China, Illinois, Brussels, Ankara, and the UN all moved from asking to building, and every one of those moves turns on the same unresolved question: who is accountable when AI acts.

If May was the week governance stopped looking like one thing, this is the week it stopped being optional.

The Week at a Glance

Where What happened Instrument Binding?

Here is each development, and what they add up to once you line them up.

China Now Regulates AI Agents as a Separate Category

China’s Implementation Opinions on intelligent agents, jointly issued by the CAC, NDRC, and MIIT, take effect on 15 July. This is the world’s first dedicated regulatory category for AI agents, and its core provisions are structural:

• Agents are defined as systems capable of autonomous perception, memory, decision-making, interaction, and execution, treated as a category separate from generative AI.

• Developers must sort agent decisions into three tiers: decisions the user makes, decisions requiring user authorisation, and decisions the agent takes alone. The user holds final decision-making power, and agents can’t act beyond authorised scope.

• Agents in healthcare, transportation, media, and public safety face mandatory filing, compliance testing, and recall provisions.

Whatever else this framework is, it’s an answer to the accountability question, and one of the first detailed answers anywhere. This puts the obligations on the agent, the thing that perceives, decides, and executes. The model sitting underneath is not the unit being governed here. That is a different regulatory instinct from the one driving most frameworks in the West, where the model is still the thing regulators reach for first. China decided the actor is what matters, and it built filing, testing, and recall around that choice. The compliance cost lands on developers, and the protection it promises, final decision-making power, lands with the user, at least on paper.

Illinois Is the First US State to Require Independent AI Safety Audits

Governor Pritzker signed the AI Safety Measures Act (SB 315), making Illinois the first US state to mandate third-party audits of frontier model safety. The mechanics:

• Applies to frontier developers with more than $500 million in revenue.

• Requires annual independent audits of safety plans, with results published.

• Enforcement sits with the Attorney General alone: civil penalties up to $1 million for a first violation and $3 million for subsequent ones, with no private right of action.

The design choice worth sitting with is that Illinois set no new safety standard at all. The law takes the safety plans companies already publish and forces them to stand up to an independent auditor once a year, in public, or answer to the Attorney General. That shifts the burden from asserting safety to evidencing it, and it does so, as I argued in May, in a world where many frontier systems have already outrun what a traditional audit can meaningfully prove. The distance between what a company claims and what an auditor can verify is exactly where this law will be tested.

What Would the GAAIA Draft Actually Freeze?

Or: how a federal pause lands on a live state experiment. The Great American AI Act discussion draft from Representatives Obernolte and Trahan proposes a three-year federal preemption, and its scope is precise:

• Frozen: state laws that specifically regulate AI model development, exactly the category Illinois just legislated.

• Untouched: post-deployment rules, laws of general applicability, and state authority granted under the draft itself.

• Status: opposed from both directions at once, by Democrats because it strips state authority and by Republicans and industry because it doesn’t strip enough.

Read next to Illinois, the timing is the story. It’s a draft that arrives just as one state finally tests the path it would close. If you read it next to Trump’s new AI executive order, you get the full picture: Congress is trying to pause state experiments while the White House writes national AI policy through directives that leave most of the model‑safety machinery undefined.

The EU Is Building the Capacity to Test AI Models Before They Reach the Market

On 7 July, the Commission presented its EU Action Plan on Cybersecurity and Artificial Intelligence. Under the headline items sits the structural one:

• An EU evaluation capacity for advanced AI models, supporting the AI Office’s regulatory function, operational in 2027.

• A secure AI testing platform expected by the end of 2026.

• The context: the AI Act already requires advanced models to be evaluated before they reach the EU market. What Europe lacked was the independent technical capacity to do the evaluating.

This is the difference between a law that exists and a law that can be enforced, and Brussels just funded the second, in hardware, software, and staff. What matters most here is who holds the evidence. Until now, the requirement to assess advanced models before they reach the market leaned on the same labs that build those models to characterise their own risks. An independent evaluation capacity pulls that evidence out of the developer's hands and into the regulator's. The catch is timing. The obligation exists now, the capacity is scheduled for 2027, and the testing platform for the end of 2026. For roughly a year, Europe holds a law it cannot yet independently enforce, which is its own kind of gap.

NATO Put Military AI Adoption on the Fast Track

Yes, NATO had briefly turned into an international Turkish cat fan club.

Of course, Ankara Summit Declaration wasn’t about cats—it was about speeding up how fast military AI systems move from concept to deployment. Two commitments matter for this file:

• The Alliance is developing an interoperable transatlantic warfighting cloud and “adopting powerful AI models,” alongside capability investments in uncrewed systems and cutting-edge technologies.

