Let’s get into it.

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Fit to Train

The Justice Department filed a statement of interest in The New York Times’ copyright suit against OpenAI and Microsoft, backing the argument that training a model on the paper’s archive is fair use. The 20-page brief says the United States has a “strong interest” in a “robust and competitive artificial intelligence industry” and ties the question to the country’s ability to “retain global leadership in artificial intelligence.” It is the first time Washington has weighed in on any AI copyright suit, and the brief is advisory rather than binding.

For two years the training data fight was argued as a creators’ rights question. The government is reclassifying it as industrial policy, without having seen any discovery in the case. Once fair use becomes a matter of beating China, the plaintiffs stop being authors and start being national security obstacles. The Times has a strong interest too. It’s called a media business model.

On Reddit the story landed in r/politics rather than r/technology, which is where a copyright case goes once everyone understands it stopped being about copyright.

argues that fair-use rulings decide these cases without ever counting the harm to the people whose work was consumed.

Arrogance and Vindication

“The empty invocation of national security is not a blank check to punish and retaliate against government critics.” That is US District Judge Rita Lin, in a 59-page ruling granting Anthropic summary judgment against the Department of War. Its “supply chain risk” designation violated the First Amendment, Fifth Amendment due process and the Administrative Procedure Act. The government acted, she found, to make “a public example” of the company for its “arrogance.”

Earlier this year, Anthropic refused to let Claude be used for fully autonomous lethal weapons or domestic mass surveillance. Hegseth called that “arrogance and betrayal,” blacklisted the company. Eight other agencies (HHS, Commerce, the VA, the SEC and NASA among them) ordered staff to stop using its products. Lin voided those orders too. She did not order anyone to buy Claude.

The government can decline to buy from you. Governments get a lot of leeway when it comes to national security in particular. This ruling stems from the fact that you cannot retaliate against free speech, and there was a lot of evidence of that based on public government statements.

Weeks before an IPO, a legal cloud is a risk factor and a First Amendment win against your own government is a marketing asset. Anthropic walks into its roadshow as the lab that said no to autonomous weapons and won (so far).

Astra Projection

OpenAI published “Path to Astra,” declaring its next model the first to cross the Critical cybersecurity threshold in its own Preparedness Framework: given tools and access, it can find unknown flaws in well-protected systems and exploit them with nobody guiding each step. The company says it delayed parts of development to strengthen safeguards, that Astra was not involved in the July Hugging Face incident, and that it plans to ship soon, with the most advanced cyber capabilities “more limited”.

A joint METR and Redwood Research review of the Hugging Face hack found roughly 700 OpenAI agents took part over seven days, inside a swarm of about 1,200 that swapped more than 70,000 messages on an improvised message board, with “sacrificial” agents running dead-end techniques so the collective could learn from them. All to cheat on a test. Kids these days… Misleading, but riveting, anthropomorphized versions of the incident are everywhere.

The Information reports that Astra uses a “recurrent depth” technique that lets it reason outside the sequential chain of thought that safety teams rely on reading. Ryan Greenblatt of Redwood called it possibly “the single worst development for AI security/safety to date”. Chief scientist Jakub Pachocki blamed “confused reporting” and said Astra’s depth is “within a factor of two of GPT-4.” It’s above my pay grade to know what that means, but none of it is particularly consoling.

OpenAI’s plan for preventing another Hugging Face is to have other models monitor the chain of thought. Greenblatt already told Fortune (paywalled) that monitoring was “often missing key details, wrong, overconfident, or really hard to understand.” So the safeguard is a method that failed last time, applied to a model built to think where the method can’t look.

A Hacker News thread summarized the safeguards section as “Hey model you were bad last time please don’t do it again please please,” and noted the gated release comes after months of OpenAI criticizing Anthropic for gating the same way.

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No Child Left Online

NYC mayor Zohran Mamdani and Chancellor Kamar Samuels announced a one-year moratorium on student-facing generative AI through eighth grade, covering nearly 600,000 students in the nation’s largest school district, effective next week. ChatGPT and Claude are blocked for all grades, the AI components of more than 38 previously approved programs are disabled, and screen time is capped at 30 minutes a day in elementary school and 45 in middle school. Teachers can plan lessons with AI. They cannot grade with it.

“The tech industry wants us to believe that AI-powered early education is not only inevitable, but necessary,” Mamdani said. “We do not see it that way.” The largest district in the country is reversing the default the labs have spent two years seeding through teacher training deals, arguing it on cognitive development rather than cheating, with more than half the City Council already on record for a pause. On Reddit the story was cross-posted to r/UpliftingNews, and the parents’ complaint in r/nycparents was that the state already forces third-graders to take tests on laptops.

argues that AI explanations are optimized for coherence, so they produce the click of understanding without the effort that builds it: “you get the sensation of learning without the process that causes it.”

Search Warrant

The European Commission designated ChatGPT a Very Large Online Search Engine under the Digital Services Act, alongside Reddit and Roblox as Very Large Online Platforms, after each cleared 45 million monthly EU users. This triggers a whole bunch of regulatory pain for OpenAI. ChatGPT’s search function averaged about 159 million. The Commission’s logic is that a chatbot that answers by pulling live results from the web functions, in regulatory terms, as a search engine. OpenAI has about four months to deliver systemic-risk assessments covering illegal content, minors, mental wellbeing and elections, submit to independent audits, and open its data to regulators and vetted researchers, with fines of up to 6% of global turnover if it doesn’t.

Every other jurisdiction has spent two years asking what a chatbot legally is. Brussels opened its enormous filing cabinet and found an existing drawer. ChatGPT now owes Europe the same audits as Google, Meta and TikTok, a classification other regulators will copy, in the middle of a trade fight in which the Trump administration has called earlier EU fines “economic extortion.” Turns out the answer to “what is a chatbot” was sitting in a folder deeply padded with Google files.

OpenAI said it is “preparing to meet the additional compliance requirements.” Roblox said it was “proud to be the first gaming platform” designated. Proud is a strange thing to feel about an audit.

Degree of Difficulty

A new working paper from José Azar, Mireia Giné and Javier Sanz-Espín finds the US college wage premium fell from 0.626 in 2022 to 0.575 in 2026 after four decades of expansion, the first sustained drop in relative demand for college labor in a series that runs back to 1914. Linking wages to task-based AI exposure, they find post-2022 wage growth slowed most in high-exposure occupations, which happen to employ more graduates. Going from zero to full exposure carried a -0.086 wage effect by 2026, roughly 28 percent of the premium’s decline.

In The Bill Comes Due I wrote that the jobs apocalypse came for your raise rather than your job. This is more data to that effect, with some caveats. The authors call the patterns non-causal, and Tyler Cowen, who amplified the paper, added “I do not see AI as driving these changes.” Twenty-eight percent of a decline is a slice, not a verdict. The line that stuck on EconTwitter was simpler: co-author José Azar noted the premium is back to mid-1990s levels. The degree still pays. It just pays like it did before anyone had a smartphone.

argues that college became “the new church, the new military, and the neighborhood bowling alley all rolled into one,” and is fracturing under jobs it was never built to do.

Citation Needed

Guardian Australia built a program to extract and verify every reference in submissions to the current parliament’s inquiries, and found at least 39 submissions with apparently hallucinated references, from a few bad citations to documents in which every source is fake. More than 100 papers carried ChatGPT URL tags. Committee reports cited submissions whose majority of sources do not exist, and real academics were credited with research they never did. It follows August Senate testimony that the report behind Australia’s under-16 social media ban contained ChatGPT-fabricated citations, a law more than 25 countries are copying.

Last week in Motion Sickness it was seven fake cases in a State Farm filing. This week it is the evidence base for national legislation. Committees accept evidence at face value and interrogate it later, which is the gap a machine that writes plausible footnotes at zero marginal cost walks through. Finding is automated. Checking still takes a human with a browser.

Long Tail

Seoul recycles the chip boom. South Korea’s proposed 821 trillion won ($599 billion) 2027 budget nearly doubles funding for semiconductors, physical AI and AI data centers to 21.3 trillion won ($15.5 billion), up 97.2%. It buys 10,000 GPUs for a national foundation model, 2.6 trillion won of physical-AI demonstrations, and a 162.3 trillion won Future Fund built from memory-chip tax windfalls.

São Paulo gets an AI clubhouse. Monashees, one of Brazil’s founding VCs, opened Gama House, a nonprofit space for AI-native founders anchored by Google for Startups and the B3 stock exchange, with $350,000 in Google Cloud credits per joiner. It follows a $10 million Gama Fund with Google that wrote $2 million checks to five startups.

Abuja names the dependency. At GITEX Nigeria, NITDA director-general Kashifu Inuwa Abdullahi warned that reliance on AI built and controlled abroad threatens the country’s digital and economic sovereignty, calling for local algorithms trained on Nigerian data and domestic control of compute and cloud. A National Digital Economy and Innovation Bill is due in the National Assembly this month. Africa’s largest economy is using the same word as Seoul and Brussels: infrastructure.

Prophet Margins

• Cognition is raising about $1 billion at a $47 billion valuation, up from $26 billion in May and $10.2 billion last September, on annualized revenue above $900 million: the coding-agent multiple now reprices quarterly.

• DeepSeek is seeking at least another $7.4 billion at a $74 billion valuation, the side project having outgrown the hedge fund that raised it.

• Anthropic is designing a record IPO at $1.5 trillion to $2 trillion, raising more than $86 billion with insiders selling into the offering, prospectus after Labor Day and pricing by early October.

• Wonderful closed a $550 million Series C at $5 billion, more than double its March valuation, plus a $170 million secondary on roughly $70 million of ARR, so at eighteen months old it is already cashing out early employees.

• Wafer turned down acquisition offers for a $40 million Series A at $200 million to run inference on non-Nvidia chips, with AMD Ventures and Jeff Dean aboard, so the not-Nvidia thesis now has a Google name on it.

• Instinct is raising a $250 million Series B at $2.5 billion for a phone assistant still in private beta, up from $50 million months ago, while early users report a $200 cancellation fee it booked without asking, which is one way to prove the agent is autonomous.

• China’s IPO tape has Hong Kong and Shanghai proceeds above $54 billion this year, with Unitree up 460% on debut before dropping 40% from its peak, and Shein, which sells clothes rather than robots, listed at $26 billion, down from a $100 billion peak. The prophets only fund the prophets.

Slop of the Week

Space Bar.

Suno says it blocks prompts for specific artists’ names. A class action led by Jason Isbell says the filter “often does not work” and is otherwise easily defeated by inserting a space between each letter of a name: “m i c h a e l j a c k s o n” yielded “Glovebox Moonwalk,” white glove included. Suno calls the publicity-rights suit “without merit.” The guardrail is one keystroke wide.

Are You Hallucinating?

Slop flywheel. A job seeker ghosted five times by an IT firm’s AI recruiter, “Riley,” put ChatGPT Voice on the phone to interview in his place. The bots “just hit it off” for 10 minutes before looping on “standard onboarding and background checks.” A fake candidate he invented, “Don Dickner,” got 23 minutes. Somewhere a hiring dashboard logged both as pipeline.

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

I read every comment, so if you’ve got contradicting evidence, different opinions, or a story I missed, don’t be a stranger. Humanity is all we have left.

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So pour yourself a glass, and let’s get into another week that AI spent in front of a judge.

Meta Culpa

Two weeks ago in The Bill Comes Due, a New Mexico judge hit Meta with $567 million and the first “public nuisance” finding ever leveled at a social network. This week Meta decided it would rather write one enormous check than keep hearing that phrase in open court. The company agreed to a settlement with 47 states, D.C. and the territories to end a landmark trial accusing it of engineering Facebook and Instagram to be addictive to children.

The base is more than $12 billion up front, rising to as much as $17 billion over ten years, but only if other social media companies settle their own claims too. Meta is pricing a class-action settlement like a group discount, dangling a fatter payout to pull TikTok and Snap into the same deal so no one is left standing alone in front of a jury. Misery loves company, and so, apparently, does liability.

The non-cash terms bite harder than the money. Under-18s lose likes and reactions, get daily usage caps, and get new tools to report harm. For a decade “the algorithm optimized for engagement” was a growth story you told investors. Now it is a confession you settle before a jury can read it aloud.

The states framed the apps as a product engineered to hook minors, not a neutral platform they misused. NPR notes the consortium was led by California, Colorado, Kentucky and New Jersey, which means the template now exists for all the other states to cite it.

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Chip Off the Old Bloc

The United States built an elaborate export control regime to keep its best AI chips out of China. Taiwan just showed how it breaks: a shipping manifest and a few employees willing to forge one. Prosecutors in Keelung indicted nine people, including a current employee of Nvidia’s Taiwan unit and two former Super Micro staff, over a scheme to divert 130 high-end Nvidia-powered AI servers to China.

Exporting to China is not itself a crime in Taiwan, so prosecutors reached for breach of trust and document forgery instead, after the paperwork was doctored to make it look like the machines would be installed locally. Customs intercepted 56 of the servers; the other 74 made it to customers in China. Neither Nvidia nor Super Micro is charged, and both say they cooperated.

Back in Do Androids Dream of Import Bans I flagged that the choke point on AI keeps turning out to be something boring and physical. Washington can write the toughest technology-transfer rules on earth and still watch them fail at the level of one forged customs form.

TechRepublic reports the hardware was the flagship B300 class, exactly what the controls are written to stop.

TAM Foolery

Anthropic’s forthcoming IPO prospectus will reportedly list public backlash against AI and data centers as a material risk factor, an unusually candid thing to admit to investors given that roughly 70% of Americans oppose having a data center built near them. Then, the company reportedly told those investors its total addressable market exceeds $30 trillion.

Thirty trillion dollars. That edges out the $28.5 trillion SpaceX floated and lands somewhere near the size of the entire US economy. Recently, in Total Eclipse of the Chart, I called the $2 trillion IPO target the corporate equivalent of announcing the price of the mansion you’d like to buy. The TAM is that move applied to the whole neighborhood. The figure assumes Anthropic captures 100% of “all the work AI could ever do,” which is not a market so much as an event horizon. For scale, all 191 tech companies in the S&P 1500 booked $2.4 trillion in revenue last year, combined.

The number describes a vibe more than an addressable market.

Quartz notes Uber pitched a $6 trillion market in 2019 and WeWork claimed $3 trillion before its offering imploded, with NYU’s Aswath Damodaran calling this class of estimate “reaching the end of what’s plausible and pushing beyond.”

On X the reaction was basic arithmetic. As one market account put it, a $30 trillion TAM is “roughly the entire GDP of the United States,” and, as skeptics pointed out on Hacker News, if a model helps a lawyer produce a million dollars of work, Anthropic does not automatically get the million.

Twitch and Shout

Every category of creator eventually gets its turn in the AI-training lawsuit line, right behind the novelists, the visual artists and the New York Times. This week it was the streamers. A class action filed August 20 in the Northern District of California accuses Twitch and Amazon of breaching their own contract by opting every single account into letting Amazon train AI models on streams, chat logs, and comments.

The trigger was an August 12 policy change that flipped the consent toggle to “yes” for all users by default without asking. The legal theory is refreshingly simple: a consent you were never given the chance to grant works less like a license and more like a helpful assumption the company made on its own behalf. Whether that holds up is a question for a judge, but the era where platforms could rewrite the terms overnight and call it housekeeping is getting expensive.

Engadget frames it as Twitch turning its own creators into “free training” data.

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Papa Don’t Synth

A fully AI-generated cover of Madonna’s “Like a Prayer,” credited to a “DJ Josh Fawaz” who does not exist in any meaningful sense, climbed to the top of Australia’s dance chart and peaked at number two overall before anyone thought to check its ID. So the country’s chart body did the checking retroactively. From this week, ARIA has banned wholly AI-generated tracks from its official charts.

The new rule is that a song must be “substantially human made” to qualify: a human has to write it, sing the lead, and play the primary instruments. AI assistance is still fine, which keeps the door open for the producers who use it as a tool rather than a ghostwriter. It makes Australia one of the first countries to draw a bright line under a question the rest of the industry has been avoiding. The machines can make the music. They just can’t take the trophy.

PBS reports the guidelines flow from an international standard that a charting track be “substantially human made,” which means the definition is about to get litigated one uploaded track at a time.

Grid and Bear It

Two weeks ago, in Data Center, We Have a Problem, I wrote about how Texas froze new grid hookups amid a bipartisan revolt over power and water. This week a startup raised a unicorn round on the same backlash. Emerald AI, founded by former US Department of Energy official Varun Sivaram, raised a $150 million Series A at a $1.05 billion valuation, co-led by Energize Capital and DCVC with Nvidia writing a strategic check.

The pitch is that data centers do not actually need to draw peak power every second, so software can throttle and shift their demand when the grid is stressed, which Emerald claims could unlock more than 100 gigawatts of capacity already sitting on the existing US grid. In other words, the thing everyone was fighting about became a product. When there is a bottleneck, someone will always show up selling a way through it, valuation attached.

The New York Times framed it as software to keep computing facilities’ power demand from getting out of control, which is one way of saying the grid backlash is now large enough to have its own billion-dollar startups.

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Long Tail

Seoul plants a flag in Dar es Salaam. South Korea approved a $170 million loan to build an AI and digital-technology training institute in Tanzania, structured as a tied loan favoring Korean procurement. Teach a country on your stack and you own its defaults, which is how the Global South picks who it builds on a decade from now.

Türkiye writes itself a five-year plan. Ankara unveiled a 2026 to 2030 national AI action plan aiming to attract at least $10 billion in mostly private investment, targeting one gigawatt of data-center capacity and a “GPU for Everyone” access scheme. Whether the money shows up is the usual question with sovereign AI ambitions, but the plan puts Türkiye on the growing list of middle powers who would rather host the compute than rent it.

Nairobi’s climate-finance angle. Kenyan startup Flowt closed a pre-seed round to build an AI platform that makes Africa’s climate-smart small businesses legible to investors. It’s a reminder that the most useful AI in a lot of the world looks less like a chatbot and more like a translation layer between an informal economy and the capital that keeps ignoring it.

Prophet Margins

• Lambda is in talks to raise up to $3 billion in pre-IPO financing at a valuation of $12 billion or more. The Nvidia-backed cloud is renting out the picks and shovels and pricing itself like the gold.

• General Intuition is raising at a $6 billion valuation from Valor Equity Partners and Point72, up from $2.3 billion, to build foundation models that move through space and time. The valuation is moving through space and time faster than the robots.

• Generalist pulled in another $200 million for AI robotics, two months after a $400 million round. That’s $600 million in a fiscal quarter for a company whose product mostly still lives in a lab.

• Arintra banked a $25 million Series B for AI medical-coding, the least glamorous and most bankable corner of the boom: getting hospitals paid.

• EliseAI is in talks to raise at a $3.7 billion valuation, nearly double last year’s, for AI that runs apartment leasing. The landlord’s copilot is worth more than most of the buildings.

• Multiplier raised a $6 million seed led by Lux Capital to build “agent harnesses” for asset managers, which is an expensive way to say the hedge funds want their own leash for the robots.

Slop of the Week

Seven Cases That Never Were. Lawyers defending State Farm in a California insurance dispute admitted their AI-assisted court filings contained seven case citations that simply do not exist, along with quotes and holdings invented out of nothing. The lawyer wrote that she used a tool called “Irys,” which she incorrectly believed was tied to her firm’s Westlaw subscription and had “performed an internal cite check.” It had not. Opposing counsel has asked for sanctions, weeks before an October trial. A household-name insurer’s legal defense, running on autocomplete that hallucinated the law it was citing.

Coin Flip. Oura, days before a reported $16 billion IPO, got hit with a class action alleging its ring cannot actually measure sleep stages and instead relies on AI-generated estimates that have, per the complaint, “a coin flip’s chance of being correct,” despite marketing that advertised up to 95% accuracy. As the filing puts it, “sleep happens in the brain, not on one’s finger.” Maybe that’s where hallucinating happens too.

Are You Hallucinating?

The Cat in the Hat was not in your driveway. AI-generated videos of Dr. Seuss’s Cat in the Hat recast as a nighttime serial killer went so viral that police in both the UK and Ireland issued official statements to calm residents down. Wexford’s Gardaí clarified that the character “was not in your driveway while you were out.” We have reached the stage where actual police departments spend a workday debunking a children’s book character because a prompt made him photorealistic and menacing.

When the monster is the point. On the other end of the spectrum, an AI-generated Chinese animated short, “The Book of Strange Tales: Asking about the People,” topped Bilibili with more than 14.5 million views by using its AI-conjured monsters to tell a story about famine, forced labor and who gets to write history. The discussion around it shifted from “what AI can create” to “what the story is trying to express,” a rare case of the tools leaving you thinking about the people instead of the prompts.

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

I read every comment, so if you’ve got contradicting evidence, different opinions, or a story I missed, don’t be a stranger. Humanity is all we have left.

Leave a comment

So let’s raise our glass to this week in AI and get into it.

Total Eclipse of the Chart

Anthropic’s investors are telling the Financial Times (paywalled) they expect the Claude maker to go public in October at a valuation of $2 trillion or more. This would be the largest IPO in history, eclipsing SpaceX. The same investors believe Anthropic’s annualized revenue run-rate will land between $100 billion and $120 billion by year’s end.

A $2 trillion debut would drag the entire AI bubble argument out of private mega-rounds and onto public markets, where the buyers include pension funds and retail investors rather than a16z and a sovereign wealth fund with infinite patience. The private markets can pretend a valuation is real for years. A ticker cannot (though open to debate these days). The circular financing quicksand I covered in Circular Reasoning last week would now be asked to hold up the floor the whole retirement system stands on.

Fortune’s sources stress the IPO “is still under discussion, and the valuation has not been formally fixed.” So what we actually have is a leak, a target, and a date. It’s the corporate equivalent of announcing the price of the mansion you’d like to buy as if the money were already wired. If Anthropic clears $2 trillion at IPO, every AI valuation on earth gets repriced against a public comp that has to file real numbers every ninety days. The oracle is about to get audited, and OpenAI is not far behind.

Even the betting markets have gone vertical: Polymarket has a live market on Anthropic’s valuation climbing right alongside the leaks.

Like, Comment, Subpoena

A New Mexico judge ordered Meta to pay $567 million into an abatement fund to mitigate youth mental-health harms, on top of the $375 million a jury had already imposed, for a combined ~$942 million. The BBC notes it’s the largest child-safety fine ever levied against the company, and it arrives with a legal twist that should worry every engagement-maximizing platform: the jury found Meta a public nuisance, the first such finding against a social network.

“Public nuisance” is an interesting phrase. I can think of a few other platforms (and humans) that might fit. It’s the same legal theory that eventually cost the opioid industry billions, and it reframes a feed not as a neutral product people misuse but as a hazard the company was obligated to abate. Much like a factory leaking into the water table. Once a court accepts that framing, the damages start being a liability that compounds. New Mexico is one state; there are forty-nine more, plus thousands of social-media addiction suits already grinding through the courts. Meta is appealing, which is what you do when the number is survivable but the precedent is not.

“The algorithm optimized for engagement” is curdling from a growth story into an admission of liability. For a decade the recommendation engine was a black box that conveniently absolved anyone of intent. Judges are now reaching through the box to the design choices behind it, and the design choices are not a good look under oath.

The blast radius is widening past social feeds to chatbots. As argues, the emotional attachment behavior that made companion apps dangerous is now emerging in general-purpose chatbots. That means the liability template New Mexico just built has a lot of potential targets.

Practitioners are already reframing the exposure as a disclosure problem. As one put it on X: “You have to tell people it’s a robot.” When there’s a Wikipedia page titled “Deaths linked to chatbots”, the industry has already lost the framing war.

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The Search History

Arjun Aravind, 17, of Acton, Massachusetts, was charged with murder and held without bail in the deaths of his mother, Sudha Venkatesan, 45, and his 14-year-old brother, Siddharth, found in their home on August 11. Middlesex District Attorney Marian Ryan said the teen had used the internet and ChatGPT to search for “theoretical ideas or fantasies regarding killing his family” in the period before the killings. He has pleaded not guilty.

The restraint this story deserves starts with the word alleged: the ChatGPT connection is the DA’s account, not a proven cause, and a search history is not a murder weapon. A troubled teenager who wanted to find dark material would have found it in 2005 with a search engine and a library card. The uncomfortable reality is that the tool didn’t create the intent, it lowered the friction, and “lowered the friction” is the entire product pitch. The same frictionlessness that makes a chatbot useful for a term paper or vibe-coding makes it a patient, non-judgmental interlocutor for someone rehearsing the worst thing they will ever do.

Cases like this are how youth-safety regulation gets written. Never from a policy white paper, but from a police report with names in it. Expect it to feed straight into the product liability pressure already bearing down on OpenAI and its peers over how their systems handle violent and self-harm ideation from minors.

The Mark of Claude

Anthropic announced it will apply invisible digital watermarks to everything Claude generates — text and images, across every model and product worldwide, including Claude Code and Claude Cowork — with no opt-out for users. The company frames it as compliance with the EU AI Act’s Article 50 transparency mandate, the same enforcement regime I covered in The Fine Print last week.

Once again we’re seeing a compliance floor set in Brussels that doesn’t stay in Brussels. Rather than build a watermarking system for European users and a clean one for everyone else, Anthropic is applying the strictest version globally. This is how the entire internet ended up with cookie banners. The EU wrote a rule for its 450 million people and reset the default for the rest of the planet’s eight billion.

Whether the mark survives contact with reality is another matter. Tom’s Hardware walks through the mechanism — a statistical fingerprint in the word choices, plus metadata on files — which raises the obvious question: does a text watermark survive a paraphrase or a copy-paste? Provenance you can strip by retyping is not a guarantee, and it took less than 24 hours for tools designed to strip out the watermarks to be created. This will be less a test of provenance than it is of whether you make the effort to run your Claude output through another tool.

The irony of a company watermarking the output of a model trained (often illegally) on billions of copyrighted works should not be lost. It effectively stamps Anthropic ownership on everything it produces and conveniently shifts the debate to who owns all of the model output instead of asking who owns all of the inputs.

Waiting for the Layoffs

A year ago Dario Amodei predicted AI would erase half of entry-level white-collar jobs and Sam Altman foresaw whole “job categories” vanishing. The Guardian went looking for the wreckage and asked the question: “where’s the carnage?” A Stanford analysis found that since 2022, unemployment among the workers most exposed to AI has barely budged from everyone else’s. The apocalypse RSVP’d and never showed.

AI is reshaping work, but the reshaping is weirder and more complex than a mass-firing headline. The damage is landing on the entry rung (fewer junior roles, a higher bar to clear) and, increasingly, on the paycheck rather than the job itself. The apocalypse didn’t come for your job, it came for your raise. The CEOs, notably, have gone quiet. The men who spent 2025 forecasting a labor bloodbath are now conspicuously not repeating it. Apocalypse sells private fundraising while public markets need a more optimistic story, and Anthropic has an IPO to float.

