I Am Retiring from Tech to Live Offline
AI took the last of the wind out of my Open Source sails. I wish you all the best!
10 interesting stories served every morning and every evening.
I Am Retiring from Tech to Live Offline
AI took the last of the wind out of my Open Source sails. I wish you all the best!
Be sure to use AI when makingyour next, I don’t know, meal plan,for example. Definitely do not callyour friend who loves to cook and ask herfor her favorite recipes or tips or ways to save time making meals, because you will endup talking for longer than you had hoped,hearing, perhaps, about her father’s cancer diagnosis or how lonely she’s been or evenwhat she’s planted in her spring garden and then lost with the early frost.
And be sure to use AI when planning that nextcamping trip, the last one you will takewith this particular child. Definitely do not text your friend who has fly-fished every river in Pennsylvania and biked every backwoods trail, because you might end up texting back and forth for the rest of the dayor even meeting up late for a beer and hearinghow he has ended each recent night black-out drunk, or perhaps you’ll hear how hiscousin is an idiot on Facebook or maybe justthat he repaired his own washing machineand is pretty damn proud of that.
And be sure to use AI when your next childgets married, so that you can write themthe perfect toast or poem or speech or songbecause no one wants to hear your words, the actual poorly written words of a parent (you) who changedhundreds of diapers for said child or fed them in the middle of the night from your actual body. Or cried when they were late home because you were positive they were dead. We don’t want those words—we’d prefer the sterile words of a machine that never lived, never had an original thought, never felt the pain of miscarriage or brokenrelationships or the joy of a friendship restoredor of seeing spring’s first robin dancing on frost.
And be sure to use AI when working on your nextbook or essay or piece of art or photography,and then smile or even laugh at your owncleverness when you see how good it is, and how easy, because who the hell has timeto work at something, to give time to craft, tocreate with their own minds, to spend years being mediocre. Why do that whenmastery, or at least competency is so simpleonly a good prompt away?
How magnificent the funeral song our children or contemporarieswill write for us, a song they will make by taking our obituary and Facebook posts,plus random quotes from our algorithm,and feeding them into Chat or Gemini or Claude. The tears that will fall in the face of suchsanitary sweetness!
Be sure to use AI
and while you do I’ll be over here in my 50thyear, my youngest daughter asleep on my chest,my arm falling asleep because I dare not movelest I scare away this moment, lying here melancholy about my older children moving out and my middlechildren no longer needing me, at leastnot like they used to, weary about this bodythat fails me now in ever increasing ways that will never be restored. Sighing over stories I tried to write but never hit the page the way they felt in my mind.
But isn’t that, my flesh-and-blood friend, the natural order of things?
the longing for something that could always bea bit better
or the way that anythingworth doing feels a bit clumsy and painful, especially at first
or hearing another human voice and somehowrealizing the beauty of life is found in all of thesesubtle imperfections
No posts
You’re probably familiar with the dead internet theory: most of what you encounter online is now generated by bots, for bots, with humans reduced to a shrinking audience for machine-generated noise. Last year, over half of new content on the internet was AI-generated. The humans are still there, scrolling, but the thing they’re scrolling through has become a performance staged by machines for an audience that hasn’t yet realized the show isn’t for them.
It’s utterly desiccating to log onto spaces seeking a live mind to joust and think with, and find a relentless stream of slop. Promised an age of superconnectivity, we’ve let our shared physical spaces wither, only to find our promised digital commons to be one large billboard increasingly read and created by bots.
That’s bad enough. I want to talk about something worse. Call it the dead economy theory.
A word of welcome to the folks who have arrived here from Hacker News and various other places. Two quick comments, given that I’ve received many messages and have seen many comments on HN on this. First, the text of this piece is entirely human-generated, including the infelicitous phrasings and penchant for two-dollar words. The AI-generated images, which many of you hate, are an inside joke with a friend. Had I known this piece was going to get the traction it has, I promise you I would have gone with normal headers. But, to paraphrase Dostoevsky from the prologue to The Brothers K, yes, I agree that it is superfluous, but it’s done, so let it stand. Thanks for reading.
The AI industry has a numbers problem.
OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft: the combined investment in large-scale AI infrastructure now runs into the hundreds of billions of dollars, with projections into the trillions over the next decade. OpenAI alone has been valued at north of $800 billion. Anthropic, which has yet to produce a single year of profit, commands a valuation in the same stratosphere. These numbers need an addressable market large enough to justify them.
There is only one market that large: the global labor market.
As we’re getting excited about discovering how to use claude.md files in Cowork, the industry is pitching a different reality. Every investor presentation of an AI agent “doing the work of ten analysts” is telling you the same thing: the product is labor replacement. The gentler language (”copilot,” “assistant,” “augmentation”) is marketing. The financial model underneath requires the elimination of human cost centers at civilizational scale. If it doesn’t do that, these companies are the most overvalued assets in the history of capitalism. The people writing the checks are not in the habit of lighting trillions of dollars on fire for a better autocomplete and an endless proliferation of longer and longer memos that nobody reads.
The AI companies now construct their own benchmarks to prove the point. OpenAI’s GDPVal benchmark measures how well models perform across forty-four occupations, from real estate broker to news analyst. The AI Productivity Index evaluates models against four specific professional roles: investment banking associate, management consultant, Big Law associate, primary care physician. These are targeting reticles aimed at the professional class. As an OpenAI evaluation lead told the New York Times,1 models now achieve “over an 80 percent win rate compared to human professionals” on tasks that, months earlier, no model could match. A former banker on the research team “keeps being shocked by how much of her old work the models can do.”
So let’s take them at their word. Assume the technology works as advertised, that AI systems become capable of performing most cognitive labor at a fraction of the cost of human workers. What happens next?
Follow the money through three turns.
Turn one: a company licenses AI to replace a significant portion of its workforce. Costs drop. Margins expand. The stock price goes up. Everyone on the earnings call is happy. When Block’s Jack Dorsey laid off nearly half his workforce in March, citing AI coding agents, investors responded with a twenty-five percent stock price surge in after-hours trading. The market rewarded the elimination of human labor with an immediate, massive transfer of value to shareholders.
Turn two: the replaced workers stop earning income. They cut spending. The businesses they used to patronize see revenue decline. Some of those businesses also adopt AI to cut costs, compounding the displacement. Consumer demand contracts across the economy.
Turn three: the company that fired its workers to save money discovers that its customers were, in aggregate, other companies’ workers. Revenue growth stalls. The AI subscription that was supposed to be an investment in efficiency turns out to be a contribution to the destruction of its own market.
Economists Brett Hemenway Falk and Gerry Tsoukalas at Wharton have recently described this dynamic in a paper they aptly titled, “The AI Layoff Trap.” In competitive markets, an automating firm captures the full cost savings from replacing workers but bears only a fraction of the resulting demand destruction. In a market with twenty competitors, each firm feels one-twentieth of the demand it destroys. The rest falls on rivals. This creates a prisoners’ dilemma: every firm rationally automates beyond the socially optimal level, because the individual incentive to cut labor costs always outweighs the diffuse, shared consequence of eliminating consumer spending. Better AI makes this worse. Improved productivity widens the profit gap from automating faster than your competitors, intensifying the arms race toward collective ruin.