• Allies announced more than $50 billion in new procurements in Ankara, on top of a $139 billion increase in European and Canadian core defence investment in 2025.

The contrast with everything above is the point: civilian regimes spent the week designing brakes and dashboards; the military track pressed the accelerator. The military track committed to adopting powerful AI models in a single sentence, with no accountability framework named in the declaration. The most consequential AI deployments of the decade may run on the track with the least governance machinery attached.

What Happened at the First UN Global Dialogue on AI Governance?

The first UN Global Dialogue on AI Governance ran back‑to‑back with ITU’s AI for Good summit in Geneva—AI’s new global governance table bolted onto the UN’s flagship ‘AI for Good’ stage.

_{I was invited to both; in the end, my contribution stayed on paper and in the consultation portal, because the visa appointment system never opened in time.:))}

Three inputs shaped it:

• The science arrived first. The preliminary report of the Independent International Scientific Panel on AI, warns that current safeguards can’t keep pace with capability growth, that there are no known technical guarantees agent systems will follow instructions consistently, and that most countries, including advanced economies, lack the technical expertise to assess the most capable models.

• The Global South named its terms. Ahead of the Dialogue, the Global Digital Justice Forum and the Global South Alliance issued a joint statement with five demands: end AI extractivism, apply the common but differentiated responsibilities principle to international AI cooperation, address corporate impunity in data and AI value chains, build a data governance framework that delivers global equity, and invest in global public compute.

• The fault line is visible. The submissions split along a familiar divide, capacity-building for the majority of countries, frontier safety for the few that build frontier models.

That split is the one to watch. Capacity-building for the many and frontier safety for the few look like parallel tracks. They are really competing for the same scarce thing: whose problem sets the agenda. When the few who build frontier models also define what safety means, the many end up governing risks they did not design and cannot yet assess. The Global South statement is an attempt to widen that definition before it hardens.

Why Does the FCA Mills Review Matter for Agentic AI?

One more entry, easy to miss. The FCA published the Mills Review on 6 July, the first review of its kind initiated by a regulator anywhere:

• FCA-commissioned research found a fifth of people, 11 million UK adults, are likely to use AI that acts autonomously on their finances within pre-set goals.

• The review maps five human roles along an autonomy spectrum, from operator to observer, and concedes that accountability under existing regimes gets hard to evidence precisely at the autonomous end.

A financial regulator just said, in its own voice, what China’s new rules assume: the accountability question moves when the AI starts acting. I’m mapping who is actually answering that question, jurisdiction by jurisdiction, in Sunday’s piece.

Until next one,

Nesibe

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Bans are what companies reach for when they have no policy that works. Three years later, most organizations do have a policy, and the same behavior is still happening underneath it.

That is the problem worth understanding. The policies exist. They assign responsibility for safe AI use, name an owner, list the rules. And then the organization around the document keeps rewarding speed, keeps tolerating workarounds, and funds no enforcement. By the time the policy circulates, the decisions that actually determine whether it works were made somewhere else, in procurement, in performance targets, in how the company is structured across regions.

I spend most of my time advising organizations that operate across borders, and I can usually predict how a policy will perform before I read it. The drafting is rarely the problem. It is everything the drafting cannot reach.

What an AI Policy Is Actually For

An AI policy is an internal governance instrument that defines which AI systems an organization may use, for what purposes, under whose oversight, and with what data. Good concept. The reason it usually stays shallow comes down to who it was written for.

A customer nervous about which AI tool can be trusted with their data.

A board that wants to be seen doing something.

A regulator waiting for evidence, though outside the EU most regulators still have no concrete compliance standard to measure against, so what companies produce gets shaped by what the audience expects rather than by any real threshold. Write to satisfy those three and the result performs seriousness without touching behavior. It only has to exist, look thorough, and carry a signature.

A policy should not be a response to external pressure. It is actually there to protect four internal things:

• Protecting the data

• Protecting the customer

• Protecting the employee

• Protecting a reputation

Treat all of that as a compliance checklist and you get a document that satisfies an auditor and defends none of it.

• The 2026 PagerDuty Shadow AI Survey of 1,250 office professionals at companies above $500 million in revenue found that 86% have an AI policy in place, and 66% used AI at work anyway, knowing it was not allowed.

Almost every company has the policy. Far fewer have one that binds.

What Actually Breaks Without a Working Policy

Here is what I keep seeing, and it is almost never a single dramatic failure.

It is a slow build-up of gaps, each one small enough to ignore until it isn’t:

• Nobody can name what is in use. A tool arrives through a free tier or a feature switched on inside software the company already licenses, a manager two levels down waves it through, and no one writes down that the decision was made. Six months later it sits in three workflows and the governance owner has never heard of it.