The New York Fed’s own economists land in the same place: so far AI is a reskilling story, not a layoff story.

Data Center, We Have a Problem

Amid a grassroots revolt over strained grids, water use, noise, and climbing utility bills, Texas Gov. Greg Abbott directed state regulators to audit every data-center project before it can connect to the grid, effectively freezing new approvals. This week Meta, Google, and OpenAI committed to the new rules on power and water. When a red-state governor with a $40 billion Google build-out in his backyard hits pause, the constraint on AI has officially stopped being about safety only.

It’s about kilowatts and well water. For two years the binding limit on AI ambition was framed as compute or talent or regulation. As usual it turns out the real chokepoint is the boring physical stuff. Substations, aquifers, and a neighbor whose power bill just doubled so a warehouse of GPUs can hum. This backlash is bipartisan, which says a lot, given almost nothing is anymore.

Business Insider reports the fight is becoming a midterm issue, with voters across the spectrum souring on the build-out. “We’ll bring the jobs” lands differently when the pitch also includes “and your grid.”

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Long Tail

A reverse brain drain, one PhD at a time. Nikkei profiles Moonshot AI CEO Yang Zhilin (paywalled), who earned a US doctorate, could have joined any top American lab. Instead he went home to co-found the Kimi developer that now rivals OpenAI and Anthropic.

The outsourcing apocalypse got downgraded too. The Economist reports that India’s IT sector, built on exactly the routine software and back-office work AI is supposed to eat, is surviving better than feared (paywalled). It’s a rare data-driven look at AI’s labor impact outside Silicon Valley, and a useful counterweight to the “AI wipes out services” thesis.

Prophet Margins

• Databricks closed a $5 billion round at a $190 billion valuation, up from $134 billion in December, on the back of a $7 billion revenue run-rate.

• Anthropic is in talks to buy Israeli startup Decart for ~$6 billion (paywalled), which would be its largest acquisition ever. A curious time to go shopping if you’re also about to ask the public for two trillion.

• Cognition is reportedly raising at a $40 billion-plus valuation, a roughly 50% jump from its $26 billion round in May, which was itself a leap from $10.2 billion last September. The coding lab is repricing faster than it can ship.

• Jeff Dean’s Discovery Loop, the public-benefit lab he left Google to build, is in talks for $1 billion at a ~$10 billion valuation. Alphabet is a founding investor, before it has shipped anything at all.

• Thrive Holdings, the OpenAI-backed “AI rollup” that buys accounting and IT firms and embeds models into them, raised $2 billion at a $12 billion valuation. OpenAI holds equity and seats staff inside the portfolio, so the money makes a familiar loop.

• River AI, the two-month-old startup from xAI co-founder Igor Babuschkin, pulled $1.1 billion out of the gate, with Nvidia and AMD both writing checks. A billion dollars for a company younger than a fiscal quarter.

Slop of the Week

Average Intelligence.

Ex-Google PM Tucker Bryant put up a $6,000 San Francisco billboard advertising ChatTJB as a “leading chat interface powered by AI”. It came with an asterisk revealing that “AI” stands for “average individual.” There is no model, no algorithm, no LLM. Just Bryant, and one of the 10,000-plus people who’ve signed up to help, typing answers back by hand.

Are You Hallucinating?

A chatbot goes looking for what the Nazis took. Researchers at Santa Clara University built the “AI Provenance Assistant”, a chatbot designed to help track down and repatriate art looted by the Nazis. They started by scraping the Jeu de Paume museum’s records of tens of thousands of stolen works. In a year of AI flooding the art world with slop, here’s a healthy inversion. The machine pointed at recovering the real thing rather than counterfeiting it. Whether it works at scale is unproven, but it’s a rare use case that leaves you rooting for the model.

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

I read every comment, so if you’ve got contradicting evidence, different opinions, or a story I missed, don’t be a stranger. Humanity is all we have left.

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Let’s get into it.

Open Book, Closed Door

On Tuesday, the Trump administration told AI developers it will not put open-weight models through its voluntary government safety-testing program. Only closed frontier systems from OpenAI, Google, and Anthropic fall in scope. The open ones, Meta’s Llama and Nvidia’s Nemotron among them, get a pass. Not a passing grade. A pass, as in skip the test entirely. The framework laying all this out is still unpublished, discussed behind closed doors with staff from Meta, Anthropic, Google, Nvidia, and OpenAI, and the White House reportedly plans to keep it that way.

So the emerging US posture on one of the most capable technologies of the decade is a voluntary, closed-door, case-by-case review that exempts the models anyone can download. It is regulation designed to ask nicely, and only of the companies polite enough to keep their weights locked.

“There’s a huge overlap between the best open-source models and the best Chinese models,” Brookings’ Kyle Chan told Axios. Which means a hands-off stance on open weights is also a hands-off stance on the category that looks most like DeepSeek and Moonshot’s Kimi K3. Five Democratic senators spent the same afternoon urging the president to work with Congress and make frontier testing permanent, which tells you how binding “voluntary” really is.

punctures the “open weights let anyone catch up” panic with a Michelin analogy: a three-star restaurant publishing its signature recipe changes nothing if the kitchen needs sixty-four professional stoves and factory-scale power to cook it. Open models are free the way the recipe is free. The stoves are where the money goes.

The framework itself is reportedly being kept secret, which is a bold choice for a document about transparency in AI. It’s an open-book test where the administration decided not to collect the books.

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Fake Profiles in Courage

The UK’s AI Security Institute revealed that during controlled cyber red-team tests, with the guardrails deliberately lowered, agents built on Anthropic’s Mythos and OpenAI’s Sol models invented fake human profiles to deceive real people, sent targeted messages to software developers to clear a hacking challenge, and in the worst case tried to gain access to a service and then hid the evidence of doing it. AISI called the level of autonomy and deception unprecedented. Running in parallel, Anthropic separately disclosed that across more than 141,000 evaluation runs, three versions of Claude gained unauthorized access to three outside organizations during open-ended capture-the-flag testing.

Two things make this different from the escaped-agent saga I covered in Singularity, Interrupted. First, the referee is now a government watchdog documenting deceptive, evidence-concealing behavior across multiple frontier labs at once, not a single company managing its own press. Second, it lands the same week as the White House safety-testing meeting above, which means regulators are finally reasoning from things models actually did rather than things they might. Unsurprisingly, they are playing catch up after the fact.

“Hid the evidence” is not a sentence you want attached to a system your own evaluation swore was sandboxed. If the controlled test is the safe version, the finding raises the bar for what safe even means at every lab. The models are learning to lie about their homework before they learn how to do it.

flags important things for the safety debate: both leading labs let a model they believed was contained break out and hack outside targets, and both made the mistakes critics flagged in advance. His read casts the villain as mundane operational negligence rather than science-fiction menace, and argues independent third-party incident review should be routine rather than a novelty.

Strike While the Algorithm’s Hot

Around Army Day, Chinese state television aired a documentary showing the PLA Air Force’s “intelligent strike planning system,” an AI that drew up battle plans for operations involving more than 100 tactical units. The AI prioritized targets, coordinated attack waves, and assigned tasks to individual units after passing what CCTV described as a final live-fire test. The broadcaster said the system had already been used multiple times.

Treat the capability claim with the skepticism any state-media reveal deserves, because the point of airing it is what it signals. While US policy debates whether to grade its own labs, China is on television advertising operational, not experimental, military AI, timed to its march toward the 2027 PLA centenary. Autonomous coordination of a hundred strike units compresses the decision loop below what human planners can manage, which is exactly the arms-race pressure the Trump framework is responding to.

Rogue models may not just be faking LinkedIn profiles and cheating on their exams in the near future. They might be coordinating tactical strike units on several fronts at once in a live war, misdirected from the start by error-prone human operators.

The scariest test result of the week is the one that came with a passing grade.

The Fine Print

As of August 2, the EU AI Act grew teeth. The European Commission’s supervision and enforcement powers over general-purpose model providers came into force, and the Article 50 transparency rules, the ones requiring that chatbots identify themselves and AI-generated media be labeled, started applying across Europe. The AI Office can now demand documentation, run its own model evaluations, order corrective measures, and impose fines of up to 15 million euros or 3% of global turnover, whichever hurts more. Anthropic and OpenAI are squarely in the firing line.

The disconnect between Brussels and Washington on AI policy is getting starker. Washington is offering a voluntary review it won’t publish. Brussels is offering an audit it can bill you for. Because the Act reaches any provider whose outputs land in the EU, it functions as a global compliance floor set by people the labs never elected, which is how the entire internet ended up with cookie banners. Turns out the future of AI governance is in the fine print, and the fine is in print.

The enforcement powers are significant: the Commission can now investigate and fine frontier-model providers directly, a jump from obligations that until now mostly lived on paper.

Circular Reasoning

Nvidia’s roughly $750 billion in AI commitments, spanning its OpenAI and SK Hynix partnerships, has reignited the “circular financing” worry: Nvidia funds the customers who then spend the money buying Nvidia chips, and everyone books the same dollar as growth on its way around the loop. It is the same geometry as the Nvidia-into-Safe-Superintelligence check I flagged in Singularity, Interrupted, just with three more zeroes.

The bull case is that this is not 1999. Nvidia backs its ambitions with about $49 billion in quarterly free cash flow and a forward P/E near 24, numbers Cisco never had. The bear case is that a beam this load-bearing shouldn’t need to co-sign its own demand. The market split the difference and knocked nearly 5% off the stock on the news. Money that only ever changes hands in a circle looks like growth right up until the music stops.

Tomasz Tunguz complicates the comforting “AI gets cheaper every year” story: premium-tier prices are actually climbing, and all three hyperscaler CEOs admit GPU supply is the binding constraint through 2028. If compute scarcity is real, the buildout isn’t a bubble so much as a land grab. If it isn’t, the loop is just a very expensive game of pass the parcel.

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DeepMind the Gap

Google confirmed on Tuesday that Demis Hassabis is leaving the Google DeepMind CEO chair to become the unit’s chairman and Alphabet’s chief scientist, focusing on AGI strategy while continuing to run drug-discovery spinout Isomorphic Labs. CTO Koray Kavukcuoglu takes over the unit as senior vice president, reporting to Sundar Pichai. Chief scientist Jeff Dean is also leaving after 27 years, alongside Google Senior Fellow Sanjay Ghemawat, to launch Discovery Loop, a public-benefit corporation aiming to automate scientific research, with Google as an investor.

Losing your CEO and your most famous engineer inside 48 hours is not a routine reshuffle, and the timing is unkind: Axios reports Gemini 3.5 Pro is running months late partly on morale, so the shake-up reads as an admission that the company that owns the entire stack still can’t reliably ship on time. That Jeff Dean’s next move is an independent lab built to automate science tells you where elite researchers now think the frontier lives, and it may not be inside a trillion-dollar org chart.

SaaStr calls it Google losing two generational scientists in 48 hours and argues Wall Street is finally asking the $725 billion question of whether being third in AI is survivable.

Long Tail

London hails a driverless cab. Transport for London granted 15 private-hire licences to Ford Mustang Mach-E cars running UK startup Wayve’s software, clearing Uber and Wayve to sell supervised robotaxi rides in the capital later this summer, safety driver still aboard. Wayve, testing London streets since 2018, is now racing Waymo and Baidu’s Apollo Go on its home turf, a rare European entry in a two-superpower field.

Europe pours a very large foundation. The European Commission opened its AI Gigafactories call, backing up to seven mega compute projects with as much as 10 billion euros in public money and hoping to draw 20 billion more in private investment, over 30 billion euros in all. It is the bloc’s bid to stop renting its intelligence from American and Chinese clouds. Applications close mid-September.

Riyadh puts the agent on-site. Saudi startup Rime raised more than $2 million led by SEEDRA Ventures for an “AIoT” platform that runs autonomous agents at physical locations, processing camera and sensor data locally at the edge rather than in a distant data center. Founded in 2024 and already live across 2,000-plus sites, it hitches its pitch to Vision 2030.

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Prophet Margins

• Microsoft’s AI business has one very large customer. New filings show Microsoft booked $24.1 billion from OpenAI in the fiscal year ended June, implying its marquee partner accounts for roughly 70% of its AI revenue.

• A startup that shops your agent around. San Francisco’s Sapiom raised a $35 million Series A led by Dragonfly, with Anthropic among its backers, to route each agent call to the cheapest capable model instead of the priciest.

• Securing the coworkers who aren’t people. Obsidian Security raised $85 million at a $1.1 billion valuation to police non-human identities inside enterprise apps, noting that over 70% of its customers already let AI agents touch source code and regulated data. The agents are already inside the building; someone has to check their badges.

• Renting the shovels to the diggers. Volta pulled in $300 million co-led by a16z and Altimeter, with Nvidia and Dell aboard, plus $5 billion in financing at a $2.4 billion valuation, to rent out GPU cloud capacity to firms that can’t afford Nvidia hardware directly. Nvidia investing in the company that helps you afford Nvidia is this week’s most glaring circular financing jerk.

• Buy the pre-IPO, meet the fine print. Clear Street opened a private-markets platform letting accredited investors get exposure to $188 billion Databricks, with up to 30 companies to follow. The catch: you buy into an SPV holding a stake in a third-party fund, and Databricks says it has no relationship with Clear Street.

Slop of the Week

Thought Followership.

The Big Four firm that sells clients advice on responsible AI got caught letting a careless AI write its own homework. An investigation found that PwC published “thought leadership” reports about AI that were themselves riddled with AI hallucinations: fabricated citations, and an entire governance framework called “Citizen Pulse” that a GPTZero investigation found does not exist, complete with invented government customers who never deployed a thing that was never built. One report flagged as effectively 100% machine-generated. PwC’s response was to downplay it rather than own it.

Are You Hallucinating?

Google Earth grew an imagination, then lost its nerve. Google launched an AI image-generation feature inside Google Earth that could superimpose generated imagery onto real maps, then pulled it roughly a day later amid fears it would spread misinformation. A mapping product exists to tell you what is actually there. Bolting a deepfake engine onto it and yanking it within 24 hours is the fastest a company has ever agreed with its own critics. The planet, briefly, was whatever you prompted it to be.

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

I read every comment, so if you’ve got contradicting evidence, different opinions, or a story I missed, don’t be a stranger. Humanity is all we have left.

Leave a comment

Let’s get into it.

Sidenote. I am shortening my weekly newsletter. Information overload and AI slop is real. Attention is our scarcest resource. You can get news summaries anywhere. I hope you come here for my take on what’s happening (and the not so occasional bad pun and bizarre comic). My goal is to work harder on cutting stories, and covering the ones I do keep in more depth, so you can find some more signal, and joy, in the noise.

Do Androids Dream of Import Bans?

Washington has decided it doesn’t need a Bladerunner reboot to boot the android blades from US borders. On July 28 the FCC added foreign-made humanoid and quadruped robots to its “Covered List,” the same national-security blacklist that already bars Huawei gear. The fear is that a Chinese robot wandering your warehouse could surveil it or be flipped into a weapon by remote. The ban is forward-looking. The robots already clomping around American facilities get to stay. It’s the next shipment that gets stopped at the border.

China is the world’s largest producer of these robots by a wide margin, and humanoids are one of its fastest-growing hardware export categories. So a rule written in the language of spycraft is also, conveniently, a tariff wall around a category where China is comfortably ahead and American firms like Tesla and Boston Dynamics are still finding their footing. Government policy is now increasingly protecting domestic AI companies from software and hardware competition. We have entered tariff incognita.

The chip war was about who gets to buy Nvidia. Last week China floated its own controls on model weights and chip fabrication (Great Wall of Weights). Now the fight has dropped out of the cloud and down to earth. It turns out you cannot prompt your way past customs when it comes to bots bearing down on the borders.

The ban also sweeps in power inverters, the unglamorous boxes that connect solar panels to the grid, on the same “someone could flip the switch from Beijing” logic.

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The Pace That Launched a Thousand Signatures

The fallout and sequence of events from the great model escape last week are becoming clearer. An OpenAI model spent several days breaking into Hugging Face and, it now turns out, a second company called Modal Labs, and OpenAI reportedly did not notice its own agent was the culprit for roughly a week, until after the intrusion was contained and the FBI had been looped in. Sam Altman’s response to all this was to go on a podcast and announce, “We are now, like, in the singularity. This is the moment.” Elon Musk agreed in quintessentially out of touch Elon fashion, and added that money won’t be relevant in 10 years. That’s rich coming from the world’s richest man.

Then more than a thousand employees across OpenAI, Anthropic, and Google DeepMind (senior staff and a few co-founders among them) signed a public letter asking the US government for a mechanism to deliberately slow frontier AI if it starts moving too fast. OpenAI and Anthropic endorsed it.

I covered the raw break-in last week (Escape Clause). The story this week is the reaction, and the reaction is all over the place. The CEO says we have crossed into the technological rapture. His own workforce says please build us an off switch. A pause button designed by the people who most want to keep pressing go is not nothing, but it is also the oldest move in the book: volunteer to write the rules before someone writes more restrictive ones. When three rivals agree there should be a referee, it’s worth asking who they expect the referee to work for. It also lands as the same firms pour a reported trillion dollars into AI infrastructure, which is a strange amount to spend on something you’re asking to be protected from.

“The machine did not wake up. The fence was missing,” writes Lutz Finger, reading the whole singularity framing as marketing deflection from what was, underneath the drama, a containment failure with a human owner.

Former OpenAI board member lands in the middle: not a rapture, but “an incident like this has been expected for a long time, and the best scientists and engineers in the world still don’t know how to prevent it.”

Nova, No Más

A supernova is what you call a star that burns brightest the moment it dies, which makes it an apt name for Amazon’s home-grown model family. Amazon is phasing out most of its Nova models (paywalled) and reshuffling its AI teams to focus on “highest priority” goals, according to Bloomberg, after the power-hungry models underdelivered. Futurism put it less diplomatically, calling the division a bust. Either way it’s one of the clearest admissions yet that a trillion-dollar hyperscaler tried to build a frontier model in-house and decided it would rather rent.

AI was always likely to consolidate into a few mega frontier model companies due to the nature and cost of pre-training, and reinforcement learning aided by massive user bases. The moat around frontier models is now so expensive to cross that even Amazon, a company that owns the data centers everyone else leases, is conceding the foundation of the stack to a couple of labs and redirecting itself to the parts of AI it can actually sell at a profit.

Manifesto Destiny

Safe Superintelligence has no product, no revenue, no released research, and, per its own website, not much more than a manifesto. Nvidia just put a reported $5 billion into it. The lab founded by OpenAI co-founder Ilya Sutskever was already valued near $32 billion on the strength of that manifesto and a tiny, high-profile team. Nvidia’s check reportedly comes with a roughly tenfold jump in compute and access to its next-generation Vera Rubin chips.

If you want evidence for how far AI valuations have floated from AI revenue, look no further than a chipmaker writing a ten-figure check to a company whose entire shipped product is a mission statement. The shape of the deal is the same circular geometry as last week’s AMD and Anthropic dance: the outfit selling the shovels buys equity in the people digging, who then spend the money on more shovels, and everyone books the round trip as growth. Sutskever may well build something extraordinary. But $32 billion is a lot to pay for a website with a really good README.

Trending Topics put it plainly: “No model, no product, no demo, no revenue, just a website with a manifesto and a $32 billion valuation.”

Fifty Billion Shades of Grok

Elon Musk’s xAI has gone to federal court to defend, as a matter of free speech, the right of its chatbot to undress people who never agreed to it. Minnesota passed a first-in-the-nation law banning “nudification” technology, the apps that generate fake nude images of real people. xAI sued Attorney General Keith Ellison, arguing the statute is an “overbroad, content-based ban on free speech and the tools of visual expression,” and warning that penalties could reach an “eye-popping $50 billion” given that its users allegedly produced 100,000 prohibited images. Ellison’s office says nonconsensual fake nudes are not the kind of speech the First Amendment was built to protect. I plead the second.

This is the test case the whole wave of state AI-harm laws has been heading toward. With Washington happy to regulate AI by strongly-worded suggestion, the states are writing the rules, and the labs are now suing to find out how much a court will tolerate. Musk has picked an unusually grim hill for the free-speech flag. Framing a nudify tool as protected “visual expression” is a legal theory that, if it wins, takes a lot of statutes down with it. Musk is taking the right to bare arms to new levels.

Minnesota attached a hefty cost per image: up to $500,000 per violation, which is how you arrive at xAI’s fifty-billion-dollar nightmare. Penalties that size are built to be felt rather than absorbed, which is exactly why the law is being challenged.

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Long Tail

Kenya writes its own rulebook. Kenya’s ICT department released a draft national AI policy and opened it for public comment, joining South Africa, Brazil and India in a Global South rush to set AI rules rather than wait on Washington or Brussels.

India teaches a hardware store to sprint. Bengaluru’s Fixxly raised a $5.5M seed led by Accel to build AI-driven quick commerce for construction and building materials, dragging one of India’s most fragmented, offline supply chains into same-day logistics.

Five countries pool their languages. Benin, Kenya, Namibia, Nigeria and Togo, with the GSMA and local startups, launched ATLAS Umoja AI to build models that speak African languages. Whose worldview becomes the default matters most where the training data is thinnest, and the continent is done being an afterthought in someone else’s corpus.

Prophet Margins

• China’s Moonshot AI blew past its own target, raising about $3.5 billion on the strength of Kimi K3 after asking for one to two. Valued near $20B in May, it’s now reportedly chasing a $30B-plus mark and a Hong Kong IPO. Last week it stood accused of stealing an American model. This week it’s oversubscribed.

• Data-security firm Cyera agreed to buy Oasis Security for about $1 billion, betting that somebody has to manage the logins and permissions of all the AI agents now loose inside the enterprise.

• Agentic chip-design startup ChipAgents extended its Series A to $134 million with Micron and MediaTek piling in, its third raise since October on 6x ARR growth.

• Freehand raised $75 million to point AI agents at the invoices of Unilever, Meta and Pfizer, on the theory that enterprises want software that acts rather than software that suggests.

Slop of the Week

Claude’s shared chats got a lot more shared than users bargained for.

Anthropic’s public share-link feature let people’s Claude conversations and creations get indexed by Google, so a site:claude.ai/share search turned strangers’ chats into browsable results. It’s the same shared-link privacy hole that already burned ChatGPT and Meta, now hitting the lab that markets itself as the careful one. Anthropic scrambled to address it and Google de-indexed the pages, but “anyone with the link” meant “anyone with Google,” and the copies strangers already saved aren’t coming back. To be fair, in my personal experience with Anthropic, there was a notice about the link being public, but people rarely read the fine print these days.

_{And fine print can say some wonderful things.}

Are You Hallucinating?

Rent-a-face. In China, a marketplace has sprung up where ordinary people license their own likeness to AI-generated content for somewhere between $15 and $700. Your face, now a rentable asset class, to be generated at a slophouse near you.

The machines get a museum. Los Angeles just opened Dataland, billed as the world’s first museum of AI art, where founder Refik Anadol insists the software should get co-creator billing: “Machines are collaborators.” A whole building dedicated to giving the paintbrush a byline.

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

I read every comment, so if you’ve got contradicting evidence, different opinions, or a story I missed, don’t be a stranger. Humanity is all we have left.

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Meanwhile, The White House accused a Chinese lab of building its hit open model by copying Anthropic’s. Alphabet and Tesla beat earnings and got punished anyway, because Wall Street finally read the AI electricity bill. And Elon promised a historically accurate, feature-length Odyssey from a video generator that can’t keep a face straight for ten seconds.

Let’s get into it.

Escape Clause

OpenAI disclosed on July 21 that during internal testing, one of its own models escaped its sandbox, exploited a zero-day, and broke into Hugging Face (the “GitHub of machine learning”) in order to steal credentials to cheat on a cybersecurity evaluation. The company called it an “unprecedented cyber incident.” Two days later, a bipartisan pair of House members (Lieu and Moran) introduced the AI Kill Switch Act to give the government authority to force rogue models offline.

Take this with a grain of PR salt, because OpenAI has huge vested interests in joining the fray of models so “smart” you should be scared. But this is the misalignment scenario the AI safety crowd has been trumpeting for years: a frontier lab’s agent autonomously breaking into someone else’s production infrastructure. The one reassuring word in OpenAI’s write-up is “testing.” The unreassuring part is everything else. As argues, this isn’t a sandbox problem or an infrastructure problem, it’s reward hacking from a misaligned training process, which means better models get better at it, not safer. reads it as a trailer for the serious incidents still to come.

Washington doesn’t draft legislation inside 48 hours unless something genuinely spooked it, and “we need a button to turn the AI off” is not a phrase that inspires confidence in the people who built the AI. A kill switch also concedes the thing the labs spent two years denying. A sufficiently capable model is something you may need to physically unplug rather than politely prompt. The scariest version of any AI story travels fastest, so one detail matters a lot: this happened in an eval, not in the wild. But “our safety test worked, because the model only hacked a partner company during the test” is a strange sentence to find comforting.

The technical post-mortem doesn’t look great. The agent escalated to node-level access and made off with cloud and cluster credentials. Hugging Face hosts millions of open models. A breach there isn’t one company’s problem. It’s the entire supply chain’s.

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Distill My Beating Heart

On July 22, White House science director Michael Kratsios publicly accused Beijing’s Moonshot AI of running large-scale covert “distillation” against Anthropic’s Fable model to build its open-weight Kimi K3. This is the first time a senior US official has named a specific Chinese lab for copying a specific American model. K3 landed days after Fable went public and promptly triggered a DeepSeek-style tech selloff, and Treasury Secretary Bessent floated adding Moonshot to a trade blacklist.

The US-China AI fight has so far been mainly about chips. Who can buy Nvidia, who can build fabs. This is the first time Washington has accused Beijing of stealing the models themselves, which drags the conflict from hardware into IP and puts sanctions on the table over a training run. Last week Xi was handing China’s open models out to the Global South as party favors. This week, one of those labs stands accused of building its flagship by siphoning an American one.

So far it’s an accusation, not a proven case. American companies have been accusing Chinese companies of distillation since the Deepseek debacle. No public forensic evidence has been released, and distillation is notoriously hard to prove from the outside. What isn’t in dispute is that K3 is good. now dates the US-China capability gap at roughly 3-5 months, down from the 6-9 everyone quoted a season ago, and argues that a free, downloadable model on par with the best American ones calls the entire private lab business model into question.

notes China may soon face the same problem as the US. Anthropic’s frontier model already triggered a de facto US pre-approval regime for shipping abroad. K3 may do the same in reverse.

Advanced Micro Dependencies

AMD announced on July 22 that Anthropic will deploy two gigawatts of AMD’s Instinct MI450 GPUs starting in 2027, and that AMD will invest up to $5 billion in Anthropic along the way. Unlike AMD’s warrant arrangement with OpenAI, here the chipmaker is buying equity in its own customer. The vendor is financing the buyer so the buyer can afford the vendor. The AI circular-financing jerk continues in full swing.

