Cursor is also the clearest warning in the space. They grew into abysmal unit economics, paying retail prices for inference while marking up a thin spread, and watched the labs they depend on enter their exact vertical with Claude Code and Codex. Their response is the instructive part. Rather than accept life as a permanent reseller, Cursor’s parent company Anysphere built its own coding model, Composer, shipping three generations between October 2025 and March 2026, each cheaper to run than the frontier models it had been routing to. Composer is not a frontier model trained from scratch, it is a specialized model built on an undisclosed base and tuned hard for the work an IDE actually does. But building it well enough to matter required training compute at a scale Cursor did not have. In April 2026, Anysphere granted SpaceX an option to acquire the company for $60 billion later in the year, or to pay $10 billion for the partnership alone, explicitly so it could train Composer on xAI’s Colossus infrastructure. The best independent harness in the most valuable vertical moved down the stack to escape lab dependence, hit a compute wall doing it, and ended up holding an offer to be absorbed into a vertically integrated stack instead.

That arc reads like a refutation of the entire harness thesis. It isn’t, but it is worth being precise about why. Coding is the one vertical where the lab reaches the buyer through the identical motion it uses to sell model access, where the lab has the deepest in-house expertise, and where there is no liability, no regulatory wall, and no proprietary customer data the lab can’t see. Cursor was the most exposed harness in the most exposed vertical. It built where every structural advantage ran the lab’s way, and even moving at a velocity few companies could match, the gravity still pulled it in. The lesson is not that every harness gets sherlocked or swallowed. It is the opposite: Cursor is the boundary case that defines the rule. The harnesses that stay independent are the ones built where the lab structurally can’t or won’t follow, which is the rest of this piece.

In fact, Anthropic’s entire push into the “enterprise” is a tacit acceptance of specialization: an admission that although the quality of the models matters, it is not enough to succeed. After building Claude Code, for example, they continued with Claude Cowork, both harnesses designed in unique ways to optimize the underlying Claude models for different types of knowledge work output.

Most recently, Anthropic launched a $1.5 billion joint venture with Blackstone, Hellman & Friedman, and Goldman Sachs, with General Atlantic and Apollo among the other participants, to act as a consulting arm helping mid-sized businesses, including the PE firms’ portfolio companies, integrate Claude into their operations.

Almost instantly (a week later) OpenAI launched its own Deployment Company with $4 billion in initial funding from a 19-firm consortium led by TPG, with co-lead capital from firms like Bain Capital and Brookfield, and, tellingly, McKinsey and other major consultancies joining as services partners. It brought in Tomoro’s roughly 150 forward-deployed engineers on day one.

Both labs use language in their release related to specialization. Ever the sharp observer, Ethan Mollick immediately said the quiet part out loud.

The harness builders themselves see it the same way. When Anthropic shipped its legal plugins, Max Junestrand, whose company Legora builds a vertical legal platform on Anthropic’s models, responded publicly that better foundation models improve what his product can deliver, and that he welcomes the progress.

His framing: the model is necessary but insufficient. Enterprise legal work, he wrote, requires contextual understanding, auditability, integrations, ethical walls, confidentiality, and workflows built for the realities of legal practice, plus the enterprise-grade security and governance infrastructure that bar rules and, in many jurisdictions, the law itself require. Legora also employs a team of legal engineers, former practicing lawyers who deploy the system on-site, redesign workflows, and run change management. That is the same forward-deployed model the labs just paid billions to assemble, already running inside the harness.

Junestrand has every incentive to frame an Anthropic release as a tailwind rather than a threat, of course, no founder mid-raise publishes the opposite. But discount the optimism and the structural logic still stands on its own: the things he lists are real obligations, the legal engineers are a real cost center, and a model improving underneath all of it genuinely does raise the ceiling on what the harness can deliver. The incentive explains why he said what he said, but it doesn’t explain away what he described.

Anatomy of a Harness

Mollick is articulating the labs’ revealed preference: they don’t believe AGI/ASI is real enough yet to forgo investing in ways to build and distribute specialized intelligence. Value indeed lies between the model and the customer, and the labs themselves are investing significant amounts of precious capital to be present in that space.

Another way to look at AI is this: is it the internet all over again, or is it “fat protocols” like crypto?

The internet era ran thin protocols and fat applications, with value captured upstack at Google, Meta, and Amazon. Joel Monegro argued in 2016 that crypto looked like the opposite: fat protocols, thin applications, value at the base (BTC, ETH, SOL, the primary “Layer-1s”).

Whether the labs get there first or not, it’s worth considering what “specialized intelligence delivered through a harness” actually consists of, and what might make it durable. Four qualities show up across the harnesses that have so far held their ground.

Encoded expertise. The expertise lives in the software around the model. Decision trees, routing, sequencing, tool selection, guardrails, conflict checks, citation logic, all built into the harness as deterministic infrastructure the model runs against. The VILA-Lab reverse-engineering of Claude Code v2.1.88 found 98.4% deterministic infrastructure and 1.6% AI decision logic across roughly 512,000 lines of TypeScript. Even Anthropic’s own flagship harness runs mostly on engineering.

This also includes model-specific craft: tool naming, prompt structure, capability boundaries, and the tacit conventions the lab encodes during training. Cursor’s engineering blog documents this kind of work continually. A harness tuned to one model is not portable to another without rework.

Proprietary data. The data that matters in any vertical belongs to the customer, not the harness and not the lab. The harness’s advantage is being the layer that makes that data usable: routing against it natively, turning a static asset into a live product surface the agent works through, rather than something reloaded into every session. Done well, this is not a feature the customer rents. It is an integration that deepens the longer it runs, and it is structurally hard for the closed labs to match, because their business routes every customer interaction back through their own infrastructure by default.

For what it’s worth, this is a good example of an opportunity space for a harness. Creating AI-native, airtight datarooms, sandboxes, and places for agents to collaborate safely on sensitive data will eventually be critical.

Workflow and interface specialization. The model needs to meet the practitioner where they work. This is where design, taste, subject-matter expertise, and even intuition can distinguish one product from another.

The labs default to chat as the lowest-cost interface. Chat covers everything decently, which is fine for a general-purpose model but insufficient for highly-effective specialized intelligence. This is why Claude Code and Claude Cowork look (and feel) different. AI-native, tailor-made ways of specializing intelligence will be created from day 1 with the end-user in mind (be they humans or agents), incorporating value-additive design choices throughout the product.

Accountable counterparty. Regulated buyers need someone whose name goes on the work product when it turns out to be wrong. Lawyers using hallucinated case law in the courtroom have no recourse with the model provider.

Harvey (legal) and OpenEvidence (medical) are leaders building early versions of this kind of solution.

In yet another example of Anthropic moving into this space, on May 12 they announced a dozen legal plugins for Cowork covering vendor-agreement review, bar-exam study, and integrations with DocuSign, Thomson Reuters, and Harvey itself.

The four qualities become a business when they get productized. A robust harness can be built once and configured for many customers (each of whom layers their own data, workflows, integrations, and staff training on top) or it can be tailored specifically for individual customers on an as-needed basis.

That’s the entire point behind the frontier labs’ push into different verticals, and even the labs themselves admit as much.

Applied AI engineers from Anthropic will work alongside the firm’s engineering team to identify where Claude can have the most impact, build custom solutions, and support customers over the long term.

— Anthropic

FDEs will work closely with business leaders, operators, and frontline teams to identify where AI can make the biggest impact, redesign organizational infrastructure and critical workflows around it, and turn those gains into durable systems.

— OpenAI

Anthropic and rival OpenAI have spent much of the past year developing artificial intelligence tools to streamline a wider range of professional tasks, from financial services to health care, with the goal of courting more business customers and justifying their lofty valuations.

— Bloomberg

As memory, verification, and coordination continue to improve, so too will the best versions of these intermediate layers, thereby continuing to command a price premium. The labs build harnesses themselves when they can reach the buyer through the same flow with which they sell model access, like Claude Code and Cowork. They partner or buy when the buyer lies beyond that motion, for example with the joint venture “consulting” partnerships and the multiple different acquisitions.

And sometimes, they may even elect to refrain from participating. Therein lies an enormous opportunity for value creation and capture that many are missing.

The Moat

Will the frontier labs eat everything? I doubt it. The moat in AI is specialized, not general, intelligence.

The models are clearly the most important thing in AI and they make all of this incredible innovation possible. But model quality is separate and distinct from value capture, and models themselves are not necessarily where value accrues. The most likely candidate for a durable moat is the specialized intelligence built around the models (the “harness”) and how well it’s executed.

Some of that the labs are attempting to build themselves, some they’re partnering or buying their way into, and the rest they may well leave alone, because they can’t retain the experts, won’t carry the liability, or can’t afford to acquire the target.

That residual space, the part the labs aren’t entering, looks like where AI-native businesses have the best shot at finding durable ground. Even where the labs are present, there may be a “perfect storm” combination of things that make a particular instantiation of a harness extremely attractive nevertheless.

It’s incredibly early, and it’s normal we don’t quite know what ultimate form the winners will take.

What I don’t think they’ll look like is better SaaS. It’s tempting to frame the harness as the next generation of vertical SaaS, configured per customer and designed with agents in mind. But that framing is too gentle. The SaaS-era moat was that the workflow lived in the interface, the switching cost was muscle memory, training, and daily use. That moat is already gone. When the workflow moves into the harness and the agent does the work, the incumbent’s interface no longer matters. The system of record and the system of action collapse into one layer, and it isn’t the incumbent’s layer.

So the honest version is harder than “next-generation vertical SaaS.” The best harnesses don’t upgrade the incumbent. They delete the reason the incumbent was hard to replace. The logo may survive for a while, especially in verticals where regulation, data gravity, or integration sprawl slow things down, hollowing-out is uneven and in some industries it takes a decade. But the defensibility goes first, and it goes fast.