• Data leaves through the fastest door. A client file or a block of source code ends up on someone else’s server, because the sanctioned path was slower and nobody built a fast safe one.

• Accountability splits until no one holds it. A model makes a call that harms someone, and responsibility divides so neatly between the person who used it, the team that bought it, and the vendor who built it that none of them carries the weight.

• High-stakes calls run with no human in them. A hiring or credit decision gets automated, and no one ever decided whether a person should look at it before it takes effect.

• The same failure repeats. An incident gets handled quietly on the spot and never changes a process, so the next team walks straight into it.

• The affected have nowhere to go. Someone turned down by an automated decision cannot ask why, because a way to challenge it was never built into the design.

None of this reads as a technology problem to me, and I do not think it is one. Each gap traces back to a decision made further upstream, before anyone sat down to write the policy. The responsibility gets assigned. The lever that would make it real stays somewhere else. That is what a working policy has to fix, and here is what one actually contains.

What a Complete AI Policy Actually Contains

A governance-grade AI policy is not a long list of rules. It is seven groups of them, each sitting at one level, each mapping to a framework someone will eventually audit you against. For an organization living under more than one regime at once, the mapping is what lets a single policy answer to ISO assessors, US state law, and the EU AI Act without being rewritten three times. The full one-page version, with every item mapped, is at the end of this post.

The seven groups run like this.

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So this is not a “best AI books” list. It is the list of books I finished, argued with in the margins, and kept coming back to. Read together, they answer the question that sits under everything TechLetter covers: who decided, with what incentive, and who pays the cost. Two founder biographies, two industry chronicles, two critiques, and one very funny experiment. Here they are, in the order I would hand them to you.

Empire of AI, Karen Hao

If you read one book from this list, read this one.

Hao interviewed over 250 people, more than 90 of them current and former OpenAI employees and executives, and it shows on every page. You are in the room for the departures, the board crisis, the Musk split.

What stayed with me is how early the mission language starts working as positioning. . The "benefit all of humanity" framing was not betrayed somewhere along the way; Hao's reporting shows it operating as strategy from the beginning, a cover under which an extraordinary concentration of economic and political power became acceptable. Her verdict, which I keep returning to, is that the mission became “a uniquely potent formula for consolidating resources and constructing an empire-esque power structure.”

_{I wrote about this in June, when OpenAI's "built to benefit everyone" manifesto landed the same week the company filed a confidential draft S-1: the vocabulary of the manifesto is nearly word for word the vocabulary of the founding, and Hao's book is in large part a record of those promises being made and quietly rewritten.}

The chapters that separate this book from every other OpenAI account are the ones far from San Francisco: Kenyan data workers paid a few dollars an hour, a Chilean community fighting a data center that would draw a thousand times its annual freshwater use.

Watch:

The Infinity Machine, Sebastian Mallaby

The opposite vantage point: three years of regular access to Demis Hassabis, and the scenes to prove it. The one I keep retelling is from right after ChatGPT went viral. Mallaby asked Hassabis how he felt. The answer: “They have parked the tanks on our front lawn.” A safety-minded scientist, describing a product launch in the language of invasion. The competitive logic of this industry has rarely been captured so precisely, and so involuntarily.

The book is also critically honest about DeepMind’s blind spot. When OpenAI went all in on large language models, DeepMind trusted its own research path and lost the lead in language, while the same stubbornness later delivered AlphaFold. There is a real lesson here about how conviction and institutional pride behave identically until the results come in.

My pushback: Mallaby is visibly fond of his subject. Risk and governance run in the background while the adventure story runs up front. The Oppenheimer question he raises about Hassabis, “He wants to do good, but can he be good?”, deserved more pages than it gets. Read it alongside Empire of AI. Same industry, opposite window.

The Thinking Machine, Stephen Witt

The Financial Times named this its 2025 Business Book of the Year, and it earns the prize in a single scene. In his final interview, Witt asks Jensen Huang one more question about the risks of the technology, and Huang detonates: “you’re interviewing Elon right now, and I’m just not that guy.” Then he compares AI risk questions to asking whether calculators would destroy math, declares the entire book project a waste of his time, and has Witt shown out. Sit with that for a moment: the CEO of the world’s most valuable company treats the question of social consequence as an insult. The book’s whole argument, compressed into one outburst.

Beyond the scene, this is the material-infrastructure book the AI debate needs. Witt traces how Huang was forced to become a political actor, cultivating Washington for chip export permissions and visa pipelines, because compute is now statecraft. Where Rivlin’s book says follow the money, this one says follow the chip.