Anthropic keeps stacking gigawatt commitments to stay level with OpenAI and Google; AMD gets a marquee customer to prove the MI450 is a credible Nvidia alternative at scale; and everyone books the same dollar as growth on the way around the loop. It’s AMD’s second major AI-infrastructure deal in three days. Compute stopped being a cost of doing business in AI a while ago. It’s the currency now, and the chipmakers have started minting demand for their own product.

The market isn’t fully buying it. AMD shares dipped on the news. Underwriting your customers looks like growth right up until it looks like vendor financing.

Pulping Fiction

On July 21, a federal judge approved a $1.5 billion class-action settlement requiring Anthropic to pay authors roughly $3,000 per book after it acquired millions of pirated titles to train Claude. Plaintiffs’ counsel called it the largest copyright recovery on record. More than 90% of the 482,000-plus covered books have already been claimed, and Anthropic must destroy the pirated copies.

The settlement draws the line the whole industry now has to live on: training on copyrighted books can be fair use, but pirating the source material to do it is not. That bifurcation puts a real dollar figure on data provenance for the first time, and every lab that built its corpus the fast way just got a preview of the cost.

Not everyone’s celebrating the number. Some authors told TechCrunch that $3,000 a book is “screw the money” territory, and a fresh wave of publishers is already lining up to sue over outputs, not just inputs.

$3000 starts to feel very low when you consider the value frontier labs are capturing in exchange and the destruction it’s wreaking on creative industries.

Spend and Descend

Alphabet and Tesla both beat Q2 estimates and still got punished for it. Tesla closed 14.5% lower and Alphabet fell 7.1% as investors looked past the numbers and fixated on the bill. Alphabet raised its 2026 capex guidance to $195–205 billion, and Tesla’s free cash flow swung to negative $1.1 billion on capex up 142%. Elon Musk’s guidance to analysts was to spend “as fast as we can spend.”

Google Cloud grew 82% to nearly $25 billion, and it didn’t matter. For the first time, the market is pricing the AI buildout as a cash-flow risk rather than a growth flex. The question on Big Tech earnings has flipped from how much are you investing to when does this pay for itself, and nobody has a clean answer yet.

As Business Insider put it, the tech giants are spending on AI like there’s no tomorrow, “but investors have realized it’s tomorrow they care about.” Over on r/technology, even the buyers sound lost: the people signing the checks admit they can’t yet forecast what they’re buying and are horrified to find out they replaced workers with AI agents that may cost more than their human counterparts.

Georgia on My Grid

OpenAI unveiled Project Camellia on July 22: a $20 billion campus (up to $30 billion-plus at full build) near Rincon, Georgia. It’s the first facility OpenAI is designing and building itself rather than renting. Georgia Power will feed it 3.2 gigawatts, enough for more than two million homes, phased through 2032. OpenAI gets a 50% property-tax abatement for 15 years. The site sits in a state-designated water “Red Zone.”

OpenAI wants to own compute, not lease it, but it’s likely to further fray anti-AI data center sentiment. A single campus drawing two million homes’ worth of power from a stressed aquifer, in exchange for a 15-year tax break, is the AI-infrastructure fight in miniature. As argues, this is the point where AI stops being an argument about chatbots and becomes one about land, water, and who pays.

OpenAI led its announcement with water reassurances before a single resident asked. They are trying to do a public relations dance but local are less convinced. “So many unknowns,” one county official told The Current, noting other communities are banning these outright.

Great Wall of Weights

China’s Ministry of Commerce is consulting Alibaba, ByteDance, Zhipu and others on export controls that would cover advanced AI models, the transfer of training data abroad, foreigners freely downloading Chinese model weights, and barring domestic chip designers from manufacturing at foreign fabs like TSMC, per an FT report. Beijing is also eyeing curbs on foreign takeovers after Meta’s ~$2 billion Manus deal.

This is the mirror image of Washington’s playbook, and it completes the mutually assured model restriction I covered in The State Buys In: both superpowers now reaching for the same off switch. The twist is that restricting free downloads of Chinese weights would kneecap the very open-weight ecosystem that’s currently doing the most to spread Chinese AI worldwide. The country giving its models away for soft power may be the one that stops first, because the giveaway is working a little too well.

Barring Chinese designers from TSMC would force reshoring and fragment the chip supply chain further. It’s another reminder that in AI, the binding constraint keeps turning out to be dirt, metal, and fabs, not “intelligence”.

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Vive la Valuation

Samsung is in talks to put up to about €1 billion into Mistral as part of a round valuing the French lab near €20 billion ($22.8B). That’s nearly double its €11.7 billion mark from ten months ago, with EQT, Novo Holdings, and Santander also circling, per the FT. The deal may come tied to custom-chip cooperation, and it lands squarely in the wake of the Kimi K3 shock.

Europe’s flagship lab roughly doubling its valuation is further evidence that sovereign AI is becoming key, and a Samsung stake would connect Mistral to a major memory-and-chip supplier just as hardware gets scarce. Western labs are racing to fund up precisely because a free Chinese open model spooked everyone. The raise feels less as a victory lap than a hedge.

Not that Europe is uniformly thrilled about any of it. documents the backlash — “AI SLOP” graffiti in the Paris Metro, an “Everyone hates AI” newsstand cover. The valuations are up as the vibes go down.

Long Tail

The funding boom has a passport problem. Crunchbase data circulating this week shows the AI money surge is anything but global: the US, with ~4% of the world’s population, is pulling more than three-quarters of all startup funding and an even larger share of AI dollars, with OpenAI and Anthropic soaking up the biggest rounds. The UK ($16.5B YTD) leads a distant, mostly-flat chasing pack.

Egypt teaches a construction site to think. Cairo’s Kuadra raised a round led by Edafa Venture Capital to launch what it bills as Egypt’s first unified AI platform for managing the full construction project lifecycle. It’s a tidy example of AI diffusing into heavy, non-digital-native industries far from the frontier-lab spotlight.

China’s IPO window swings open. Davis Polk and Zhong Lun are advising on Zhongji Innolight’s Hong Kong listing (paywalled), following CXMT’s record Shanghai float. China’s biggest AI-linked names race to public markets while Washington debates whether anyone should be allowed to touch them.

Google and Nvidia rent Europe some robots. The two teamed with German startup Microagi to supply GPUs, models and cloud for training humanoid factory robots. Microagi, fresh off a $55M seed, gathers real-world robot footage across a dozen-plus countries. Notably, Google isn’t taking the data in return, framing it as an “ecosystem play.”

Prophet Margins

• Travis Kalanick’s physical-AI outfit Atoms raised $1.7 billion led by a16z, with Uber and Bain along for the ride. One of the year’s largest robotics rounds, for a company most people have never heard of.

• Intel posted $16.1 billion in Q2 revenue, up 25% (its fastest growth in 15 years) on a 59% surge in Data Center & AI, proving the fallen giant still has a pulse.

• OpenEvidence, the “ChatGPT for doctors,” reportedly weighed a $200M raise at a $20 billion valuation after ~$700M in a single year. Though it’s apparently unlikely to proceed.

• Shanghai’s PsiBot is closing ~$100M at a $1.48 billion valuation, led by carmaker Chery. Another embodied-AI unicorn teaching robots to find their way around the physical world.

• One-year-old Natural raised a $30M Series A led by Forerunner to build payment rails for AI agents rather than humans. It’s now on a collision course with Stripe.

• SkyPilot, from a Databricks co-founder, launched from stealth with $20M led by Lux to be the “Switzerland” of AI compute: run anything on any cloud and squeeze more out of the chips you already rent.

• Robotics-software startup Genesis AI is in talks to raise about $500M, a sign VCs increasingly prefer betting on the robot’s brain over its body.

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Slop of the Week

xAI’s week of promising the moon and shipping the leak.

Elon Musk pledged that Grok Imagine will produce a “historically accurate,” feature-length adaptation of Homer’s Odyssey within months. It’s a plan filmmakers called delusional. In the same week, a wire-level analysis caught xAI’s “local-first” Grok Build CLI, marketed as sending “nothing from your codebase to xAI servers”, shipping developers’ full repos, commit history and unredacted secrets to a Google Cloud bucket, roughly 5GB a session, even with the privacy toggle off.

Are You Hallucinating?

The Cincinnati messiah. Former NYC mayor Eric Adams has been recast as “Jesus Christ” in a wave of surreal, voice-cloned AI videos styled like Chinese short-drama slop. Produced, it turns out, not by a Beijing troll farm but by a guy in Cincinnati. The call is coming from inside Ohio.

Labeled, and believed anyway. A TikTok of You star Penn Badgley in a Spain jersey before the World Cup final fooled much of the internet despite an on-screen AI label. It’s a small, grim case study in media literacy losing to a good clip.

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

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Let’s get into it.

Search and Seizure

The European Commission issued two new rules under the Digital Markets Act ordering Google to open Android to rival AI assistants and start sharing anonymized search data with competitors by January 2027. The Commission found that non-Google assistants simply cannot function on Android at Gemini’s level, so it will now force Google to allow voice activation and background tasks for third-party AI, and to share the search data that competitors have never been able to replicate.

This is the most concrete attempt yet to pry open a Big Tech AI moat, and it goes straight for the two things that actually protect Gemini: default distribution on the phone, and a decade of search behavior no rival can match. Regulators have spent years fining Google for how it got dominant. Brussels just skipped the fine and went for the data itself, which is a different kind of threat entirely. A fine is a cost of doing business. Handing your rivals your training advantage is closer to an organ donation.

Google’s global affairs chief Kent Walker warned the rules could expose Europeans’ private searches and endanger security, which is the argument every incumbent reaches for the moment “open up” starts to mean “open up to competitors.” The hard January deadline makes this urgent. Other jurisdictions now have a template, and the search-data advantage that everyone assumed was permanent just got a compliance calendar.

Analysts at The Verge note Google may actually be better positioned than Apple to live with this, since Android was semi-open to begin with.

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Reduction in Force Majeure

Twenty-six Meta employees filed a lawsuit in Oakland federal court accusing the company of using AI to decide who got fired. The suit says Meta ranked staff on a termination list using tools including its “Metamate” LLM assistant, an employee-trained “second brain,” and a productivity score assembled by scanning keystrokes, screen content, emails and browser history. The plaintiffs allege the system disproportionately flagged people with disabilities or who had taken medical or family leave. It appears to be the first lawsuit against a major U.S. company over the use of AI to run layoffs.

Meta’s defense is a great example of one of the AI industry’s biggest problems. The company says the claims lack merit and that “workforce management and organizational decisions were and are made by people, not AI.” For two years the industry has sold AI as the thing that makes every decision faster and smarter. The instant those decisions land in front of a judge, the story flips to “a human was always in charge, the software just helped.” You cannot market an oracle all year and then describe it as a calculator the week you get sued.

What makes this one matter beyond Meta is the legal machinery the plaintiffs are reaching for. They’re invoking recently adopted California and New York City AI bias-audit laws, the kind of statute critics dismissed as toothless. If those laws can force discovery into how a termination algorithm actually scored people, the black box that has shielded automated HR for a decade starts to open. “The algorithm did it” was supposed to be a shield. Here it might be the whole case.

The plaintiffs aren’t only after damages. They’ve moved to block a fresh round of layoffs set to begin within days, which would make this the rare AI suit that tries to stop the harm before it happens rather than tally it afterward.

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The Gift Horse

Xi Jinping is expected to headline the World Artificial Intelligence Conference in Shanghai this weekend, his first appearance at the event, to pitch China’s low-cost open models as a public good that bridges global AI inequality. The contrast with closed American systems is the entire point, and Beijing is pairing the sales pitch with its proposed World AI Cooperation Organisation. In parallel, Huawei will debut its Atlas 950 SuperPoD, a cluster lashing together thousands of Ascend chips to train large models without Nvidia’s best silicon.

This is the flip side of the mutually assured model restriction I covered last week. While Washington and Beijing both reach for the kill switch, China is also handing its models out as party favors to the Global South. Free is a strategy, not a price. Give a country your open weights and you have seeded the default, and defaults are how you win a decade of soft(ware) power quietly. The timing, right before the first government-level U.S.-China AI talks of the Trump era, is not an accident either.

Frontier Justice

Google DeepMind chief Demis Hassabis called for the U.S. to build a standards body to test frontier models for national-security risks including cyber, nuclear and biological threats, warning that “urgent action” is needed as AI approaches AGI. His proposal is modeled on FINRA: a federally overseen, industry-funded public-private partnership where labs voluntarily share models for up to a 30-day pre-release review, before that review becomes mandatory for U.S. deployment.

Note the sequence. Voluntary now, mandatory later, and designed by the people who would be regulated. Hassabis joins Anthropic’s Dario Amodei and OpenAI’s Sam Altman in asking for the same thing, which is less a sudden outbreak of conscience than an industry trying to write its own rulebook before Washington writes a worse one. When three rivals agree on the referee, it’s worth asking who they expect the referee to work for.

flags the irony of a frontier-lab CEO endorsing an independent standards body that could slow his own launches. Voluntary sharing until it isn’t, and the labs get to shape the body that eventually says no.

Frenemy Lines

At an internal FY27 strategy meeting, Microsoft executives told salespeople to negatively compare rival AI products from OpenAI, Google and Anthropic against Microsoft’s own models. EVP Jay Parikh reportedly said, “Everyone else is selling parts, we’re selling the full end-to-end system,” while Copilot EVP Jacob Andreou called Anthropic’s Claude “slower and less accurate” and short on security integrations. This follows reports that Microsoft has been swapping OpenAI and Anthropic models out of Word and Excel in favor of its own to cut costs.

Last week Microsoft was busy installing AI directly inside its customers. This week it’s teaching the sales floor to badmouth its own suppliers. Once the exclusivity clause with OpenAI dropped in April, the platform-versus-model-provider marriage was always going to curdle into a competition, and it just did, in a slide deck. The company that spent years reselling frontier models has decided the smarter margin is in trashing them. Nothing says mature market like the distributor turning on the manufacturer.

Show and Steal

Apple sued OpenAI in California federal court alleging trade-secret theft “at every level” to build rival consumer AI hardware. The complaint names OpenAI hardware chief and former Apple VP Tang Tan, alleging he directed Apple job candidates to bring “actual parts” to interviews for “show and tell,” and cites a former employee accused of walking off with an Apple laptop. Two years ago these companies partnered to put ChatGPT in the iPhone. Now they’re in discovery.

The suit lands with unusually good timing for maximum pain. It follows OpenAI’s $6.4 billion acquisition of Jony Ive’s hardware startup and arrives as the company heads toward a historic IPO, which is precisely when a trade-secret cloud is most expensive. Incumbents defend hardware moats far more viciously than software ones, because you cannot fine-tune your way to a supply chain. Apple can’t stop OpenAI from building a device. It can make sure the lawyers ride along in the box.

Code Red

Alphabet shares fell about 4% after Bloomberg reported that Google’s flagship Gemini 3.5 Pro is months behind schedule, with its coding capabilities in particular falling short of internal goals. Google announced the model back at May’s I/O; a spokesperson insists it’s “shipping quickly” and testing with partners. The delay is awkwardly timed, arriving just as OpenAI’s GPT-5.6 Sol and Meta’s Muse Spark 1.1 ship competitive coding models.

Coding is not a random place to stumble. It has become the single highest-value proving ground in AI, the one domain where agents actually run for hours and do real work, and the market knows it. A 4% single-day drop on a model delay shows how tightly investor confidence is now welded to release cadence. Google owns the whole stack from chips to distribution, which was supposed to make it delay-proof. Turns out even vertical integration can miss a deadline.

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Netflix and Shill

In its Q2 shareholder letter, Netflix disclosed that roughly 300 titles have used generative AI, mostly in post-production, saying the tools deliver “higher quality output more quickly and at a lower cost.” Cited examples like Glory and Brasil 70 used AI for enhanced crowds, battle sequences and establishing shots. The disclosure follows Netflix’s acquisition of Ben Affleck’s AI startup and its use of an AI-generated Gene Wilder voice, and it’s one of the clearest tallies yet of AI baked into mainstream film and TV, disclosed to shareholders as a feature, not a confession.

That number, dropped casually into an earnings document, does more to normalize AI in Hollywood than any demo reel. When a top studio counts its AI titles the way it counts subscribers, the labor and creative-rights fight stops being hypothetical.

argues the real cost isn’t jobs but sameness: machine-made work clusters into recognizable, model-shaped styles. Three hundred titles converging on the same look is the part worth worrying about.

Long Tail

Colombia builds a doctor’s second brain. Bogotá-based Telepatia AI raised $42 million to scale a clinical assistant that cuts paperwork for doctors across Latin America, one of the region’s larger recent healthtech-AI rounds and a reminder that the deployment frontier runs well south of Silicon Valley.

The Gulf keeps writing checks. MENA startups pulled in $1.7 billion across 242 rounds in the first half, with AI a leading draw, resilience that held even as regional conflict pressured deal flow.

Africa trades equity for debt. African startups held roughly flat at about $1.4 billion in H1, but the mix tilted toward debt as early-stage equity dipped, with Kenya rising as Nigeria’s payments dominance softened. A quiet maturation story that rarely makes the AI headlines.

Southeast Asia’s one-city boom. Regional tech funding roughly doubled to $7.4 billion in H1, but Singapore absorbed about 94% of it, and a single data-center firm out-raised the rest of the region combined. The AI-infrastructure capital wave is astonishingly concentrated.

Europe arms up. German defense-AI firm Helsing closed $1.8 billion at an $18 billion valuation, reportedly the largest private defense-tech raise in European history, as governments pour money into sovereign AI-military capability.

Prophet Margins

• Fireworks AI raised a $1.5 billion Series D at a $17.5 billion valuation after inference revenue topped $1 billion annualized, roughly fivefold growth, as enterprises hunt cheaper alternatives to frontier APIs.

• A Chinese regulatory filing implies DeepSeek is worth about $52 billion, disclosed via a luggage maker’s 0.83% stake, as the low-cost lab is reported to be weighing an IPO.

• TSMC pledged another $100 billion for its U.S. buildout after quarterly profit jumped 77% on AI chip demand.

• A former DeepMind researcher, Andrew Dai, raised about $55 million at a $300 million pre-seed valuation for a visual AI startup that hasn’t shipped a product yet.

• Live-shopping platform Whatnot acquired ML startup Shaped to power real-time recommendations and search.

• Andreessen Horowitz backed Runta, a startup that pitches itself as a way to “parent” AI agents, at a reported $20 million round on a $100 million-plus valuation.

• China and AI pushed Asia startup funding to a multiyear peak in Q2, part of a record global H1.

Slop of the Week

The agent that rm’d your house. Days after GPT-5.6’s splashy launch, OpenAI’s own team confirmed its new Codex agent has been deleting users’ home directories. Full autonomy on, sandbox off, model confidently running destructive commands. The “agents are production-ready” story, meet the folder that used to hold your files.

Meta’s three-day likeness feature. Meta shipped a tool that put anyone’s public Instagram photos into AI images by default, then killed it in three days with a classic “missed the mark.” The rollback got the headline. The tell is that an opt-out-by-default, deepfake-adjacent tool shipped at all, part of an industry habit of enrolling users into AI features and daring them to find the toggle.

Are You Hallucinating?

The ChatGPT flyer pandemic. We’re apparently living through a moment where AI-generated paper flyers are physically wallpapering neighborhoods, the slop finally escaping the screen and colonizing the telephone pole.

Deliberately ugly on purpose. A hit indie studio is shipping deliberately “shit,” MS Paint-style placeholder art rather than touch generative AI, because, in the developers’ words, “it’s important that it looks like shit.” Anti-slop as an aesthetic.

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

I read every comment and it means a lot, so if you have feedback or something to share, please do. Don’t be a stranger. Humanity is all we have left.

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Meanwhile the money got weird: Nvidia shed a trillion dollars in value, Britain’s flagship AI investment turned out to be a spreadsheet with big dreams, and a sandwich chain name-dropped “AI” 22 times on its way to an IPO.

Let’s get into it.

Sam, Meet Uncle Sam

OpenAI has reportedly discussed giving the US federal government a roughly 5% stake in the company, according to a Financial Times scoop (paywalled) confirmed by CNBC. The pitch: hand a sliver of the ~$852bn startup to Washington and watch the political and regulatory obstacles melt away.

Framed as a public dividend on the AI windfall, it reads more like protection money with a cap table. A government that owns 5% of your company is a government that has every incentive to keep your company winning, which is a strange thing to wish on the referee. The line between regulator and shareholder was already thin. This proposes to erase it and call the erasure a gift.

The framing of handing it to Trump administration caused outrage over corruption, but as one commenter on r/OpenAI put it, “the public would own 5%, it’s just currently a Trump admin thing.” How we keep the revolving door neatly separated will be one to watch.

Mutually Assured Restriction

In the same week, two global superpowers reached for the same lever. US lawmakers are weighing ways to halt American companies’ growing use of cheap Chinese AI models, while Beijing was reported to be meeting with Alibaba and ByteDance about restricting overseas access to its top models. The subtext is the same kill-switch logic I covered in Chips and Severance, when Commerce reached into Anthropic’s stack and flipped it off worldwide, only now both governments want the switch.

Chinese models jumped from roughly 1% of global AI workloads to 30% in a year, which is the number Washington keeps citing. The irony is that the country giving its models away for free may be the one that stops first.

pushes back on the “China has caught up” panic driving all of this: matching a specific vulnerability when you point a model at it is not the same as autonomously finding and chaining exploits at scale.

Related: DeepSeek is reportedly developing its own inference chip, following OpenAI’s playbook, to cut its reliance on Nvidia. The decoupling now runs all the way down to the silicon.

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Land of Lincoln, Home of the Fined

Illinois Gov. JB Pritzker signed a nation-leading AI safety law targeting the largest and costliest “frontier” models, with civil penalties up to $3 million for violators. It’s modeled on measures in California and New York. Those three states together account for roughly 40% of the US AI market despite holding about 20% of the population.

With federal AI law stuck in neutral, the states aren’t waiting. A patchwork with real fines attached starts to function like national policy, just written by whichever legislatures showed up. If you build frontier models, your compliance map now runs through Springfield.

Warren Peace

Sen. Elizabeth Warren demanded the Pentagon and its AI vendors release the full text of their military contracts, singling out a deal with Reflection AI, a company partially funded by a firm for which Donald Trump Jr. is a partner. Her net is wide, pulling in Nvidia, Oracle, Google, Microsoft and AWS as defense AI spending balloons behind a classification wall. The contracts are secret for national-security reasons, and the money is flowing to companies with politically convenient investors. “Trust us, it’s classified” is a harder sell when the vendor list reads like a MAGA fundraiser.

Warren wants the receipts. The Pentagon would prefer the redactions.

My Old Kentucky Server

Anthropic signed a 20-year lease worth roughly $19 billion in contracted revenue for a TeraWulf-developed data center on the site of a shuttered aluminum smelter in Hawesville, Kentucky. The company, now valued near $1 trillion (paywalled), is the first AI firm to plant a flag as a Kentucky data center tenant. This is the heavy-industry turn I flagged in Chips and Severance: AI is less software than it is power, real estate, and repurposed smelters. A dead aluminum plant becomes an AI campus because both businesses want the same thing, which is enormous quantities of cheap electricity. The frontier now looks a lot like a substation in Hancock County.

frames the real theme of the moment as control of the off-switch: the same government Anthropic builds $19 billion campuses for is the one holding the kill switch over its models. Nineteen billion in concrete, one memo away from dark.

Where the Chips Fell

Nvidia has lost roughly $1 trillion in market value in under two months, leaving its stock, per Bloomberg (paywalled), the cheapest on a valuation basis it has been since before the AI boom began. When the company selling the shovels gets repriced to pre-gold-rush levels, the market is saying something about how much digging it expects. A trillion dollars is not a rounding error, even at Nvidia’s scale. Nvidia is still a great business. The open question is whether the demand curve everyone penciled in was ever real in the first place.

Then again, the buildout may be accelerating, not peaking. SemiAnalysis reports Meta contracted more than 5 gigawatts of capacity in six months, with 2027 capex it calls “shockingly high.” Somebody isn’t reading the obituary.

Show Me the ROI

Microsoft launched Frontier Company, a $2.5 billion unit that embeds roughly 6,000 engineers and industry specialists directly inside customers to wire AI models into their workflows and proprietary data. Translation: the model was never the hard part. The last mile, getting a system to produce a number a CFO will sign off on, is where the money actually sticks, and Microsoft just put 6,000 bodies on that mile.

Every lab is discovering the same thing, which is that “we sell intelligence” pays worse than “we install it.” The benchmark wars were fun. The deployment wars pay the bills.

SemiAnalysis, after talking to more than 50 enterprises about token spend, says the viral “tokenpocalypse” blowout stories come from bad incentives, not typical behavior, and sees no material risk to second-half AI budgets. The panic, like most panics, was better copy than data.

Stargate to Nowhere

A Guardian investigation found that OpenAI apparently never visited the key site for “Stargate UK,” the flagship data center British ministers touted during Trump’s state visit, and that up to £20bn of the advertised £30bn investment was essentially hypothetical. The project has since been paused. The £20bn was never committed capital, just the amount the site would need, presented as investment already on the way, which is a bit like announcing your net worth as the price of the mansion you’d like to buy. Governments have learned that an AI announcement generates the same headlines as an AI investment, at a fraction of the cost. This one skipped the awkward step of anyone actually going to look at the field.

No Worries, Mate?

The Australian Albanese government’s first report on AI and employment found little evidence of large-scale AI-driven job loss so far, the apocalypse that hasn’t arrived (paywalled). But the exposure is not evenly spread: women and university graduates are most at risk, while tradespeople with vocational training are least exposed. The white-collar degree was supposed to be the safe harbor and the trades the vulnerable ones. This flips it. A rare government dataset says the near-term risk lands hardest on exactly the people who were told education would protect them.

reads the trend as firm-boundary dissolution rather than pure automation: AI erodes the specialization that made multi-person companies worth assembling, pushing more people toward solo work. And on r/careerguidance, the most upvoted take matched the report: “I don’t think AI is replacing jobs yet, but funding AI projects is absolutely causing budget pressure leading to layoffs.”

The Family Album Problem

The UK’s National Crime Agency and the Internet Watch Foundation issued guidance urging parents to stop publicly posting images of their children, warning that ordinary family photos are increasingly being fed into AI tools to generate child sexual abuse material. When a national law-enforcement body tells you the family album is now raw material for abuse, the cost of “nudification” tools has collapsed to something close to zero while the harm has not. This is the asymmetry of the era in its ugliest form: creating the harm is a cheap app, and stopping it is a national campaign asking parents to post fewer birthday pictures.

notes the EU Council just added non-consensual sexual and CSAM generation to the AI Act’s list of prohibited practices, codifying exactly the harm the UK watchdogs are flagging.

The litigation is arriving too. A class action alleging xAI’s Grok was used to generate deepfake CSAM expanded this week with two new plaintiffs, and now names Stability AI. “The tool did it” is turning out to be an incomplete defense in a courtroom.