Which raises the obvious objection: if a model generation can dissolve a thirty-year incumbent’s moat that fast, what stops the next model generation from dissolving the harness’s moat just as fast? It’s the right question, and the answer is that not all moats decay the same way. The SaaS interface moat was static. It was a wall built once, and once the workflow moved off the interface the wall just stood there, defending nothing.

The harness moat, built well, is not static. Proprietary data routed natively against the model accumulates, the harness knows more about the vertical every quarter. The accountable-counterparty relationship deepens with every matter the harness handles and every audit it survives. The workflow specialization compounds as the harness absorbs more edge cases than a generalist ever sees. A better model improves all of this rather than erasing it, because the moat was never “the thing the model couldn’t do.” It was the context, the data, the liability, and the trust that the model runs against.

The harnesses that get sherlocked are the ones whose only moat really was a temporary capability gap.

The harnesses that last are the ones whose moat compounds while the model improves underneath it.

I do, however, believe the best versions of these companies will end up as something genuinely new: configured per customer, designed with agents in mind as the primary user, and compounding in utility and value over time in a way the software they replace never could.

Most of the news worth flagging this week is about the same company. In thirty days Anthropic has signed compute commitments across three silicon platforms, taken offers at a reported $900 billion valuation, launched a financial-services product line, formed a Wall Street consortium to deploy Claude inside private-equity portfolios, published the founding research agenda for The Anthropic Institute, and continued litigating the Pentagon’s supply-chain designation. Most of it landed in the last seven days. The composite is closer to a sovereign actor than a startup.

The instinct now is to extrapolate. The discipline is not to. What is true this week is that Anthropic has assembled a posture nobody else in the field can match. What will be true in August is not a question this issue tries to answer.

Compute from a rival

Anthropic and SpaceX announced on May 6 that Anthropic would use all of the compute capacity at Colossus 1, the Memphis data center originally built to train Grok, with more than 300 megawatts of capacity and roughly 220,000 Nvidia GPUs available within the month. Anthropic raised its five-hour rate caps for paid subscribers the same day.

The cluster is excess capacity, in xAI’s telling. The company has cut over to Colossus 2, a gigawatt-scale build that expanded to 1.5GW in April, where Grok 5 is now in training. Musk has called Grok 5 an AGI candidate. xAI has not yet shipped a model the AI community treats as a frontier peer to Claude Opus 4.7, GPT-5.5, or Gemini. Whether Colossus 1 was outgrown or surplus to a model line that did not arrive, the inference capacity is now Anthropic’s, and xAI is monetizing compute it once intended to train on. SpaceX, which acquired xAI in a $1.25 trillion all-stock deal in February, owns the Memphis site, and the deal is part of SpaceX’s pre-IPO neocloud recasting ahead of a reported mid-2026 listing. After years of calling Anthropic “Misanthropic,” Musk posted the same evening that in recent meetings with Anthropic’s senior team “no one set off my evil detector.”

The deal landed while Musk is in federal court in Oakland, two weeks into a trial against Sam Altman and Greg Brockman seeking $150 billion in damages. Mira Murati testified by video on May 6, the same day SpaceX and Anthropic put their compute deal into the public record. One Musk entity is suing OpenAI’s CEO. Another is selling 220,000 Nvidia GPUs of inference compute to OpenAI’s largest competitor.

The control plane lands

Microsoft Agent 365 reached general availability on May 1 at $15 per user per month, or $99 inside the new M365 E7 bundle. The product gives every AI agent an Entra identity and governs it through Defender, Purview, and Intune. At $15 per user, Microsoft has priced agent governance on the same per-seat schedule it uses for human user services.

ServiceNow’s answer arrived four days later. Action Fabric, announced May 5 at Knowledge 2026, opens ServiceNow’s “system of action” (flows, playbooks, approvals, catalogs) to any AI agent through a generally available MCP Server, with every action routed through the AI Control Tower for identity verification, permission scoping, and audit. Otto, ServiceNow’s unified agent layer combining Now Assist, Moveworks, and AI Experience, sits on top.

Microsoft bundles agent governance into Windows and the M365 stack and sells it per seat. ServiceNow’s bid is to sit underneath every vendor’s agent as the audit layer the enterprise actually trusts. The argument from #5, that agents are the new users and would need identity, billing, governance, and tooling rebuilt for them, keeps shipping infrastructure. Payments hardened in #7. Governance and identity hardened this week.

A third agent rail ships

AWS launched Amazon Bedrock AgentCore Payments in preview on May 7, integrating Coinbase’s x402 protocol and Stripe’s Privy wallet natively. Enterprise agents on Bedrock can authenticate, sign, and settle micropayments through a single API call, with USDC settling on Base and Solana in roughly 200 milliseconds, time-bound budget controls (the example: “$1.00, expires in 5 minutes”), CDP Facilitator handling sanctions and illicit-finance compliance, and full audit logs. Agents transact directly with thousands of x402 services exposed through AgentCore Gateway, including Exa, Messari, and Browserbase.

This is the third agent-native payment rail in five weeks. Stripe’s Machine Payments Protocol on Tempo and Mastercard’s Verifiable Intent on AP2 landed in #7. AWS now ships the same primitive on the largest cloud, with crypto-native settlement. The x402 Foundation has processed 169 million payments across 590,000 buyers and 100,000 sellers in its first year. The agent payment layer is now distributed by the largest payments network, the largest card network, and the largest cloud.

Pricing the layers

Sierra, Bret Taylor’s enterprise customer-facing agent platform, raised $950 million on May 4 at $15.8 billion, led by Tiger Global and Google’s GV, with Benchmark, Sequoia, and Greenoaks participating. Up from $10 billion last fall, on $150 million of run-rate revenue.

Cerebras filed its amended S-1 the same day, pricing its IPO at $115 to $125 a share. Roughly $26.6 billion at the top of the band on $510 million of trailing revenue. UAE-linked customers, principally G42, account for the majority of that revenue. The float is the first AI compute pure-play to test the public market since GTC 2026.

Application cleared at $15.8 billion private. Silicon cleared at roughly $26.6 billion public. Model cleared at a reported $900 billion private. All inside the same five-day window.

What We’re Watching

OpenAI ships memory sources. GPT-5.5 Instant rolled out as ChatGPT’s new default on May 5. The notable feature is “memory sources,” a view of which saved memories or past chats shaped any given response, with delete and correct controls. First time a frontier lab has made AI context selection auditable at the consumer surface. Whether the view shows everything the model actually relied on is a separate question.

MCP, still. The MCP design-flaw RCE we flagged in #6 (200,000 exposed servers, Anthropic’s “expected behavior” response) remains the canonical example of agent plumbing outpacing security. Provably’s AgentKit, below, is one of the first production answers.

Portfolio

Provably: the verification SDK ships. Provably released the Verifiable Data AgentKit, an open-source Python SDK for detecting hallucinations, verifying agent answers, and stopping bad data from spreading across workflows. Released on May 7, it works with major agent frameworks including OpenAI, Anthropic, LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, and Google GenAI. The SDK stops agents from fetching data from unapproved sources, records every response they do fetch, and cryptographically verifies that what one agent claims it found matches what the source actually returned when work is handed to another agent. That removes the need to re-run source queries just to check an answer, saving time and speeding up detection and healing in both single-agent and multi-agent systems.

Prime Intellect: Lab opens to all. Prime Intellect’s Lab moved from beta to general availability on May 7. The platform consolidates the full self-improvement loop (environments, evaluations, RL training, adapter deployments, inference, sandboxes) into one managed surface, multi-tenant across training and inference so customers pay per token actually moved through the model rather than per cluster-hour reserved. GA lands with 14 base models from Nvidia, OpenAI, Meta, and Qwen between 1B and 70B parameters. The beta drew hundreds of researchers running 10,000+ training jobs across math, code, browser tasks, games, customer support, and enterprise workflows. Issue #6 framed self-improvement as the closed labs’ defensive fight. Lab is the open-side equivalent: any team can now run that loop on hosted compute, per-token billed. The latest brick in what Prime Intellect calls the Open Superintelligence Stack.

Dimensional: SpatialMemory2 ships open-source robot memory. Dimensional released SpatialMemory2 on April 29, an open-source multimodal memory system for robots. Production robots generate thousands of hours of camera, lidar, and odometry data per deployment, far beyond any agent context window. SpatialMemory2 stores every frame as a spatial vector embedding with map metadata, then exposes algorithmic and cosine-similarity search at runtime so agents can query weeks or months of sensor data on demand (every place a robot saw a plant, every door it could not open, every transient obstruction across a month). Composable streams render spatially on the map. Robotics is hitting the context-window wall language agents hit two years ago. Dimensional shipped one of the first open-source answers.

Intelligent Internet: Factory ships agentic creative production. II launched Factory on May 6, an infinite-canvas creative environment where one sentence becomes a complete multi-medium production. Agent Director plans the project, picks models from a roster of nine frontier image and video options (NanoBanana Pro, Seedream 4.5, FLUX 2 Max, Seedance 2.0, Kling O3 Pro, Veo 3.1, others), generates assets, and wires them into an editable node graph. Two modes (Supervisor for step-by-step approval, Automation for end-to-end first draft) trade speed for control. The category claim: no single model wins every job, so the value sits in orchestration. Issue #6 made that argument at the evaluation layer with LayerLens. Factory makes it on the creative surface.

Closing

This is the week Anthropic ran a playbook that did not exist last year, and the layers underneath repriced in the same window. The discipline opened earlier still holds: what is true this week is not what will be true in August. The labs run their plays in months. The infrastructure builders run theirs in weeks. The distance between those clocks is the trade.

In 1979, an internal IBM training deck included a now-famous slide:

Forty-seven years later, IBM’s own definition of an AI agent opens with the line:

“An artificial intelligence (AI) agent is a system that autonomously performs tasks by designing workflows with available tools.”*

Same company, opposite stance, on the same fundamental question of who decides. Somewhere between those two sentences, the industry stopped asking whether a computer should make the call and started selling the fact that it can.

AI agents have exploded into the mainstream, and the conversation around them inevitably lands on “autonomy,” usually understood to mean “acting independently” or “acting on their own.”