AI Valley, Gary Rivlin

Rivlin embedded with Reid Hoffman for over a year and set out to find the next great startup. His conclusion, delivered on his book tour with admirable honesty: “the next Google was probably going to be Google.” The book accidentally documents, in real time, what happens to a well-funded startup when the giants decide a category belongs to them. As a chronicle of concentration, it is more persuasive than any policy paper I read this year.

The framing my readers will reuse is Hoffman’s own: zoomers, who push ahead regardless of risk, versus bloomers, who want the potential with safeguards. By the Paris AI Summit in February 2025, the zoomers had won, and the book records the surrender without quite calling it that. Rivlin’s own one-line diagnosis of the technology holds up too: “it seems to know everything, but it doesn’t understand a thing.”

The AI Con, Emily M. Bender and Alex Hanna

The most useful sentence in this book is about the debate itself. Boosters insist AI is inevitable, imminent, and superpowerful, and will solve everything. Doomers insist AI is inevitable, imminent, and superpowerful, and will kill us all. As Bender puts it, there is “not a lot of daylight between those two positions.” Both camps inflate the same asset. Once you see this, you cannot unsee it, and half the panels at every AI conference become legible as a single conversation.

Bender and Hanna refuse the term AI altogether where they can, preferring the gloriously deflationary “synthetic text extruding machines.” The book grew out of their podcast, and the tone survived the transition: sarcastic, precise, occasionally unfair in ways that are usually productive.

The Ethics of AI: Power, Critique, Responsibility, Rainer Mühlhoff

The theory layer under everything above. Mühlhoff, who holds a chair in ethics of AI at Osnabrück, treats AI as a sociotechnical system entangled with power from the ground up, and he rejects the individualistic framing that dominates AI ethics: the idea that responsibility lives with the single developer, the single user, the single bad actor. His alternative is collective responsibility, enforced through regulation and systemic change, and the book closes with a manifesto for what he calls a power-aware ethics of AI.

Why it matters for this list: Hao and Bender and Hanna document empirically what Mühlhoff argues philosophically. Reading him afterward feels like being handed the grammar of a language you had been speaking by ear.

P.s.the book is fully open access from Bristol University Press, so you can start reading it five minutes after this email.

I Am Not a Robot, Joanna Stern

The beach read, and I mean that as a compliment. Stern, after twelve years as the Wall Street Journal’s consumer tech columnist, spent a year saying yes to every AI experiment: an AI boyfriend with her wife’s consent, an AI therapist with her human therapist’s blessing, an AI research assistant instead of a human one. Her line about the cooking robot deserves framing: “robots won’t kill us with lasers, they’ll kill us with salt.”

Underneath the comedy is real field data. She describes her emotional attachment to the systems as unsettling, which is the most honest sentence in the companion-AI debate this year. And the ending lands harder than she could have planned: the year with AI became a factor in her decision to leave the Journal and start her own company. The labor effects of this technology stopped being a projection and happened inside the book’s own author.

My pushback: the consumer lens is a deliberate choice, and it means the power questions never arrive. That is exactly why it belongs here. Six books tell you how the system was built. This one tells you how it feels to live inside it.

What I took from the pile

Reading these together, one pattern kept surfacing. The insiders’ books, Mallaby and Witt, show brilliant people who treat consequence questions as interruptions. The outsiders’ books, Hao and Bender and Hanna and Mühlhoff, show why those questions are the whole game. Rivlin sits in the middle, watching the money decide. Stern shows us absorbing the results in our kitchens.

If your summer only has room for two: Empire of AI for the system, I Am Not a Robot for the life inside it.

What did you read this year that changed how you think about this technology? Reply and tell me. I am already building the winter pile, and I would rather build it with you.

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We do owe Sam Altman and his team a great deal. They did not simply scale a technology. They moved its center of gravity from the lab to ordinary life, collapsing the distance between frontier research and public experience. Today even grandmothers chat with an AI on their phones. But access is not the same as authority. The grandmother can use the system; she has no say in what it is allowed to do, when it changes, or on whose terms it reaches her. Reach expanded. The power to shape it did not.

That is the gap the manifesto talks around. Democratization is not the same as distribution of power. Putting a capability into everyone’s hands is not the same as giving anyone a meaningful voice in how it is built, deployed, or withdrawn. The essay rests its case on the second promise while demonstrating only the first.