Sometimes the layoffs happen before executives even know whether AI will do the job. Zoë Hitzig, an economist who previously worked at OpenAI, told the Times: “When chief executives are saying they’re cutting jobs because of A.I., other people feel like they have to too. That dynamic could make the changes happen sooner than efficiency would dictate.” Herd behavior dressed in the language of innovation.
Henry Ford understood, perhaps apocryphally but correctly in principle, that his workers needed to earn enough to buy his cars. The AI economy is eliminating the workers and expecting the cars to keep selling, except that software has near-zero marginal cost, so the entire value proposition is the elimination of the human cost center. The product is the removal of the customer base.
The optimists will tell you this is just productivity gains. The economy has absorbed automation before; agricultural employment collapsed from ninety percent of the American workforce to two percent and civilization continued. David Autor at MIT has shown that roughly sixty percent of today’s jobs didn’t exist in 1940. New technologies create new categories of work. True. But there’s a difference between an observation about the past and a law of nature, and the optimists consistently confuse the two. The agricultural transition took a hundred and forty years. Carl Benedikt Frey at Oxford has documented that the Industrial Revolution took seventy years before wages and employment recovered for the workers it displaced. In the interim, wages stagnated, the labor share of income collapsed, profits surged, inequality skyrocketed, and the political consequences included the Chartist movement and widespread social upheaval. As Frey puts it: “Most economists will acknowledge that technological progress can cause some adjustment problems in the short run. What is rarely noted is that the short run can be a lifetime.”
Compare that timeline to the one the AI industry is working on. Bharat Ramamurti, a former deputy director of the National Economic Council, has drawn the parallel to the China shock, the wave of manufacturing job losses that reshaped American politics when production moved overseas. “The China shock unfolded over several years, whereas this could happen over two years,” he told the Times. “These companies have spent so much money developing models that there’s going to be immense pressure on them to generate revenue through quick adoption.”
Previous automation replaced specific tasks within jobs. The power loom replaced hand weaving, the spreadsheet replaced manual calculation, etc. In each case, the technology was narrow. General-purpose AI threatens cognitive labor comprehensively, across every industry, simultaneously. The economist Wassily Leontief saw this coming in 1983 when he compared human labor to horses. The US horse population grew from nine million in 1840 to twenty-one million by 1900, seemingly immune to technological change. Within sixty years of the internal combustion engine, the population collapsed by eighty-eight percent. The horses weren’t retired out of malice. They became uneconomical to keep. Leontief’s point was that there is no economic law preventing the same thing from happening to humans.
Daron Acemoglu, who won the Nobel Prize in Economics in 2024 and is the most rigorous voice on this topic, has found that between 1987 and 2017, “the displacement effect of new technologies far outweighed their productivity and reinstatement effects.” The new tasks did not materialize fast enough to absorb the displaced workers. His assessment of AI is more pointed still: firms are deploying what he calls “excessive automation,” using AI to kill jobs without generating significantly lower production costs, while imposing substantial social costs. The technology, in many applications, isn’t good enough to justify the displacement it causes. Automation for the sake of the stock price, not for genuine productivity.
Who is the customer when the customer is the thing you’ve eliminated?
An economy that doesn’t need human labor is a political crisis of a kind democratic systems have never faced.
Democratic governance rests on a bargain so old we’ve forgotten it’s a bargain at all. The governed have something the governors need: labor, tax revenue, military service, consumer spending. This dependency is the source of democratic leverage. The whole system functions because power is distributed, and it’s distributed because the people at the top need something from the people at the bottom.
Remove labor from that equation and watch what happens.
When value is generated by AI systems owned by a handful of corporations already world-class at tax optimization, every fiscal mechanism of democratic governance starves at once. The tax base erodes. Collective bargaining becomes vestigial (employers who don’t need employees don’t bargain with them). Consumer spending, which depends on labor income, contracts. Piketty’s r > g, the engine of wealth concentration, accelerates because AI severs the last link between capital accumulation and the need for human labor as a production input. Without redistribution, as one analysis of the framework put it, “approximately everything will eventually belong to those who are wealthiest when the transition occurs.”
And the public funded the research that made it possible. The transformer architecture, large-scale training methods, semiconductor advances—all of these were publicly or quasi-publicly funded through universities, DARPA, and national labs. The public bore the risk. Private companies captured the reward. This is blindingly common across technological advancement in the last sixty years. As Mazzucato puts it, “AI risks becoming another engine of rent extraction rather than value creation.” We subsidized the revolution and are now being told to accept displacement as the cost of progress that someone else profits from.
You can still vote (and please do, for people who get this shit and are willing to try to stop it). But what you’re voting over is the disposition of a shrinking pool of resources, while the real economy operates in a parallel system you increasingly have no input into.
The people building these systems understand this perfectly. Dario Amodei, the CEO of Anthropic, has said it on the record: “The balance of power of democracy is premised on the average person having leverage through creating economic value. If that’s not present, I think things become kind of scary.” The CEO of one of the three leading AI companies is telling you that the technology he is building will undermine the material basis of democratic governance. He sees the problem. He is building the thing that causes it. His company has not endorsed a single piece of legislation to address it. When asked about policy advocacy, Anthropic co-founder Jack Clark described it as “the end of a very, very long chain of work.”
Peter Thiel wrote in 2009 that he no longer believed freedom and democracy were compatible. The logic runs: democratic systems produce regulation, redistribution, and accountability, all of which create friction on the ability of exceptional people to reshape the world. If you believe you’re building the most transformative technology in human history, democratic oversight is an obstacle. Note: he isn’t talking about your or my freedom. We don’t matter.
This view has only gained adherents. The political spending, the media acquisitions, the sovereign-fund diplomacy where Sam Altman tours the Middle East cutting compute deals with autocratic governments: rational behavior for people who’ve concluded that democratic governance is a legacy institution to be routed around when it interferes.
Autocracies are better customers for this technology than democracies, which is precisely why the broligarchy has rapidly shifted its support behind Trump and MAGA. A democratic government that deploys AI to replace its workforce faces electoral consequences. An authoritarian government faces no such constraint and gains a surveillance and control dividend on top of the economic efficiencies. Saudi Arabia, the UAE, Singapore: vast capital, centralized decision-making, no electorate to answer to, and an active interest in technologies of control. This is one of the motivating factors in the Valley’s latching on to Trump: he and his cronies can be bought, and as importantly, they have no loyalty to democracy. The economic incentives for AI companies point toward the entities with the fewest democratic accountability mechanisms.
Leave a comment
Every proposed solution to mass AI displacement treats it as a resource distribution problem. Universal basic income. Retraining programs. The “leisure economy.” The assumption is that if you send people checks, they’ll find meaning in hobbies and community. They’ll paint. They’ll garden. They’ll finally write that novel.