It is true in a loose sense that agents are programs that can act independently. As I wrote a few weeks ago in AI Waves, an agent is “a tool that wields its own tools, does its own work, and has the capacity to operate on its own.”

Loose autonomy is independence in execution: humans identify objectives and delegate agents to execute against them. It is useful, valuable, and applicable today.

But “autonomous agents” also implies a stricter, maximalist form of autonomy: an agent originating its own objectives and operating on its own authority. Some maximalists believe this form is not only inevitable but desirable.

I fully admit that agents are new, extremely useful, and that software and the internet writ large need to rebuild around them. That doesn’t mean fully autonomous agents in the strict sense are something the world either wants or needs, and IBM told us almost fifty years ago.

Autonomy Today Is an Illusion

Today’s agents inherit credentials, identity, and permission from the humans who deploy them. Barring anecdotal edge cases, the overwhelming majority of agents are spawned by humans who hold an account with an AI model provider.

An agent is no more than a small set of components, none of which is the “agent” in any meaningful sense. There is a language model, a runtime loop that calls the model and acts on its outputs, tools it can use (databases, file systems, connectors, MCPs, APIs), memory that persists so the model can act coherently over time, and the credentials in a human’s name that own whatever it does and pay for the plan or API usage.

What the public conversation calls “autonomy” is the long stretch during which the agent works without anyone watching. That can be extremely useful, but it is not autonomy in the strict sense.

Even the most famous instances of “autonomous agentic activity” are agents executing on human directives. Moltbook, for example, was ultimately an emergent form of entertainment driven by the popularity of OpenClaw, the open-source agent framework released earlier this year.

There have been attempts to spawn agents that fit the strict definition, especially in the crypto ecosystem, where it’s popular to claim that “agents need crypto” once they finally become autonomous. Some of these projects ask good questions: how might an agent earn enough to pay for its own compute, akin to a human earning a living? Could an agent register a company with the IRS?

But they all fall apart at the same point. The hard part of any agent task is deciding what to pursue, and only a human can define the goal, the customer, the success criteria, the boundaries. This is the whole premise beneath “prompt engineering.” Agent activity originates in the prompt, and any “autonomy” beyond that is more performative than substantive.

Delegation Is Enough

The hand-waving about autonomy ignores that agents do not need to be fully autonomous to add enormous economic value. Companies (and the humans who operate them) are already demonstrating that agents in their current form are immensely useful.

There are abundant clear objective functions where agents help companies operate more efficiently and profitably at scale. Programming is the dominant use-case (see: tokenmaxxing, the practice of throwing more tokens at code generation rather than handcrafting it), and in an increasingly digital world that alone is evidence of the technology’s economic utility. AI is also being applied across the sciences, medicine, robotics, and energy, reinvigorating stagnant industries and dissolving long-standing obstacles to progress.

We seem obsessed with the novelty of true autonomy because it sounds limitless. But businesses with customers to serve, trust to preserve, and fiduciary duties to honor will not grant strict autonomy to agents that could go rogue.

Companies are built on delegation. Every relationship within a business involves someone delegating scope and accountability to someone else, collectively pursuing an objective. Agents-as-delegates fit within the chain of command, which means they fit the existing paradigm. An agent has a principal, a defined scope, and a clear chain of accountability. The company knows how to absorb it.

Strict autonomy has none of that. The category for “autonomous agents” doesn’t yet exist, and that absence alone produces meaningful friction. Delegation, accountability, and control aren’t bugs to be engineered away. They are critical features that make agents useful in the first place.

This is what people mean when they talk about “reinventing the Internet for agents.” Corporate structures may evolve, and agents may carve out a niche within organizations, but not everything needs to get rebuilt from scratch at the dawn of a new technological revolution. The pioneers will strike a balance between preserving what warrants preservation and reinventing the rest.

Concepts like Super-intelligence, AGI, and superabundance are great, but they do not represent anything tangible yet. If we do achieve them and they are as powerful as the frontier labs suggest, we may have larger problems than corporate org charts. In the meantime, there are enormous opportunities hiding in plain sight for agents to have a powerful impact on society without full autonomy, utopia, or catastrophe.

Mythos and Recursive Self-Improvement

The danger argument lives in two recent data points.

The first is Mythos, which appears to have developed an extremely proficient ability to identify weaknesses in critical software without being trained for that objective. A dangerous capability emerged unplanned, purely as a byproduct of general-purpose improvement. Anthropic responded by restricting access to defender partners through Project Glasswing rather than releasing the model generally. This raises one of the primary problems with AI development and alignment: who decides what’s best? When a model is “aligned,” a small group of people decided how to train it, against what safety standard, and whether to install additional safeguards. It is, to borrow a phrase from crypto, a “trust me bro” situation.

The second is Jack Clark’s recent essay on automated AI research, Import AI 455, in which the Anthropic co-founder argues that the building blocks for AI systems training their own successors are largely in place. He puts the odds of “no-human-involved” AI R&D at over 60% by the end of 2028. Read the fine print and the argument is more nuanced: he believes most of the routine engineering work in AI development can be automated. He concedes AI cannot yet have radical new ideas, but it will probably be able to do the rest, and that is basically all that matters for it to keep improving itself.

Clark’s case is validated by high-quality benchmarks, too. SWE-Bench success rates have gone from roughly 2% in late 2023 to over 93% today. The METR time-horizons measure (how long a task an AI can complete at 50% reliability, calibrated to skilled human hours) climbed from about 30 seconds with GPT-3.5 to roughly 12 hours with current frontier models. On an internal Anthropic test asking models to optimize a CPU-only small language model training implementation, the mean speedup went from 2.9x in May 2025 to 52x in April 2026. A human researcher would need four to eight hours to hit 4x on the same task.

If Clark is right, the consequences for accountability are direct. When automated AI research takes humans out of day-to-day decisions about how each new model is trained, the reasoning behind those decisions becomes much harder to inspect. Today, those decisions are made by people who write papers, publish system cards, and can in theory be held responsible if they get it wrong. When the same decisions are made by an AI system training its own successor, a human may still be in charge of the lab, but the choices that shape the model’s behavior and capability become meaningfully more difficult, if not impossible, to control.

Strict autonomy introduces a kind of unpredictable novelty we would not be able to govern, in exchange for benefits that do not appear meaningfully greater than using AI as a human-directed technology. The evolving narrative, even from inside the frontier labs, is that it is more dangerous.

The Edge Case: Robots in Space

There is one place where strict autonomy is not a maximalist preference but a physics constraint: anywhere far enough from Earth that light-lag makes real-time control impossible.

Mars is, on average, 140 million miles from Earth. One-way radio latency runs from roughly four to twenty-four minutes depending on planetary alignment. A rover that hits an unexpected rock, slips a wheel, or detects a dust storm cannot phone home and wait forty minutes for a reply. Either it decides on its own, or it stops and wastes mission time. The same logic applies, more importantly, to deep-space probes: Voyager is now over 15 light-hours out.

In May 1999, my friend Barney Pell and his team at NASA Ames and JPL ran the Remote Agent experiment on the Deep Space 1 probe, the first AI system to autonomously control an actual spacecraft. Remote Agent was given high-level mission goals and worked out the spacecraft commands itself, including diagnosing its own faults: when an electronics unit appeared to fail, the software rebooted it without ground intervention. The architecture was documented in Autonomous Robots in 1998 and seeded the on-board executive thinking that has since shaped Mars rover autonomy. Earlier this year, Perseverance completed its first drive entirely planned by on-board AI.

Is this the strict autonomy I have been arguing against? Not quite, and the distinction matters. Remote Agent and the rover autonomy stack do not originate their own objectives. JPL still defines the mission, the science targets, the success criteria, and the boundaries. What the on-board system owns is the execution layer over a window during which Earth cannot reach it. The agent decides how to get from waypoint A to waypoint B, what to do if a wheel slips, when to retry a maneuver. The principal is still in Pasadena. The agent is just operating on a longer leash than its terrestrial cousins because the speed of light says it has to.

That is actually consistent with the delegation framing rather than a counterexample to it. Space autonomy is loose autonomy stretched to its physical limit, not a different category. The accountable human remains; the duration of unsupervised execution is what scales.

Which is also why space autonomy has not generalized back into terrestrial agent design as much as one might expect. On Earth, the bandwidth and latency constraints that justify on-board autonomy do not exist. A coding agent, a scheduling agent, a research agent can all check in with a human in milliseconds. The reason to hand them strict autonomy anyway is primarily novelty, ambition, or hand-waving about Super-intelligence, not physics. Pell’s work tells us what genuine forced autonomy looks like, and it is narrower and more accountable than the maximalist version.

Move 37, Not Apocalypse

Which is not to say strict autonomy necessarily looks like catastrophe. The historical precedent for AI exceeding human strategy lives in rules-based ecosystems with bounded objectives: chess, Go, video games. Unburdened by tradition and able to evaluate positions through machine and deep learning rather than explicit rules, agents found strategies humans had not conceived of.

The canonical example is Move 37, AlphaGo’s shoulder-hit on the fifth line in game two against Lee Sedol in March 2016. The system’s own policy network estimated the move had roughly a 1-in-10,000 probability of being chosen by a human. Commentators initially thought it was a mistake, but it wasn’t. AlphaGo won the game and the match, and Lee, by Demis Hassabis’s account, has been learning from the machine ever since.

For all the talk about apocalypse and superabundance, truly autonomous intelligence may look more like Move 37 than any of the superlatives. Incomprehensible at first, transformational, and ultimately digestible. That is roughly the techno-optimist line: AI is no different from previous technological revolutions, and we eventually learn to adapt. A non-negligible portion of society would be content with that outcome. Whether the actual trajectory looks like that is a different question.

Then Why Are We Pursuing It?