The week it was published makes the point more sharply than I could. On the same day, OpenAI filed a confidential draft S-1 with the SEC, the opening move toward a public listing. OpenAI will say its structure guards against exactly this: the listed entity is a public benefit corporation, controlled by a nonprofit foundation that appoints its board. But a benefit corporation’s duty to the public is enforceable in practice mainly by its shareholders, and a foundation that owns roughly a quarter of the company and names its own directors is not what anyone means by broadly distributed power. A listing monetizes the mission. It does not distribute it. Two days later it expanded its arrangement with Oracle.

The day after that it announced the acquisition of a startup called Ona. Set an essay about distributing power next to a single week of consolidating it, and the asymmetry stops being rhetorical.cThis is not a story about a company that changed. It is a story about which parts of it were allowed to change and which were not.

A nonprofit founded in 2015. A capped-profit subsidiary added in 2019. A board that, on paper, owed its duty to humanity rather than to investors. What followed was a steady and asymmetric evolution: investment rights and corporate flexibility expanded with great care, while board design, independent oversight, and enforceable public commitments stayed thin and improvised. As Karen Hao writes in Empire of AI, the mission became “a uniquely potent formula for consolidating resources and constructing an empire-esque power structure.”

And here is what I keep returning to. Is any of this new? The vocabulary of this manifesto, the appeal to all of humanity, the insistence that power must not concentrate, is nearly word for word the vocabulary of the founding. Hao’s book is, in large part, a record of those promises being made and then quietly rewritten. Years before OpenAI, Altman had already described what he was doing in revealing terms: the most successful founders, he wrote, are not really setting out to build companies at all, but something closer to a religion, and forming a company simply turns out to be the easiest way to do it. Read the manifesto with that line in mind and its cadence makes sense. It is written less as a plan than as a creed.

The eloquence was never the problem. The eloquence is the method. Hao’s sharper charge is about where it directs the eye. By dwelling on the long-term, speculative stakes of AGI, the missionary register draws attention away from the harms that are already here: the labor, the water and the power, the quiet consolidation of knowledge into a few hands. This manifesto is a clean specimen. It offers electricity, prosperity, a personal AGI for everyone on Earth, an automated AI researcher by 2028, and almost nothing about who is paying for any of it now. A promise so vague that it can be reinterpreted the instant it becomes inconvenient is not a commitment. It is a mood.

The manifesto’s own metaphor turns against it, too. Electricity did not become broadly beneficial because one firm chose to be generous. It became beneficial because societies built public utilities and antitrust safeguards around private power, often over the objections of the companies that held it. The word “everyone” deserves the same scrutiny. The people who labeled the data and absorbed the harms, the workers across the less powerful countries that Hao documents in detail, are part of the “everyone” who built these systems. They are not part of the “everyone” who will hold the equity when the shares are priced.

So if I take the manifesto seriously rather than literally, the question is not “how do we restore the old OpenAI,” and it is certainly not “do we trust the current leadership.” Leadership and incentives change. The real question is whether we can build governance that does not depend on any one leader’s goodwill, or talent for the right sentence.

It is worth noticing that OpenAI already endorses several of the right ideas. Altman has told Congress he supports mandatory evaluations for the most powerful systems. The manifesto calls for international coordination, even for slowing frontier development “when needed.” The trouble is the distance between endorsing a principle and accepting a constraint. In its December 2023 framework, OpenAI committed to having its safety evaluations audited by independent third parties. In April 2025 it removed that commitment, without noting the change. Its current framework leaves the decision to deploy a high-capability system with company leadership, and openly contemplates relaxing its own requirements if a competitor moves first. The principle survives. The binding version is the one that keeps disappearing.

A credible baseline would close exactly that distance. At minimum:

Mandatory independent evaluation, as a non-waivable precondition for deploying frontier systems above a defined threshold. Not the discretionary, “when feasible” testing that exists today, but the binding third-party audit OpenAI promised in 2023 and deleted in 2025.

Dual-key deployment, so that for capabilities that materially shift security, information integrity, or economic baselines, release requires the sign-off of an independent public body and not only an internal committee reporting to the CEO.

Mission lock, with unbundling. Public-benefit obligations written as constraints that are enforceable by parties other than shareholders, and the decisions now concentrated in one board, profit, research, and deployment, separated into organs that can check one another.

Polycentric oversight, with governments, independent experts, and civil society holding durable roles that carry real authority rather than an advisory seat, so that the capture of any single center does not collapse accountability.

Governance by design, with auditability, interruptibility, and standardized safety metrics built into systems from the start, rather than described in a framework the company can rewrite at will.

None of this is hostility toward scale. OpenAI did not go wrong because it sought to be large. It went wrong where many transformative institutions go wrong: it let the governance imagination fall behind the technical imagination, and then asked us to trust the people closing the gap.