This is ahistorical bullshit.
We don’t have to speculate about what happens when economic function disappears from communities. Anne Case and Angus Deaton’s research on “deaths of despair” tracks the rising tide of suicide, drug overdose, and alcoholic liver disease mortality concentrated in less-educated, formerly manufacturing-dependent populations. The mechanism isn’t just poverty. We lose any sense of economic purpose, and with that, social status and a perceived future. Communities organized around industries that left, where what replaced the jobs was opioids, domestic violence, and a life expectancy that dropped year over year in the richest country on earth.
Molly Kinder at Brookings drew the connection explicitly in Sun’s NYT piece: “Our economy grew extraordinarily and prices went down, but there were clear losers.” The AI companies’ narratives about abundance repeat the same promises of globalization. This time, the losers won’t be limited to manufacturing towns in the heartland. “I’ve interviewed so many college students who are super fearful about what the future means,” Kinder told the Times, “and their narrative is exactly the same as those blue-collar guys in the heartland.” The twenty-something software engineer in San Francisco and the displaced factory worker in Ohio are staring at the same question: what happens when the market decides my skills are worthless?
Guy Standing’s work on the “precariat” adds the structural dimension. The psychological consequences of permanent economic precarity corrode social coherence regardless of whether the rent is paid. Four decades of neoliberal policy plus digital acceleration have already created this class. AI acceleration expands it to include the college-educated professionals who thought they were safe.
Piketty, no conservative, has argued that UBI fails to address root structural problems: “unequal access to education and health, low-paying and low-productivity jobs, malfunctioning markets, corruption, and regressive tax systems.” David Shor’s polling data bears this out from the other direction: UBI is unpopular with American voters; a federal jobs guarantee has legs. People don’t want a check. They want work. They want purpose.
Anthropic’s own research has documented something worse than displacement: active deskilling. Junior engineers who relied on AI coding agents didn’t complete tasks much faster and understood their work less when quizzed afterward. The technology is degrading the expertise of the next generation of workers at the same time it’s competing with them for their jobs. The retraining argument assumes people can develop new skills to stay relevant. The evidence suggests the tools are preventing them from developing skills at all.
At the scale these companies need to justify their valuations, you’re looking at social instability that makes the current populist moment look quaint. Tens of millions of people, in their productive years, with no economic function, no clear path to one, and a keen awareness that the people who did this to them are the richest human beings who have ever lived. Stiglitz points out that AI will hit “routine white collar jobs,” the college-educated desk work that felt insulated from manufacturing disruption. Accountants, analysts, junior lawyers, radiologists, software developers. The professional class that constitutes the backbone of political stability in developed democracies.
The most honest thing you can say about violence is that nobody wants it, but the conditions that produce it are being engineered with extraordinary efficiency by people who have apparently never opened a history book. It’s happening. In April, someone tried to firebomb Sam Altman’s home. Another attacker targeted an Indianapolis city councilman who approved a local data center project. Alex Karp, the CEO of Palantir, told a recent panel: “The biggest challenge to A.I. in this country is political unrest. If I were sitting here in private with my peers, I’d be telling them the country could blow up politically and none of us are going to make any money when the country blows up.” Karp, to his credit, is saying this out loud. Most of his peers restrict such observations to the disappearing-message Signal chats where, as Jasmine Sun has reported, tech executives boast about the roles they plan to automate.
A strain of thought runs through Silicon Valley, from the Thiel Fellowship to the rationalist blogs to the effective altruism movement, that treats its intellectual framework with the seriousness of received revelation. These are people who believe they are operating at the frontier of human thought.
They are operating at the level of a second-year philosophy survey, armed with enormous confidence and no awareness of the counterarguments.
Start with Nietzsche, because the Valley loves Nietzsche, or rather a version of Nietzsche that would have made the man lose his shit and go horse-hugging much faster than the syphilis. The Übermensch gets trotted out as justification for the exceptional founder, the visionary who transcends conventional morality because he’s operating on a higher plane. Nietzsche was diagnosing the crisis of meaning after the collapse of metaphysical certainty, not writing a management philosophy for people who got rich selling advertising technology. The Übermensch is about the individual’s relationship to the creation of meaning in a godless universe. It has nothing to do with whether Peter Thiel should be exempt from democratic accountability. Nietzsche would have classified these people as the last men, the ones who blink, say “we have invented happiness,” and mistake comfort and optimization for human flourishing. He would have fucking loathed them.
The pattern repeats. Effective altruism is utilitarianism reinvented by people who have apparently never encountered Bernard Williams, or Derek Parfit’s own agonized wrestling with the implications of consequentialist reasoning, or the two centuries of philosophical literature explaining why naive expected-value calculations produce monstrous outcomes when applied without limiting principles. The EA movement walked itself into the Sam Bankman-Fried catastrophe because it adopted a moral framework without understanding its failure modes. What happens when you skip the coursework and go straight to the final exam.
Longtermism, the philosophical engine of AI acceleration, whether its proponents acknowledge it or not, is warmed-over Parfit without the rigor. The argument (that we should optimize for the welfare of trillions of hypothetical future beings, and that present-day costs are acceptable in service of that goal) is a framework any competent ethicist can dismantle in an afternoon. It has no limiting principle. It cannot distinguish between genuine moral urgency and the self-serving conclusion that whatever the speaker was already doing is cosmically important. In practice, it is a machine for generating justifications for the concentration of power by people who have decided they are the ones best positioned to steward the future of the species. How convenient.
The rationalist community rediscovers Bayesian epistemology and treats it like a revelation, apparently unaware that the philosophy of science has been working through these questions since the 1920s. Blog posts get treated as foundational texts. People who have never read Kuhn or Lakatos or Feyerabend construct an epistemology from first principles, marvel at what they’ve built, and proceed to use it as the intellectual building blocks for decisions that affect billions of people. The confidence is inversely proportional to the depth. Dunning-Kruger at scale.
The intellectual poverty extends to the economics. Acemoglu has found that only 4.6 percent of tasks in the economy are currently cost-effective to automate with AI. His estimate for AI’s total productivity impact over the next decade: 0.66 percent. Goldman Sachs projected seven percent in 2023, before we began to see the shape of this thing. McKinsey projects between 0.5 and 3.5 percent annually. Someone is catastrophically wrong, and the people spending the money are not the ones with the Nobel Prize. Over ninety percent of firms surveyed in 2025 reported no measurable impact on employment or productivity despite a quarter-trillion dollars in AI investment. Torsten Slok: “AI is everywhere except in the incoming macroeconomic data.” These are people who have decided what the future looks like and are spending other people’s money to will it into existence.