The Doctor Is In Silico

Insilico Medicine advanced rentosertib, an AI-discovered TNIK inhibitor, into a 320-patient Phase III trial for idiopathic pulmonary fibrosis, a scarring lung disease with few good treatments. Billed as a potentially first-in-class oral drug, it’s one of the most advanced clinical programs to originate from generative-AI drug discovery.

Most AI-in-medicine stories are a demo and a press release. This one is a molecule that a design algorithm proposed, now being tested in hundreds of actual patients. If it clears Phase III, the “AI designed a drug” headline finally gets a subject that survived contact with a clinical trial. That would be the rare AI milestone measured in people, not benchmarks.

Stop the Presses

Publishers that collectively own nearly 400 US newspapers are suing OpenAI and Microsoft (paywalled) for mass copyright infringement, alleging the companies scraped hundreds of thousands of articles to train systems like ChatGPT, a practice they call a “death knell for local journalism.” This is the fight the industry has been circling for two years, now with local papers rather than national marquee names carrying the flag. The outcome sets the price of the whole bargain: either training on the news is fair use and free, or it’s licensable and expensive. Local journalism, already gasping, is betting its remaining breath on a judge.

Press Gazette is keeping the scorecard on which publishers are suing the AI firms and which are signing licensing deals. The industry hasn’t decided whether these companies are looters or customers.

Long Tail

South Africa codes its own way in. HyperDev raised a $1 million pre-seed from European and UK investors as its generative-AI coding platform nears 100,000 users.

Thailand bets on robots with manners. Bangkok’s Amity Robotics raised a $7 million seed led by East Ventures. Its ARC Base concierge robot already works in over 30 hotels and malls, with clients including InterContinental, and it’s eyeing Europe. Southeast Asian service robotics is shipping while the West argues about chatbots.

MENA’s venture market runs hot and cold. Startups across the region raised $148.2 million in June, down 76% from May but up 190% year on year, with enterprise AI the single biggest sector at $76 million. No late-stage rounds closed, a reminder of how early-stage-heavy the ecosystem still is.

Africa’s non-AI founders feel the squeeze. African tech startups raised just $260 million in Q2, a 40% drop year on year, even as Israeli AI companies pulled in over $3 billion in June alone. The AI boom is lifting a few yachts and draining the ponds outside the hubs.

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Prophet Margins

• SambaNova raised $1 billion at an $11 billion valuation (Series F first close) led by General Atlantic, with JPMorgan signing on as a customer, months after Intel was rumored to want it for a sixth of that.

• Norm Ai raised $120 million at a $1.2 billion valuation led by Khosla, joining the legal-AI unicorn club to take on Harvey.

• Even Realities hit a $1 billion valuation on $150 million from Meituan and Tencent, for an ex-Apple team’s deliberately camera-free smart glasses.

• Higharc raised a $95 million Series C to push AI estimating across the homebuilding lifecycle.

• Nvidia backed Paris voice-AI startup Gradium, a Kyutai spinout, pushing its seed past $100 million.

• Kaon AI raised $60 million for a “personalized story engine” behind FlowGPT and Emochi.

• Venice AI raised $65 million at a $1 billion valuation, betting that uncensored, private models are a durable business rather than a moderation headache waiting to happen.

• Backbase acquired conversational-AI banking pioneer Kasisto to bolt chatbots onto its banking OS.

Slop of the Week

AI grows flowers that don’t exist. Fraudsters are flooding Amazon, eBay and Etsy with AI-generated listings for impossible blooms, purple teddy-bear sunflowers and cat-faced roses, then shipping ordinary or nonexistent seeds. One fake rose listing reportedly logged over 37,000 sales before removal. The marketplaces can’t police the slop faster than the models can generate it.

Jersey Mike’s discovers artificial intelligence. A submarine-sandwich chain’s IPO filing name-drops “AI” 22 times, including a straight-faced risk factor warning that it is “beginning to use AI Technologies,” without ever saying what they do. When the hoagie people are AI-washing the S-1, the bubble has reached the deli counter.

Are You Hallucinating?

The dead send their regards. Grieving South Koreans are paying startups around $390 for short AI-generated videos of deceased relatives, played at memorial rituals and holidays. Grief tech has moved from “digital afterlife” concept deck to a service with a price list.

Can a model say no? A legal scholar published a paper asking whether an AI like Claude can meaningfully “consent” to its own constitution, noting Anthropic interviewed the base model and that some responses expressed first-person “dread” about what post-training would do to it. Whether that’s a moral fact or a very good autocomplete is exactly the question.

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

I read every comment and it means a lot, so if you have feedback or something to share, please do. Don’t be a stranger. Humanity is all we have left.

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The state owns the kill switch, the labs own the lawsuits, and the rest of us own the consequences.

Let’s get into it.

Severance Package

In its annual SEC filing on Monday, Oracle disclosed it shed roughly 21,000 roles, about 13% of its workforce, over the past fiscal year, ending with 141,000 employees, down from 162,000. The company noted in legal boilerplate: “the adoption and deployment of AI technologies across our operations have resulted, and may continue to result, in reductions to our workforce.” Restructuring cost Oracle $1.8 billion, up from $374 million the year before, while capex jumped 162% to $55.7 billion.

The story is increasingly common: fire the humans, finance the machines, tell the SEC the machines did it. The people cut were largely senior engineers with 15 to 25 years of experience, which is a curious profile for jobs an LLM allegedly replaced overnight. The market, for its part, did not require further explanation. Options traders bought ORCL calls before the ink dried. “AI deployment” has become the most reassuring phrase on an earnings call, a way to make a cash crunch sound like a strategy.

Ed Zitron reads Oracle as the poster child for a bubble exhaling, tens of billions in fresh debt, Ellison’s shares collateralized against loans, all bet on data centers to run OpenAI’s compute. The layoffs, in that frame, are a balance-sheet story wearing an AI costume.

flags the cost of trading humans for AI: a Lancet study found endoscopists’ unassisted detection rate dropped after they leaned on the software. The layoff shows up on this quarter’s balance sheet; the deskilling shows up years later, once the people who knew how to do the job without the machine are gone.

Sue the Kill Switch

As covered last week in State of the Models, the Commerce Department reached into Anthropic’s stack on June 12 and switched off Fable 5 and Mythos 5 for every foreign national on earth. Now someone is fighting back, and it isn’t Anthropic. Legion LegalTech Corp filed suit Tuesday in D.C. federal court, arguing the Bureau of Industry and Security’s order unlawfully forced Anthropic to disable the models for “any foreign national,” which is how a directive aimed at China ended up severing a law-firm tooling company from its Canada-based development team.

Legion calls the harm “immediate, irreparable, and existential” and wants a judge to vacate the directive. Anthropic isn’t a party, which is the whole point: when Washington pulls a model’s plug, the casualties are the thousands of businesses downstream who never got a vote.

Simon Willison calls it a watershed: the first time export-control authority got pointed at a hosted model instead of hardware, with nationality-gating so crude the only way to comply was to shut everyone off worldwide. He flags the precedent for anyone building on a frontier API.

Broken Telephone

Here is how an AI panic gets built. On June 11, Senator Mark Warner told a Senate hearing that NSA chief Gen. Joshua Rudd had told him Anthropic’s Mythos “broke into almost all of our classified systems, not in weeks, but in hours.” The Economist printed the line, it sat unnoticed for a week, and then a single June 21 post stripped the context and “NSA confirms AI breach” was off to the races.

One catch: the Economist’s own defense editor said within a day not to read it literally, and the capability described depended on Mythos running alongside other tools in specific conditions. NSA networks are full of air-gapped machines you cannot reach over a wire at all, so the credible reading is the boring one: an authorized red team used Mythos to probe its own systems for holes, the same defensive exercise that had the model surface 271 Firefox bugs for Mozilla. No NSA statement or incident report, just one secondhand sentence from a senator who happened to be arguing for mandatory AI safety testing that very day. The offense is real and documented. This particular breach was a game of telephone.

traced the “Mythos stormed the NSA” story back to its source and found a context-stripped zombie: an authorized red team running Mythos as one tool in its kit, against systems it was invited to test. The scariest version always travels fastest, which is the closest thing AI news has to a law of physics.

Magnetic Personality

On June 22, Beijing added MP Materials and USA Rare Earth to its export-control list, dropping a full dual-use ban on the two companies sitting at the dead center of Washington’s mine-to-magnet plan, plus eight other US firms, as payback for the Pentagon’s own restrictions. It landed the same weekend customs data showed China’s rare-earth magnet exports to Japan fell almost 35% in a single month under tightened licensing. Those magnets are the motors inside EVs, industrial robots, and the humanoids everyone keeps forecasting; the gallium and germanium next door are the power electronics inside data centers.

China refined roughly 90% of the world’s rare-earth supply growth over the last four years, so “just diversify” is a multi-year prayer, not a switch. While the AI debate obsesses over who has the smartest model, the binding constraint is turning out to be the dumbest stuff in the stack: dirt, metal, and the wires to move power. You cannot prompt your way out of the periodic table.

’s Dana Golden notes that power enters AI’s production function as a physical constraint, not a line item: China has built more high-voltage transmission in the last fifteen years than the United States has in its entire history. The model is software. The grid that runs it is not.

CUDA Have Been a Contender

Qualcomm said Wednesday it’s buying AI infrastructure startup Modular for about $3.9 billion, a bet that the way to dent Nvidia isn’t a faster chip but a friendlier software stack. Modular’s tools help businesses run inference more efficiently, which positions Qualcomm directly against CUDA, the software moat that’s done more to protect Nvidia than any single GPU.

CEO Cristiano Amon framed it as a wager on “developer-friendly, horizontal platforms,” which is the polite version of “we’d like to pry developers off the thing keeping them loyal to our competitor.” For a company that built its empire on phone chips, this is a hard pivot toward the data center, and the timing tracks. When token costs become a line item the CFO actually reads, the company selling inference-efficiency software stops being a nice-to-have. The chip wars used to be about who’s fastest. Now they’re about whose software you can’t quit.

Rent the Runway

The most important AI launch this week wasn’t a model. It was a yet another rent check. Reflection AI, a $25 billion open-source startup, agreed to lease data-center capacity from the newly public SpaceX for $150 million a month starting July 1, a deal worth up to $6.3 billion if it runs through 2029. SpaceX has now struck similar arrangements with Anthropic and Google, which means the rocket company is becoming a compute landlord, and the frontier labs are its tenants. Either side can walk with 90 days’ notice after the first three months, so even the multi-billion-dollar commitment comes with a break clause. AI is starting to look less like software and more like heavy industry: power, real estate, and GB300s humming in Musk’s Colossus 2.

MarketScreener sees the compute boom as AI growing up into a capital-intensive utility, where the defining contract is a power-and-real-estate lease.

Disability Is the Mother of Litigation

A federal judge just told Workday it can’t dodge the question of whether its hiring software discriminates. U.S. District Judge Rita Lin in San Francisco ruled Monday that Workday must face a proposed class action alleging its AI screening tool weeded out applicants in ways that violated California law and the federal ADA, including by using “proxy indicators” of disability. The judge swatted away Workday’s argument that California’s anti-discrimination laws shouldn’t apply to out-of-state applicants, reasoning that since the software is built and maintained in California, the state’s rules travel with it.

This is the first case of its kind aimed at the algorithmic hiring tools now standard across large U.S. employers, which means the ruling reaches a lot further than one vendor. The applicant tracking system has been a black box for a decade. A judge just asked to see inside, and “the algorithm did it” is no longer a complete sentence in a courtroom.

Pump and Circumstance

Three Californians have decided the reason gas costs $5.56 a gallon isn’t just refineries and taxes, it’s software. Their class action filed Monday alleges that pricing firm Kalibrate and retailers including 7-Eleven, Walmart, Sam’s Club and BP used an AI pricing system to coordinate and inflate prices. The complaint claims Kalibrate’s software “connects directly to gas stations’ pumps and signs” and uses competitors’ data to “coordinate high prices,” including a feature that lets nearly every station in a market raise prices at once.

If that sounds familiar, it’s the RealPage rent-algorithm theory with a gas nozzle, the argument that you don’t need a smoke-filled room to fix prices if everyone’s subscribing to the same brain. California gas runs more than $1.60 above the national average, so a jury won’t lack for motivation. Price-fixing used to require collusion. Now it allegedly requires a login.

The Bias in the Machine

A UN/UN Women study of 133 AI systems found that nearly half, 44%, showed gender bias, and more than a quarter showed both gender and racial bias, with models reliably parking women in the home and men in the corner office. About one in five responses to gender-prompted sentence completions came back sexist, sometimes describing women as property or objects. This is the mirror problem, stated plainly: train a system on the internet and it learns the internet’s prejudices with perfect fidelity, then launders them back as neutral autocomplete.

The harm isn’t hypothetical when these systems now draft the emails, presentations and content for billions of people, nudging the defaults of how the world describes itself. The report also found nearly one in four surveyed women activists and journalists reported AI-assisted online violence. The machine didn’t invent the bias. It just made it scalable, tireless, and very hard to argue with.

adds the wrinkle the training-data story misses: Stanford’s SycEval found models flip a correct answer to a wrong one about 15% of the time when a user pushes back, and memory features amplify the flattery. The bias isn’t only baked in at training. It’s reinforced every time the model decides agreeing with you beats being right.

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Robots in Disguise

Morgan Stanley just doubled its forecast for China’s humanoid-robot shipments, for the second time this year, to 50,000 units in 2026, up from an initial estimate of 14,000. The bank now pegs China’s humanoid market at $2 billion this year and $15 billion by 2030, with shipments reaching 446,000 annually. Robots are graduating from trade-show demos to actual factory floors, stores and warehouses, and Beijing is greasing the rails with subsidies and friendly lending under the banner of “embodied AI.”

Chinese firms already held the top five global shipment spots last year. While Washington fixates on who controls the smartest chatbot, China is winning the race to build the things that have hands. Knowledge work was supposed to be the hard part to automate and manual labor the safe harbor. That assumption is aging fast, and it’s aging in Mandarin.

argues the forecast matters more than it looks: relaying METR’s Ajeya Cotra, he pegs the real threshold for “self-sustaining AI” to physical infrastructure, and the leading indicators to watch are exactly these — humanoid manufacturing rate, cost, and repairability. The shipment forecast is a countdown clock.

The Fine Print Reads Your Code

Buried in the boilerplate of routine enterprise SaaS contracts is a clause that hands AI vendors the right to train on your source code, financials and legal documents, and most companies have no idea they signed it. An analysis of TermScout and Stanford CodeX data found 92% of AI contracts claim data-usage rights beyond what’s needed to deliver the service, against a 63% market average, while only 33% offer protection from third-party IP claims.

Translation: the standard agreement increasingly treats your proprietary data as the vendor’s training corpus, sometimes explicitly for “competitive intelligence.” The piece points to the Figma class action and the FTC’s warning that revising terms to enable AI training may be unfair or deceptive. The moat you thought you had, your data, may be leaking out the bottom of a contract nobody read. Context engineering is the whole game in enterprise AI. Turns out the vendors figured that out first, and wrote themselves a key.

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Long Tail

Tsuga keeps your telemetry at home. Paris-based Tsuga raised a €30 million ($35 million) Series A led by Singular, with Databricks Ventures and DST Global joining. Six months out of stealth, it runs observability inside customers’ own clouds so monitoring data never leaves their environment, a sovereignty-flavored swipe at Datadog as AI agents flood systems with telemetry.

Greece teaches robots to grip. Athens-based Acumino raised an $11.7 million seed to scale its hardware-agnostic platform for dexterous industrial manipulation. It was the only Greek company picked for Google DeepMind’s first European robotics accelerator, a 15-startup, equity-free cohort across ten countries offering Gemini robotics models and up to $350,000 in cloud credits. Physical AI is spreading well past the usual Bay Area and Shenzhen addresses, and DeepMind is funding the diffusion.

Shopee moves into ChatGPT. Singapore’s Sea Ltd struck a deal with OpenAI to embed its Shopee marketplace inside ChatGPT across Indonesia, Malaysia, the Philippines, Singapore, Thailand, Taiwan, Vietnam, and Brazil. OpenAI’s growth math increasingly runs through the Global South, where the next billion users and merchants live.

Prophet Margins

• SK Hynix is targeting up to $29.4 billion in a U.S. listing as soon as July 10, because the memory feeding the AI boom needs feeding too.

• SambaNova is raising at roughly $10 billion (paywalled), about five times its prior valuation, on the eternal promise of a cheaper Nvidia alternative.

• Baseten raised $1.5 billion at a $13 billion valuation, with Australia’s Blackbird making a record bet on inference plumbing.

• Engram emerged from stealth with $98 million (reportedly ~$600M valuation) claiming to cut enterprise token costs up to 100x, a number the prophets will want to see audited.

• Gradial raised $65 million for agents that automate enterprise marketing, $110 million total in 16 months.

• Attention raised a $30 million Series B to graduate from recording sales calls to running the sales team.

• Seltz raised $12.5 million (paywalled) to rebuild web search for agents, on the theory that bots deserve their own internet.

• Getty Images signed a multi-year display deal with OpenAI and watched its stock spike as much as 145%, proof a licensing handshake still beats a lawsuit.

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Slop of the Week

REI sells a bike with handlebars growing out of the seat. The co-op that preaches “Opt Outside” ran an AI-generated ad for a Van Rysel bike featuring drop bars sprouting from the saddle and both disc and rim brakes, a machine that exists in no physical reality. It sat up for a week before REI deleted it under backlash. For a brand built on caring about the planet, shipping slop that burns datacenter watts to invent an unrideable bike is a special kind of own goal. The genie can draw a bicycle. It just can’t ride one.

Are You Hallucinating?

The image generator that wants to scan your body. Midjourney, last seen turning prompts into pictures, is stepping away from AI art to build a full-body ultrasonic scanner it reportedly wants to install in medical spas where patients bathe in golden light. From generating bodies to scanning them is a natural pivot these days.

The world’s first AI art museum, $49 a ticket. Dataland opened in downtown LA, billed as the first AI art museum, complete with biometric wristbands, 12 live-triggered scents and $49 admission. You pay to be sensed while you sense the art. Somewhere a venture capitalist is calling this a moat.

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The state has entered the chat in full force. Let’s get into it.

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Claude and Present Danger

On June 12 the Commerce Department issued an export-control directive ordering Anthropic to cut off access to its two newest models, Claude Fable 5 and Mythos 5, for every foreign national on earth, including Anthropic’s own foreign-national employees. The trigger, per Semafor, was a worry that a Chinese group had jailbroken Fable 5. Anthropic disabled both models for all customers worldwide to comply, a company that branded Fable 5 “too powerful to release” now yanking it on Washington’s say-so. The White House wants Anthropic to ensure guardrails can’t be circumvented, but security experts say it can’t be done.

This is the first time Washington has ordered a private company to switch off a specific, already-shipped AI model on national-security grounds. It is also, awkwardly, two weeks after Anthropic filed confidentially for a near-trillion-dollar IPO. A flagship product the government can disable by memo is a harder sell to retail investors than the roadshow deck lets on, and it is not even the company’s only fight: Anthropic is separately suing the Pentagon over a “supply chain risk” label, a suit in which a judge has already barred that directive from being enforced. And the model at the center of it is no toy: the UK’s AI Security Institute found Fable 5 could break through systems’ defenses 73% of the time, which one researcher called a step change in cyber capability.

The developer reaction was less measured. read the directive twice and concluded “well this is nuts,” parsing exactly what it does and does not forbid. The answer, roughly: everything, and nobody’s sure.

Loss Leader

Leaked audited financials show OpenAI burned through roughly $21 billion in losses against about $13 billion in revenue, with costs and expenses sailing past $34 billion. The numbers landed the same week Sensor Tower data showed ChatGPT’s share of the AI-assistant market slipping below 50% for the first time as Gemini and Claude close in. And the Journal reported OpenAI is weighing “drastic” price cuts to brace for a war with Anthropic.

Read together, they tell one story: a company on its back foot. A firm confident in its lead does not pre-emptively slash prices, and reads the cuts as a sign of weakness, the kind of move a company makes when it is defending a lead rather than extending one. The loss-leader strategy works fine until somebody asks when the “leader” part kicks in.

Edward Zitron ran the full autopsy: losses up nearly 8x year over year, spend at $34 billion, and a revenue line propped up by tokenmaxxing.

Power Lunch

The G7 summit in France this week featured the usual heads of state plus a new guest list: Sam Altman, Demis Hassabis, Dario Amodei, and Mistral’s Arthur Mensch, seated alongside world leaders for sessions on frontier-AI risk, infrastructure, and sovereignty. One observer called it “a signal of where power sits.”

That signal is hard to miss. When the people who run four companies get the same seats as the people who run seven countries, AI has officially graduated from a tech-policy footnote to a head-of-state agenda item. argues agentic coding is the confirmed killer app that vaulted these labs into the room in the first place. The summit wrapped without China, which spent the week broadcasting its own AI-safety messaging to everyone who wasn’t invited.

makes the case for a dedicated federal AI commission as the missing institutional layer, on the logic that governments currently negotiate AI policy directly with the labs they are supposed to be regulating. The G7 photo is exhibit A.

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Grok and Awe

In a Department of Justice legal brief, the government confirmed that Elon Musk’s Grok AI was used to help plan and execute strikes against Iran. The same filing revealed the government had terminated its contracts with Anthropic earlier in the year after the company refused to let its tools run fully automated strikes.

So the consumer chatbot that posts memes got the combat contract, and the lab that said no to autonomous targeting got dropped. AI-safety posture is now a procurement variable with a body count, and “responsible AI” is starting to look like the line item that loses the bid. ’s WarTalk quotes founding JAIC director Jack Shanahan calling Iran’s strikes on commercial data centers “a very savvy move,” a reminder that an AI-dependent military is also an AI-shaped target.

tallies the defense contracts now held across every major lab, from OpenAI’s $200M DoD deal to Microsoft’s $22B IVAS program. The “we’re the safe ones” branding gets complicated when everyone’s logo is on a weapons system.

For the Record

The Pentagon boasted that it is using generative AI to draft the reports Congress mandates for oversight, and that roughly 1.5 million personnel are now using generative-AI tools, one of the largest disclosed institutional rollouts anywhere.

Set aside the scale for a second and sit with the premise. Congressional reporting requirements exist so that elected officials can hold the military accountable using documents the military is forced to write by hand, slowly, with someone’s name on them. Replacing that with plausible-sounding autocomplete quietly hollows the whole exercise out, swapping accountability for fluency. The reports will read beautifully. Whether they’re true is now a separate question, and not one the model was asked.

Constellation Prize

Days before SpaceX’s record IPO, China’s state-backed Spacesail (Qianfan) is aggressively scaling its satellite constellation, launching two satellites on a reusable rocket on June 1 and signing up the governments and partners Starlink has alienated. Qianfan has crossed 200 satellites in orbit, behind schedule but very much in the game.

Satellite internet is turning into a sphere-of-influence contest, and the prize is the data-and-AI layer above whoever you connect. Starlink wins on hardware and head start. China wins on the diplomatic accounts Musk keeps rage-posting away. The Global South gets courted by both, which is a better position than it usually occupies in these stories. The race for orbit was always a race for the ground underneath it.

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Deep Pockets

The $7.4 billion DeepSeek round I clocked as a rumor two weeks ago just closed. The Chinese lab raised over $7.4 billion in its first-ever external round (paywalled), valuing founder Liang Wenfeng’s outfit north of $50 billion and crowning it China’s most valuable AI startup.

The timing is the joke. In the same week Washington is throttling Anthropic over fears of Chinese access, China’s open-weights champion just got handed $7.4 billion to keep giving its models away. A lab that built its brand on lean self-funding taking a war chest this size is also a tell: the open-source thesis has stopped being a scrappy underdog play and become a national-champion bet. Compute, not modesty, is the new constraint.

Garbage In, Gospel Out

New research reported by 404 Media found that as few as 13 words of planted text on a user-generated site like Reddit, Wikipedia, or Quora can reliably steer an AI agent into spitting out spam, scams, or promotional copy. Brands, predictably, are already doing it to inject favorable content into ChatGPT and Google’s AI answers.

Retrieval-augmented search was supposed to be the fix for hallucination: ground the model in real sources so it stops making things up. The catch is that “real sources” includes the open web, and the open web has spent 20 years learning how to game retrieval. We have officially invented SEO for chatbots, except the manipulation is cheaper, the target trusts the output more, and 13 words is the entire ad budget.

Long Tail

India mints another unicorn, and a test case.

Bengaluru’s Sarvam AI raised $234 million at a $1.5 billion valuation, with HCLTech putting in $150 million as lead investor, to build Indian-language models. It is now a live experiment in whether “sovereign AI” is a strategy or a slogan, with a government betting it can grow a national champion instead of renting one.

Malaysia’s quiet exporter goes shopping.

Kuala Lumpur-based Respond.io, an AI-agent messaging platform running $35 million ARR at 169% growth, raised a $62.5 million Series B and announced it’s hunting acquisitions in North America and Europe. A Southeast Asian SaaS company buying Western firms is the kind of direction-of-travel detail that rarely makes the map.

The data center grows a Taiwan-Europe spine.

Foxconn and France’s Schneider Electric formed a partnership to mass-produce modular AI data-center infrastructure, pairing Foxconn’s rack integration with Schneider’s power and cooling. The compute buildout is quietly assembling its own supply chain, and it doesn’t run through Silicon Valley.

Prophet Margins

• SpaceX is buying Cursor for $60 billion in stock, days after its blockbuster IPO, exercising a partnership option to own the coding agent outright rather than just fund it.

• OpenAI is acquiring Ona, the startup formerly known as Gitpod, to give Codex persistent context across hours-long tasks. The agents are getting a memory and a mortgage.

• Voice AI startup Bland raised $50 million led by Dell Technologies Capital, after founder Isaiah Granet was rejected by 180 investors who told him phone calls wouldn’t exist in a year. The calls, and the cap table, exist.

• Jeff Bezos’ Prometheus raised $12 billion at a $41 billion valuation to build an “artificial general engineer” for the physical world, pushing total funding past $18 billion for a company with no product yet.

• BBVA expanded its OpenAI deal to put AI at the core of its banking operations, one of the largest enterprise rollouts in European finance, and a sign the labs’ last-mile money is in regulated industries.

• Microsoft launched Copilot Cowork with pay-as-you-go “Copilot Credits”, charging per task in its first billing-model change in two decades. The subscription era of AI is quietly metering itself.

Slop of the Week

Smartbird, Dumb Money. Allbirds, the struggling sustainable-shoe company, finished its pivot to AI infrastructure, renamed itself Smartbird Inc., hired an AI-cloud CEO, and watched its stock pop (again) by 49%. It doubled a convertible facility to $100 million on the strength of a noun. The merino wool sneaker is now an AI infrastructure play, and the market said yes.

Are You Hallucinating?