I think Altman believes much of what he writes. That has never been the issue. He has always been able to say the right thing, at the founding and now. The real test of distributed power is not what a company says about everyone. It is what it agrees it should no longer be allowed to do alone. By that test, a public offering is not redistribution.

No more promises. We need actions. And no governance model that takes a promise as its primary safety mechanism.

Until next week,

Nesibe

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SpaceX signed a $60 billion all-stock deal to acquire Cursor, expected to close in Q3 2026 pending regulatory approval. Musk couldn’t accept falling behind in the AI race, and today he put freshly minted post-IPO stock on the table instead of cash. Within hours the internet had split into three competing readings of what just happened. Here is each one, what it gets right, and what they add up to when you put them together.

My read

The way I see it, this is one move, not a shopping spree: SpaceX is turning raw compute into something developers actually touch, then using that to win the layer that matters most.

Back in April there was an option agreement on the table. SpaceX opened up its Colossus compute to Cursor and the two started working together. In return, it held a one-sided right: by year end, if it chose to, it could buy Cursor for $60 billion; if it didn’t, it would pay $10 billion for the partnership and the acquisition would fall away. Today SpaceX exercised that right.

Why now? Because the picture changed:

• The IPO went through, so SpaceX now holds extremely valuable stock as currency

• Cursor has 1M+ daily users, tens of thousands of enterprise teams, and a large share of the Fortune 500

• Its annual revenue sits in the $2-3 billion range and is growing fast

• Every day, inside the editor, it generates tons of high-signal code plus developer behavior data

You have to read the move across three dimensions:

• Compute to product. A supercomputer on its own impresses no one. You have to turn it into a real product that touches developers. Cursor is exactly that: a VS Code based IDE, Composer, long context, terminal. People already live inside it.

• Model to data. To build the best coding model, you need to see the best code. Cursor does this perfectly: which suggestion got accepted, which got deleted, refactor, debug, test. All of it real developer behavior, a data mine far richer than classic web scrape.

• Distribution to power. For developers the question is no longer which LLM but which workflow. Cursor puts its model straight inside the editor, with code, terminal, tests and repo in one place. Whoever wins the battle to become the default model wins the game, and SpaceX just took that default.

The stack now sits in one hand:

• Hardware: Colossus

• Model: Grok and xAI

• Application: Cursor and Composer

• Distribution: X and IDE integrations

From hardware all the way to millions of developers, the chain belongs to a single figure. SpaceXAI and Cursor have reportedly been jointly training a model for months, set to ship in both Cursor and Grok Build soon.

So the Copilot-Cursor race now maps cleanly onto a Microsoft vs SpaceX axis. Microsoft reportedly looked at buying Cursor and walked away, and OpenAI was turned down twice. Cursor, which wanted to stay independent, finally gave in to fresh stock. xAI is moving from chatbot to the heart of the developer ecosystem.

But two other angles are circulating

The cynical one: the real sucker is Musk. Cursor is a thin-moat VS Code fork, and once users drift to Claude and Codex, all he is left with is an editor, while the founders cashed out at the top in inflated paper.

The kernel is real. The moat question is legitimate and the all-stock exit read is sharp. But the premise is shaky. A product with $2-3 billion in revenue and fast growth is not sinking, and the whole thesis rests on a single bet, that Musk is foolish. That is a feeling, not an analysis.

The visionary one: this acquisition is the sequel to last week’s AI1 announcement. SpaceX unveiled AI1, its first orbital data center satellite, just before the IPO, so the connection is real and well spotted.

But the closed loop story, write code in Cursor, transmit over Starlink, run it on compute in orbit, is years away. Prototypes are slated for early 2027, the 1 GW target for late 2027, and the million-satellite figure is aspiration rather than a committed plan. SpaceX’s own filing flags that it may not secure enough chips. “Save this, you’ll understand in three years” feels profound precisely because it cannot be falsified.

What the three add up to

Each read owns a piece of the truth, but none gives the whole picture alone. The thesis that survives is this: Musk is vertically integrating the entire AI supply chain:

• Chips through Terafab

• Energy through solar

• Compute through Colossus and AI1

• Model through xAI

• Application and data through Cursor

• Distribution through X

It is a campaign to break free, one by one, of the bottlenecks everyone else is stuck behind: Nvidia silicon, grid power, the cloud. Cursor is the application and data capstone on a compute-sovereignty play.

The honest counterweight lives in the same place. SpaceX’s own IPO paperwork says the chips may not be there. So the same bottleneck thesis that explains the move also marks its biggest risk.

Which is why this is not “they bought another editor.” The real question is what it means when the AI economy leaves everyone dependent on the same few bottlenecks, and a single actor sets out to buy all of them at once.