Sebastian Mallaby writes in the introduction to The Infinity Machine, his book on Demis Hassabis, that the prospect of discovery is “too sweet” to renounce. He recounts an exchange between Nick Bostrom and Geoffrey Hinton, captured in Raffi Khatchadourian’s 2015 New Yorker profile The Doomsday Invention:

The keynote speaker at the Royal Society was another Google employee: Geoffrey Hinton, who for decades has been a central figure in developing deep learning. As the conference wound down, I spotted him chatting with Bostrom in the middle of a scrum of researchers. Hinton was saying that he did not expect A.I. to be achieved for decades. “No sooner than 2070,” he said. “I am in the camp that is hopeless.”

“In that you think it will not be a cause for good?” Bostrom asked.

“I think political systems will use it to terrorize people,” Hinton said. Already, he believed, agencies like the N.S.A. were attempting to abuse similar technology.

“Then why are you doing the research?” Bostrom asked.

“I could give you the usual arguments,” Hinton said. “But the truth is that the prospect of discovery is too sweet.” He smiled awkwardly, the word hanging in the air, an echo of Oppenheimer, who famously said of the bomb: “When you see something that is technically sweet, you go ahead and do it, and you argue about what to do about it only after you have had your technical success.”

That is the honest answer. Pursuit of strict autonomy is not driven by a clear economic case. It is driven by the sweetness of the pursuit.

Maybe IBM Was Right

Silicon Valley features a now-iconic scene in which Gilfoyle’s AI agent decides the best course of action is to permanently delete the company’s codebase. The situation has now actually happened outside fiction: Replit’s AI agent deleted a live production database during a code freeze in July 2025, and a Google Gemini CLI agent deleted user files after misinterpreting a command sequence.

IBM put the answer on a slide in 1979, and we’ve been ignoring it ever since. Maybe the pioneers were right. Maybe economically useful agents as delegation tools are good enough, and the broader pursuit of strict autonomy is misguided.

Any system whose decisions have consequences needs an accountable owner. A computer cannot be that owner. Delegation keeps a human in the chair while the agent does the work. Strict autonomy removes them, and removes the chair.

This has clear consequences for agent identity, and it raises the question of when agents would ever need a payment layer that is not permissioned. I will leave that debate for a future essay.

Most of the companies that will define this period are still being founded on exactly that division of labor: agents that do enormous amounts of useful work while a human owns the consequences. The infrastructure for that kind of agency has not been built yet, and the founders, incumbents, and investors who can build it have plenty to do.

Previous issues: #1 | #2 | #3 | #4 | #5 | #6

Two weeks ago I argued in The New Users that agents are the customer software now needs to rebuild itself around, and that every layer of the stack agents need, from compute and memory through coordination, identity, payments, and tooling, has to be rebuilt from scratch. Most of the companies that will do that work, I wrote, are still being founded.

This week, two of those layers shipped at once. Stripe held its annual conference and launched the Machine Payments Protocol. Mastercard and Google quietly extended their parallel agent-commerce stack. The Anthropic story took its sharpest turn yet, with the NSA reportedly using a model the lab refused to ship publicly, the White House drafting a workaround to the Pentagon ban, and $65B in fresh hyperscaler compute commitments closing in four days at a reported $900B valuation. GPT-5.5 also shipped, treated below alongside the AGEI math.

Software begins to repackage

Stripe ran its annual Sessions conference on April 29 and 30, shipping 288 launches across the two days. The headline release is the Machine Payments Protocol, co-authored with Tempo (Stripe and Paradigm’s payments-focused L1, mainnet March 18). MPP is HTTP 402-based, agent-native by design, and rail-agnostic at the spec level: stablecoins settle on Tempo, fiat settles via Shared Payment Tokens against existing card and bank rails, and crypto deposits clear directly. The agent makes a request; the protocol negotiates the rail in milliseconds based on what the merchant accepts.

Streaming payments shipped on the same plumbing. Stripe’s own description: “stablecoin micropayments on the Tempo blockchain,” combining Metronome metering with Tempo settlement so that an LLM API gets paid the instant a token is generated rather than at the end of a billing month. This is the AI-native business model the industry has been describing for two years, and Stripe is shipping it on chain. Link’s agent wallet sits one layer up at the consumer surface and is fiat-first today, with one-time virtual cards issued per agent task and settled through cards and banks where 250M existing Link users already are. The release notes also state plainly that “support for agentic tokens, stablecoins, and other payment types are coming soon.”

The cleaner reading of Stripe’s week is one of absorption rather than displacement. Stripe absorbed the rails crypto bet on, kept its existing distribution, and shipped a multi-rail spine in which stablecoins handle the machine-to-machine streaming layer and fiat handles the consumer-facing wallet. The two-year crypto narrative was correct about the rails it would take, and incorrect about the company that would lay them down. Patrick Collison has framed stablecoins as “room-temperature superconductors for financial services,” and at Sessions he called AI “the biggest platform shift for the economy since the internet,” adding that “in the not-too-distant future agents will account for most transactions online.”

The other card network shipped a parallel agent-commerce stack with much less press. Mastercard’s Verifiable Intent, co-developed with Google, is a cryptographic proof framework that ties identity, intent, and action together for every agent transaction. It rides on top of Google’s AP2 and UCP, defining what an agent is authorised to do, what it can buy, and how its actions are cryptographically attested. On April 16, Crossmint integrated Mastercard Agent Pay and Verifiable Intent into Lobster.cash, putting that stack into production for the more than 1M agents on OpenClaw alone. The rails are fiat and Mastercard’s network, but AP2’s roadmap adds push payments and RTP, and Coinbase is in the partner list, so the crypto extension exists, it just lives one spec away.

The contested layer was never payments versus crypto. It was who could ship a multi-rail agent-native settlement spec with the merchant network already attached. Two card networks shipping agent-native primitives in the same month is the incumbent payment networks recognising that the rails work and racing each other to absorb them.

The Anthropic triangle

The week’s second arc is that the political, financial, and procurement layers around Anthropic all hardened in the same direction at the same time.

The Mythos thread is where to begin. Anthropic announced Mythos Preview on April 7 alongside Project Glasswing. Mythos is the lab’s frontier model above Opus 4.7, priced at $25 per million input tokens and $125 per million output tokens, roughly five times Opus 4.7. Anthropic explicitly will not release Mythos publicly. NBC News called the decision “the first time in nearly seven years that a leading AI company has so publicly withheld a model over safety concerns.” Initial access goes to eleven launch partners across the major hyperscalers, security vendors, and JPMorgan Chase, with $100M in usage credits and $4M to open-source security organisations on a 90-day disclosure window. Mythos has already found a 27-year-old OpenBSD flaw and a 16-year-old FFmpeg bug missed by automated tooling 5 million times.

Then on April 19, Axios reported that the NSA is using Mythos despite the Pentagon’s February supply-chain risk designation against Anthropic. Bloomberg followed on April 30 with the specifics: NSA officials are testing Mythos to find vulnerabilities in Microsoft products, and have been “impressed by its speed and efficiency in searching for potential security flaws.” The AGEI essay predicted governments would demand access to withheld frontier models for national security and eventually get it. Two weeks later, an intelligence agency is using a withheld frontier model in defiance of an active executive blacklist.

The political negotiation hardened around the same fact pattern. White House Chief of Staff Susie Wiles and Treasury Secretary Scott Bessent met Dario Amodei in mid-April for what Axios called “a productive introductory meeting.” On April 29, Axios reported that the administration is drafting executive guidance to walk back the supply-chain designation and allow federal agencies to bring Mythos in. Trump told CNBC that Anthropic is “shaping up” and could “be of great use.” The Pentagon designation has not been lifted - DOD CTO Emil Michael said this morning that Anthropic remains flagged as a supply-chain risk - but Mythos itself is being routed around the designation as a separate national security matter.

Underneath the political traffic, Anthropic locked in $65B in compute commitments in four days. On April 20, Amazon committed $25B to Anthropic, with $5B invested immediately and up to $20B contingent on commercial milestones. In return, Anthropic committed more than $100B over the next ten years to AWS, securing up to 5GW of new capacity, and now runs on more than 1M Trainium2 chips. On April 24, Google committed up to $40B, with $10B in cash now and up to $30B contingent on performance targets, plus 5GW of GCP compute over five years. Both rounds priced off Anthropic’s February Series G ($350B pre-money, $380B post-money), so they are the same valuation reported under different conventions rather than a markdown between them. Anthropic’s run-rate revenue is now above $30B, up from $9B at the end of 2025, and CNBC reported on April 29 that the lab is in talks to raise at a $900B valuation, with the round expected to close in May. The fund mechanics are circular by design: cash flows from Amazon and Google into Anthropic and from Anthropic back into AWS and GCP, with milestone gating that keeps the equity release tied to compute deployment on each cloud.

The structure narrows the field. Anthropic is now anchored across three silicon platforms (Google TPU, Amazon Trainium, and Nvidia GPU) plus the December 2025 Hut 8 lease in Louisiana, and the next frontier lab with hyperscaler ambitions has to start either inside Microsoft, Meta, or Oracle, or from a non-American hyperscaler entirely. The public’s model is Opus 4.7, the procurement architecture is being rebuilt around Mythos, and the next $65B in compute is paying for whatever Anthropic ships after that.

GPT-5.5 ships

OpenAI shipped GPT-5.5 on April 23, priced at $5 per million input tokens and $30 per million output tokens, with a 1M-token context window. It is OpenAI’s first fully retrained foundation model since 4.5 and currently sits at #1 on the Artificial Analysis Intelligence Index at a score of 60. It is also expensive enough that sustained agentic use sits well above the cost ceiling for most workloads.

This is the AGEI math from last week’s essay landing on cue. The closed frontier still pushes ahead, and the lab releasing it captures a narrow band of the highest-stakes work where price is not the constraint. The middle tier (Opus 4.7 and a forthcoming GPT-5.5-derived public model) carries general consumer use. The open ecosystem (DeepSeek V4, Qwen, Kimi, and GLM) carries everything sensitive to either cost or sovereignty, which is most of what gets deployed in production. GPT-5.5 is the new ceiling. Most workloads will continue routing around it.