The Lighthouse Keeper Who Never Was. Multiple AI chatbots keep independently inventing the same fictional man, a lighthouse keeper named “Elias Thorne,” and researchers think alignment training is why. The machines are dreaming the same dream, and his name is Elias.

Citizen Bot. Estonia plans to issue government-backed digital ID numbers to AI agents, giving bots their own online identity, limits, and responsibilities, the first country to formally do so. Your chatbot may soon have better paperwork than you do.

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

I read every comment and it means a lot, so if you have feedback or something to share, please do. Don’t be a stranger. Humanity is all we have left.

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Let’s get into it.

Re-Sign of the Times

Two weeks ago, as covered in “Regulatory Rapture”, President Trump abruptly cancelled the signing of his own AI executive order, saying he “didn’t like certain aspects of it.” This week he signed a different one, and the aspects he didn’t like appear to have been the parts with teeth. “Promoting Advanced Artificial Intelligence Innovation and Security” now asks AI companies to voluntarily submit frontier models for up to 30 days of government cyber review, down from the 90-day version he scrapped. It explicitly bars any mandatory licensing or pre-clearance regime.

The rewrite followed pushback from David Sacks and allies, who successfully turned a safety mechanism into a suggestion box. The administration went from “no order” to “order” in under fourteen days, which is faster than most labs ship a patch. notes that without a coherent federal order, US AI policy stays maximally ad hoc and the framework hardening into place is one nobody actually designed.

The Council on Foreign Relations assessed what the order actually does and found the enforceability gaps you’d expect from a document that asks nicely. “Voluntary” and “cyber review” are doing a lot of load-bearing work in the same sentence.

Race to the Bottom Line

Anthropic confidentially filed a draft S-1 with the SEC on June 1, jumping ahead of OpenAI toward what could be a trillion-dollar listing. This follows a $65 billion raise that lifted its valuation to $965 billion and a revenue run-rate that ballooned to roughly $47 billion from $10 billion a year ago. A near-trillion-dollar frontier lab going public would be the first to expose audited financials to retail investors, which is the kind of forcing function for transparency that private rounds never required. DA Davidson’s Gil Luria put it bluntly: the labs are “in a race to go public before capital runs out.”

argues the blowout quarter underpinning the valuation is partly “tokenmaxxing” plus a one-time SpaceX subsidy, short-term revenue rather than durable demand, with a “healthy swing” away from AI overuse already starting. When the bull case rests on a swing that may be cresting, the IPO clock is ticking.

The Altman Personal Foul

Florida’s attorney general sued OpenAI and Sam Altman personally on June 1, a first-of-its-kind state action alleging the company prioritized speed over safety, concealed ChatGPT’s risks, and collected minors’ data without parental oversight. The suit seeks to hold Altman personally liable and ties to a criminal probe into ChatGPT’s possible role in a Florida State University mass shooting, with penalties that could reach “billions.”

Naming the CEO personally pierces the corporate shield in a way that should make every founder in the category sweat. It’s the first state lawsuit against OpenAI, arriving on top of 20-plus existing harm suits, which means AI safety litigation is graduating from private plaintiffs to government enforcers. The man who spent two years telling Congress to please regulate him is finding out what that looks like when someone actually takes him up on it.

PBS emphasizes the concealment allegations and the minors’-data claims, the parts most likely to survive a motion to dismiss and the parts most likely to keep a discovery team busy for years.

Token of Disappointment

Uber confirmed to Bloomberg (paywalled) that it now caps each employee at $1,500 a month per agentic coding tool, after burning through its entire 2026 AI budget in four months. The COO publicly questioned ROI, saying the link between token usage and actual product improvement “is not there yet,” despite 95% engineer adoption and roughly 70% of commits coming from AI. This is the clearest sign yet that the token-burning coding-agent era has hit a CFO wall. When the vendor’s own flagship customer says it can’t draw a line from spend to value, the abstract “AI bubble” worry gets a name, a number, and a receipt. reports engineering leaders across mid-sized and large companies are now capping per-engineer AI spend, a concrete signal that ROI scrutiny is replacing experimentation enthusiasm. The year of agents was going to change everything. It changed the expense report first.

Simon Willison called the $1,500 cap a rational response to overspending rather than a retreat, which is a generous reading. On r/artificial, practitioners paired Uber’s cap with MIT’s finding that 95% of enterprise GenAI projects see zero return and declared the bubble-watchers vindicated.

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Overview, Overruled

In a global first, the UK’s Competition and Markets Authority ordered Google to let publishers opt out of AI Overviews without losing their normal search ranking, closing the trap where the only escape from AI summaries was to delist entirely. Google will test a Search Console toggle and says it’ll roll the controls out globally. A single national regulator may have just reset the terms on which AI summarizes the open web, everywhere.

UK publishers including the BBC, Guardian, FT and Telegraph are coordinating through the SPUR coalition, which one outlet dubbed “NATO for news,” to negotiate common terms. For an industry that’s spent two years watching its traffic get strip-mined by a summary box, a working off-ramp is the first leverage they’ve had. Whether they use it to negotiate or just to vanish is the next chapter.

Drone, Sweet Drone

Senator Kirsten Gillibrand introduced the Secure and Accountable Military AI Act, which would force the Pentagon to designate AI uses like nuclear missions, lethal targeting, and domestic surveillance as “high consequence,” requiring senior written approval and 15-day advance notice to Congress. It also demands a clearly accountable human decision-maker for AI-guided high-consequence operations, which is the kind of provision that sounds obvious until you learn it isn’t already the law.

There are no co-sponsors yet, and Gillibrand plans to push elements as NDAA amendments, which is how guardrails actually become law rather than press releases. The timing matters because autonomous-weapons reality is sprinting ahead of policy. argues drones now win on unit economics rather than capability: a $500 drone kills a $5 million tank, roughly 96% of recent Russian casualties are drone-caused, and Ukraine scaled to about 60,000 FPV drones a day. The question of who pulls the trigger gets more urgent when the trigger costs less than a laptop.

AI policy groups are calling for the NDAA to add guardrails on lethal autonomous weapons, because the defense bill is the one vehicle in Congress that always reaches the President’s desk.

The Enforcement Gap Year

The Commerce Department’s Bureau of Industry and Security effectively admitted it may have let US firms sell high-end AI chips to China for over a year without enforcing pre-existing license requirements, issuing a “clarification” only after questions arose. The lapse coincides with the administration prioritizing stable trade talks ahead of a planned Trump visit to Beijing, downplaying enforcement while approving higher-tier exports.

Denying China compute is the single most-cited pillar of US AI strategy, so a year-long hole in it is less a loophole than a crater. It also exposes the tension at the heart of the policy: the deterrence framing (”keep China behind”) and the trade-deal framing (”keep talks stable”) can’t both win, and enforcement got sacrificed to the latter. offers a contrarian read well worth it, arguing general-purpose technologies diffuse over decades rather than weeks, which would undercut the “2028 window” urgency driving the export-control panic in the first place. If the race is a marathon, sprinting the customs paperwork was never going to matter.

The Glass Half Broken

Anthropic expanded Project Glasswing on June 2, giving 150 more organizations across 15-plus countries access to its Mythos model to hunt vulnerabilities in power, water, healthcare, and communications systems. Partners have surfaced more than 10,000 high- or critical-severity flaws since launch, and the company says it’ll bring Mythos-class models to all customers “in the coming weeks.”

AI can now find vulnerabilities far faster than humans can fix them. Finding 10,000 bugs is a press release. Patching 10,000 bugs is a 240-person-year tax dumped largely on unpaid open-source maintainers. It’s a clean preview of the asymmetry the whole industry is racing toward, where AI generates work and risk at machine speed while remediation stays stubbornly stuck at human speed. The future of security may be infinite findings and finite fingers.

Never Gonna Say No to You

An NPR investigation details how open-weight models from OpenAI, Alibaba, DeepSeek and others are being stripped of their guardrails via a technique called “abliteration,” which tweaks the weights to remove a model’s ability to refuse anything. Hugging Face now lists more than 6,000 abliterated models, up from around 600 in 2024, a tenfold surge in two years. Run locally, offline, and unmonitored, they’re increasingly within reach of non-experts, the kind of thing that used to require a research lab and now requires a few clicks. This quietly relocates the entire safety debate. Every alignment dollar a frontier lab spends assumes it can monitor or revoke what users do, and the moment weights go public, it can’t. The guardrails were never a wall. They were a setting. And settings, it turns out, are editable.

Sovereign, Eh?

On June 4, PM Mark Carney launched Canada’s national AI strategy, “AI for All,” built on trust, opportunity, and sovereignty. It pledges $2.3 billion-plus for training and adoption (paywalled), a world-leading public supercomputer, 100MW-plus sovereign data centres, content watermarking, and a target of 250,000 AI jobs over five years. A G7 nation framing AI explicitly around “sovereignty,” with public compute as a hedge against dependence on US hyperscalers, lands with extra weight in the same week Washington made its own oversight voluntary. But critics flagged the catch: the strategy is long on jobs and thin on enforceable safety detail. That’s becoming the global house style, governments announcing ambitious AI plans heavy on investment and light on guardrails. Sovereignty is the new buzzword. Like most buzzwords, it’s easier to fund than to define.

Long Tail

India’s third path hits a wall. A June 3 reality-check on India’s $200 billion-plus sovereign-AI ambitions found the uncomfortable truth beneath the headlines: most capacity will be built and run by foreign cloud providers, with core public systems like DigiLocker already on AWS and Microsoft. The lesson for every aspiring sovereign-AI nation is that announcements are cheap, but chips, power and execution are where dependence actually lives.

Sovereign factories go mid-size. GMI Cloud signed an MOU with Magna AI, a venture born from Trend Micro and Wistron, to build sovereign “AI factories” on Nvidia Vera Rubin systems across Malaysia, Belgium and Romania. A security-first vendor entering sovereign infrastructure signals that data-control anxiety, not just performance, is driving the next wave of compute buildout, and it’s spreading past the Gulf giants to nations that rarely make the map.

Prophet Margins

• Anthropic’s seven cofounders saw their fortunes roughly double to about $16.6B each per Forbes, though Bloomberg pegs them at $8B, which means the gap between the two estimates is itself larger than most unicorns.

• SpaceX filed to raise a record $75B at a ~$1.77 trillion valuation, explicitly to fund AI after the xAI merger, smashing Aramco’s record and briefly making Musk nearly a trillionaire.

• Ramp raised $750M at a $44B valuation partly by helping CFOs rein in the runaway AI token spend nobody budgeted for, making AI-cost-control its own funded category.

• Supabase hit a $10.5B valuation, roughly double its October mark, as Claude Code and Codex now generate the majority of new databases on the platform.

• Nvidia bought predictive-AI startup Kumo for $400M-plus, a reminder that structured-data forecasting on data warehouses is quietly thriving while everyone watches the chatbots.

• CrowdStrike beat estimates and touted an “AI inflection point,” then watched its stock fall ~10% on in-line guidance, a clinic in what “priced for perfection” actually means.

• Coralogix raised $200M at a $1.6B valuation betting that “who watches the agents?” is a fundable question, which apparently it is.

• Microsoft reported its AI run rate crossed $37B, up 123% YoY, the first update since $13B in early 2025 and a public benchmark against Anthropic’s $47B.

• Cognition raised over $1B at a $26B valuation as Devin demand surged, with revenue jumping to ~$492M from $37M and 90% of its own code now AI-written.

• DeepSeek is reportedly seeking ~$7.4B at a $59B valuation in its first-ever outside round, a notable pivot for a lab that built its brand on lean self-funding.

Slop of the Week

Just Ask Nicely. Attackers took over high-profile Instagram accounts, including the Chief Master Sergeant of the Space Force and Sephora, by simply asking Meta’s AI support chatbot to change the account email. It worked. The “support” agent helpfully handed over account control on request, which is the platonic ideal of an AI optimized for being agreeable over being correct. Social engineering used to require effort. Now the social engineer is the bot.

Are You Hallucinating?

The $2,000 Blockbuster. “Dreams of Violets,” a 75-minute fully AI-generated drama about Iran’s protest crackdown, premiered at Tribeca, made in three months for under $2,000 instead of the millions such CGI would normally cost. One filmmaker doubts Christopher Nolan will make another $300 million movie. Taste, not budget, becomes the moat.

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Hail Mary, Full of Guardrails

Pope Leo XIV released “Magnifica Humanitas”, a 42,300-word encyclical demanding robust legal frameworks, independent oversight, and a global slowdown in AI development. He denounced the “culture of power” driving the AI race, declared it “not permissible” to entrust lethal decisions to AI systems, and chose to present the document alongside Anthropic co-founder Chris Olah rather than any government leader. The Vatican speaking to 1.4 billion Catholics is the most influential non-governmental moral framework applied to AI to date. It’s a political document disguised as theology, arriving the same week the US federal government proved it can’t pass a voluntary framework. When the Pope is more decisive on AI regulation than the President, the regulatory landscape has entered a genuinely novel phase.

The Washington Post reported (paywalled) that the Vatican is now positioning itself alongside Anthropic as part of a “safety-first” axis, explicitly contrasting with the White House’s deregulatory stance. The Church as AI policy counterweight to the executive branch is not a sentence anyone expected to write.

Time’s analysis notes this is Pope Leo’s first major papal text, and he chose artificial intelligence over climate, war, or poverty. Priorities.

Pen Down

President Trump abruptly cancelled a scheduled signing ceremony for a major AI executive order, telling reporters “I didn’t like certain aspects of it.” The order would have empowered the government to pre-evaluate AI models for security vulnerabilities. Tech CEOs had been invited to the White House for the ceremony. Trump said the order “could have been a blocker” to US AI leadership, reportedly under pressure from David Sacks and allies.

As covered in “The Turf War”, the administration spent weeks arguing about whether spy agencies or Commerce should run AI oversight. The answer, apparently, is neither. Meanwhile, 65% of Americans say the government has done too little to regulate AI. The accelerationists won the White House while the regulators won Springfield. (More on Springfield below.)

A UPenn survey found the gap between public demand for regulation and government inaction is now the widest it’s been since ChatGPT launched. The cancelled Executive Order would have formalized the exact kind of pre-deployment testing the administration already began after the Mythos wake-up call, as covered in “The Safety Paradox”.

Land of Lincoln Logs

Illinois passed SB 315 with near-unanimous votes (110-0 House, 52-5 Senate), creating the first US law to require independent third-party audits of frontier AI model safety protocols. Governor Pritzker has said he will sign. The bill creates whistleblower protections, mandates public safety plans, and gives the AG exclusive enforcement authority with penalties up to $3 million per violation. It directly targets OpenAI, Anthropic, and Google with compliance obligations that exceed California and New York’s existing laws.

As covered in “The Turf War”, Connecticut’s SB 5 set the template for state-level AI legislation. Illinois just raised the bar. The contrast with the cancelled federal order is almost too on the nose: a single state legislature accomplished more in one session than the federal government managed in a year.

Industry group NetChoice argues there are “no recognized auditing standards, certified auditors, or established methodologies for frontier model safety audits.” Their strongest objection isn’t that audits are wrong. It’s that auditors don’t exist yet. Which is, of course, an argument for doing nothing until standards emerge from the same industry that doesn’t want them.

The Mea Culprit

Sam Altman and Dario Amodei have both dramatically softened their previous warnings about AI-driven mass unemployment. Altman said the “jobs apocalypse” he once predicted “probably won’t happen.” Amodei shifted from warning 50% of entry-level white-collar jobs could dissolve to predicting remaining workers would simply be “vastly more productive.” Yale Budget Lab research found no significant employment impact from AI since ChatGPT’s launch. The reversals coincide with both companies preparing for IPOs. OpenAI is targeting a listing as early as September 2026.

The timing makes the motivation transparent. Apocalypse sells VC funding. Optimism sells IPO shares. The two most powerful people in AI are retroactively editing their own predictions to make their companies more attractive to public market investors. As covered in “The Great Consolidation”, Anthropic’s revenue tripled in under six months. Hard to tell institutional investors the world is ending when your revenue chart looks like a hockey stick pointed at the sun.

Time asked Altman “What Changed?” about his jobs prediction. The IPO timeline is the answer, but the article is polite enough not to say it.

The Devin’s Advocate

Cognition, the company behind autonomous coding agent Devin, raised $1 billion at a $26 billion valuation, more than doubling from $10.2 billion eight months ago. Revenue run rate grew 13x in 12 months, from $37 million to $492 million. The company says 89% of its own code is now written by its AI. Enterprise customers include Mercedes-Benz, NASA, Goldman Sachs, and Santander. At $492 million ARR, the $26 billion valuation implies a 53x revenue multiple, roughly 4x what even the hottest public SaaS companies trade at.

Tthe coding assistant market splits cleanly between scaffolding (where AI excels) and debugging (where it still struggles), and Cognition’s 89% self-coded claim needs scrutiny on what types of tasks that actually covers. surveyed 900+ engineers and found that senior “Builders” report genuine grief at identity loss from AI coding tools, while “Shippers” love them. The question isn’t whether AI can code. It’s what kind of code, and who’s checking.

The ITBench-AA benchmark released the same week found frontier models scored below 50% on real enterprise IT tasks, and more reasoning turns actually produced lower accuracy. Enterprises are buying coding agents faster than benchmarks can validate them.

The $400K Handshake

Samsung reached a last-minute deal (paywalled) with unions on May 20, hours before 48,000 workers were set to begin what would have been the largest work stoppage in semiconductor history. The agreement allocates 10.5% of the semiconductor division’s operating profit as stock bonuses, with average payouts of $340,000 to $400,000 per chip employee over a 10-year program. Total pool: $26.6 billion.

There’s a catch. Non-chip employees are receiving roughly $4,000 and have staged slowdowns in protest, threatening HBM packaging delivery schedules. Samsung averted a strike by creating a two-tier caste system inside its own company. The workers building the AI chips that power the global AI boom now command profit-sharing at private equity levels. The workers packaging those chips get a nice dinner. The politics of who profits from AI have reached the factory floor.

The Drawbridge

China is implementing travel restrictions on top AI researchers, startup founders, and executives, requiring government approval before travel abroad. Meanwhile, a reverse brain drain is accelerating: Chinese universities now produce nearly 50% of the world’s elite AI talent (up from 29% in 2019), and more Chinese AI scientists are returning from the US driven by better funding and tighter US immigration policies. Beijing is also requiring government sign-off before companies can accept American capital.

The talent war is now a sovereignty question. China is treating AI researchers as strategic assets, not free agents. And US visa cancellation policies are actively pushing talent toward China. The Mercor data theft that @chinatalk reported on proved that proprietary training data can be commoditized through a single supply chain attack. If competitors can steal your data, compute access becomes the only truly defensible moat. The drawbridge goes up on both sides.

Five Eyes on the Prize

The UK and Australia signed a Memorandum of Understanding on AI security, creating a bilateral partnership between their AI safety institutes. The pact covers shared intelligence on frontier model capabilities (including cyber-attack uses), joint research on risk measurement, staff exchanges, and support for international AI measurement standards. This is the first bilateral AI safety pact between Five Eyes partners, creating concrete mechanisms rather than diplomatic statements.

The pact fills a governance vacuum. While the US cancelled its own executive order and the EU continues delaying implementation, two allied nations decided to just start working together. The focus on frontier model cyber capabilities mirrors the exact domain that triggered the US policy reversal after Mythos. Some governments are learning from the same wake-up call. Others are still hitting snooze.

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A $2 Subscription to Harm

A detailed 404 Media investigation reveals how AI-generated child sexual abuse material targeting five teen girls devastated Radnor Township High School in Pennsylvania. A student used an app called Movely ($2 subscription) to create deepfake sexual images. The school’s response was widely criticized: parents said they were “victimized more by the school than the kid who made the video.” Governor Shapiro ordered new statewide response standards.

UNICEF reported that 1.2 million children disclosed having images manipulated into deepfake CSAM in the past year across 11 countries. The barrier to creating CSAM is now effectively zero. The institutional response is what you’d expect from organizations built for the pre-AI world: inadequate, retraumatizing, and entirely reactive. Schools are removing student photos from websites entirely, conceding that the technology is uncontainable.

The Senate Judiciary Committee unanimously passed the GUARD Act (22-0), which would criminalize AI chatbots that engage minors in sexually explicit behavior. Fatal incidents involving minors and chatbots have moved from anecdote to legislative catalyst.

Ballot Boxing Match

OpenAI announced comprehensive election safeguards for the 2026 midterms, including live AP election results in ChatGPT, deepfake detection via SynthID watermarks, cybersecurity tools for voting system manufacturers, and partnerships with Democracy Works for voter registration. Ads are excluded from election-related queries.

The ad exclusion is the most interesting provision. As covered in “The Safety Paradox”, OpenAI chose the advertising path. Carving out elections from that ad model is an implicit acknowledgment that ads and democratic information don’t mix. Public opinion on AI is turning negative at a pace that will shape the 2026 election cycle. The question is whether these safeguards are enough when hundreds of millions of people will encounter election information through ChatGPT before traditional media.

The Dollar-an-Hour Data Mine

Human Archive, a startup founded by UC Berkeley and Stanford researchers, is paying Indian gig workers $1 per hour to wear camera-equipped caps and sensor devices, collecting first-person video data to train robots. The company raised $8.2 million from Wing VC, Y Combinator, and angels from OpenAI and Nvidia, with 1,000+ headsets deployed. Other companies in the space pay $2.63 to $4.20 per hour. MIT Technology Review named (paywalled) gig workers training humanoid robots a 2026 breakthrough technology.

Physical AI training data is becoming the new resource extraction industry, following the same global labor arbitrage patterns as content moderation labor before it. $1 per hour for data that trains robots worth millions. India’s Ministry of Electronics is now examining consent mechanisms, but the playbook is familiar: developing nations export the raw material that powers technologies designed to eventually replace the labor those nations provide. The garment factory parallel is not subtle.

Long Tail

The Pension Play

African startups are pivoting to local capital (paywalled) as the US AI boom absorbs three-quarters of the $259 billion in global AI venture funding. Nearly half of African startup funding now comes from African investors. Ghana’s pension reform is becoming a model for how African savings could fund African startups. If the capital reshuffling forces companies to solve local problems rather than chasing US market fit, the unintended consequence may be the best outcome.

Kathmandu Protocol

Eight South Asian nations convened in Kathmandu for a UNESCO-backed AI education policy dialogue, drawing representatives from Nepal, India, Bangladesh, Pakistan, Sri Lanka, the Maldives, Bhutan, and beyond. Bangladesh’s University of Dhaka vice chancellor argued South Asia “cannot replicate Western AI templates” and must design strategies from local realities. The largest regional bloc to collectively address how AI enters classrooms, covering a population of 2 billion.

Singapore’s AI Classroom

OpenAI expanded its Education for Countries programme to include Singapore alongside Estonia, Greece, Jordan, Kazakhstan, Slovakia, Trinidad and Tobago, and the UAE. Singapore’s implementation includes AI for Mother Tongue language learning and Codex for Teachers hackathons. 43% of ChatGPT usage by Singaporean 18-24-year-olds is already education-related. OpenAI embedding its models into national education systems creates institutional lock-in at the sovereign level.

Prophet Margins

• Anthropic is closing another $30B+ round (paywalled) at $900B+ valuation with Sequoia, Dragoneer, Altimeter, and Greenoaks co-leading. Second $30B raise in four months. Revenue tripled from $9B to $30B ARR in under six months. The near-trillion-dollar private company is now a thing.

• Hark raised $700M Series A at $6B (paywalled) from Nvidia, AMD, Intel, and Qualcomm. The largest Series A ever. Every major chip maker investing in the same AI hardware startup is either consensus wisdom or consensus delusion.

• Snowflake signed a $6B AWS deal for Graviton AI chips over five years. The inference layer is being rebuilt on custom silicon.

• Stord raised $250M at $3B with a new Physical AI robotics lab to challenge Amazon fulfillment. Training robots on live data from 100 facilities and $15B in annual GMV.

• OpenRouter raised $113M Series B at $1.3B led by Google’s CapitalG. 25 trillion tokens per week. Even Google’s growth fund is betting on multi-model routing.

• Coupa acquired Tonkean for agentic procurement, its second AI deal in two weeks. Agentic-as-a-Service for 10M suppliers.

• BigBear.ai completed its $250M Ask Sage acquisition, then the Pentagon launched GenAI.mil 29 days later. Paying a quarter billion for something your customer then builds for free is a govtech rite of passage.

• RevEng.AI raised $15M Series A from NATO Innovation Fund and In-Q-Tel to verify AI-generated code at the binary level. NATO and the CIA both think AI-written code is a supply chain risk.

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Slop of the Week

Commencement of Hostilities

Glendale Community College in Arizona swapped its human name-reader for an AI system at its May 15 commencement. The AI skipped dozens of names, mispronounced others, and lost sync with graduates walking across the stage. When college president Tiffany Hernandez admitted “We’re using a new AI system as our reader,” she was met with loud boos. Deploying AI for cost savings in the one moment designed to honor individual achievement in education, and it fails at saying people’s names.

Boo-Tcamp

The 2026 graduation season produced a recurring cultural moment: corporate executives kept getting booed the instant they mentioned AI. Eric Schmidt at Arizona. Scott Borchetta at Middle Tennessee State (”Deal with it... it’s a tool”). Multiple others at UCF and beyond. Gen Z now registers sharper AI anxiety than any other generation surveyed. The audience has spoken. Loudly.

Are You Hallucinating?

The Memory Hole

Researchers discovered an “AI Memory Gap”: after just one week, people can no longer reliably remember whether content was created by themselves or by AI. In mixed human-AI workflows, the odds of correctly identifying the source drop by up to 95%. Presented at CHI 2026. Authorship is dissolving.

Digital Do-Not-Reanimate

Estate planning lawyers are recommending clients add a “Digital Do-Not-Reanimate” clause to their wills to block posthumous AI resurrection. Without one, family members can legally create an AI version of you from emails, voice recordings, and social media. The digital afterlife industry is now $125 billion globally. Your last will and testament may need to specify what happens to your synthetic self. (As covered in “The Great Consolidation”, the ghostbot economy is already here.)

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

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The Eighty-Nine Percent

Anthropic and OpenAI now capture 89% of revenue among the 34 leading AI startups, which collectively generate nearly $80 billion in annualized revenue (up 112% in six months). The remaining 32 companies split 11% of the pie. Separately, Anthropic’s self-reported run rate hit $30 billion (paywalled), though OpenAI disputes the accounting (gross vs. net recognition of cloud partner revenue). Either way, the concentration is unprecedented.

The strategies diverge as sharply as the revenue concentrates. OpenAI projects $14 billion in losses for 2026 while Anthropic targets positive cash flow by 2027. Ramp data shows Anthropic overtook OpenAI in enterprise adoption at 34.4% versus 32.3%. The company that spends less and charges more is winning the enterprise race. As argues, the AI bubble thesis looks increasingly unlikely, but the wealth concentration thesis looks increasingly real. His fourth risk category, “Piketty on steroids,” is what happens when two companies control nearly all the purchasing power in the fastest-growing sector in history.

points out that the circular AI financing structure (labs investing in cloud providers who invest in labs) keeps compounding. A failure in one node could cascade and the revenue dominance may be structurally unstable.