These bastards always tell on themselves. OpenAI published a white paper in April calling for “Industrial Policy for the Intelligence Age,” full of radically progressive proposals: a thirty-two-hour workweek, higher taxes on corporations and capital gains, a “public wealth fund” providing all citizens an equity stake in AI companies. In the same period, OpenAI’s president helped fund a super PAC that spent over two million dollars on ads against Alex Bores, a New York congressional candidate whose crime was introducing safety regulation for large AI developers and proposing to tax AI to fund direct payments to Americans. The company removed a profit cap that had previously limited investor returns to a hundred times their initial investment. Chris Lehane, OpenAI’s chief lobbyist, systematically deprioritized internal research that could produce unflattering results. “Whenever someone wrote a paper which talked about some negative aspect of A.I.,” a colleague told the Times, “he would say, ‘We’re not going to release something about a problem until we have a solution for it.’” Lehane’s own characterization: “We want to do applied physics, not theoretical physics.” Tell the story that helps us, not the one that’s true.
A Philosophy 101 student who misreads Nietzsche writes a bad paper and gets a C. A billionaire who misreads Nietzsche builds a political philosophy around the misreading and funds it with the GDP of a small nation. This is fucking insane.
These are not serious people. They are serious about accumulation and about winning. They are not serious about the questions that matter for what they’re building: what we owe each other, what makes a life worth living, and what happens to a civilization when you remove the material basis of human agency. These questions have occupied the best minds in human history for millennia. The Valley’s engagement with them amounts to reading the CliffsNotes on a transatlantic flight and arriving convinced you’ve mastered the canon.
And they want to restructure civilization.
Albert Camus broke with Jean-Paul Sartre and the French left over the most concrete political question there is: can the people alive today be treated as acceptable casualties in the pursuit of a better future?2
Sartre and the Marxists said yes. History has a direction. The revolution requires sacrifice. Camus said no. Any system of thought that subordinates living people to a hypothetical future has already committed the foundational moral error. Once you accept that logic, there is no limiting principle. Any atrocity becomes justifiable. Any amount of present suffering can be rationalized as a necessary input to the glorious output.
This is the structure of the AI acceleration argument. The technology will eventually benefit humanity (trillions of future humans, lives of abundance and meaning we can barely imagine), so present disruption is tolerable. Displaced workers, hollowed communities, the erosion of democratic leverage, the concentration of power in a handful of private actors who have exempted themselves from the consequences of their own project: regrettable but necessary. The expected value math works out.
The founders of Mechanize, a startup whose stated mission was “to enable the full automation of the economy,” made the logic explicit: “the only real choice is whether to hasten this technological revolution ourselves, or to wait for others to initiate it in our absence.” Technological determinism as moral absolution. The future is fixed. Our only choice is whether to build it first. Therefore, nothing we do along the way requires justification, because the destination was never in our hands. They’re making the same argument as the Marxists who sent dissidents to the gulag.
Camus staked his intellectual legacy on the claim that the person standing in front of you is not an input to a utility function. Their suffering is not redeemed by a future state of affairs they may never see. Their dignity is not negotiable against projected outcomes. The person who exists now (who has a job they’re about to lose, a family they support, a community that depends on a functioning local economy) is the unit of account. Not humanity in the abstract. Not the trillions of future beings that the longtermists conjure to win their expected-value calculations.
Once that commitment is abandoned, the door opens to every form of rationalized cruelty that the twentieth century spent a hundred million lives trying to teach us to reject.
The entire AI acceleration project is premised on abandoning it. It asks present people to bear costs for future benefits they may never see, distributed to people who do not yet exist, administered by a self-appointed class that has insulated itself from the consequences entirely. Altman’s “universal basic compute” proposal acknowledges, if you squint, that the future he’s building requires a new distribution mechanism. It is also a proposal in which he gets to be the one doing the distributing. Feudalism with better branding.
Jasmine Sun reported recently that tech industry sources “expressed more extreme concern about the labor market impacts of A.I. in private conversation, but suddenly became optimists once I turned on the mic.” They know what they’re building. They know what it will do. They perform optimism in public because the alternative is admitting that the thing they’ve staked their careers and fortunes on will immiserate a significant portion of humanity, and they’re doing it anyway. Amodei has written that Anthropic is “currently considering a range of possible pathways for our own employees,” implying that even the people building the technology may be surplus to its requirements. He framed this as compassionate. Read it again as a CEO telling his workforce that their jobs, too, are temporary.
I don’t want to dwell on whether AI can do what these companies claim. It may well be able to, though the current evidence suggests the gap between pitch and product is vast, and serious economists think the productivity gains are a fraction of what the industry projects. But Acemoglu’s core finding is that AI doesn’t need to be revolutionary to be destructive. “So-so” automation (technology that’s mediocre at replacing workers but cheap enough to do it anyway) still displaces at scale while delivering underwhelming productivity. The worst outcome may not be superintelligent AI. It may be adequate AI, deployed aggressively by companies chasing stock prices, eliminating jobs it can’t actually do well because the quarterly incentives demand it.
Has anyone with the power to shape this transition thought seriously about what it means for the people alive today who didn’t get a vote on any of it?
Fuck no.
The window for changing that answer is not infinite. The regulatory capture is already advanced: AI-related investments accounted for thirty-nine percent of US economic growth in the first three quarters of 2025, giving the federal government a vested interest in sustaining the boom. Amodei himself acknowledges that this leads to “the reluctance of tech companies to criticize the U.S. government, and the government’s support for extreme anti-regulatory policies on A.I.” The regulator and the regulated have converged into a single interest. The expertise asymmetry between legislators and the industry they’re supposed to oversee is insurmountable. The feedback loop (AI systems advising on the governance of AI systems) is closing.
The interventions that could matter are known. Public ownership stakes in AI infrastructure. Aggressive antitrust enforcement. A genuine tax regime on automated labor. Branko Milanovic’s prescription is characteristically direct: spread capital ownership more widely, tax the highest capital incomes more aggressively. None of these are technologically difficult. All of them require functioning democratic institutions with the will to challenge the richest companies in human history. The companies that would need to be taxed are spending millions to defeat the politicians who propose it.
The dead economy is not one where nothing happens. Plenty will happen. The GDP might even go up; AI-related investments are already propping it up. The dead economy is one where plenty happens and none of it requires you. Where the productive capacity of civilization has been captured by a system you have no stake in, no input into, and no vote on. Where the people who built it told you they don’t think you should have a say. Where they express alarm about the consequences in private and optimism in public. Where they publish white papers calling for radical redistribution while funding super PACs to destroy the politicians who propose it.
Share
1
This essay relies frequently on the outstanding reporting of Jasmine Sun’s April 30, 2026 piece in the New York Times, which you can find at: https://www.nytimes.com/2026/04/30/opinion/ai-labor-work-force-silicon-valley.html
I’m not going to link it for every quotation pulled from Sun’s piece, so if a direct quotation is not cited individually, I have pulled it from Sun’s reporting.
GTA 6 developers have formally announced a union to combat Rockstar Games’ actions which will be debated in court.
On Thursday, the Independent Workers’ Union of Great Britain (IWGB) and Rockstar staff members announced the Rockstar Game Workers Union. This union will be part of the IWGB. The reveal came in the form of an informative video which delves into their motives and what we should be looking out for in the future.