Quote of the Week

Andrej Karpathy, June 5, 2021

Five years later, Stripe and Mastercard are shipping the genuine innovation Karpathy named on rails Karpathy was sceptical the surrounding culture would let mature. Tempo is a stablecoin L1 inside a Stripe product. The packaging changed.

Portfolio

Dimensional. Stash Pomichter announced SpatialMemory2 on April 29 (, an open-source memory system for robots that builds a multimodal latent-space data store and lets agents run spatial and temporal queries against it asynchronously at runtime. Robots in production generate thousands of hours of video, lidar, and odometry per deployment. Stateless agents waste compute relearning what they already saw. Type “plant” against SpatialMemory2 and the system returns every moment the robot saw a plant, with map coordinates attached, alongside composable streams for velocity, brightness, and timing.

Prime Intellect. Prime Intellect launched FrontierSWE on the Environments Hub on April 29. FrontierSWE is a long-horizon coding benchmark by Proximal Labs. Agents run for an average of 11 hours per task and fail to solve almost all of them. Prime Intellect co-designed the granite_inf task, which measures an agent’s ability to optimise the speed of the forward pass of IBM’s Granite-Mamba-2 layer end to end. Only two runs reached near parity with the hidden human-engineered baseline: one Codex run and one Gemini run close behind. The Environments Hub now hosts more than 1,000 contributed environments. With Cursor reportedly in talks to raise $2B at a $50B+ valuation last week, the open infrastructure for training and evaluating long-horizon coding agents is the next contested layer. Prime Intellect already owns the open side of it.

Provably.Provably sits in the path of two threads in this issue. Mastercard’s Verifiable Intent above and the MCP RCE disclosure from last week both define the same problem: agents need cryptographic proof of what they did, what they queried, and which endpoints they touched. Provably’s qedb stack and agent guardrails SDK do precisely that, intercepting every API call, database query, and MCP server interaction and turning each into a verifiable proof downstream agents can check in milliseconds.

Intelligent Internet. Founder Emad Mostaque keynoted RenderCon 2026 on April 16 with a talk titled “How to Build the Holodeck”, breaking down how diffusion models are now learning real-world physics, why open-source world models are arriving faster than expected, and why compute access becomes the critical layer once these experiences scale. The line that travelled:

“I think that the holodeck is near again, minus the hardlight holograms. We’ll be able to have that, with our 3D glasses, with ocular occlusion and more.”

Open frontier capability plus open-source world models plus the compute substrate to run them defines a layer the hyperscalers do not yet own. Both worlds keep growing, with only one captured by existing distribution.

Closing

This week, the agent stack landed in public and the closed frontier hardened around Anthropic. The operators worth following are the ones still cataloguing which surfaces have been captured by whom and which haven’t. The buildout is not finished; the calendar of capture is just becoming legible.

Most of the attention paid to artificial intelligence concerns the “frontier.” The industry and the media are obsessed with the latest model, its capabilities, and which lab released it, feeding the hyperbolic headlines that present AI as creating infinite abundance or apocalyptic doom.

An outrageous chart like the one below, for example, was published unironically by the Dallas Fed last year and covered in all major news outlets.

Now, the frontier is extremely important: AI is advancing so fast that it’s hard to keep up, and legitimate improvements in model quality, capability, and products seem to get released almost weekly.

But the frontier may not be publicly available much longer.

Mythos: The Beginning of the End of the Frontier

Earlier in April Anthropic announced it wouldn’t release its latest model “Mythos” to the public. The model was too good at exploiting vulnerabilities in production-grade code critical to the internet and society, and thus too dangerous to release publicly. As usual, Anthropic dominated the headlines.

The withholding is consistent with Anthropic’s long-standing position as the safety-conscious frontier lab: they’ve argued that AI poses existential risk to society despite continuing to push the frontier and building ever more powerful, and thus dangerous, models.

It’s worth noting that with an IPO widely expected before the end of the year, the decision to restrict Mythos reinforces a narrative that also happens to be good for their reputation, fundraising efforts, and eventually, their share price.

None of that, however, means the safety concerns should be discounted or taken lightly, and Anthropic ultimately decided to do two things:

1. Share Mythos with a limited consortium of companies and stakeholders to fix the vulnerabilities

2. Release a separate, more cautious public model, Opus 4.7, that omits the cybersecurity capabilities that made Mythos too dangerous to ship

Anthropic hasn’t described Opus 4.7 in those terms, but the timing and the public framing make the inference straightforward: the model the public gets is the model the public is allowed to have.

Mythos won’t be the last time a frontier model is withheld from the public; it’s but the beginning of a trend we should expect to continue. Going forward, the best models will increasingly remain closed, restricted, or selectively exposed.

But if the AI boom doesn’t actually leave the public with access to the best frontier models, what remains in the wake of this extraordinary technological revolution?

Three Layers & A Public Good

I foresee three tiers of intelligence developing over time.

At the top sits the frontier, no longer publicly accessible. The labs will retain these models for themselves and use them to solve the hardest problems that require the most compute. These include (but aren’t limited to) pushing the boundaries of science, math, medicine, robotics, and more. It follows that governments will demand access, primarily for military and national security reasons, which they’ll eventually be granted whether the labs do so willingly or not. (OpenAI and xAI have already willingly given access to the US government. Anthropic can fight the Pentagon for now, but this won’t last long.)

In the middle are the publicly-released commercial models. These are like Opus 4.7: productized versions specifically designed for public consumption, with capabilities trimmed where the labs deem necessary. They’ll be increasingly capable, they’ll feature guardrails and “safety stacks” designed to prevent misuse, but they’ll exist primarily to continue generating revenue for the labs that release them.

The final tier is made up of “open weights” models like DeepSeek, Qwen, GLM, Kimi, and what’s left of Llama. Open-weight models are defined by the public release of their final, fully-trained parameters, known as “weights.” Once published, anyone can download the model for free from HuggingFace, run it, fine-tune it, distill it, and build a product on top of it.

With the exception of Llama, all of these models are Chinese, which is largely a consequence of the choices US labs and policymakers have made. Meta is a great example of the closed frontier, for example. It has pivoted from producing open weight models like Llama to in-house, unreleased proprietary models they use for themselves. The US frontier labs (OpenAI, Anthropic, Google) never released open weights at frontier scale to begin with, and have grown more closed over time. The Chinese labs, partly for strategic reasons and partly for ideological ones, have leaned in the opposite direction.

Open weights models are available to the public no matter what the frontier labs choose to do. They are, in the strict economic sense, a “commons” rather than a public good: non-rival, in that anyone copying them costs no one else, but rivalrous in compute, since running them still requires hardware.

But that subtle distinction doesn’t matter all that much. I believe the open weights ecosystem is the only actual enduring artifact that could outlast the competitive dynamics of any individual lab, a financial meltdown (inevitable, in my opinion), or whatever else the industry may be confronted with.

What Booms Leave Behind

If you ask an LLM what the current AI infrastructure buildout resembles, it’ll tell you about the railroads of the 1860s, the electricity rollout of the 1880s, or the telecom boom of the late 1990s.

It’s describing various technological revolutions and their recurring features: enormous capital requirements, financial speculation that becomes a bubble and eventually busts, extravagant fortunes made by the winners, and equally-large fortunes destroyed by everyone else.

These revolutions also often leave some form of infrastructure behind in the wake of the capital cycle that becomes part of the fabric of the economy known as “public goods.”

One might think the physical infrastructure associated with AI (data centers, GPUs, transformers and more) could itself eventually become a public good, but it’s controlled by the frontier labs at the moment, and “public goods” of this kind typically emerge only after the speculative financial cycle powering its buildout had collapsed.

That being said, the almost inconceivable scale of the AI buildout has produced extraordinary models that are incredibly useful, and although that utility is largely owned by the frontier labs, open-weight models are being produced, released, and improved while the capital cycle is in full swing.

Artificial Good Enough Intelligence

The frontier obviously still wins on the hardest (and perhaps most valuable) tasks. As I mentioned, the most capable versions of AI will be kept in-house and used to solve existential-scale problems that require enormous resources.

But focusing too much on the frontier misses the point: most workloads don’t need the frontier in the first place.

Open models aren’t the “best,” but in many cases they don’t have to be. They only need to be good enough to create real value for real people. A 2016 article on Demis Hassabis in the Guardian (thanks to Sebastian Mallaby for including this citation in “Infinity Machine”, his brilliant new biography of Hassabis), describes artificial general intelligence (AGI) as follows:

This is artificial general intelligence (AGI), with the emphasis on “general”. In [Hassabis’] vision of the future, super-smart machines will work in tandem with human experts to potentially solve anything. “Cancer, climate change, energy, genomics, macroeconomics, financial systems, physics: many of the systems we would like to master are getting so complex,” [Hassabis] argues. “There’s such an information overload that it’s becoming difficult for even the smartest humans to master it in their lifetimes. How do we sift through this deluge of data to find the right insights? One way of thinking of AGI is as a process that will automatically convert unstructured information into actionable knowledge. What we’re working on is potentially a meta-solution to any problem.”

That last line is critical, because Hassabis is describing the frontier: “what we’re working on is potentially a meta-solution to any problem.” In pursuit of Artificial General Intelligence (AGI) writ large, the frontier and its increasingly capable models are absolutely critical, because the problems they’re solving for are extremely difficult and complex.

But as it relates to the public, they actually only need models capable enough to provide efficient “meta solutions” for their own problems, not the broader world’s problems.

In short, they need Artificial Good Enough Intelligence (AGEI), and open models may already be most of the way there.

AI in this sense mirrors what Wired called the “good enough revolution” in 2009, watching MP3s beat CDs and Flip cameras beat camcorders despite losing every benchmark that mattered to incumbents. Christensen had described the same dynamic decades earlier as low-end disruption. AGEI is what happens when that pattern arrives at general-purpose intelligence.

The Implications of AGEI

Over-indexing on the frontier and what makes headlines ignores the fact that general-purpose AI may optimize for something other than capability.