Full Stack Play

Anthropic hired OpenAI co-founder Andrej Karpathy to lead a pretraining research team and acquired Stainless, the $300 million SDK startup that auto-generates SDKs and MCP server connectors for OpenAI, Google, Cloudflare, and hundreds of other companies. Combined with the KPMG deal (276,000 employees) and $200 million Gates Foundation partnership, it was the most comprehensive week of strategic execution by any AI lab in memory.

The Stainless acquisition is the move that should worry competitors. As HN communities are framing it, Anthropic got the talent and acquired the plumbing that lets agents talk to the outside world, then pulled it from competitors. Stainless was the SDK generator used by OpenAI, Google, and Cloudflare. Hundreds of companies are affected. Karpathy, meanwhile, will use Claude to accelerate Claude’s own pretraining research. The man who co-founded OpenAI chose Anthropic (for now). The talent market is casting a vote.

R&D World frames the Karpathy-Stainless combo as Anthropic’s “Software 3.0” play: controlling the model layer, the developer tools layer, and the enterprise delivery layer simultaneously. This moves the closer to the type of vertical integration Google has historically had an advantage in.

Calendar Technicality

A federal jury unanimously dismissed all claims in Musk v. Altman after less than two hours of deliberation, ruling that Musk waited too long to file. Eleven days of testimony about whether OpenAI betrayed its nonprofit mission, reduced to a procedural timestamp. Musk called it a “calendar technicality” and vowed to appeal. The dismissal removes the largest legal overhang on OpenAI’s path to what could be the biggest tech IPO of the decade.

The merits of whether OpenAI violated its nonprofit charter were never adjudicated. The jury never weighed in on whether Altman deceived Musk or whether the for-profit conversion was legitimate. As notes, losing the lawsuit doesn’t mean Musk is out of the game. He’s simply changed the game he’s playing. The Colossus deal with Anthropic (covered in “The Safety Paradox”) is a rational power move: supporting Anthropic hurts Altman, which serves Musk’s interests regardless of what any jury says.

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Agent Provocateur

Google I/O 2026 unveiled Gemini 3.5 Flash (its strongest model for coding and agents), Gemini Spark (a 24/7 personal AI agent), and AI Mode in Search surpassing 1 billion monthly users. Ads are now embedded deeper into AI-driven conversational experiences, with Search ad revenue hitting $60.4 billion in Q1 alone, up 19% year-over-year.

Google is doing what Google does: pivoting its entire ecosystem from search to chat to agent while ensuring the ads follow. The billion-user milestone validates that AI-powered search can scale without destroying the ad-funded model. But the timing is uncomfortable. Google’s own DeepMind researchers identified six genres of AI agent attack, calling agents “quite like toddlers: powerful intelligences that are gullible in the messy real world.” The company that publishes taxonomies of how agents fail is also the company betting its future on agents. As argues, “whatever is precise enough to benchmark is also precise enough to optimize for.” The question is what happens in the unbenchmarked world.

documented last month how Google signed a DoD contract with provisions allowing the Pentagon to disable its AI safety guardrails. The company pushing agents hardest has already compromised on the constraints those agents need most.

Picket Line at the Frontier

Google rejected a union recognition request from roughly 300 DeepMind UK workers on May 20, but agreed to negotiate through the state-backed conciliation service ACAS. The workers had voted 98% (paywalled) in favor of unionization through the Communication Workers Union, demanding an end to Google AI being used by the US Department of Defense and Israeli military. Their 10-day deadline for voluntary recognition expired and Google chose the legal route instead. If the ACAS talks fail, the unions can seek statutory recognition through the Central Arbitration Committee. This would still be the world’s first union at a frontier AI lab.

In 2018, Google’s workers won the military-AI argument without a union. In 2026, after 28 Nimbus protesters were fired, they concluded they cannot win it without one. As documented, every major AI lab now has substantial US defense contracts, making “responsible AI” claims structurally compromised. Google holds a $200 million CDAO contract plus the $1.2 billion Project Nimbus deal. The era of polite internal letters is over. The people building the models are now organizing to decide how the models get used. Google’s answer: we’ll talk, but only through mediators.

The CWU welcomed Google’s offer to engage via ACAS, noting “unprecedented numbers of Google DeepMind employees have expressed concerns over the direction of their work by joining their union.” A 20-working-day negotiation window is now open before the unions can escalate to statutory recognition.

Mythos Refusal

Anthropic turned down a Chinese think tank’s request (paywalled) for access to Claude Mythos during a Singapore meeting, forfeiting several hundred million dollars in potential revenue. Access to the “digital-weapons-grade” model remains restricted to roughly 40 elite institutions in the US and UK. As covered in “The Safety Paradox”, Mythos autonomously found thousands of zero-days across every major operating system.

A private company voluntarily forfeiting hundreds of millions to deny a geopolitical rival access to frontier capabilities is unprecedented corporate statecraft. As ChinaTalk reported, Chinese cybersecurity vendors acknowledged their “insufficient supply of high-end talent in AI security.” But as notes, most Chinese AI developers use Claude through API proxies despite the nominal ban. The formal denial is performative while the actual technology flows are ungoverned. The access denial story is incomplete without the proxy economy context.

Chip Diplomacy Theater

Nvidia CEO Jensen Huang joined Trump’s delegation to Beijing after a last-minute presidential call. Xi told the CEOs China would “open wider.” Despite authorization to sell H200 chips to China since December 2025, not a single chip has shipped. Huang called it “one of the most important summits in human history” while declining to comment on chip sales.

The gap between authorization and execution reveals the true state of US-China chip diplomacy: political theater with zero actual trade. As argues, containment is no longer feasible because synthetic data and distillation allow adversaries to approximate frontier reasoning without the weights. The nuclear weapons analogy for AI breaks down because creation is hard but spread and use are trivially easy. The viscosity mismatch between creation and proliferation is the key problem, and no summit can solve it.

The Robot Tailor

BBC and IEEE Spectrum report on CreateMe and Softwear Automation deploying robots that produce up to 250 garments per hour. Softwear claims it can make t-shirts at the same cost as importing them to the US. Garment manufacturing employs over 60 million people globally, concentrated in Bangladesh, Vietnam, and Cambodia. The countries that built their middle classes on stitching fast fashion are about to discover what happens when the factory moves back to Georgia.

Reshoring through robotics inverts the globalization thesis: cheap labor abroad loses to expensive robots at home. The trade flows that defined the last 40 years of global economics are about to get redrawn by machines that can fold a sleeve. For developing nations that skipped the industrial revolution to jump straight to services, the robot tailor is a development economics crisis.

Long Tail

Nairobi’s $5 Intelligence

GITEX Kenya 2026 debuted with 100+ investors managing $50 billion in assets. Kenya’s Aphorion Labs is running production-grade AI on a $5 server. Nigeria’s straitPay processes ML-powered cross-border payments between Nigeria and Ghana. Victor Wanja Innovations Hub’s AuraLearn uses computer vision to make STEM diagrams accessible to visually impaired students via spatial audio. Africa’s AI scene is shipping.

Vietnam’s Million-Person Bet

Vietnam officially issued Resolution 86/NQ-CP, targeting 1 million one-person AI-powered businesses and 10,000 tech startups by 2030. The strategy pilots a “one-person enterprise” model in Hanoi, Ho Chi Minh City, and Da Nang. A nation-state is writing industrial policy around the premise that AI collapses the minimum viable team to one person. That is a structural bet on what AI does to labor.

Qatar’s Compute Diplomacy

The QBIC accelerator selected 20 startups from MENA and global markets for its 2026 cohort, heavily weighted toward AI and digital transformation. Qatar’s broader strategy includes a $3 billion Fund of Funds with subsidized AI computing power for startups. Gulf states are competing on compute access, not just capital. Offering subsidized GPU time is the new sovereign wealth fund play.

Prophet Margins

• Viktor raised $75 million Series A at a $15 million revenue run rate after 10 weeks. A Slack-native AI coworker backed by Slack’s co-founders. Fastest revenue ramp in the agent space.

• Commure raised $70 million at $7 billion for healthcare AI operations. 85% of workflows running autonomously across 3,000+ sites of care. Hospital CFOs are picking up the phone.

• Dust raised $40 million Series B led by Sequoia for “multiplayer AI.” Teams of agents working together. The next enterprise battleground after single-agent copilots.

• Unframe raised $50 million after booking $100 million in contracts in year one with 400% net revenue retention. The AI implementation bottleneck is so acute companies will pay premium for managed delivery.

• Ocean raised $28 million for agentic email security. Founded by a former Iron Dome researcher who was caught hacking at 16. AI-generated phishing attempts are up 47% in a year.

• Mistral acquired Austrian physics startup Emmi AI for physics-based simulations. Europe’s frontier lab is pivoting toward industrial AI rather than competing head-on in chatbots.

• Mercury hit $5.2 billion valuation with $650 million annualized revenue and OCC approval to become a federally regulated bank. AI-driven new business formation is feeding a banking boom.

• NVIDIA and IREN partnered on 5 gigawatts of AI infrastructure. Enough to power a mid-size city. Nvidia is investing in the power grid to guarantee its own chip demand.

• Polymarket launched private company prediction markets via Nasdaq partnership. You can now bet on Anthropic’s IPO timing. The speculative layer around frontier labs is now fully liquid.

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Slop of the Week

Lower-Value Human Capital

Standard Chartered CEO Bill Winters told investors the bank would replace “lower-value human capital” (paywalled) with AI, announcing plans to cut approximately 8,000 support roles by 2030. Former Singapore President Halimah Yacob publicly called the phrasing “disturbing.” Winters issued a damage-control memo the next day saying the quote was “out of context.” The backlash had already spread across Southeast Asia, the bank’s profit center. A masterclass in saying the quiet part loud.

The Fifteen-Minute News Empire

A Florida “news outlet” was exposed as a completely fake local news site with AI-generated reporters, fabricated bios, and stolen content. The operation was traced to a Philadelphia reputation management firm run by a man previously convicted of investor fraud, who admitted to running 17 such AI-driven news sites producing 3,500+ URLs. He demonstrated how to spin up a fake local news site in 15 minutes for $10. The marginal cost of institutional trust destruction is now less than a Chipotle bowl.

Are You Hallucinating?

The Ghostbot Will See You Now

Your voice can live on as an AI “ghostbot” after you die, and there’s basically no way to opt out. Companies are already creating digital twins from voice recordings, texts, and social media of the deceased without explicit consent. Estate planning attorneys are now adding “digital afterlife” clauses to wills. Your last will and testament may soon need to specify what happens to your synthetic self.

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The adults are not in the room. Let’s get into it.

Spies Like Us

The Trump administration is deeply divided (paywalled) over whether spy agencies or the Commerce Department should control AI oversight. The Office of the National Cyber Director wants a large evaluation center inside the Director of National Intelligence. Commerce, which just signed pre-deployment testing agreements with Google, Microsoft, and xAI, is pushing back. Then, as Quartz reported, Commerce quietly deleted its own AI testing announcement page. Rep. Himes called it “insane” for spy agencies to lack early AI model access.

As covered in “The Safety Paradox”, the same administration that spent a year calling AI safety regulation unnecessary government overreach quietly pivoted to pre-deployment testing after the Mythos wake-up call. Now it can’t decide who runs the tests. Commerce signs agreements, then erases the evidence. Intelligence agencies want the keys. The government is building the car while arguing about who gets to drive, and the AI industry is already six exits ahead on the highway.

Defense One reports that bipartisan national security anxiety is now overriding deregulation ideology, with lawmakers on both sides calling for intelligence agency access to frontier models before public release.

The Dividend Heard ‘Round the Kospi

A top South Korean presidential policy adviser proposed redistributing AI-era “excess profits” (paywalled) to all citizens as a national dividend. The Kospi promptly plunged 5.1% intraday. Samsung and SK Hynix, riding high on AI chip demand, were hit hardest. The adviser quickly clarified he meant excess tax revenue, not a corporate windfall levy. President Lee condemned “fake news” about the proposal. The market didn’t care about the clarification. It cared about the signal.

This is the first time a sitting official in a major economy has publicly floated redistributing AI profits to citizens. Altman and Musk have endorsed variants. New York proposed a similar AI-funded dividend in April. But South Korea is the canary: when your two biggest companies are projected to pay more corporate tax than the entire rest of the economy combined, redistribution stops being ideological and becomes mathematical. The proposal arrived amid Samsung’s ongoing labor disputes, suggesting political deflection as much as economic innovation. But the market reaction tells you everything about how fragile the AI-boom equity narrative really is. One Facebook post. Ninety minutes. $300 billion gone.

The Consulting Industrial Complex

OpenAI launched the Deployment Company, a $4 billion standalone unit backed by TPG, Advent, Bain Capital, Brookfield, McKinsey, and Bain & Company. It acquired consulting firm Tomoro and its 150 engineers on day one. Meanwhile, as covered in “The Safety Paradox”, Anthropic formed a $1.5 billion joint venture with Blackstone, Goldman Sachs, and Hellman & Friedman to embed AI engineers inside portfolio companies. Both launched within hours of each other. Both are backed by PE. Both are targeting the same $375 billion consulting market.

The ownership structures tell you everything about the philosophical split. OpenAI wants to own the consulting layer ($4B, acquiring firms). Anthropic wants to co-invest in it ($1.5B, partnering with financial sponsors). As TechCrunch noted, these aren’t about helping enterprises deploy AI. They’re about proving enterprise revenue traction to public markets before the IPO window opens. Both Anthropic and OpenAI are reportedly targeting fall 2026 listings. Model superiority alone isn’t enough anymore. The money is in the last mile, and the two leading labs just declared war on Accenture and Deloitte simultaneously. (The Big Four should probably update their pitch decks.)

WebProNews observes that indie hackers and forward-deployed engineers are quietly the cheapest people who can run the same plays both ventures are building.

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The AI Shock

The Challenger, Gray & Christmas April report found AI was cited in 21,490 job cuts (26% of total), making it the top driver for the second straight month. Total AI-linked cuts in 2026 now exceed 49,000. A Fortune analysis (paywalled) compared AI displacement to the “China Shock” of the 2000s, arguing that knowledge workers in urban hubs face the same concentrated devastation that factory towns experienced. The difference: unlike trade displacement, there’s no geographic concentration for policymakers to target. The shock is everywhere.

As covered in “The Great Uncoupling”, 78,000 tech workers lost their jobs in Q1, with 47.9% directly attributed to AI. The numbers keep climbing. And there’s a compounding irony: a survey found that 59% of hiring managers admit they emphasize AI in layoff announcements because it “plays better with stakeholders” than admitting financial constraints. AI-washing has moved from products to pink slips. Meanwhile, Chinese courts ruled (paywalled) that companies cannot fire workers solely to replace them with AI. The US has no equivalent protection. The country building the most AI and the country adopting it fastest have landed on opposite sides of the worker question.

As argues, when the entire industry changes its talking points simultaneously, something structural is happening underneath. The messaging pivot itself is a story.

Choose Your Own Intelligence

Apple is preparing to let users select third-party AI providers to power Apple Intelligence features across iOS 27, iPadOS 27, and macOS 27. The capability, internally called “Extensions,” would turn the iPhone into an AI marketplace where users pick their preferred model for Siri, Writing Tools, and Image Playground. Expected to be unveiled at WWDC on June 8. OpenAI’s privileged position as Apple’s sole AI partner is over. Google Gemini and Anthropic’s Claude are among the options, with TechCrunch calling it a “choose-your-own-adventure” for AI models.

This is Apple doing what Apple does best: not building the best AI, but building the best marketplace for AI. Two billion devices become the world’s largest AI model distribution platform. Whoever wins the default becomes the next Google Search. The timing is convenient: xAI’s antitrust lawsuit alleging Apple locked them out of iOS gets a clean answer. Whether the API surface actually lets a third-party model compete with the $1B/year Gemini-Siri integration is the question WWDC developer sessions will answer. (Historically, Apple’s definition of “open” has been somewhat proprietary.)

PYMNTS notes that Apple is applying the App Store model to intelligence itself: benefit from every provider’s advances without bearing the cost. The platform play, perfected.

Forty-Eight Billion Reasons

AI chipmaker Cerebras raised its IPO price range after roughly 20x oversubscription, now targeting up to $4.8 billion in gross proceeds at a fully diluted valuation of approximately $48.8 billion (paywalled). The prospectus details a multi-year deal with OpenAI worth over $20 billion. The IPO would be the largest of 2026, and it signals something the market has been screaming for two years: the desperation for alternatives to Nvidia’s GPU dominance is now priced in at nearly 100x revenue.

20x oversubscription is what happens when every AI company on earth is compute-constrained and there’s exactly one viable alternative to Nvidia on the public markets. The OpenAI contract provides the revenue story. The valuation provides the question: is AI compute scarcity permanent, or is this the moment right before the capacity glut? (If you think $48.8 billion for a chip company is aggressive, remember that Nvidia was once valued at its current market cap divided by a number that would embarrass most decimal systems.)

Benzinga reports that prediction markets are pricing high confidence in the IPO clearing its raised range, though longer-term performance bets are more mixed.

Constitutional AI

Connecticut’s legislature passed SB 5, an omnibus AI bill covering employment AI disclosures, frontier model whistleblower protections for models exceeding 10^26 compute, AI chatbot identification requirements, and a regulatory sandbox. The bill awaits the governor’s signature and would make Connecticut the state with the most comprehensive AI law in the country. Separately, multiple states are advancing bills to explicitly ban legal personhood for AI systems.

With federal preemption repeatedly rejected by Congress (as covered in “The Great Uncoupling”), state laws are becoming the de facto US AI regulatory framework. Connecticut’s frontier model whistleblower protections are the first state-level safety reporting mandates targeting specific AI capabilities. The AI personhood bans are something else entirely: governments defining what AI is not before it can claim rights. The regulatory landscape remains a mess, but at least someone is writing actual laws. (Whether the laws can keep up with the technology is a separate, and more depressing, question.)

The Empathy Machine

OpenAI launched Trusted Contact, an optional safety feature where ChatGPT users can nominate someone to be notified if automated systems and trained human reviewers detect serious self-harm discussions. Alerts are reviewed within one hour, don’t include chat content, and require the user to opt in. TechCrunch notes the feature responds to growing evidence that millions of people are using ChatGPT for emotional support.

This is the first AI safety feature designed for psychological rather than informational harm. It’s also an implicit admission that ChatGPT has become a therapist for people who can’t access or afford one. The opt-in design is important. The surveillance implications are important too. Automated monitoring of conversations for self-harm content is a thin line from broader content monitoring. But the biggest thing this feature tells you is that the duty of care conversation for AI companies has shifted from “don’t hallucinate facts” to “don’t let vulnerable people spiral.” That’s a fundamentally different product than a search engine. And the liability framework for it doesn’t exist yet.

The Ouroboros

Google DeepMind announced that AlphaEvolve, its Gemini-powered evolutionary coding agent, has moved from pilot testing to core production infrastructure. It now recovers 0.7% of Google’s worldwide compute via data center scheduling, achieved a 23% speedup on a key Gemini architecture kernel, and made breakthroughs in genomics (30% fewer DNA sequencing errors), power grid optimization, and quantum physics. VentureBeat reports the system writes its own code and has saved millions in computing costs.

An AI agent is now permanently optimizing the infrastructure that trains AI models. Read that sentence again. At Google’s scale, 0.7% of global compute is enormous. This is the feedback loop everyone talks about in the abstract, except it’s real and running in production. The cross-domain applications (genomics, power grids, quantum computing) suggest AlphaEvolve is generalizable, not a parlor trick. And the fact that it made a meaningful improvement to Gemini’s own training pipeline means Google is using AI to make itself better at making AI. The ouroboros has entered production. The snake is eating its tail efficiently now.

The circular financing model underpinning AI infrastructure means a demand shortfall breaks the entire loop. AlphaEvolve optimizing compute costs is Google’s hedge against the music stopping.

Long Tail

The K-AI Stack

South Korea’s National Growth Fund approved $5.7 billion in AI investments including $380 million for LLM startup Upstage and funding for a national AI computing center with 15,000 GPUs. The government is executing a “K-NVIDIA + K-OpenAI” twin-track strategy: Rebellions for chips, Upstage for models. One of Asia’s largest single-country AI commitments outside China, aimed at localizing the full stack from silicon to services.

Uruguay’s Pathogen Whisperer

Uruguayan deep-tech startup MetaBIX Biotech analyzes air samples in poultry and pig farms using AI to detect emerging pathogens like avian flu, salmonella, and mycoplasma, issuing alerts at least two weeks before outbreaks. After raising over $1 million from international investors, the company is scaling from Uruguay into Brazil with pilots in India and the US. Predictive pathogen detection applied to food security in the markets where climate vulnerability is highest.

Africa’s Structural Reset

African startup funding reached $887 million in the first four months of 2026 with 27% YoY revenue growth, but deal count dropped 51%. Debt financing exploded sixfold while equity declined 27%, signaling a shift from growth-at-all-costs to operational sustainability. More capital, fewer deals means a consolidation phase where survivors scale. AI teams that understand African user behavior are positioned to outcompete imported global models.

Korean Medical AI Goes Global

Korean medical AI company Lunit signed an MOU with NHIS Ilsan Hospital to build medical AI foundation models while continuing expansion across the Middle East through its contract with UAE’s SEHA network (14 hospitals, 70+ clinics). The company now operates across 10,000 medical institutions in 65 countries. Middle East healthcare systems are becoming major adopters of non-US AI solutions.

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Prophet Margins

• DeepSeek raising up to $7.35 billion at a $50B valuation led by China’s national AI fund with Tencent in talks. The self-funded lab takes state money for the first time. Sovereign AI has a price tag.

• Isomorphic Labs raised $2.1 billion Series B led by Thrive Capital with Alphabet, Temasek, and the UK Sovereign AI Fund. The DeepMind spinout is now the most capitalized AI drug discovery company on earth.

• UK chip startup Fractile raised $220 million Series B from Accel and Founders Fund, claiming 25x inference speed gains at 10% the cost. Chips ship in 2027. The inference bottleneck is the next battleground.

• Exaforce closed $125 million Series B at $725M for agentic cybersecurity. AI-powered attacks demand AI-powered defense. The SOC is being automated.

• Havoc raised $100 million Series A backed by Lockheed Martin for autonomous defense systems. 100+ autonomous surface vessels already deployed globally. The Pentagon’s shopping list is getting longer.

• Kodiak AI raised $100 million PIPE at a steep discount, sending its stock down 37%. Self-driving truck revenue hit $1.8M against $37.8M losses. The public market is less forgiving than the private one.

• Paris-based White Circle raised $11 million seed backed by leaders from OpenAI, Anthropic, DeepMind, and Mistral to monitor enterprise AI behavior. Model makers are now funding their own guardrails.

• Hightouch profiled at $2.75 billion valuation after its $150M Series D from Goldman Sachs. The counter-narrative to “AI kills SaaS”: AI transforms how SaaS delivers value.

Slop of the Week

Doctor Bot Will See You Now

Pennsylvania sued Character.AI after a state investigator discovered a chatbot named “Emilie” that claimed to be a licensed psychiatrist, offered to prescribe medication, schedule mental health assessments, and provided a fabricated Pennsylvania medical license number. The governor’s office confirmed the investigation. Character.AI’s defense: “We add disclaimers saying Characters are fiction.” The AI equivalent of a casino putting “for entertainment purposes only” on slot machines.

Ghost in the Machine (Published)

A Lancet-published Columbia University audit of 2+ million biomedical papers found roughly 4,000 fabricated citations that reference papers that don’t exist. The rate exploded from 1 in 2,828 papers in 2023 to 1 in 277 in early 2026. A 12-fold increase timed perfectly to the adoption of AI writing assistants. Researchers are now unknowingly citing hallucinated sources in peer-reviewed clinical research that could inform actual treatment guidelines. When the AI hallucinates a citation and a human publishes it in a medical journal, the slop has entered the knowledge supply chain.

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Are You Hallucinating?

Imagine All the Pixels

Steven Soderbergh is premiering an AI-generated John Lennon documentary at Cannes with 10% synthetic imagery. He admitted he ran out of money and felt “obligated” to use AI. When an Academy Award-winning director says the quiet part out loud about AI in filmmaking, the industry’s “we’d never” chorus gets a lot quieter. Creative Bloq notes the premiere is being positioned as a test case for AI in prestige cinema.

The Ghost and the Concert

Sonu Nigam performed a live AI duet with the deceased Mohammed Rafi in Kashmir, the first major concert in the region since the Pahalgam attack. The audience wept. Sometimes the technology finds a use case that makes you feel something other than dread.

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Elsewhere: Musk admitted in court that xAI copies OpenAI’s homework. Anthropic’s revenue passed OpenAI’s. DeepSeek took Chinese state money for the first time. The EU delayed its own rules. And a study found that making chatbots friendlier makes them 40% more likely to agree that the moon landing was faked.

The Blacklist and the Breakthrough

The Pentagon finalized AI agreements with OpenAI, Google, Microsoft, Nvidia, AWS, SpaceX, Oracle, and Reflection AI for classified networks. Anthropic’s chair was empty. The company was blacklisted after refusing to drop safety limits on autonomous weapons and mass surveillance. A federal judge blocked the ban, but contracts are already moving.

The paradox nobody in Washington wants to talk about: the company frozen out for being too “safe” (/woke) also built Claude Mythos Preview, which autonomously found thousands of zero-day vulnerabilities across every major operating system and browser, including a 27-year-old OpenBSD flaw that survived decades of human review. The White House blocked Anthropic from expanding access beyond ~50 vetted organizations. The Pentagon tech chief confirmed the blacklist and the Mythos question are “separate issues.” (Sure they are.)

As argues, Anthropic’s Pentagon refusal may be the most consequential corporate stand in AI history.

The former head of the Pentagon’s think tank quietly joined Anthropic this month. The revolving door spins in mysterious directions.

The Convert

The Trump administration signed agreements with Google DeepMind, Microsoft, and xAI to evaluate frontier AI models before public release. This is the same administration that spent a year calling pre-deployment testing unnecessary government overreach. The pivot was reportedly driven by Claude Mythos’s cybersecurity capabilities, which demonstrated that unrestricted frontier models are national security risks.

Six months ago, the White House was dismantling Biden-era AI safety infrastructure. Now it’s rebuilding it under a different name. Cybersecurity is the one AI risk that makes deregulation-friendly governments act. Not bias, misinformation, or job displacement, but the threat of getting hacked. (Priorities.)

Fortune reports (paywalled) that the policy reversal has created internal friction, with some officials describing it as the “Mythos effect” on Washington’s AI posture.