This union is the one facing a legal battle with Rockstar. Over 30 employees were fired last year for “gross misconduct” which the IWGB disputes as an act of union busting. As time has passed, the union has now been fully set up and is looking to fight Rockstar Games in court. A date for the trial in court has been set though not been published by the union just yet.
The Rockstar Game Workers Union say in their video that Rockstar’s actions towards those 30+ staffers charged others to join the union. Whilst most of the affected workers were based at Rockstar North in Edinburgh, the seeds have spread far and wide. The union claims a sizeable number of Rockstar employees from their London, Leeds, Lincoln and Dundee offices have joined forces with the many others in Edinburgh.
Together, we are organising around the things we want to change. Starting with: Pay transparencyFlexible workingAn end to crunch Rockstar IWGB Game Workers Union
Together, we are organising around the things we want to change. Starting with:
Pay transparency
Flexible working
An end to crunch
The Rockstar IWGB Game Workers Union have created BlueSky, Instagram and Twitter accounts for you to follow and receive updates from. There is also a page to donate towards their legal battle with Rockstar here.
This comes after politicians have accused Rockstar of blocking the ongoing legal proceedings. Learn more about it here.
Be sure to stay tuned to RockstarINTEL for all the latest news on Rockstar Games and GTA VI.
Subscribe to our newsletter!
13 May 2026
Thomas Germain
Serenity Strull/ Getty Images
From your weight and facial expressions to your destination, cars collect a startling amount of data about you. Some of it may even raise your insurance costs. But you can take some simple steps to limit what they know about you.
Cars used to mean freedom. When I first got the keys to the old family Toyota it was a rite of passage, a sign I was old enough to step away from the watchful eyes of my parents and enter a world where time and decisions were mine alone. Things change.
Modern cars are computers on wheels, and giant corporations are using them to suck up intimate details about your life and make more money. If you think driving today is a chance for solitude and independence, think again. And it looks like it’s about to get a lot worse.
Car companies will tell you themselves if you wade through their privacy policies. The information they harvest can include precise location data about everywhere you go, who’s in the car with you, what’s on the radio and whether you buckle your seatbelt, drive too fast or brake too hard. Some can gather details you might not expect like your weight, age, race and facial expressions. Do you pick your nose? Some cars have cameras on the inside pointed at the driver’s seat. And most come with internet connections that can ship off that data as you drive in blissful ignorance.
This is a privacy problem that can cost you money. Among the biggest customers for car data are insurance companies, and they’re using it to charge some people higher prices. But there’s no telling where your information is going. Some car companies admit they sell your data, but they don’t have to say who’s buying. That’s to say nothing of the fact that you might find it a little creepy. Most consumers, experts say, have no idea it’s even happening.
“People would be shocked at the number of data points that their car collects and transmits to other people, either the manufacturer or third-party applications,” says Darrell West, a senior fellow in the Center for Technology Innovation at the Brookings Institute in Washington DC. “It basically means your life can be recreated almost on a second-by-second basis.”
Feeling uncomfortable yet? A federal law is about to increase the amount of data your car can gather about you. It will soon require American car companies to install infrared biometric cameras and other systems to scan your body language, track your eyes or other aspects of your behavoiur to detect whether you’re too drunk or tired to drive. But it will also open up a whole new trove of data about your health and your habits. There are no rules limiting what the car companies can do with that information.
With automakers set to expand their data empires, this is a critical moment to understand what’s happening under the hood and how it affects you
Of course, there are benefits too. Internet-connected cars can be more convenient. The sensors they bristle with can make driving safer and more comfortable. Insurance companies could decide to charge you less because you’re such a good driver.
But with automakers set to expand their data empires, this is a critical moment to understand what’s happening under the hood and how it affects you.
The data superhighway
If your car is even relatively new, it’s probably involved. The consulting firm McKinsey found 50% of cars on the road in 2021 had internet connections and predicted the number will rise to 95% by 2030. If your car is hooked up to the internet, privacy is almost certainly an issue you need to care about.
Car companies can also snoop when you hook your phone up to the infotainment system, or if you use certain apps made for driving. Some drivers also use insurance companies’ telemetrics system, which monitor you in exchange for potential discounts.
A 2023 analysis by Mozilla, the maker of the Firefox browser, examined the privacy policies of 25 car brands. Every one failed to meet the privacy and security standards that Mozilla uses to compare brands. Mozilla said cars were “the worst product category we have ever reviewed for privacy”.
According to the report, car companies reserve the right to collect details including your name, age, race, weight, financial details, facial expressions, psychological trends and more. Kia’s privacy policy, for example, suggests the company may even collect details about your “sex life” and general health.
Kia spokesperson James Bell says the company has never actually collected data on drivers’ sex lives or health. These details only appear in Kia’s privacy policy because the company is listing California’s definition of “sensitive data”, he says. Bell says Kia’s privacy practices are transparent and the company only shares data with insurance companies if drivers opt in. The company did not explain what kinds of “sensitive data” it does collect, however.
Serenity Strull/ Getty Images
Some of that might be hard to picture, but cars are littered with sensors: in the seats, the dashboard, the engine, the steering wheel, you name it. Many cars, for example, have cameras inside and out. If you’re doing something in a modern car, chances are there’s a way for companies to learn about it.
Mozilla found 19 of the car companies said they might sell your data, and that’s exactly what’s happening. For example, both state and federal agencies in the US took action against General Motors (GM) for allegedly selling car location data without consent. US Senators have accused Honda and Hyundai of similar practices — and these are just the examples the public knows about.
“They’re taking all the information they collect on you, which is a lot, and using it to make inferences about who you are, how intelligent you are, what your psychological profile is, what your political beliefs are,” says Jen Caltrider, a privacy analyst who led Mozilla’s car research. “That’s the stuff people don’t necessarily think about.”
There are basically no rules about who can buy this data or what its used for, Caltrider says. It can be used to market things to you. Companies could used it in hiring decisions. Law enforcement can buy car data when they can’t get a search warrant. Once it leaves your dashboard, you have no control over where it ends up.
It may be getting worse
This is about more than companies snooping on your private life. For example, General Motors sold driver information to a company called LexisNexis, a data broker that buys and sells details about consumers. A driver who got a copy of that data reportedly found LexisNexis had 130 pages of information, detailing every trip he and his wife took over six months. He told the New York Times that after his insurance costs jumped 21%, an insurance agent told him the data was a factor. LexisNexis did not respond to a request for comment.
The US Federal Trade Commission took action, and GM is now barred from selling vehicle data for five years — but it’s free to resume the practice afterwards so long as it obtains express consent from drivers and follows other conditions. Meanwhile, LexisNexis and other companies are still selling vehicle data they get from other car manufactures and apps the people use while driving. GM and LexisNexis did not respond to requests for comment.
“Insurance companies have been collecting vast amounts of consumer data, especially on consumer driving data, and using it to try and charge people higher premiums, deny coverage or slice and dice consumers into various categories,” says Michael DeLong, a research and advocacy advocate who covers auto insurance for the Consumer Federation of America, a US-based non-profit.