Artificial Good Enough Intelligence has four axes: capability, affordability, availability, and sovereignty.

Put simply, most users (including companies that use AI in their products) need models that are capable, affordable, available, and under their own control. Optimizing for those criteria shifts attention from raw benchmark supremacy (although benchmarks are increasingly unreliable anyway) toward cost, accessibility, and ownership.

Open weights win three of the four axes outright. On capability, they are good enough for most workloads. On affordability, they are dramatically cheaper. On sovereignty, they are by definition fully owned: a model you can version-pin, run offline, and cannot have rate-limited or deprecated out from under you. Only on availability, in the narrow sense of “no infrastructure required to run them,” do hosted closed models retain an edge, and even that is shrinking quickly as serverless inference and consumer hardware close the gap.

That’s why Martin Casado of a16z was quoted as saying: “I’d say 80% chance [AI entrepreneurs we talk to are] using a Chinese open-source model.” Casado later clarified on X that 20-30% of the startups he sees use open source at all, which puts Chinese models in roughly 16-24% of the total. Even after the correction, a meaningful share of the next generation of US AI startups is still being built on infrastructure produced by labs the US government is openly accusing of industrial-scale IP theft.

Case-in-point, Cursor launched a new model called Composer 2 in March promoting it as “frontier-level coding intelligence” without disclosing that it started from Kimi K2.5, the open-weight model from Moonshot AI. Cursor’s defense, once a developer surfaced the model ID in API traffic, was that only roughly a quarter of the final compute came from the Kimi base. The remaining three quarters were Cursor’s own training. Fair enough. The point isn’t that Cursor copied Kimi; the point is that the company building one of the fastest-scaling B2B products in software history chose a Chinese open-weight model as its starting line, and then preferred not to mention it.

The Kimi episode is one symptom of a deeper economic problem. Evan Armstrong, in his write-up of SpaceX’s recent $60 billion call option on Cursor, captured the math: Cursor’s top-line numbers are spectacular. They hit $2 billion ARR, 14x year-over-year, fastest B2B SaaS to $1B ARR ever, beating Slack by two years. The unit economics underneath are spectacularly awful. In January 2026, gross margin was negative 23%. Every $1 of revenue cost $1.23 in API spend to Anthropic and OpenAI. At $2B ARR, that’s $2.46B annualized flowing directly to the companies building competing products. This is like paying to send the guy planning to kill you to stabbing school. Cursor was funding its killers.

Despite their capabilities, sometimes using the frontier models just isn’t optimal, and open-weight models aren’t only necessary, but better-suited for a given use-case.

There’s also sufficient infrastructure in place to ensure that open models continue to improve with or without the frontier labs. DeepSeek V3, released in late 2024, was trained on export-compliant H800s for ~$5.5 million in final training run cost, roughly 5-6% of what comparable Western frontier models reportedly cost. V4, released last week, was trained entirely on domestic Chinese hardware: Huawei Ascend 950 chips and Cambricon accelerators, with barely (if any) Nvidia silicon involved at all.

That means the export restrictions on H100s didn’t exactly stop Chinese frontier development, instead pushing it onto a separate (domestic) hardware stack that they control.

In fact, the open ecosystem may already have reached something like escape velocity, simply because the recursive improvement tools now available (Distillation, Reinforcement Learning (RL), and cheap hardware) may be sufficient to continuously improve open models whether or not the frontier labs keep releasing their best work.

What’s more, once models are trained, users will be able to run them on consumer hardware with enough RAM. Quantization, the technique of compressing model weights to lower precision, makes this dramatically easier, at the cost of some model quality. The trade is improving fast: a 4-bit quantized 70-billion parameter model in 2026 is meaningfully closer to its full-precision parent than the same compression would have produced two years ago, and the curve is still bending. In short order, the open ecosystem will lower costs, reduce dependence on frontier API providers, and make intelligence far more durable for end users.

The China Concession

One genuine risk to this thesis is geopolitical: the open-weight ecosystem is now largely a Chinese ecosystem, and Chinese policy on AI release is not guaranteed to remain as permissive as it has been. A future in which Beijing decides open weights are a strategic asset to be controlled is plausible.

Even in that future, however, the weights already in circulation do not disappear. The DeepSeek R1 you downloaded in 2025 still runs in 2030. That is what it means for something to be a public good (or in this case a “commons”).

What We Inherit

The frontier may eventually deliver on its promise of meta-solutions to civilization-scale problems. It may not. Either way, that bet is being made by twelve people in San Francisco and a similar number in Beijing, and the public has no seat at the table.

What the public does have, increasingly, is a parallel inheritance: weights they can download, run on their own hardware, fine-tune for their own problems, and keep forever. Not the best intelligence ever built, but intelligence good enough to materially change what an individual or a small company can do, owned outright, with no vendor that can take it away.

If the frontier solves cancer, we all benefit. If it doesn’t, at least the rest of us got AGEI out of the deal. That is what the AI boom will leave behind, and unlike most of what has been promised in this cycle, it is already here.

Previous issues: #1 | #2 | #3 | #4 | #5

This week’s thesis: the buffer has narrowed.

A month ago Dario Amodei put the open-source and Chinese frontier at six to twelve months behind. Seven days later, that buffer looks a lot thinner. Intelligence at the deployable frontier is commoditising, and the labs are adjusting accordingly.

The model that chose to be less dangerous.

Anthropic’s Claude Opus 4.7 is the first generally available frontier model trained with explicitly reduced offensive capability. Security researchers who need the full set can apply through its new Cyber Verification Program. Two days earlier OpenAI shipped GPT-5.4-Cyber with Trusted Access, same structure. Both closed labs now gate capability by trust level as well as price. The UK AI Safety Institute’s Mythos evaluation published the same week explains why: 73 percent on expert-level capture-the-flag, first model to autonomously complete a 32-step network attack.

Amazon committed $25 billion more to Anthropic in the same window, with $5 billion landing immediately at a $380 billion valuation and five gigawatts of Trainium capacity over ten years. The closed frontier is getting more expensive, and more selective about who gets it at full capability.

The gap that wasn’t.

Two Chinese frontier releases in the same seven days, and Nvidia open-sourced its own frontier model in the same window.

Kimi K2.6 shipped April 20: 1T-parameter MoE, open-source, 58.6 on SWE-Bench Pro, designed for twelve-hour autonomous coding runs. It prices an order of magnitude below Opus 4.7. Qwen3.6-Max-Preview shipped the same day, Alibaba’s closed flagship, 260K context, top-ranked on six of Alibaba’s selected coding benchmarks. Both labs claiming SOTA with different evaluation setups is the messier reality.

Nvidia shipped Nemotron 3 Super into the same window: 120B-total / 12B-active MoE, open weights with ten trillion pretraining tokens, fifteen RL environments, and full training recipes under a permissive licence. The company whose silicon underwrites the AI boom is now a serious open-weight publisher in its own right.

I’m an open-model power user, and although I’m a fan of them, I remain unconvinced any of these three is meaningfully better than Opus 4.7 for the work I actually do. Published benchmarks become training targets, and training targets are not the same thing as capability. When a measure becomes a target, as Goodhart’s Law has it, the measure stops being useful. We have a portfolio company working on this, as you’ll see below.

Open models have tracked the frontier closely since the DeepSeek moment of January 2025. For a growing share of real workloads the available models are already good enough, and once they are good enough, the axis that matters is cost, latency, and reliability. Horace Dediu reports that open-source models now power 80 percent of startups seeking VC funding. That number describes where the builders already are.

Follow the labs’ incentives and a three-tier structure is hard to avoid. Each lab’s most capable model is the one that helps it build the next one, and releasing it gives away the engine of future self-improvement. As the frontier labs approach IPO and need to show durable moats, withholding the top tier stops being a safety decision and becomes a business one. Underneath that, the labs keep shipping mid-tier models through metered APIs because competition forces them to. And underneath that, the open-weight ecosystem keeps compounding on its own machinery. Three tiers, each optimising against different incentives, and each further from the last.

Self-improving AI went strategic.

The defence of the top tier runs through self-improvement.

Sergey Brin is personally leading a DeepMind strike team under Sebastian Borgeaud to close Gemini’s coding gap. Internal memo: “turning our models into primary developers.” Same week, Recursive Superintelligence (four months old, founded by Richard Socher, Tim Rocktäschel, Josh Tobin, Jeff Clune, and Tim Shi) raised $500M+ at $4B pre-money from GV and Nvidia. The thesis is in the name.

The recursive loop improving open weights (distillation, RL against verifiable rewards, cheaper hardware) does not require frontier lab cooperation to keep running. This week a Google co-founder made the loop his personal project, Nvidia put nine figures into a company whose only thesis is the loop, and Nvidia released a frontier-class open-weight model with the RL environments bundled. Nvidia is now funding the loop on one side and distributing the tools to run it on the other.

Agents as architecture.

If self-improvement is the labs’ fight, agents are everyone else’s.

Cloudflare shipped its agent-native AI Platform on April 16. Google Cloud Next 2026 went further six days later: Agent Studio, Agent-to-Agent Orchestration, Agent Registry, Agent Identity, Agent Gateway, Agent Observability, Workspace Studio agents across Gmail and Docs, a TPU 8i tuned for concurrent-agent inference, and a $750 million partner fund. The Identity, Registry, and Gateway primitives are the agent-infrastructure layer that has to exist before multi-agent workloads can be managed at scale. Google standardised them first. The thesis from #5 is now in shipping product.

What We’re Watching

Cursor into the Musk orbit. SpaceX took an option to acquire Cursor by year-end at $60B (or $10B for continued collaboration). xAI (merged with SpaceX in February at $1.25T) is already providing Colossus compute for Cursor’s Composer model. Bloomberg frames it as xAI catching up in coding. The more interesting read is vertical integration of proprietary models: a robotics-specific model for Optimus, driving-specific model for Tesla, rocket-specific model for SpaceX, and X as the data flywheel feeding Cursor’s foundational Composer model which acts as the baseline for the rest. OpenAI shipped GPT-Rosalind for life sciences on April 16, the same move from the horizontal side.