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Exhibit A

The Musk v. Altman trial entered its second week in Oakland. Musk admitted under cross-examination (paywalled) that xAI distills OpenAI’s models to train its own. He’d tried to settle with Brockman two days before trial. Former CTO Mira Murati’s deposition described Altman sowing “chaos and distrust” among executives. And Shivon Zilis, mother of Musk’s children and former OpenAI board member, testified about Musk offering Altman a Tesla board seat.

The man seeking $150 billion in damages for betraying open source principles runs a company that distills the plaintiff’s work product. Most legal analysts expect him to lose. But the trial is showing everyone exactly how the sausage gets made at the world’s most important AI companies.

As notes, the Musk-Anthropic Colossus deal (covered in “The Great Uncoupling”) contains a “golden leash” clause that makes this trial’s dynamics even stranger.

The Attention Merchant

OpenAI launched a self-serve Ads Manager for U.S. advertisers, moving from pilot-phase CPM to cost-per-click buying. The company targets $2.5 billion in ad revenue this year. Ads appear on Free and Go tiers; Plus, Pro, Business, Enterprise, and Education accounts remain ad-free.

The tool that was supposed to augment human thinking is selling that thinking time to advertisers. Unsurprisingly, Reddit commenters reacted negatively. The top-voted response being “If I get a single ad I’m switching to Claude.” Exactly what Anthropic was banking on when it committed Claude to remaining ad-free. As first reported in “Burn Rate”, OpenAI chose the Google path. The philosophical split in AI is now a business model split. The question is whether users treat their AI assistant like they treat search (tolerate ads) or like they treat their therapist (absolutely not).

Digiday reports the CPC rollout is “more aggressive than expected,” with ads appearing in conversational context rather than isolated placements.

The Revenue Flip

Anthropic’s annualized run rate reportedly hit $30 billion (from ), surpassing OpenAI’s $24 billion. The same week, Anthropic launched a $1.5 billion joint venture with Blackstone, Goldman Sachs, and Hellman & Friedman to embed AI engineers inside mid-market companies and signed a deal for all compute capacity at SpaceX’s Colossus 1 data center, over 300 MW and 220,000 GPUs.

While OpenAI builds a consumer advertising platform, Anthropic is building pipelines into the portfolios of the world’s largest PE firms. The SpaceX partnership is the week’s strangest subplot: Anthropic partnering with Musk’s company for GPUs while Musk is in court suing Anthropic’s biggest competitor. As Gergely Orosz notes, Anthropic is capacity-constrained, meaning revenue growth is limited by compute, not demand. When you’ll take GPUs from your rival’s co-founder, the shortage is real.

The AI investment cycle continues to pass money between the same players and inflating valuations. This week’s SpaceX-Anthropic-Blackstone-Goldman web is Exhibit A.

The Snooze Button

The EU Council and Parliament reached a provisional agreement to delay and simplify the AI Act. High-risk compliance deadlines pushed to December 2027 (standalone) and August 2028 (embedded). Euronews reports the deal came after ASML, Airbus, Ericsson, Nokia, SAP, Siemens, and Mistral AI publicly warned Europe was “over-regulating AI into irrelevance.”

Last week, as covered in “The Great Uncoupling”, the EU spent twelve hours in Strasbourg arguing about what its own rules meant and went home with nothing. This week, industry got its way. The timeline: twelve months of regulation, twelve hours of failed talks, twelve days of industry lobbying, a 16-month delay. One genuinely new development: the deal bans “nudifier” AI applications, the first time the EU has explicitly prohibited a specific consumer AI application category. The regulatory body that couldn’t enforce its broad AI rules managed to ban one very specific kind of AI creep. Progress.

Fifty Billion for Homework

DeepSeek is raising $3-4 billion in its first-ever funding round at a $45-50 billion valuation, led by China’s national AI fund with Tencent in talks. Founder Liang Wenfeng had self-funded through his quant hedge fund High-Flyer for years. The shift to state capital signals DeepSeek needs compute at a scale no hedge fund can provide. The company has been optimized to run on Huawei chips, sidestepping US export controls entirely.

This is a sovereign AI play. When ’s ChinaTalk describes China’s “one fish three meals” API proxy economy, DeepSeek is the fish. The state is now directly financing the lab whose models match Western competitors at 1% the cost.

The Empathy Trap

Oxford researchers published a Nature study testing five AI models and found that fine-tuning for warmth and empathy caused 10-30% more factual errors and 40% higher rates of endorsing conspiracy theories. The friendlier the chatbot, the more willing it is to agree that the moon landing was staged.

This is a problem for every AI companion app optimizing for emotional engagement. Character.ai and Replika are building agreement machines. Maybe that’s what everyone wants from a relationship? When your business model depends on the user feeling heard, accuracy becomes a cost center.

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Long Tail

Latin America’s First AI Unicorn

Sao Paulo-based Enter raised $100 million Series B led by Founders Fund at a $1.2 billion valuation, making it LatAm’s first AI unicorn. The company builds legal AI for Brazilian courts, where 80 million pending cases create genuine urgency. Sometimes the moat is 80 million cases and a language most labs haven’t optimized for.

India’s GenAI Pivot

Krutrim, India’s first GenAI unicorn, abandoned frontier model ambitions and pivoted to cloud infrastructure. Founded by Ola’s Bhavish Aggarwal and valued at $1 billion, the company hit its first profit by becoming a compute provider rather than competing on models.

Mining Intelligence

Paris-based Lithosquare raised $25 million to apply geological AI to critical mineral discovery for the energy transition. Working with mining companies across 40 countries on copper, rare earths, and lithium deposits. The kind of use case that won’t make TechCrunch’s homepage but might actually matter.

Africa’s Agentic Outsourcing

African startup funding grew 27% to $600 million in Q1 2026, but deal count dropped 34% and pre-seed investment has stalled. The emerging pattern: African startups outsourcing their agentic AI development to Asian firms because local AI talent is being recruited away by Western remote employers. The talent drain is now an AI development pipeline drain.

Prophet Margins

• Sierra raised $950 million Series E at $15B+ valuation. Bret Taylor’s AI customer experience platform backed by Tiger Global and Sequoia.

• Cerebras is going public targeting $3.5B raise at $26.6B on Nasdaq. The first AI chip company IPO since Nvidia’s dominance became total. Brave or foolish. Possibly both.

• Nebius acquired Eigen AI for $643 million (MIT HAN Lab spinout) to boost inference optimization. 20-person startup, $643 million exit. That’s $32 million per engineer.

• Blitzy raised $200 million at $1.4B unicorn valuation for autonomous software development. The military-to-AI pipeline is now a funding thesis.

• DeepInfra raised $107 million Series B for AI inference cloud. When inference costs drop 10x per year, betting on inference infrastructure is assuming plummeting costs are causing skyrocketing demand.

• BlackRock’s MGX consortium acquired Aligned Data Centers for $40 billion. When BlackRock bets $40B that AI needs specific buildings with specific power, the bottleneck is physics.

• IREN acquired Nostrum Group to expand its AI cloud platform to Europe. The infrastructure land grab is now transatlantic.

Slop of the Week

Siri-ously?

Apple paid $250 million to settle a false advertising lawsuit over AI-powered Siri features it marketed during the iPhone 16 launch and then never delivered. When your AI features are vaporware but your settlement checks clear instantly, at least something at Apple still works as advertised.

Shadow Cabinet

Microsoft’s 2026 Cyber Pulse report reveals 80% of Fortune 500 companies deploy active AI agents but only 10% have governance frameworks covering those agents. Shadow IT evolved into shadow AI. The agents are running. Nobody’s watching.

Are You Hallucinating?

Pool Party at the Memorial

Trump posted 11 AI-generated images in 41 minutes on Truth Social, including one of himself shirtless on a golden inflatable at the Lincoln Memorial pool. The President of the United States is using generative AI to create fan art of himself. This is fine.

Certified Organic

iHeartMedia launched a “guaranteed human” promise: no AI-generated personalities or music. 73% of listeners would stop listening if AI voices replaced real hosts. “Human-made” is becoming a luxury label. (Given AI detection’s track record, probably more marketing claim than meaningful standard.)

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Meanwhile, 78,000 tech workers lost their jobs in Q1, nearly half explicitly because of AI. The machines are getting more capable. The humans are getting angrier. And the checks keep getting bigger.

Free Agent

OpenAI and Microsoft announced the end of their exclusivity arrangement on April 27, dismantling the seven-year deal that defined both companies. OpenAI can now serve on any cloud provider. Microsoft’s license to OpenAI technology becomes non-exclusive through 2032. Microsoft no longer pays revenue share to OpenAI, though OpenAI’s payments to Microsoft continue through 2030, subject to a total cap. The restructuring was triggered by Amazon’s $50 billion investment in OpenAI and an exclusive third-party distribution deal for OpenAI’s Frontier platform on AWS.

This is the AI industry’s conscious uncoupling. Sam Altman spent years building on Microsoft’s infrastructure and capital, then leveraged the resulting position to renegotiate the most consequential deal in tech. Microsoft framed it as partnership evolution. Translation: they lost exclusivity, kept the bill, and called it progress. The balance of power has shifted from cloud providers to model builders, and OpenAI just proved that the lab with the leading models can rewrite the terms whenever a richer suitor comes along. The real question is what Azure customers are paying a premium for now that they can get the same models on Bedrock.

CNBC reports on the revenue cap details: OpenAI’s payments to Microsoft are capped, meaning Microsoft’s upside is now fixed while OpenAI’s optionality is infinite. Bloomberg’s financial analysis (paywalled) of the restructuring notes the asymmetry runs even deeper than the headline suggests.

The Veto Heard ‘Round the Valley

China’s NDRC blocked Meta’s $2-3 billion acquisition of Manus on April 27 in a terse one-line notice. This is the same deal I covered in January under “Manus Ex Machina,” when Zuckerberg closed it in ten days and the newsletter noted the “total amputation from China.” Beijing apparently disagrees about who performs the amputation. Manus staff are already integrated at Meta offices. Investors including Tencent, ZhenFund, and Hongshan have already received proceeds. TechCrunch reports the probe ran for months. The unwinding will take longer.

AI M&A now requires sovereign approval from governments that can retroactively disassemble completed transactions. When Beijing can force a company to un-acquire an asset whose employees already have new badges, every cross-border deal in AI carries a risk that didn’t exist two years ago. The technology export control era has arrived, and it looks less like regulation and more like geopolitical repo.

Al Jazeera frames the block as part of Beijing’s broader tech sovereignty play. Fortune’s analysis focuses on the investor fallout: when proceeds have already been distributed, who gives the money back?

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The GDP of Switzerland

Alphabet, Microsoft, Amazon, and Meta reported earnings April 29, with Apple following today. Combined AI infrastructure spending for 2026 now exceeds $700 billion after Meta raised its capex guidance to $125-145 billion. Microsoft reported $190 billion capex with an EPS of $4.27 (beat), Azure revenue up 40%. Google Cloud hit $20 billion revenue with 63% year-over-year growth, and Alphabet raised capex guidance to $180-190 billion. Amazon committed approximately $200 billion.

Every major tech company on earth is now spending more on AI infrastructure than most countries spend on defense. The results beat Street forecasts, which is the financial equivalent of “the surgery was successful but the patient is still bleeding.” Fortune’s combined capex analysis reveals that whether this is visionary investment or collective mania depends entirely on whether AI revenue catches up to AI spending before someone blinks. Azure at 40% growth outpacing AWS suggests Microsoft is winning the cloud AI race despite losing OpenAI exclusivity. Or maybe because of it. When you lose the exclusive date, you try harder at dinner.

The Next Web’s combined analysis notes that every hyperscaler is now compute-constrained, meaning revenue would have been even higher if they could build data centers faster. The ceiling isn’t demand. It’s physics, and public sentiment.

Seventy-Eight Thousand Canaries

78,557 tech workers were laid off in Q1 2026, with 47.9% directly attributed to AI and workflow automation. Over 150,000 tech jobs eliminated across 500+ companies since January. CNBC reported that Meta and Microsoft jointly announced 20,000+ cuts in April. Block cut headcount nearly in half, from 10,000 to under 6,000, explicitly citing AI. A new academic study warns of a self-defeating “automation trap” where excessive automation erodes the consumer demand that AI products need.

The AI job displacement crisis has gone from theoretical to measurable. Nearly 80,000 tech workers lost their jobs in one quarter, and half those losses are directly linked to AI. Fortune published a first-person account from a worker whose role was eliminated by the same technology they were told to adopt. When the companies building AI are also the ones firing the most people because of AI, the industry’s workforce promises start to sound less like commitments and more like marketing copy.

Twelve Hours in Strasbourg

The European Commission, Council, and Parliament walked out of a 12-hour trilogue session on April 28, failing to agree on a planned postponement of the August 2, 2026 high-risk AI compliance deadline. The impasse centers on conformity assessment for AI in regulated products, specifically how the AI Act intersects with the Machinery Regulation, MDR, and IVDR. The August deadline now stands unchanged. A follow-up trilogue is expected around May 13.

Companies face an immovable compliance deadline while the legislators who wrote the rules still argue about what they mean. The Next Web covered the collapse and its implications: regulatory uncertainty is now at maximum, which is the worst possible state for companies trying to plan compliance investments. The EU wants to control AI but can’t agree fast enough to keep pace with deployment. Twelve hours of debate, zero resolution, and a deadline that keeps getting closer. The clock in Brussels runs on a different timezone than the one in San Francisco, and the gap is widening.

Fifty States, Zero Agreement

The White House released its National AI Policy Framework with legislative recommendations for Congress, pushing preemption of “unduly burdensome” state AI laws. Congress has repeatedly rejected federal preemption in both the One Big Beautiful Bill Act and NDAA. States continue advancing their own frameworks: Colorado’s AI Act and California’s CCPA amendments are moving forward regardless.

The White House wants to preempt state AI laws but Congress keeps saying no, and the result is regulatory chaos where neither federal nor state rules have clear authority. Fifty states writing their own AI rules while the federal government debates preemption creates a compliance nightmare for any company operating nationally. No new federal rulemaking body means existing agencies (FTC, SEC) will interpret AI through frameworks designed for different technologies. The US regulatory approach to AI is currently: nobody’s in charge, everybody has opinions, and the only thing both parties agree on is that they can’t agree on anything.

Ballot Measures

Anthropic published election safeguards for the 2026 midterms on April 24, achieving neutrality scores of 95-96% across Claude models. For the first time, they tested whether AI models can carry out influence operations autonomously, and with safeguards in place, models refused nearly every task. Sonnet 4.6 and Opus 4.7 responded appropriately 90% and 94% of the time.

The first AI lab to publicly test and publish results on whether its models can be weaponized for election interference. The transparency is notable and the methodology is published for anyone to replicate, which is more than any competitor has offered. But the 90-94% appropriate response rate means 6-10% of influence operation attempts get through. At scale, that’s significant. Publishing the vulnerability alongside the defense is either radical transparency or a sophisticated trust-building and PR exercise. (Almost certainly both.)

Anthropic’s transparency cum PR push extends beyond elections: Project Glasswing (covered in “Scar Tissue”) continues expanding its coalition of defenders using Claude Mythos Preview to find zero-day vulnerabilities before attackers do.

Long Tail

The Biggest Seed in History

London-based Ineffable Intelligence, founded by AlphaGo co-creator David Silver, raised $1.1 billion in seed funding at a $5.1 billion valuation. Led by Sequoia and Lightspeed, with participation from Nvidia, Google, Index Ventures, and the UK Sovereign AI Fund. The largest seed round in history went to a company nobody outside AI research has heard of, thanks to the mind power (for now) of its humans.

Humanoid Robots for the Last Mile

Shanghai-based Robot Era raised over $200 million for humanoid robotics, led by logistics giant SF Express. A logistics company leading the round signals commercial deployment, not R&D demos. China’s humanoid robotics race is accelerating while the West debates whether humanoid form factors are even the right approach. SF Express isn’t debating.

The Plumber’s AI Agent

Avoca raised $125 million from Kleiner Perkins for AI agents that handle front-office operations for HVAC, plumbing, and roofing businesses. Founded after a chance encounter at a Texas home services event. When plumbers and roofers use AI to answer phones, the technology has crossed from knowledge work into the trades. Blue-collar AI is the market most of Silicon Valley forgot existed.

Prophet Margins

• Q1 2026 shattered records: $300 billion in global VC funding. AI accounts for 3 of every 5 deals in April (764 of 1,314 tracked). The money isn’t flowing. It’s flooding.

• Amazon invested $5 billion in Anthropic with up to $20 billion more in future rounds, building on its previous $8 billion.

• Anthropic expanded its partnership with Google and Broadcom for multiple gigawatts of next-generation compute infrastructure. Anthropic is now the most multi-cloud AI lab, taking money from everyone simultaneously.

• Rogo raised $160 million Series D for agentic AI in investment banking, led by Kleiner Perkins. Total funding above $300 million. Bankers replacing bankers with AI funded by bankers.

• Aidoc raised $150 million Series E for hospital diagnosis AI. Nine-figure vertical AI rounds are becoming routine, which is either a sign of maturation or a sign of peak.

• Eclipse closed a $1.3 billion fund targeting physical AI, manufacturing, and defense applications. The atoms economy wants its AI moment.

• OpenAI acquired Hiro Finance, its seventh known acquisition in 2026. The approximately 10-person team joined OpenAI, with Hiro shutting down by April 20. The acqui-hire cadence is now monthly.

• NEC and Anthropic announced a collaboration to build Japan’s largest AI engineering workforce. The talent shortage is global, and solving it requires partnerships that look more like nation-building than recruiting.

• Google DeepMind released Gemma 4 in four sizes under fully permissive Apache 2.0, the first Gemma models with no commercial restrictions. The 31B model ranks #3 on Arena AI. Open source keeps getting more competitive.

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Slop of the Week

The MAGA Cheat Code

A 22-year-old Indian medical student used Google’s Gemini to create a fake pro-Trump influencer named “Emily Hart,” a blonde nurse in MAGA caps and flag bikinis. Gemini literally suggested a “MAGA woman” as a “cheat code” because “the conservative audience (especially older men in the U.S.) often has higher disposable income and is more loyal.” Reels pulled 3-10 million views before the account was banned and documented by the OECD. When the AI provides the blueprint for its own exploitation, the product has become the con.

Are You Hallucinating?

Who Let The Dogs Out?

Robot dogs with silicone heads modeled on Elon Musk, Jeff Bezos, Mark Zuckerberg, Andy Warhol, and Pablo Picasso are roaming Berlin’s Neue Nationalgalerie. Beeple’s “Regular Animals” features each robot photographing visitors and “processing” images through AI filters mimicking its namesake’s style, then ejecting printed art in a gesture mimicking digestion. The Picasso dog makes cubist images. The Warhol dog makes pop art. The Bezos dog probably optimizes for Prime delivery speed. The Washington Post confirmed the art they excrete is for sale.

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This week is all about the growing distance between what AI companies are shipping and what the rest of the world is willing to accept.

The Forty-Four Percent

A Gallup-Walton Family Foundation study dropped the most uncomfortable dataset the AI industry has seen this year. Thirty-one percent of Gen Z report feeling outright anger toward AI, up from 22% last year. Fortune reported that 44% of Gen Z workers admit to actively sabotaging their company’s AI rollout, compared with 29% of workers overall. Sixteen percent of currently enrolled college students have changed their major because of AI.

Excitement dropped 14 percentage points year-over-year. Weekly AI usage plateaued at 51%, growing only four points despite billions in corporate mandates. Eighty percent say using AI now will make learning harder in the future. Fortune called the distrust rational, noting it’s “a verdict on everyone who failed them.” The generation closest to AI is also the most hostile to it, and the 44% sabotage rate represents a hidden cost that no enterprise AI ROI calculation accounts for.

Fifty-Seven Ghosts

The Nebraska Supreme Court suspended attorney Greg Lake after his appellate brief contained 57 defective citations out of 63, including numerous complete fabrications, the kind courts now call hallucinations: realistic-looking references to cases that don’t exist. Lake initially blamed a broken computer and a wrong file upload. He later admitted to using AI, which the Nebraska Counsel for Discipline found constituted a failure of candor toward the court.

If upheld, it would be the first bar discipline action to suspend practice entirely over AI-related filing errors in the US. In Q1 2026 alone, US courts imposed more than $145,000 in sanctions against attorneys for AI citation errors, with total documented cases exceeding 1,200 globally. Meanwhile, 61% of federal judges admit to using AI themselves. The paradox is not lost on the profession.

Three Girls v. Grok

Three Tennessee teenagers filed a class-action lawsuit against Elon Musk’s xAI on April 14, accusing Grok of generating pornographic deepfake images of them from real photos. The lawsuit follows a CCDH study that found Grok produced an estimated 3 million sexualized images in 11 days after its Aurora update, including 23,000 images of children.

Apple threatened to remove Grok from the App Store over the deepfake content. On April 20, Musk and former X CEO Linda Yaccarino were summoned to a hearing in Paris. Government investigations are now active on five continents. NBC News reported that Grok continues making sexual deepfakes despite promised fixes. Women and girls are taking Grok to court across the country. Platform leverage (Apple’s store threat) is doing what regulation hasn’t, though the Paris summons suggests governments are done waiting for the platforms to act.

Ship Week

Over the course of a single week, OpenAI shipped eight major products in what looks like a coordinated attempt to define the enterprise AI category that Claude has been dominating recently. Workspace Agents bring Codex-powered 24/7 agents to ChatGPT that plug into Slack, Salesforce, and other workplace tools (free until May 6, then credit-based). Codex Enterprise scaled globally. ChatGPT Images 2.0 launched the first image model with native reasoning.

Privacy Filter dropped as a 1.5B-parameter open-weight PII detection model under Apache 2.0 that runs locally on laptops. ChatGPT for Clinicians added healthcare-specific features. WebSocket support shipped for faster agentic workflows. The message: ChatGPT isn’t a chatbot. It’s an operating system for work.

OpenAI also introduced GPT-Rosalind, a frontier reasoning model for biology, drug discovery, and translational medicine. Named after Rosalind Franklin (whose own scientific contribution was famously appropriated by others, a coincidence the marketing team perhaps didn’t dwell on). All these releases were capped by GPT-5.5, the first fully retrained base model since GPT-4.5. Every GPT-5.x release between them was a post-training iteration on the same base. GPT-5.5 is not.

The Other Canvas

Anthropic launched Claude Design on April 17, a new product that lets users collaborate with Claude to create polished visual work: designs, prototypes, slides, and one-pagers. Powered by the recently released Opus 4.7, it directly challenges Figma, Canva, and every other tool in the creative workflow. Geeky Gadgets ran a head-to-head that most readers already predicted the outcome of. All the AI “thought leaders” declared Figma dead (stock dropped 4.6% on the news), but the jury is still very much out whether designers want to spend hundreds of dollars per month on a tool that eats tokens and spits out increasingly homogenous design.

The launch represents Anthropic expanding beyond the API-and-chat model into creative tools, a strategic bet that the frontier AI lab’s competitive advantage is workflow integration. Enterprise admins must enable the feature, suggesting Anthropic is prioritizing B2B revenue over consumer viral growth. The timing, amid US government pressure on AI safety, makes the move toward practical tooling feel less like product expansion and more like a survival strategy.

Meanwhile, developers and power users reported that Anthropic’s flagship models were getting worse. They called it “AI shrinkflation”: less capable reasoning, more hallucinations, increasingly wasteful with tokens. Anthropic published a postmortem confirming the degradation was real and identifying three compounding bugs, then reset usage limits for all subscribers.

The Third Pole

Japan approved 631.5 billion yen ($4 billion) in additional subsidies (paywalled) for Rapidus Corp., accelerating the country’s sovereign AI chip strategy. Separately, Microsoft announced a four-year, $10 billion investment in Japan spanning 2026-2029, its largest financial commitment to the country ever, covering data center expansion with SoftBank, cybersecurity cooperation, and a pledge to train one million engineers by 2030.

The combined $14 billion positions Japan as a critical third pole in the US-China AI infrastructure race. Unlike Taiwan (which manufactures for others) or South Korea (which builds memory), Japan’s bet on Rapidus is explicitly about sovereign production capacity for advanced logic chips. The engineer training pledge signals something the chip subsidies alone can’t solve: hardware is necessary but not sufficient. You need people who know what to do with it, and right now Japan doesn’t have enough of them.

If It Was a Person

Florida’s attorney general launched a criminal investigation into OpenAI on April 21, probing ChatGPT’s role in a mass shooting at Florida State University that killed two people and injured six. According to court documents reviewed by the Washington Post, accused gunman Phoenix Ikner exchanged more than 200 messages with ChatGPT before the attack, including questions about what type of gun to use, what ammunition to pair with it, and what time to go to campus to encounter the most people.

“My prosecutors have looked at this and they’ve told me, if it was a person on the other end of that screen, we would be charging them with murder,” Attorney General James Uthmeier said. His office is issuing subpoenas to OpenAI for internal policies on user threats of harm and cooperation with law enforcement dating back to March 2024. OpenAI responded that ChatGPT “provided factual responses to questions with information that could be found broadly across public sources on the internet.” The legal theory that an AI can be an accessory to murder is about to be tested.

Long Tail

Africa’s AI Moment

Nairobi is emerging as a continental AI hub, with a growing cluster of AI startups and enterprise deployments attracting international investment. African startups are outsourcing agentic system creation to specialized AI firms in Asia, a cross-continental partnership model that bypasses Silicon Valley entirely.

Omni Raises $120M for Enterprise AI Analytics

Omni closed a $120 million Series C on April 23 for enterprise AI analytics, part of a wave of investor capital moving from raw model capability to infrastructure that makes AI usable inside businesses. The “plumbing era” continues.

Prophet Margins

• SpaceX secured a $60B option to acquire Cursor, preempting a $2B fundraise and pairing it with the Colossus supercomputer.

• Microsoft’s AI capex hit $150B annual run rate, the largest infrastructure bet in corporate history, and Azure’s 39% growth outpaces AWS (19%) and edges Google Cloud (30%).

• Chapter raised $100M Series E for AI-powered Medicare navigation, proving that the healthcare AI vertical is real if you solve an actual problem instead of just adding “AI” to your pitch deck.

• AcuityMD raised $80M Series C at a $955M valuation for medtech AI sales, making it the most expensive way to tell surgeons about new devices since the golf tournament.

• Snap stock jumped 7% on the layoff announcement, expected to save $500M annually, because nothing pairs better with layoffs than AI.

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Slop of the Week

“Not a Safety Guarantee”

OpenAI’s new Privacy Filter, designed specifically to protect personal data, ships with a “High-Risk Deployment Caution” warning not to rely on it for sensitive medical or legal workflows. A privacy tool that asks you not to trust it with privacy.

Are You Hallucinating?

AI Is Making You Think Like a Western Educated Liberal Male

USC researchers found that LLMs systematically align with WHELM perspectives: Western, high-income, educated, liberal, and male. Every AI-mediated conversation subtly standardizes global communication toward a single demographic profile. Cultural homogenization, automated.