Until next week,

Nesibe

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Hello everyone. This week, a reading in context rather than a summary. If you read my earlier piece, America's AI Action Plan, think of this as the next chapter.

What triggered the order: a model too dangerous to release

The proximate cause has a name: Claude Mythos Preview.

On April 7, 2026, Anthropic announced a general-purpose frontier model whose coding and reasoning capabilities proved so effective at finding and exploiting software vulnerabilities that the company chose not to release it publicly. According to Anthropic, the model identified thousands of previously unknown vulnerabilities across major operating systems and browsers.

The UK AI Security Institute independently evaluated it and found it could execute multi-stage attacks and discover and exploit vulnerabilities autonomously, tasks that would take human professionals days of work.

Sit with that for a moment. The most consequential AI safety decision of 2026 so far was made by a private company, voluntarily, about its own product. The June 2 order is Washington’s institutional response to that fact.

Three phone calls in one night

The order that was signed is not the order the administration drafted.

The original version was scheduled for a White House signing ceremony on May 21. It contained a voluntary 90-day pre-release review window, the timeline national security officials inside the administration had pushed for. Hours before the ceremony, Trump called off the signing, saying he did not want to do anything that could threaten America’s lead in the AI race with China.

According to reporting by the Washington Post and Politico, the cancellation followed calls from Elon Musk, Mark Zuckerberg, and David Sacks between Wednesday evening and Thursday morning, warning that the review system could slow AI development. Politico reported that Sacks called the president on Thursday morning without telling his own staff, arguing that the voluntary review process could one day be made mandatory.

The accounts are disputed. Musk denied the reporting publicly, saying he still did not know what was in the order and spoke to the president only after the signing was declined. Meta also disputed it, saying Zuckerberg spoke to Trump only after the order was rescinded. Who placed which call may never be settled. What is not disputed is the outcome: the draft had 90 days, the signing was cancelled, and the version signed twelve days later has 30 days, no ceremony, and an explicit prohibition on anything mandatory.

Hold that sequence next to the trigger. A model deemed too dangerous to release prompted a review mechanism. The mechanism was then negotiated down, in private, by the industry it would have applied to. The security concern that produced the order survived the editing process. The oversight did not.

The sequence: four moves since January 2025

Move one came on January 20, 2025, Trump’s first day back in office, when he revoked Biden’s Executive Order 14110. The revoked requirements included the obligation for developers of the most powerful AI systems to share safety testing and red-teaming results with the federal government, plus directives for over 100 specific agency actions across eight policy areas. Imperfect, but the only federal AI accountability structure the U.S. had. The deck was cleared before anything existed to replace it.

Move two came in July 2025, when the White House published America’s AI Action Plan, outlining more than 90 policy recommendations to promote American AI dominance. I covered it in detail at the time. Real content on compute, export controls, and education pipelines. But it set direction without setting rules: no enforcement, no mandates, no accountability framework.

Move three came in December 2025, when Trump signed an order turning the federal government against state AI laws. More on that below, because it is the key to reading the June order correctly.

Move four is the June 2 order. Revoke, plan, preempt, build. Each step covered as a separate story. They are one story.

What is actually in the order

The order has five operative components, on 30-day and 60-day deadlines running to early August:

• A voluntary pre-release window. Developers can voluntarily give the federal government early access to frontier models for up to 30 days before releasing them to other trusted partners. No lab is required to participate. No consequence is named for staying out.

• A classified capability threshold. Within 60 days, Treasury, the NSA, and CISA, in consultation with NIST and the National Cyber Director, must develop a classified benchmarking process to determine when a model becomes a “covered frontier model,” sharing assessments with developers and researchers only “as appropriate.” The NSA Director makes the designation determination.

  • If the benchmarks are classified, independent researchers cannot verify them, civil society cannot scrutinize them, and international partners cannot align with them. A safety framework built on classified standards is not a transparent governance framework. It is a national security program. Both are legitimate things to have. They are different things, with different accountability logic.

    • In that kind of regime, I expect the next shoe to drop not in public law, but in access. Once a Mythos‑class model is folded into a classified national‑security order, mos likely future move is to suspend access for users rather than to write a public rulebook for its commercialization.

• An AI cybersecurity clearinghouse. Within 30 days, Treasury must form a voluntary body with the AI industry and critical infrastructure operators to coordinate vulnerability scanning, validate discovered flaws, and prioritize patch distribution. The direct institutional answer to the Mythos problem: AI now discovers vulnerabilities faster than anyone can fix them.