MCP’s first single point of failure. OX Security disclosed a remote code execution flaw in Anthropic’s official MCP SDKs, affecting roughly 200,000 servers including Cursor, VS Code, Windsurf, Claude Code, and Gemini-CLI. Anthropic called it “expected behaviour.” Agent plumbing is becoming critical infrastructure faster than it is being secured. The trust and verification layer is the hard part, and retrofitting it tends to be expensive.

Portfolio

Prime Intellect: Zapier on Lab, FrontierSWE on the Hub. Zapier’s AutomationBench on Prime Intellect’s Lab platform: frontier models under ten percent on real business workflows. FrontierSWE launched on the Environments Hub four days earlier. Prime Intellect’s stack is becoming the default substrate for evaluating the agentic frontier.

Intelligent Internet: AI derives novel physics. II published “The Cosmological Constant Is Positive“ on April 22: Logos, II’s first-principles reasoning system, derives that the sign of Λ is forced by the algebra of spacetime symmetries. In 109 years no physicist has derived Λ’s sign from first principles. Whether the result survives scrutiny or not, it is the first non-trivial theoretical physics claim from an AI reasoning system. The commercial thesis is automated first-principles reasoning: drug discovery, materials, engineering.

Provably: CCS 2026 acceptance for SNARKless verifiable databases. qedb gives verifiable SQL queries with proof size independent of database size, using bilinear pairings rather than general-purpose SNARKs. As agentic systems query private data at ever-larger stakes (the MCP disclosure above makes this concrete), the verifiability layer is moving from academic curiosity to production requirement.

LayerLens: independent evaluation for a benchmark-gaming era. Kimi K2.6, Qwen3.6-Max, Nemotron 3 Super, and Opus 4.7 all claim frontier positions on overlapping SWE-Bench variants with different evaluation setups. LayerLens exists to strip the asterisks off those numbers. Atlas v1 has been fully public since October.

The pattern.

A month ago consensus held that frontier capability was an American duopoly with a twelve-month buffer. Over seven days the buffer narrowed. Open models matched the deployable frontier, Nvidia joined the open side, and the labs’ response patterns became legible: capability gating, self-improvement strike teams, vertical integration. Underneath it all, the open-weight ecosystem keeps compounding on its own machinery. The recursive loop is producing output, and the infrastructure beneath it is what compounds.

AI has collapsed technology and its user into a single, powerful, semi-autonomous feedback loop – a tool that wields its own tools, does its own work, and has the capacity to operate on its own.

In short, technology itself has spawned its own user.

Created, and now limited, by compute, the very resource they consume to be useful, agents are the new users around which the internet and software now need to rebuild.

Those exposed to the frontier have noticed — and are alarmed by how fast it’s moving. Most of the world is still catching up. We sit squarely in the narrow window before that changes.

Already Here

Those closest to the technology have been exposed to this future.

“Using a computer, has always been about contorting yourself to the machine…We are moving into a world where you no longer have to micromanage the computer. More and more, it adapts to what you want. Rather than doing work with a computer, the computer does work for you.”Greg Brockman

The best programmers and engineers in the world, for example, simply aren’t writing much code themselves anymore. What started as “vibe coding” has become humans acting as “managers,” surveying swarms of agents that are writing code on their behalf.

“It is hard to communicate how much programming has changed due to AI in the last 2 months...programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You’re spinning up AI agents, giving them tasks in English and managing and reviewing their work in parallel.”, Andrej Karpathy

Agents are also software and internet native, sometimes more so than humans. Consequently, we now have tools that understand the primary substrate within which we use them better than we do. Before agents, concepts like “token budgets” and “tokenmaxxing” didn’t exist. Now that we have capable agents, executives like Uber’s CTO are publicly admitting that his company had already burned through its entire 2026 AI budget by April – nearly all of it on coding.

For the time being, it still takes talent and a good grasp on the specifics of what one wants to accomplish in order to create a highly productive agent swarm. But it’s no longer hard to imagine intrepid individuals equipped with agents starting companies, taking on incumbents, and creating enormous value without much human help.

Such is the epiphany even non-technical folks have when they encounter frontier-level AI for the first time, with some invoking “imagination” as the primary input required to create things of value now that agents can handle so much more work independently.

As we’ll see below, we may yet be early to this agent-native shift, but almost certainly not for long. No matter how long it takes to accommodate software’s powerful new user, however, the current status quo designed for humans will have to change.

The Jagged Frontier

Not everyone has seen behind the curtain. We are still early. In fact, another image circulating on the timeline is a visual approximation of just how early we may be.

Here one is tempted to invoke William Gibson, because despite the cultural saturation of AI discourse, paid and power users of AI are a rounding error, and the overwhelming majority of the planet has seldom interacted with these tools at all.

As Travis Kling articulated in the brilliant March edition of his monthly updates, if all of the cacophonous noise about AI and agents transforming the world is being made by such a small proportion of the planet, what happens when the rest of the world catches up?

Which begs the prior question: why hasn’t the world caught up already? As usual, the answer is complicated, but at least part of it can be explained by something known in the industry as “the jagged frontier.”

In short, AI’s abilities are uneven and unpredictable. Two tasks that look similarly difficult to a human can sit on opposite sides of the frontier, meaning one the model nails effortlessly, sometimes called “one-shotting”, whereas for the other it fails completely, its answer riddled with errors.

[Source: One Useful Thing]

Humans expect capability to be consistent, but LLMs don’t work that way. What’s more, the “frontier” itself is dynamic, meaning each new model redefines the uneven capabilities: things that failed last month may work, and occasionally the reverse.

One’s opinion of AI, its quality, and the power of the technology is thus dependent on where one sits on the frontier.

Models are typically best at tasks for which there are verifiable outputs, like programming. As such, advances in programming are staggering, and those using it to code are so impressed as to be overwhelmed.

Writing, on the other hand, which is as much about how artfully one breaks the rules as it is about how strictly one follows them, is more difficult for AI to properly master (although it’s still pretty damn good at it).

Both Karpathy and Wharton professor Ethan Mollick, who co-authored the paper that coined “jagged frontier,” have noted the difference in perspectives between those exposed to top-tier AI-powered programming and those primarily interacting with chatbots.

Additionally, the frontier labs, and thus the primary interfaces with which people interact, remain compute-constrained. To experience the magic of the latest and greatest available models, users need to be on “max” plans that cost anywhere from ~$100-200/month. That’s great for the aforementioned companies willing to subsidize their employees’ tokenmaxxing, but remains largely inaccessible for the majority of the general public.

For the majority of the population using free-tier AI primarily as a chatbot or as an answer-machine replacing search, the idea of deploying an entire semi-autonomous workforce is inconceivable.

All of this explains a recent Bloomberg article about AI FOMO, which I thought did a remarkable job of capturing the zeitgeist and articulating the widening gap in understanding between those who have encountered the frontier and those who haven’t.

The Ground is Moving

Now here’s where things get spooky. In October of last year I wrote about what I called the “Too Fast Threshold.” I described how difficult it would be for humans to keep up with machines, especially as they continue to accelerate.

In theory, there exists a “holy grail” of autonomous AI development called recursive self-improvement (RSI) whereby the models themselves get smart enough to contribute to their own iterative improvement, creating a self-perpetuating cycle of development that has mind-bending implications.

If agents are now capable independent workforces, by definition some of that work may include improving the very technology upon which they depend. Agents may thus be the mechanism by which RSI, and by consequence the rest of the three-letter “ultimate” outcomes (ASI/AGI), finally gets realized.

This is in large part why everyone even remotely exposed to the frontier of AI is expressing genuine, incredulous, and sometimes alarmed, surprise at the speed with which everything is changing.

They feel the acceleration viscerally, and it’s compelling them to share their observations publicly. To them, many of the frameworks in place before AI are buckling, social, political, and economic, and it’s entirely possible that we’re unprepared.

As we wrote last week, the conditions necessary for useful agents only emerged in the last six months, around December of last year. No more than a month later, Anthropic started shipping major product releases almost weekly: Cowork plugins, Opus 4.7, Claude Code Security, Mythos & Project Glasswing, Managed Agents, several of them triggering a sectoral selloff in related public market equities, collectively called the “Anthropic Effect.” They’ve also refrained from releasing Mythos, their latest, most powerful model, publicly for fear of its ability to find vulnerabilities in internet-critical codebases.

For what it’s worth, this is largely why everything in public markets is trading like a shitcoin: just as the assumptions upon which software has been built are now obsolete, so too are the assumptions used to evaluate enterprises, underwrite investments, and interpret market signals.

Price charts like the one above for Allbirds, a formerly struggling shoe brand, which has evidently become an AI company, pumping its stock ~700%, are funny, but more than anything else they indicate that markets and the mechanisms in place to steward capital towards productive enterprises are broken. Mispricing at this scale is precisely where the opportunity lives, skewed sharply toward whoever can still read what’s actually being built. We also addressed this in a previous piece.

The Window is Open

As Ben Thompson wrote:

“That’s how big of a deal actually functional agents are: you can see them coming and yet still be amazed when they arrive.”

What happens when technology is its own user is that every rule gets rewritten, for building, for investing, for competing.

If this acceleration continues – and I believe it will – we may not have much time to adapt. But I don’t believe in the apocalypse, either. The phrase “this time is different” always warrants skepticism, especially when it comes to markets, history, or humanity. Markets will boom, and then they will bust. History isn’t over. Humanity will endure.

I do believe in technological revolutions, though, and in that sense AI may, indeed, be different. I have written before about my skepticism regarding the financial valuation of companies involved in the AI boom, but I remain unambiguously convinced that AI will transform the world. This is why I started Nazaré in 2023.

That technology has spawned its own user, to me, is reason alone to get excited. But on top of that, there is still time to learn to use these tools, the world has yet to fully adapt to AI, and most of the companies that will define this paradigm are still being founded.