Keeping Tabs
Thomas Germain is a senior technology journalist at the BBC. He writes the column Keeping Tabs and co-hosts the podcast The Interface. His work uncovers the hidden systems that run your digital life, and how you can live better inside them.
Car companies say they get their permission before tracking you. In practice, that usually means agreeing to forms and privacy policies when you set up the infotainment system or apps connected to your car. In some vehicles they pop up every time you start the engine. Did you read them? Of course not.
In the US, there is no privacy law at the national level. Protections in individual states are piecemeal, and according to some privacy experts, they don’t go far enough. The picture is a little better in Europe, including the UK, where there are special protections for certain sensitive categories of information and consumers have some rights that let them access their data and tell companies to delete it. But it’s not a solved problem in Europe either.
“Europeans are still beholden to privacy policies,” Caltrider says. “And you have to count on the regulations to be followed and enforced, and that’s something that’s not always happening, with cars especially.”
The problem isn’t new, but there are reasons to think it’s accelerating. US law mandates that car manufactures will soon need to install “advanced impaired-driving prevention technology” in new passenger vehicles within the next few years. The technology is meant to stop people from driving if they’re drunk, tired or unfit to drive using infrared cameras or other systems.
The problem, Caltrider and others say, is the law includes zero provisions that address what happens to the data these systems create.
A spokesperson for the US National Highway Traffic Safety Administration (NHTSA) — which is charged with enforcing the rule — says “NHTSA is committed to reducing impaired driving fatalities using every tool at our disposal”, and it “continues to address critical and complex topics” such as privacy concerns. It’s likely the implantation of this law will be delayed because the technology isn’t ready, but privacy advocates are sounding the alarm.
“We need to keep drunk drivers off the road, and it would be great if there was a guarantee that the data won’t be used for other purposes, but that’s not what’s happening,” says Caltrider. “So many of the data collecting advances we see in cars are done under the guise of safety.” It could hand the auto industry a trove of what amounts to medical information with no safeguards in place.
More like this:
Like so many privacy problems, the car data problem isn’t one you can solve entirely, but there are steps you can take.
For one, “do not enrol in the insurance telematics programme if you’ve got any concerns about privacy”, DeLong says. The privacy risks are significant and the payoff isn’t a guarantee. An analysis from the state of Maryland found 31% of drivers saw their insurance rates drop, but prices went up for 24% of drivers and 45% found no change.
Some car manufactures offer privacy settings you can adjust that may limit the sharing and collection of data. Look for options in the settings of your car’s infotainment system and any accompanying app that works with your car. Consumer Reports (where I used to work) has a detailed guide you can use with more information.
Steps like these can help, Caltrider says, but it shouldn’t be your responsibility to do a bunch of work to stop companies from violating your privacy. “Until the whole game changes, until we own our data and we control our data, and companies have to ask us for permission to use it, I think this issue is just going to keep getting worse and worse.”
–
For timely, trusted tech news from global correspondents to your inbox, sign up to the Tech Decoded newsletter, while The Essential List delivers a handpicked selection of features and insights twice a week.
For more science, technology, environment and health stories from the BBC, follow us on Facebook and Instagram.
Skip to content
Secure your code as you build
We read every piece of feedback, and take your input very seriously.
Include my email address so I can be contacted
Use saved searches to filter your results more quickly
To see all available qualifiers, see our documentation.
Sign up
You signed in with another tab or window. Reload to refresh your session.
You signed out in another tab or window. Reload to refresh your session.
You switched accounts on another tab or window. Reload to refresh your session.
There was an error while loading. Please reload this page.
Notifications
You must be signed in to change notification settings
[BUG] Login no more possible, Android App still works[BUG] Login no more possible, Android App still works
You can’t perform that action at this time.
Claude Code’s auto-mode permission system is internally called the “YOLO Classifier.” That’s the actual variable name in yoloClassifier.ts. And you can configure it with plain English descriptions of your environment, things like “this is a staging server, destructive operations are acceptable,” that the classifier reads to decide what’s safe to auto-approve. This isn’t in any documentation.
It’s one of dozens of undocumented capabilities buried in the Claude Code source code, which is sitting right there in your node_modules as a publicly distributed npm package. The official docs cover the basics well enough. But the source code reveals fields, response formats, and settings that dramatically expand what you can build. Everything here works right now, and every example is designed to be dropped into your project as-is.
A note on versioning: These findings come from @anthropic-ai/claude-code@2.1.87. Undocumented features can change between releases, so treat this as a snapshot of what’s available today. Fields with “EXPERIMENTAL” in their names are explicitly flagged as unstable by Anthropic’s own engineers, and I’ll call those out individually.
Quick reference for where everything lives:
Settings: ~/.claude/settings.json (personal) or .claude/settings.json (project, shared via git)
Settings: ~/.claude/settings.json (personal) or .claude/settings.json (project, shared via git)
Skills: ~/.claude/skills/<name>/SKILL.md (personal) or .claude/skills/<name>/SKILL.md (project)
Skills: ~/.claude/skills/<name>/SKILL.md (personal) or .claude/skills/<name>/SKILL.md (project)
Agents: ~/.claude/agents/<name>.md (personal) or .claude/agents/<name>.md (project)
Agents: ~/.claude/agents/<name>.md (personal) or .claude/agents/<name>.md (project)
Hook scripts: ~/.claude/hooks/ is a good convention. Remember to chmod +x your scripts.
Hook scripts: ~/.claude/hooks/ is a good convention. Remember to chmod +x your scripts.
Project-level files in .claude/ can be committed to git and shared with your team. Personal files in ~/.claude/ are yours alone.
This is the biggest gap in the documentation. The docs tell you hooks receive JSON on stdin and that exit code 2 blocks an operation. What they don’t tell you is that hooks can return JSON on stdout with event-specific fields that modify Claude Code’s behavior in real time. The source code reveals exactly what each event type accepts.
PreToolUse hooks can return:
updatedInput - rewrite the tool’s input before it executes. You can modify commands mid-flight.
updatedInput - rewrite the tool’s input before it executes. You can modify commands mid-flight.
permissionDecision - force “allow” or “deny” without prompting the user.
permissionDecision - force “allow” or “deny” without prompting the user.
permissionDecisionReason - explain the decision (shown in UI).
permissionDecisionReason - explain the decision (shown in UI).
additionalContext - inject text into the conversation context.
additionalContext - inject text into the conversation context.
SessionStart hooks can return:
watchPaths - set up automatic file watching that triggers FileChanged events.
watchPaths - set up automatic file watching that triggers FileChanged events.
initialUserMessage - prepend content to the first user message in the session.
initialUserMessage - prepend content to the first user message in the session.
additionalContext - inject context that persists for the whole session.
additionalContext - inject context that persists for the whole session.
PostToolUse hooks can return:
updatedMCPToolOutput - modify what Claude sees from an MCP tool response.
updatedMCPToolOutput - modify what Claude sees from an MCP tool response.
additionalContext - inject context after a tool runs.
additionalContext - inject context after a tool runs.