• Federal hardening directives. By July 2, CISA must issue Binding Operational Directives to expedite cyber defense of civilian federal systems and facilitate access to AI-enabled defensive tools for federal agencies, state and local authorities, and critical infrastructure operators including rural hospitals, community banks, and local utilities.

• Criminal enforcement. The Attorney General is directed to prioritize prosecution of AI-enabled computer crime under existing statutes covering identity fraud, computer fraud, and wire fraud, explicitly including the use of AI agents. No new offenses. A prosecutorial signal, pointed at existing tools.

That is the whole structure: defensive hardening, voluntary intelligence-sharing, classified evaluation, prosecution. Nothing in it binds a private AI developer to do anything.

How this differs from Trump’s own December order

The sharper comparison is internal: the June order against the administration’s previous one, signed less than six months earlier.

The December order is a demolition instrument. It directs the DOJ to challenge state AI laws as unconstitutional regulation of interstate commerce and leverages federal funding against states that keep them. Its named targets include state statutes on transparency and automated decision-making, the only binding AI-specific rules that exist anywhere in the American system. Its carve-outs are narrow: child safety, compute and data center infrastructure, and state government procurement.

The June order is the administration’s first construction project. A clearinghouse, a benchmarking process, a review window, prosecutorial priorities. After nearly a year and a half of removing oversight, this is the first time the administration has built any.

Read together, the two orders reveal the actual policy. The December order dismantles oversight that is public, legislated, and enforceable in open court. The June order constructs oversight that is classified, voluntary, and run by an intelligence agency. The administration does not oppose AI oversight as such. It opposes oversight it does not control and the public can see. Visible rules are treated as obstruction. Invisible review is treated as security.

One Biden-era contrast still matters for anyone who tracked the earlier framework: the direction of obligation has reversed. Under the revoked 2023 order, developers of the most powerful systems were required to share safety testing results with the government. Under the June 2026 order, developers decide what to share, and when. Everything else about the old comparison is now historical.

Why classified benchmarks are a governance problem

There is a defensible version of the secrecy argument: publishing exact capability thresholds could help developers train models that pass the tests without becoming safer.

But notice what the order does not contain: a definition of “covered frontier model.” The threshold is whatever the classified benchmarking process says it is. The scope of the regime is itself classified. Developers will not know where the line sits until the NSA tells them, on terms the NSA controls.

If the benchmarks are classified, independent researchers cannot verify them, civil society cannot scrutinize them, and international partners cannot align with them. A safety framework built on classified standards is not a transparent governance framework. It is a national security program. Both are legitimate things to have. They are different things, with different accountability logic.

The expert commentary has caught pieces of this. CSIS analysts question why Treasury leads while the agencies holding most of the government’s cyber expertise sit in consulting roles. Others note the government may simply lack the technical capacity to evaluate frontier systems at the pace AI is advancing. Both critiques are sound. Both stop short of the incentive question: the order assigns oversight to the actor with the strongest institutional incentive to keep it opaque, and makes participation voluntary for the actors with the strongest commercial incentive to skip it.

What this means beyond the U.S.

If your organization operates globally, you now manage two regulatory environments built on incompatible theories of responsibility.

On the EU side, the timeline just moved. On May 7, 2026, EU negotiators reached provisional agreement on the Digital Omnibus on AI, postponing high-risk obligations for Annex III systems from August 2, 2026 to December 2, 2027, and for product-embedded Annex I systems to August 2, 2028. Formal adoption is expected before August 2, and the new dates bind only once published in the Official Journal. Until then, August 2, 2026 remains a live compliance date.

The deadlines moved. The architecture did not. Risk classification, conformity assessments, human oversight, and penalties reaching 7% of global turnover all remain. The EU’s answer to the responsibility question is still developers and deployers, under public oversight. The U.S. answer is the market, watched by the national security apparatus from behind a classification wall.

There is a third audience here: everyone else. As Oxford’s Matthias Holweg put it, framing safety and competitiveness as a dichotomy will fuel calls for digital sovereignty, because using U.S. models becomes a riskier proposition. For governments and enterprises outside the U.S. and EU, a U.S. framework whose thresholds are classified offers nothing to assess, audit, or align with. That strengthens the case, already gathering force from Brussels to Ankara to New Delhi, for sovereign and open-source alternatives.

What to watch

Three things between now and August. Which labs opt into the pre-release window, and which quietly do not. Whether classified designations start leaking into procurement, insurance, and liability decisions that are never publicly explained. And whether the Mythos pattern, a lab unilaterally deciding what is too dangerous to release, becomes the de facto governance model this order quietly ratifies.

That last one is the real story. The U.S. has not built a system for governing frontier AI. It has built a system for watching the companies that govern themselves.

Until next week,

Nesibe

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