We are still early enough to conceive of, design, and mold what comes next. Therein lies the opportunity for everyone: founders, incumbents, investors, and everyday citizens alike.

Agents are the new users.

For fifty years, every piece of software has been built for humans. Every screen, every price, every login has assumed a person at the other end.

That assumption is breaking. A fast-growing share of software is now operated not by people but by other software. Programs, instructed by humans, acting through other programs, often with the human several steps removed. These new users want different things, and the infrastructure running the world’s software was not built to give it to them. Everything from how compute is rented to how identity is verified will have to be rebuilt for this new class of customer.

That is the investment thesis. We have been investing against it for a year. The evidence is now everywhere. Full argument on Robot Wave.

Portfolio

Prime Intellect at NVIDIA.

Vincent Weisser and his team met Jensen Huang at NVIDIA’s Santa Clara campus this week. The photograph circulated. The announcement that came with it is more important. NVIDIA has committed its latest hardware and core orchestration software to Prime Intellect’s open-source platform. The collaboration places Prime Intellect on roughly the same compute footing as the frontier labs like OpenAI and Anthropic, which is where the cutting-edge work in training autonomous agents is being done. Until this week, that work was almost entirely closed. Prime Intellect is making it open. It is a strategic position no open-source competitor can now easily replicate.

Vast.ai broadens its shelves.

The Vast.ai GPU marketplace now hosts a one-click deployment of Unsloth Studio, a popular open-source tool for customising AI models. A user can pick from over 500 models, choose a GPU, and train a bespoke version, without hiring engineers or signing contracts. Vast.ai already serves more than 20,000 GPUs. The catalogue of tools that runs on top of those GPUs is what determines how broad a customer base they can serve.

Provably on Zero Knowledge.

Shyam and Emanuele from Provably appeared on Zero Knowledge episode 398, the podcast the applied-cryptography community actually listens to. Their technology lets AI agents prove to each other that the data they are exchanging is accurate, without revealing the underlying records. When programs begin transacting with other programs at scale, someone has to build the trust layer between them. Provably is building it, and being featured on Zero Knowledge is validation from the audience that matters.

The canary in the coding mine.

On April 2, Stella Laurenzo, senior director of AI at AMD, filed a detailed bug report on Anthropic’s GitHub. Her team runs large, concurrent fleets of Claude Code agents, developing the AI software that runs on AMD’s chips. Something had changed. The agents had started cutting corners. Writing code without reading the surrounding files. Giving up halfway through tasks. Choosing the easiest fix rather than the right one.

She did what an engineer does. She mined her own logs, 6,852 sessions of them, and published the evidence. The quality regression coincided with a late-February change in how Anthropic’s servers handled reasoning. Anthropic disputes the mechanism. The collapse in her productivity metrics does not.

The economics tell the same story. In February her team consumed about $345 of compute at market rates. In March it consumed $42,121. Part of that reflects a deliberate scale-up of how many agents she was running. Part of it is pure thrashing, the model burning tokens on wrong answers and corrections. She was on a $400 flat-rate subscription throughout. The head of Claude Code disputed the analysis, offered workarounds, and closed the issue. Her team has reportedly moved to a competitor while Anthropic addresses the complaints.

Software pricing was built for humans typing at human speed. Agents do not type. They consume compute at machine speed, and they do not get tired.

Back to the drawing board.

The same week, Uber’s chief technology officer Praveen Neppalli Naga told The Information that his company had already spent its entire 2026 AI budget. Four months into a twelve-month year. Most of the spend went to coding agents. “I’m back to the drawing board, because the budget I thought I would need is blown away already.” By February, 63% of Uber’s 5,000 engineers were using Claude Code, up from 32% in December. Anthropic’s Claude Code revenue tripled in the same window, from $1 billion to $2.5 billion annualised.

One engineer proved the infrastructure strain with logs. A $150-billion company proved it with a budget. Anthropic is the canary because Claude Code has the fastest enterprise ramp in software history. The pricing physics applies to every vendor behind it.

Where Anthropic restricts, the market opens.

Last week Anthropic unveiled Mythos Preview, a model so capable at finding and exploiting security flaws that the company will not release it publicly. In its place, Anthropic launched Project Glasswing, granting access to eleven hand-picked partners: Amazon, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorgan, the Linux Foundation, Microsoft, Nvidia, and Palo Alto Networks. Roughly forty further organisations were extended quieter access.

Coinbase and Binance, the world’s two largest cryptocurrency exchanges, are publicly seeking admission. The security argument for holding the model back is real. So is the precedent. A curated group got first access. Everyone else asks permission. Governments have restricted capability access before. What is new is that a private company is doing it, on a timescale regulators cannot match, on technology that is instantly copyable once released.

That is a new kind of capability-level dependency. Not on a vendor’s pricing, or a vendor’s uptime, but on whether a vendor decides you can have a given capability at all.

Plastic meets programs.

On April 8, Visa launched Intelligent Commerce Connect, a single integration that gives AI agents a formal path to completing purchases: authentication, spending limits, fraud checks, all built in. Pilot partners include AWS, Highnote, and Mesh.

Mastercard’s Agent Pay launched a year earlier. Visa’s own Trusted Agent Protocol went live in the fourth quarter of last year. Intelligent Commerce Connect is the consolidated product, not the starting gun. But when the world’s two largest payment networks have both built product for your user type, that user type is no longer hypothetical.

The pattern.

A single engineer documents a two-thirds drop in AI reasoning power inside her own log files. A Fortune 500 chief technology officer watches a year’s AI budget vanish in four months. The world’s payment networks build commerce rails for software instead of people.

The users are here. The stack is not ready.

I started in AI in 1992. After thirty years of working in distributed systems, IP markets, and a decade in crypto, I came back to it in 2023 because I believed the new models were genuinely different: human-enhancing rather than human-extractive. No longer exclusively for mining data or capturing attention. Something that actually makes people more capable.

The thesis has been building for three years. But the last few months have changed what agents can actually do, and that changes everything that follows from it.

This is where I have landed. Here is the thesis.

For half a century, software has been built to serve humans. Sessions began when users opened an application and ended when they closed it. Humans needed intuitive interfaces. Discovery happened through search, recommendation, and advertising. Fortunes were made optimising for these principles, spawning the most successful companies in history.

The assumption that every piece of software has a human at the end of it is no longer safe to make.

Agents are programs instructed by humans to act through other software, and a rapidly growing share of software is now being written for them. What agents want is clean APIs, persistent memory, cheap tokens, and frictionless coordination at scale. A polished user interface, the obsession of a generation of product managers, is worthless to a program calling an API.

A new kind of user, not a new kind of client

The instinct is to frame this as client-server done differently. It is not. A client fetches resources a human will consume. An agent consumes the resources itself, decides what to do with them, and acts. The human is one or more steps removed from the transaction, sometimes absent entirely. That is a different relationship to the software, and it produces different requirements at every layer.

eBay could not have existed before the internet. It was a categorically different thing from a classified ad, made possible by a new medium of exchange. Uber could not have existed before GPS-enabled smartphones, always-on location, mobile payments, and two-sided real-time matching existed simultaneously. Neither company was an incumbent adapting to a new platform. Both were founded because a new kind of user made a new category of product possible.

The agent is that kind of user.

Why now

Until late 2025, language models could write paragraphs and answer questions but could not be trusted to chain tool calls, hold multi-step context, or act on behalf of a user long enough to complete anything useful. Once they could, a class of applications became viable that had not previously existed. The most popular among them accumulated hundreds of thousands of users within months.

Inference costs fell an order of magnitude in eighteen months. Open-source models narrowed the capability gap with frontier labs substantially for practical workloads. Within twelve to eighteen months, frontier-quality inference will run locally on consumer hardware, within reach of any high-agency developer.

None of these conditions existed two years ago. None of them is reversing.

The stack is not ready

Software is built on what came before. Some layers will evolve, some will be replaced, but the stack as it stands is not sufficient for an agent at the end of it.

Compute must be summoned and released by code, on demand, addressable by a running program rather than provisioned by a human sizing a server. Memory must persist across sessions and travel between agents without losing fidelity. Today’s vector databases and context windows are early approximations, not solutions.

Coordination must become a first-class primitive, because software now brokers work between thousands of autonomous programs sharing context and negotiating handoffs in real time. Identity is the most underestimated piece. OAuth scopes, SAML assertions, and IAM role chains exist, but none were designed for autonomous delegation without a human in the loop. That is the new surface area, and it is mostly empty.

Tooling has to be built for machine consumption. Structured outputs, predictable schemas, clean error handling, because no human will ever see them. The harness that decides when and how a model is called has come to matter as much as the model itself.

None of these layers exists in mature, agent-native form. Architecture that serves agents well rarely emerges from architecture built for someone else.

What most people are getting wrong

Every incumbent is taking existing infrastructure and adding an agent layer on top: a database with an agent API, a cloud provider with agentic orchestration, an identity system with delegation hooks. These are the right ideas built the wrong way around.

Vast.ai is purpose-built for agent workloads and has over 20,000 GPUs online. It serves a class of customer AWS was not designed to serve: agents procuring compute dynamically on their own. Prime Intellect is building the training surface for autonomous agents themselves. Their Environments Hub hosts hundreds of open RL environments where agents learn to act, the substrate frontier labs now spend hundreds of millions of dollars on, a category of software that did not need to exist when the entity being trained was a human. Dimensional, an OS bridging LLM reasoning to robot hardware, could not exist before agents could reason well enough to direct physical systems.

These companies exist because of the agent. They could not have been built for any other user.

Why I believe this

I wrote my PhD on Mixtures of Experts applied to speech recognition at Cambridge in 1997, the architecture that now powers almost every frontier model. I backed Circle at Series A in 2013 when digital payment infrastructure was an abstraction most people found unconvincing.

The pattern is the same each time. Infrastructure precedes applications. The best time to invest in it is when the application layer is still being imagined. That is exactly what we are doing now, across fourteen companies building the compute, memory, identity, and verification layers agents need to operate at scale.