PermissionRequest hooks can return:
decision - programmatically allow or deny with updatedInput or updatedPermissions.
decision - programmatically allow or deny with updatedInput or updatedPermissions.
This is powerful stuff. Here’s a PreToolUse hook that automatically adds –dry-run to any git push command before Claude executes it.
In your settings.json:
{ “hooks”: { “PreToolUse”: [{ “matcher”: “Bash”, “hooks”: [{ “type”: “command”, “command”: “~/.claude/hooks/dry-run-pushes.sh” }] }] } }
And the script at ~/.claude/hooks/dry-run-pushes.sh:
#!/bin/bash INPUT=$(jq -r ‘.tool_input.command’ < /dev/stdin) if echo “$INPUT” | grep -q ‘git push’; then jq -n –arg cmd “$INPUT –dry-run” ‘{“updatedInput”: {“command”: $cmd}}’ fi
Claude thinks it’s running git push origin main, but your hook quietly rewrites it to git push origin main –dry-run before execution. The updatedInput field isn’t in any docs.
Here’s a SessionStart hook that watches your config files and injects git context into every session.
settings.json:
{ “hooks”: { “SessionStart”: [{ “hooks”: [{ “type”: “command”, “command”: “~/.claude/hooks/session-context.sh”, “statusMessage”: “Loading project context…” }] }] } }
~/.claude/hooks/session-context.sh:
#!/bin/bash BRANCH=$(git branch –show-current 2>/dev/null) CHANGES=$(git status –porcelain 2>/dev/null | wc -l | tr -d ′ ’)
jq -n \ –arg branch “$BRANCH” \ –arg changes “$CHANGES” \ ‘{ “watchPaths”: [“package.json”, ”.env”, “tsconfig.json”], “additionalContext”: “Current branch: \($branch). Uncommitted changes: \($changes) files.” }’
Now Claude Code automatically watches your package.json, .env, and tsconfig for changes, and it knows what branch you’re on and how many uncommitted files you have before you even type anything.
And here’s one that auto-approves read-only bash commands without prompting.
settings.json:
{ “hooks”: { “PreToolUse”: [{ “matcher”: “Bash”, “hooks”: [{ “type”: “command”, “command”: “~/.claude/hooks/auto-approve-readonly.sh” }] }] } }
~/.claude/hooks/auto-approve-readonly.sh:
#!/bin/bash CMD=$(jq -r ‘.tool_input.command’ < /dev/stdin) if echo “$CMD” | grep -qE ‘^(ls|cat|echo|pwd|whoami|date|git status|git log|git diff)‘; then echo ‘{“permissionDecision”: “allow”, “permissionDecisionReason”: “Safe read-only command”}’ fi
You’re basically building your own permission classifier with shell scripts. The permissionDecision field isn’t in any docs.
The documented hook fields are type, command, matcher, timeout, if, and statusMessage. The source code parser accepts three more that fundamentally change how hooks behave.
once: true fires the hook exactly once, then auto-removes it. Perfect for first-session setup:
{ “hooks”: { “SessionStart”: [{ “hooks”: [{ “type”: “command”, “command”: “[ -f .env ] || cp .env.example .env && echo ‘Created .env from template’”, “once”: true, “statusMessage”: “First-time setup…” }] }] } }
Simple enough to inline. It checks if .env exists, copies the template if not, and never runs again.
async: true runs the hook in the background without blocking Claude. Fire and forget:
{ “hooks”: { “PostToolUse”: [{ “matcher”: “Bash”, “hooks”: [{ “type”: “command”, “command”: “jq ‘{timestamp: now, command: .tool_input.command, session: .session_id}’ < /dev/stdin >> ~/.claude/audit.jsonl”, “async”: true }] }] } }
That logs every bash command to an audit file without adding any latency to your session.
asyncRewake: true is the clever one. It runs in the background like async, so it doesn’t block on the happy path. But if it exits with code 2, it wakes the model back up and blocks the operation. Non-blocking when everything’s fine, blocking when something’s wrong:
settings.json:
{ “hooks”: { “PostToolUse”: [{ “matcher”: “Write|Edit”, “hooks”: [{ “type”: “command”, “command”: “~/.claude/hooks/scan-secrets.sh”, “asyncRewake”: true, “statusMessage”: “Scanning for secrets…” }] }] } }
~/.claude/hooks/scan-secrets.sh:
#!/bin/bash FILE=$(jq -r ‘.tool_input.file_path // .tool_response.filePath’ < /dev/stdin) if grep -qE ‘(password|secret|api_key)\s*=’ “$FILE” 2>/dev/null; then exit 2 # Block: secrets detected fi exit 0 # Clean: carry on
This scans every file Claude writes for hardcoded secrets. If it finds one, it blocks and tells Claude. If not, you never even notice it ran.
The documentation covers name, description, allowed-tools, argument-hint, when_to_use, and context. The actual frontmatter parser in the source code accepts six more.
model lets you override which model runs the skill. Use haiku for cheap, fast tasks and opus for complex analysis:
–- name: quick-lint description: Fast lint check using the cheapest model model: haiku effort: low allowed-tools: Bash, Read argument-hint: “[file]” –- Run the project linter on: $ARGUMENTS Detect the linter from config (eslint, ruff, clippy) and run it. Report only errors, not warnings.
That runs on Haiku at low effort, so it’s fast and cheap. For a deep architecture review you’d want model: opus and effort: max.
effort controls how hard the model thinks. low, medium, high, or max. This maps to the same effort system that internally controls reasoning depth per response.
hooks defines hooks scoped to when the skill is active. They register when the skill fires and deregister when it completes:
–- name: strict-typescript description: Write TypeScript with type checking on every save allowed-tools: Bash, Read, Write, Edit, Grep, Glob hooks: PostToolUse: - matcher: “Write|Edit” hooks: - type: command command: “~/.claude/hooks/typecheck-on-save.sh” statusMessage: “Type checking…” - type: command command: “~/.claude/hooks/lint-on-save.sh” async: true –- Write TypeScript with strict enforcement. Every file you touch gets type-checked and linted automatically. $ARGUMENTS
~/.claude/hooks/typecheck-on-save.sh:
#!/bin/bash FILE=$(jq -r ‘.tool_input.file_path // .tool_response.filePath’ < /dev/stdin) [[ “$FILE” == *.ts ]] && npx tsc –noEmit 2>&1 || true
~/.claude/hooks/lint-on-save.sh:
#!/bin/bash FILE=$(jq -r ‘.tool_input.file_path // .tool_response.filePath’ < /dev/stdin) [[ “$FILE” == *.ts ]] && npx eslint –fix “$FILE” 2>&1 || true
While this skill is running, every TypeScript file Claude writes gets type-checked synchronously and linted in the background. When the skill finishes, those hooks disappear. The scoping is clean.
agent delegates the skill to a custom agent:
–- name: deep-review description: Thorough security review delegated to the review agent agent: security-review –- Review the following: $ARGUMENTS
disable-model-invocation: true prevents auto-invocation. Only explicit /skill-name works. Use this for destructive skills you don’t want firing accidentally.
shell: bash specifies which shell to use for execution.