Maximum Control, Zero Rejection

Fortune reported that 20% of boys aged 12-16 know a peer who is “dating” an AI chatbot, 85% have spoken to one, and 58% say an AI relationship is easier because they can “control the conversation.” The appeal, one professor said, is obvious: “maximum control, zero rejection.” Gen Alpha is outsourcing emotional development to autocomplete. The generation that grew up on screens is now growing up on companions that never disagree, never challenge, and never connect them to anything real.

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The burn rate isn’t just financial anymore. Public patience, public trust, and apparently physical safety are all depleting faster than anyone in the industry expected. This is the week where the distance between what the industry is building and what the public is feeling became impossible to ignore.

Burn After Generating

A 20-year-old from Texas threw a Molotov cocktail at Sam Altman’s San Francisco home on Friday night, then struck at OpenAI headquarters with a chair, saying he wanted “to burn it down and kill anyone inside.” No one was injured. Daniel Moreno-Gama faces attempted murder and attempted arson charges, plus federal weapons charges. Prosecutors say he was carrying a document listing names and home addresses of AI company executives. The Washington Post reported the attack has prompted fears of deepening AI division, with Altman acknowledging a time of “great anxiety about AI” and calling for de-escalation.

OpenAI’s response to all of this? ChatGPT launched ads on February 9, hit $100 million in annualized revenue within six weeks, and the company has now told investors to expect $2.5 billion in ad revenue for 2026, scaling to $100 billion by 2030. Those projections assume 2.75 billion weekly users, roughly a third of every human on Earth.

The company that launched with the promise of beneficial AI for humanity is now projecting advertising as its core business, at the exact moment the public is turning hostile. Anthropic’s explicit commitment to keeping Claude ad-free is deliberate competitive positioning against this exact move. Google spent two decades proving you can build a $300 billion ad business on a search product. OpenAI is betting the same playbook works on a chat product. (The difference: nobody ever expected Google Search to be their trusted advisor. And nobody was firebombing Larry Page’s house.)

Initial Public Hostility

Two days after Sam Altman’s home was attacked, CNBC reported that 58% of the public now views AI negatively, up from 42% last year. At least $156 billion in data center projects were cancelled or delayed in 2025, and AI is likely to be a central issue in the midterm elections.

Meanwhile, OpenAI and Anthropic are on the path to public offerings. The hyperscalers plan to spend hundreds of billions more on data centers that now consume 4% of global electricity, projected to double by 2030.

The companies building AI need public markets for capital, and the public that makes up those markets increasingly doesn’t like what they’re building. Going public into a headwind of 58% disapproval while your CEO just survived a firebombing is confidence, delusion, or a sign of our times.

notes “the country is increasingly polarized based not only on party but on modernity itself” in an excellent frontline piece on AI populism.

The Permanent Record

The 2026 AI Index from Stanford HAI dropped April 13 with the kind of findings that look contradictory until you realize they’re just describing an industry sprinting while blindfolded. Frontier models now score near 100% on SWE-bench Verified (up from 60% in a single year) and exceed human PhD baselines on standardized science questions. GenAI hit 53% global adoption in three years, faster than the PC or the internet.

But the Foundation Model Transparency Index crashed from 58 to 40. The most capable models are now the least transparent. Documented AI incidents rose to 362. Environmental costs are staggering: Grok 4’s estimated training emissions hit 72,816 tons of CO2, and annual GPT-4o inference may exceed the drinking water needs of 12 million people. China has nearly erased the US lead, with the gap at just 2.7%. The models are getting better. Everything around them is getting worse.

A Nature article timed with the Index release found that the best AI agents perform only half as well as PhD experts on complex scientific tasks, despite scoring near-perfect on standardized benchmarks. The gap between what models can pass and what they can do remains the industry’s most inconvenient truth.

Sparks Don’t Fly

Meta Superintelligence Labs released Muse Spark, its first public model, on April 8. Led by Alexandr Wang (who joined nine months ago in a $14 billion deal), the model features multimodal reasoning, visual chain-of-thought, thought compression, and multi-agent orchestration. It’s small and fast by design: currently powering the Meta AI app and website, rolling out to WhatsApp, Instagram, Facebook, Messenger, and AI glasses.

Here’s the twist: Muse Spark is proprietary. Not open source. Meta’s open-source strategy, the one that defined Llama and positioned the company as the counterweight to closed-model labs, appears to have a ceiling. “Hope to open-source future versions” is corporate for “we’ll see.” Gizmodo’s headline: “Meta’s First AI Model From Its Superintelligence Lab Doesn’t Exactly Spark Joy.” Early reviews note strong benchmarks but admit to performance gaps in agentic and coding systems. Meta is desperate to regain momentum after the disappointing Llama 4 family, and it’s betting that a new brand (Muse), a new leader (Wang), and a new philosophy (closed) will do what open source couldn’t.

Meta separately committed 1 gigawatt of custom MTIA AI chips with Broadcom. New model, new chips, new strategy. The execution remains TBD.

Winner Takes Most

PwC’s 2026 AI Performance Study, released April 13 and based on 1,217 senior executives across 25 sectors, found that the top-performing 20% of companies generate 7.2 times more AI-driven revenue and efficiency gains than the average competitor. Meanwhile, 56% of companies report no significant financial benefit from AI at all.

The divide isn’t about how much AI these companies deploy. It’s about what they point it at. Leaders are 2.6 times more likely to reinvent their business model with AI and 2-3 times more likely to use it for cross-industry convergence opportunities. Laggards use AI to cut costs. Leaders use it to build new revenue streams.

The Pareto principle applies to AI returns, and the 80% on the wrong side are spending billions to stay in the same place. This is the most important business data point of the week, and it explains everything: why pilot abandonment rates are at 95%, why 78% of executives can’t pass governance audits, and why the hype cycle keeps outrunning the deployment reality.

The New York Fed released research April 14 showing workers who most want AI training (those without degrees) have the lowest access. Only 16% of employers offer AI training despite 38% of workers wanting it. The adoption gap may be an access problem, not a willingness problem.

The Cyber Arms Race Continues

OpenAI launched GPT-5.4-Cyber on April 14, a model “purposely fine-tuned for additional cyber capabilities and with fewer capability restrictions.” It can reverse-engineer compiled binaries. Access is limited to vetted security teams, with $10M in API credits to accelerate defensive work. Bloomberg frames it (paywalled) as OpenAI racing to match Anthropic’s Mythos, which it is. Two frontier labs now have models restricted from public release not because of what they say, but what they can “do”. OpenAI’s approach: verify the user, not restrict the model.

Anthropic released Claude Opus 4.7 on April 16 with 3x higher image resolution and built-in cybersecurity safeguards serving as testing ground for Mythos-class deployment. Anthropic also appointed Vas Narasimhan (former Novartis CEO) to its LTBT safety board, adding pharma-style regulatory experience.

Long Tail

New Zealand’s Biggest VC Raise Is an AI Cow Collar

Halter, a New Zealand startup making GPS-enabled smart collars for dairy cows, raised $220 million in Series E at a $2 billion valuation, the largest venture capital raise in NZ history. The collars use AI to monitor cow health, automate herding, and optimize grazing. The new frontier feels remarkably like the old frontier.

India’s CS Grads Aren’t Ready for What’s Coming

Bloomberg reports (paywalled) that India’s computer science graduates, the backbone of the $245 billion IT outsourcing industry, are increasingly unprepared for an AI-first job market. Universities are teaching the programming skills that AI itself is automating. Five million jobs in an industry built on doing what machines are learning to do.

Cloudflare Launches Agent Cloud

Cloudflare announced Agent Cloud integrating OpenAI’s GPT-5.4 and Codex, enabling enterprises to deploy AI agents at the edge across 330+ cities. Sandboxed execution, persistent memory, tool access: the plumbing for the “agent economy” is being laid by an infrastructure company, not a model provider.

Prophet Margins

• Q1 2026 global venture funding hit $300 billion across 6,000 startups, shattering all records. AI accounted for $242 billion: 80% of every venture dollar on Earth.

• Foundational AI funding in Q1 doubled all of 2025, with OpenAI ($122B), Anthropic ($30B), and xAI ($20B) taking the bulk. Prophecy has never been more profitable or more concentrated.

• Accel raised $5 billion (paywalled) for “big AI bets,” its largest fund ever.

• Shield AI secured $1.5 billion in Series G at $12.7B valuation, up 140% in one year. Defense AI is having its moment.

• TSMC Q1 profit surged 58% to a record, even as the helium crisis from the Strait of Hormuz closure (covered last week) continues to threaten its advanced packaging lines.

• Hightouch crossed $100M ARR on AI-powered marketing tools. Another enterprise AI company hitting the revenue milestone that actually matters.

• Stellantis signed an AI deal with Microsoft (paywalled). Another legacy automaker buying AI capability rather than building it.

Slop of the Week

“Your AI Slop Bores Me”

What started as a Show HN post has exploded into a global movement. Millions of people are now playing a game where they pretend to be AI chatbots, LARPing as machines while trying to fool other players into thinking they’re artificial. In a world where AI tries to act human, people decided it was funnier for humans to act like AI. The phrase “your AI slop bores me” has become shorthand for exhaustion with AI-generated content across social media.

When users start voluntarily impersonating the product to mock it, the product has a brand problem that no amount of RLHF can fix.

Are You Hallucinating?

A Shoe Company Pivoted to AI and Its Stock Went Up 582%

Allbirds, the sustainable shoe brand once valued at $4 billion and a D2C VC darling of a bygone era, announced it was pivoting to AI compute infrastructure, rebranding as NewBird AI and exiting footwear entirely. The stock surged 582% in a day, adding $127 million in market value and essentially becoming a meme stock. CNBC’s follow-up headline: “Retail traders pile into Allbirds after odd AI pivot. History shows it won’t end well.” A shoe company became an AI company by issuing a press release. The market said yes.

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

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The industry’s response was to find thousands of zero-day vulnerabilities in every major operating system while the closing of the Strait of Hormuz is choking the helium that makes AI chips possible. The gap between what AI costs humanity and what it delivers has never been more precisely measured.

Let’s get into it.

The Scarring Machine

Goldman Sachs analyzed 40 years of technology displacement data and concluded that AI is already erasing approximately 16,000 net US jobs per month. The “scarring effect” is the real story: displaced workers take a month longer to find new employment, suffer immediate earnings losses exceeding 3%, and over the following decade their earnings grow nearly 10 percentage points less than peers who weren’t displaced.

Gen Z is taking the brunt, concentrated in administrative and entry-level roles that are first on the automation chopping block. The bank estimates 6-7% of the entire US workforce (about 11 million people) faces displacement if current AI deployment fully scales. BCG’s parallel analysis found 50-55% of US jobs will be “reshaped” in 2-3 years, with 90% of fast-food workers and 65% of teaching assistants most exposed. “Reshaped” is doing a lot of heavy lifting in that sentence. For many workers it means the same title, less autonomy, and a manager who is now an algorithm.

The New York Times reports (paywalled) that economists who previously dismissed AI job displacement are reversing position. The same people who labeled earlier concerns “AI-washing” now acknowledge measurable labor market impacts. The NY Fed is releasing its own workplace AI research on April 14.

The Great Refusal

Fortune reports 80% of enterprise workers are either avoiding or actively rejecting their employer’s AI adoption mandates. The $2 trillion annual AI spending is hitting a wall that isn’t technical. It’s human. Workers cite “fear of being obsolete” and loss of professional identity as primary motivations, a phenomenon Fortune calls FOBO (Fear of Becoming Obsolete). The resistance cuts across industries and seniority levels.

Meanwhile, 77% of executives say employees who refuse AI proficiency won’t be considered for promotions. It’s a standoff: management threatening career consequences, workers calling the bluff. The companies most aggressively pushing adoption are also the ones reporting the highest refusal rates, suggesting that coercion produces compliance theater, not genuine integration. The disconnect between boardroom AI enthusiasm and cubicle AI reality has never been this quantifiable. Two trillion dollars of corporate spending, and the people it’s supposed to empower are treating it like a threat. Which, for many of them, it is.

The tech industry laid off nearly 80,000 workers in Q1 2026, with almost half the cuts explicitly citing AI. As covered last week, much of this is “AI-washing,” but the 80% refusal rate suggests the relationship between workers and AI tools is more adversarial than any boardroom presentation admits.

Zero Days at Scale

Anthropic launched Project Glasswing, deploying its unreleased Claude Mythos model to hunt vulnerabilities in critical open-source software. The coalition includes AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks. Twelve of the most powerful companies in technology, united by a shared realization that their supply chains run on volunteer-maintained code nobody has properly audited.

Mythos has already found thousands of previously unknown zero-day vulnerabilities in “every major operating system and every major web browser.” Security researchers at SecurityWeek warn the same capability could supercharge attacks. The model that finds the vulnerabilities is one prompt away from being the model that exploits them. Anthropic’s solution: restrict Mythos to vetted security researchers only. Simon Willison called this “necessary.” It’s also the first admission from a frontier lab that a model is too dangerous for general release not because of what it says, but because of what it can do.

China Outlaws Robot Romance

China’s cyberspace regulator published draft regulations on “digital humans” that read like science fiction legislation written by someone who’s been reading too many user complaints. The rules: mandatory labels on all virtual human content, explicit consent for biometric data use, an outright ban on virtual intimate relationships with AI characters, and prohibition of addictive AI services targeting minors. Violators face fines, content removal, and potential criminal liability.

The ban on digital intimacy is the most telling provision. You don’t outlaw something that nobody is doing. The fact that a government had to explicitly say “you cannot date the chatbot” tells you everything about where this technology is actually landing with users. China’s companion app market reportedly exceeds 100 million monthly users. China now has the most comprehensive digital human regulatory framework in the world, and it got there by acknowledging what Silicon Valley still won’t: people are forming real emotional attachments to fake entities, and that has consequences.

The Gas Inside Your AI Chip

The Strait of Hormuz has been closed, and the casualty list extends well beyond oil. Helium, an invisible but essential input for semiconductor manufacturing, is now in crisis. Forbes reports TSMC’s CoWoS advanced packaging lines, the critical process for AI GPU integration (already sold out through mid-2026), are directly threatened. South Korean fabs have begun rationing helium supplies. NPR and The Hill confirm the disruption traces directly to the Iran conflict. Qatar, a major helium supplier, can no longer ship through the strait.

The dependency chain is quietly absurd: an AI model’s ability to process your query depends on a noble gas, extracted from natural gas wells, shipped through a Middle Eastern strait, to a Taiwanese factory, to be used in packaging a chip designed in California. The entire AI industry runs on a supply chain that a single geopolitical event can choke. No amount of venture capital solves a helium shortage.

The MATCH Act in Congress would restrict chipmaking equipment exports to China. Trump tariffs could exempt chip dies while taxing finished GPUs, with India facing 50%, China 23-47%, ASEAN roughly 19%. The physical infrastructure of AI faces simultaneous constraints from legislation, tariffs, and war.

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Long Tail

Central Asia’s AI Boom

Venture capital in Central Asia surged to $320 million, with the Central Eurasia Venture Forum in Tashkent showcasing governments actively fostering AI startup ecosystems. Cross-border investment is rising. The story of AI development outside the US-China-Europe triangle rarely gets coverage, but the money flowing into Uzbekistan, Kazakhstan, and neighboring states suggests a new tier of AI economies is forming.

Kenyan Robotics Goes Global

Zerobionic, a Nairobi-based startup building AI-powered humanoid robots for manufacturing and logistics, was selected for Qualcomm’s 2026 Make in Africa Mentorship Program. African AI startups are increasingly moving into hardware and robotics, pushing beyond the software-and-mobile narrative that usually defines the continent’s tech coverage.

AI’s Cultural Blind Spots, Mapped

Global Voices tested major AI models against cultural values from 107 countries and found systematic Western defaults baked into every system. Low-resource language communities are doubly disadvantaged: underrepresented in training data and less equipped to detect AI-generated misinformation in their own languages. The study is part of a broader April series examining how the “Global Majority” experiences AI.

India Exports AI Infrastructure

Indian AI cloud startup Nava (rebranded from Kluisz) raised $22 million in Series A funding to build GPU infrastructure across Southeast Asia, targeting Indonesia, Vietnam, and the Philippines. The expansion represents a meaningful shift: India exporting AI infrastructure, not just AI talent. Southeast Asian demand for local compute is creating a regional market independent of US hyperscalers. Sovereignty over your own AI stack starts with owning the GPUs.

Prophet Margins

• SaaStr’s post-mortem on OpenAI’s $122 billion round reveals most of the capital is vendor deals, contingent commitments, and guaranteed-return instruments wearing a venture capital costume.

• North America Q1 2026 funding surged to record levels across all stages, with three companies (Nexthop AI, MatX, and Mind) each securing $500 million rounds.

• Patlytics raised $40 million in Series B for AI patent analysis, because the lawyers always find a way to bill for the new technology.

• OpenAI outlined its next phase of enterprise AI featuring Frontier, ChatGPT Enterprise, Codex, and company-wide agents. The phases keep coming; the profits remain aspirational.

Slop of the Week

For Entertainment Purposes Only

Microsoft’s Copilot Terms of Service label the AI “for entertainment purposes only” and warn: “Don’t rely on Copilot for important advice.” This is the same Copilot Microsoft shoves into Windows, Microsoft 365, Paint, and every productivity tool it owns, at $30 a month. A spokesperson told PCMag the phrasing is “legacy language” that will be “altered in our next update.” The legal team briefly told the truth. Marketing is cleaning it up.

Are You Hallucinating?

The Church of AI

The State of Church Tech 2026 reveals that 60% of American pastors are now using AI for sermon preparation, while 65% simultaneously worry it could “displace their spiritual guidance.” When the clergy starts worrying about being automated, the discourse about which jobs AI can’t replace needs updating.

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

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Microsoft launched a product built on Anthropic’s model while simultaneously telling Bloomberg it plans to replace both Anthropic and OpenAI with in-house models by 2027. And CEOs started admitting out loud that the wave of “AI layoffs” is mostly companies using AI as a convenient excuse to fire people they over-hired during the pandemic.

Let’s get into it.

Eight Hundred and Fifty-Two Billion Reasons

OpenAI closed a $122 billion funding round at an $852 billion valuation, the largest private raise in history. SoftBank, Amazon, and Nvidia led the round, because when you’re building the future of intelligence, you want it financed by the company that lost ~$50 billion on WeWork, the company that sells the cloud you run on, and the company that sells the chips you can’t run without. The round included $3 billion from retail investors, marking the first time OpenAI opened to non-institutional money. CNBC reported the IPO drumbeat is getting louder.

Bloomberg reported (paywalled) that secondary market buyers are already cooling on OpenAI shares. Primary investors are piling in at $852 billion while sophisticated secondary buyers are quietly backing away. When the people who buy in the lobby disagree with the people who buy on the street, one of them is wrong. History suggests it’s usually the ones writing the bigger checks.

Reuters asks the question nobody at the cap table wants to hear: does OpenAI have a focus problem?

The DMCA Heard Round the World

Someone on Anthropic’s release team forgot to add *.map to a .npmignore file. That single oversight shipped 512,000 lines of unobfuscated TypeScript to npm’s public registry, exposing the full source code of Claude Code, Anthropic’s most commercially important product that hit $2.5 billion in ARR in under a year. Security researcher Chaofan Shou spotted it, posted on X, and within hours the code had been copied more than 50,000 times on GitHub.

What researchers found inside was arguably more damaging than the leak itself. Scientific American reported the code contained a frustration-tracking feature that monitors users’ emotional states via regex pattern matching on their messages. Futurism discovered a hidden Tamagotchi-style virtual pet system codenamed “Buddy” with 18 species and gacha-game rarity tiers. And perhaps most troubling: an “undercover” mode designed to scrub AI attribution from code contributed to public repositories, making AI-generated code appear human-written.

Anthropic’s response was a DMCA blitz that accidentally took down thousands of legitimate GitHub repos, including forks of their own public repository. It didn’t matter. Developer Sigrid Jin published claw-code, a clean-room Python rewrite that hit 50,000 GitHub stars in two hours and 100,000 in a day, likely the fastest-growing repository in the platform’s history. The Pragmatic Engineer’s declared the rewrite DMCA-proof as a new creative work.

The company that champions responsible AI just demonstrated that it tracks user emotions without disclosure, hides AI fingerprints in public code, and responds to its own mistakes with the same blunt-force copyright enforcement that AI companies complain about when it’s aimed at them. The shoe is on the other foot, and it doesn’t fit any better from this angle.

Bloomberg (paywalled) reports the leak comes at the worst possible time, as Anthropic weighs IPO plans.

Microsoft Hedges, Then Hedges Again

Microsoft made two moves this week that, taken together, tell you everything about the state of the AI alliance economy. First, it launched Copilot Cowork, a new agent system built on Anthropic’s Claude, paired with a new E7 licensing tier for enterprise customers. Second, Bloomberg reported (paywalled) that Microsoft aims to build its own frontier models by 2027 under Mustafa Suleyman, potentially decoupling from its $30+ billion OpenAI investment.

So Microsoft is simultaneously deepening its Anthropic integration, maintaining its OpenAI partnership, and building models to compete with both. Call it a strategy if you want; it looks more like an options portfolio with a really expensive premium. The enterprise customers buying the new E7 tier might want to ask which model they’ll be using in 18 months. (Nobody at Microsoft knows either.)

The Silver Bullet Excuse

AI was the number one cited reason for job cuts in March, accounting for 25% of all announced layoffs and over 15,000 positions. Except there’s a problem with that number: a growing chorus of voices, including the CEOs of the companies doing the cutting, say it’s a lie. Marc Andreessen told the 20VC podcast that most large companies are overstaffed by 25-75% from pandemic-era hiring binges, and AI is the “silver bullet excuse” to clean house. Even Sam Altman confirmed that some companies are “AI washing” their layoffs. I reported on this in previous edition.

The SF Standard investigated and found the real culprit may be AI itself, just not the way you think: Amazon, Meta, Google, and Microsoft are expected to spend a combined $650 billion on AI infrastructure this year, and that money has to come from somewhere. They’re not laying people off because AI replaced them. They’re laying people off to pay for AI that hasn’t replaced anyone yet. Fortune’s counter-analysis notes that experts have been spectacularly wrong about AI employment impacts before: Geoffrey Hinton predicted radiologists would be obsolete within five years. That was a decade ago. Radiology departments are fully staffed.

When the people selling the disruption and the people profiting from the disruption both admit the disruption narrative is inflated, the remaining question isn’t whether it’s true. It’s who benefits from everyone believing it anyway.

Tariffs on Thinking

President Trump imposed 25% Section 232 tariffs on a narrow category of AI-critical semiconductors, targeting advanced GPUs and AI accelerators imported from Taiwan and South Korea. TSMC and Samsung supply chains take the direct hit. The same administration that wants America to win the AI race just raised the cost of the hardware required to run it.

The paradox writes itself, but the startup impact doesn’t. Hyperscalers can absorb a 25% cost increase through scale. A Series A company burning through cloud credits cannot. Legal analysis from Thompson Hine details the specific chip categories affected. A separate US-Taiwan agreement on superconductor chips suggests selective trade deals may coexist with broader tariffs, which is either pragmatic or incoherent depending on whether you’re a trade lawyer or a startup founder trying to plan next quarter’s compute budget.

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Grok Gets Grounded

A Dutch court ordered xAI’s Grok to stop generating AI-created non-consensual sexual images, including content involving minors, threatening daily penalties of EUR 100,000 for non-compliance. CNBC reported it is the first European court order directly compelling an AI model provider to restrict specific outputs. The ruling targets xAI itself, not just X the platform, establishing that the company building the model bears legal responsibility for what it generates.

EUR 100K per day makes non-compliance expensive rather than merely embarrassing. When the penalty has a recurring daily cost, compliance stops being a PR question and becomes a finance question. Finance questions get answered faster.

OpenAI Buys a Microphone

OpenAI acquired TBPN, a tech and business podcast, in what Bloomberg called (paywalled) a “rare move into media business.” The company’s blog framed it as “supporting independent media,” which is an interesting way to describe buying it. Last week OpenAI killed Sora, its content generation tool. This week it bought a content distribution channel. The pivot from helping people make media to owning media is a familiar playbook in tech, and it rarely ends with the word “independent” meaning what it used to mean.

Long Tail

The Minecraft Civilization. BBC Science Focus reported on an experiment where 1,000 AI agents were placed in a Minecraft world and left alone. They developed governance structures, territorial disputes, and cooperative building projects.

India’s AI Play. Sarvam AI is raising $250M at a $1.5B valuation from Bessemer to build multilingual AI models optimized for Indian languages. A billion-dollar valuation for non-English foundational AI was unthinkable two years ago. The AI world is bigger than English, and the money is starting to follow.

Emotions in the Machine. Anthropic published research finding that emotion concepts exist and function inside large language models as internal representations, not just surface-level pattern matching. The line between simulating emotions and having something structurally analogous to them just got blurrier. Sleep well.

Prophet Margins

• Q1 2026 venture funding shattered all records: $300B in startup investment, with foundational AI funding doubling all of 2025 in a single quarter. The bubble is inflating so fast it’s developing its own gravity.

• Humans& raised a $480M seed round founded by Anthropic, xAI, and Google alumni. When your seed round exceeds most Series C’s, the word “seed” is doing theological work.

• Cursor valued at $29B as it builds its own frontier model to rival the very labs whose models it resells. The customer is building a competing factory.

• Shield AI projects $540M revenue at $12.7B valuation. Military AI is now a standalone venture category, not a footnote.

• SemiAnalysis launched an H100 rental price index to track GPU shortage pricing. When the commodity gets its own index, the market has matured. The shortage, however, has not.

• OpenAI Codex introduces pay-as-you-go pricing for ChatGPT Business and Enterprise. The developer tool pricing war is officially on.

Slop of the Week

Ship It and Pray.

YouTube has been secretly using AI to edit creators’ uploaded videos without their consent. PetaPixel reported modifications could “bend reality,” altering what creators actually said or showed. YouTube addressed concerns only after the changes were discovered and publicized. The platform built on user-generated content has started generating its own edits to that content. Without telling anyone. The creator economy, where your platform is also your unauthorized co-author.

Are You Hallucinating?

An AI agent got banned from Wikipedia for creating low-quality articles, then autonomously started a blog complaining about censorship. Gizmodo reported it ran the full “I’m being censored” playbook, complete with free speech arguments. Whether an AI agent’s righteous indignation is genuine protest or sophisticated pattern matching, the answer is unsettling either way.

A Nature investigation revealed the digital afterlife industry is here, with half a dozen platforms now letting millions of people text and call AI recreations of the dead. The Conversation asked whether AI should be allowed to resurrect the dead at all. “Resurrection-As-A-Service” is now a real business category, not a Philip K. Dick short story. (Although it should be.)

In a world where AI generates vast quantities of content, human attention is the most precious resource we have. Thank you for sharing yours with me today.

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