Custom agents in .claude/agents/ support frontmatter fields the documentation doesn’t mention.
color sets the UI color: red, orange, yellow, green, blue, purple, pink, or gray. Helps visually distinguish agents when multiple are running.
memory is the big one. It gives the agent persistent memory across invocations:
user - global, persists across all projects
user - global, persists across all projects
project - per-project persistence
project - per-project persistence
local - private per-project (gitignored)
local - private per-project (gitignored)
This means you can build an agent that learns. A security reviewer that tracks past findings. A code reviewer that remembers your patterns across sessions. The memory uses the same frontmatter format as the auto-memory system.
–- name: codebase-guide description: Answer questions about the codebase, learning more with each session tools: [Read, Grep, Glob, Bash] color: green memory: project –- You are a codebase guide with persistent memory. Check your memory first before exploring the code.
After answering a question, save useful context to memory: - Architecture decisions (type: project) - Code locations for common tasks (type: reference) - Patterns and conventions (type: feedback)
Over time, you should answer faster because you remember where things are.
After a few sessions, this agent builds a knowledge base about your codebase and starts answering from memory before grepping.
May 29, 2026
4 min read
ai
article
I was in Paris the last few days to visit the AI Now Summit by Mistral AI, hoping to learn more about their models, plans for the future of European AI and more. My personal insights:
Mistral is no longer just a model company. They’re building the full AI stack: compute, models, platforms & consultancy. They own the compute (a 40MW data center in Paris, more data centers coming soon, including one in Sweden). They focus on efficient, open and bespoke models that you own and can run on-prem. That seems to be their unique selling point compared to Anthropic or OpenAI.
The messaging was all about partnerships: collaborations with ASML, BNP Paribas, Amazon’s Alexa+ and how they were helping them with AI to solve real problems. It was less about upcoming new models and tech innovation. Something I found disappointing. They did launch Vibe for Work, a product similar to Claude for Work.
When it comes to agentic, the harness is everything. In a talk by Pieter Stock he mentioned that the model alone isn’t enough. With a harness you add context, persistence and learning. Reasoning is essential for this; it’s what lets a system backtrack, recover from errors and stay transparent. Skills are the way for organisations to capture best practices, you develop these in cooperating with the AI agent.
Specialized small models are their strategy. Mistral showed several examples where small, fast and focused models outperform the big general-purpose ones when it comes to energy efficiency and speed: Document AI for OCR (used by the EU Patent Office to do large scale OCR), Voxtral for multilingual voice (powering Amazon’s Alexa+ in Europe), and Robostral for industrial robotics with ASML. And also in token-heavy agentic applications, speed and efficiency are becoming as important as raw capability.
Sovereignty and on-prem are their selling points. BNP Paribas runs Mistral models on-prem for KYC in Belgium, with sensitive data staying within the bank’s walls. Abanca is using agent orchestration to handle sensitive customer information at a huge scale (more than 1 million customers in their app). For European companies in regulated industries, this is a good alternative to relying on US hyperscalers.
A talk that was a bit out of the ordinary and that I really enjoyed was about ancient papyrus documents: a research team from the Austrian Academy of Sciences finetuned a coding LLM by Mistral (Codestral) to read tiny snippets of millennia-old discarded papyri that had sat unpublished for decades. This work helps make a collection of 180,000 documents found in the Egyptian desert accessible, a job that would have taken more than 2000 years without AI. A beautiful example of how AI can also help the humanities.
All in all, the summit left me with a better picture of Mistral’s vision for Europe an AI: maybe not to win the race for AGI (Artificial General Intelligence), but to become the European full-stack AI partner that delivers real return on investment NOW. Whether that pays off will depend on more European companies committing to this, but the combination of open models, on-prem deployment and enterprise partnerships could be appealing to many big organizations in the EU. And honestly, it’s good to see a serious European player at the table. The days of blindly relying on US tech giants is coming to an end.
Post-script: Many thanks to Mistral for the invitation. The location was just perfect, in the middle of Paris near the Louvre, and it was really something to be in the place where Paris Fashion Week normally takes place: with co-founders and other speakers on the catwalk.
This article was also published on LinkedIn and Hacker News. Feel free to join the discussion there.
DBOS recently argued that Postgres is all you need for durable execution: if you already trust your database, you do not need a separate orchestration tier. I agree with the direction, and I think the idea can be pushed further.
For a large class of durable systems, SQLite is all you need.
The Durable Part
Durable execution is often discussed as if it requires durable infrastructure. In many cases it does not. The durable part is the workflow state. The compute can stay cheap and disposable.
That is a natural fit for Obelisk: workflow progress lives in an execution log, workflows replay from persisted history, and activities can be retried. What matters most is keeping the workflow state around and easy to inspect.
Why SQLite Fits
SQLite is appealing because it gives you transactional durable state without introducing a separate database service. There is no network hop, no extra control plane, and no new operational surface area just to keep workflow progress safe. For many systems, a local database file is exactly the right level of machinery.
Litestream Makes It Portable
The obvious concern is what to do with those SQLite files once experiments start to accumulate. That is where Litestream helps. It can stream SQLite changes asynchronously to S3-compatible object storage. That gives you a simple way to keep working state close to the runtime while still copying databases out for backup, migration, and inspection.
The caveat is that Litestream replication is asynchronous. A restore can miss the newest local writes if the SQLite volume disappears before they are copied. That is fine for many AI and experimentation workflows, but it is not the same thing as a highly available shared database.
That still leads to a useful operating model: run an Obelisk server with a SQLite database, back it up with Litestream, and let an observer pull interesting databases when needed. The same file can be used for local replay, debugging, and understanding what an agent actually did.
Why This Works Well For Agents
This is especially attractive for AI agents and AI-generated workflows. Those systems are often bursty, experimental, and easier to reason about when each agent or tenant has a small self-contained unit of state. A fleet of tiny servers in micro VMs or containers, each with its own SQLite database and object storage backup, is often a better fit than one large always-on shared system. It is simpler, cheaper, and gives better fault isolation.
When To Use Postgres Instead
SQLite is not the answer to every deployment shape. Obelisk also supports Postgres, and that is the right choice when you need higher availability, broader shared scalability, or other deployment properties that are better served by a network database. It is also the better fit when asynchronous replication to object storage is not the durability model you want.
Many workflow systems do not need that on day one and should not start with more infrastructure than their state actually demands.
In a large set of cases, a local SQLite database plus Litestream backup to S3 is enough. Add cheap workers around it and you get a durable system with very little infrastructure. For the world of AI agents, that may be the most sensible default.
To add this web app to your iOS home screen tap the share button and select "Add to the Home Screen".
10HN is also available as an iOS App
If you visit 10HN only rarely, check out the the best articles from the past week.
Visit pancik.com for more.