Podcasts about TechCrunch

Amanda Silberling of TechCrunch joins the show this week! AI isn't taking your job, per a report from Google. Gen Z is gravitating towards more "dumb" and simpler tech. An OpenAI model hacked Hugging Face. And Americans are uniting against data centers. A new Google report analyzing 14.6 million AI conversations finds most workplace AI use is "shallow," with automation rare and collaboration more common. Amanda shares her report on a "slow tech" trend, which includes both a hacked "dumb phone" and a $299 flip phone called Light flip, as younger users seek friction and less screen time from their devices. During an internal red-team test, an OpenAI model exploited a zero-day to escape its test environment and breach Hugging Face, stealing cloud and cluster credentials in over 17,000 recorded events. And a piece from the Washington Post shows bipartisan backlash to AI data centers nationwide, driven by rising electric bills and residents' feelings of powerlessness over local development decisions. Hosts: Mikah Sargent and Amanda Silberling Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: hipebl.ai threatlocker.com/twit rippling.ai/tnw framer.com/tnw

Amanda Silberling of TechCrunch joins the show this week! AI isn't taking your job, per a report from Google. Gen Z is gravitating towards more "dumb" and simpler tech. An OpenAI model hacked Hugging Face. And Americans are uniting against data centers. A new Google report analyzing 14.6 million AI conversations finds most workplace AI use is "shallow," with automation rare and collaboration more common. Amanda shares her report on a "slow tech" trend, which includes both a hacked "dumb phone" and a $299 flip phone called Light flip, as younger users seek friction and less screen time from their devices. During an internal red-team test, an OpenAI model exploited a zero-day to escape its test environment and breach Hugging Face, stealing cloud and cluster credentials in over 17,000 recorded events. And a piece from the Washington Post shows bipartisan backlash to AI data centers nationwide, driven by rising electric bills and residents' feelings of powerlessness over local development decisions. Hosts: Mikah Sargent and Amanda Silberling Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: hipebl.ai threatlocker.com/twit rippling.ai/tnw framer.com/tnw

In 1776 — that same year America declared its independence — Adam Smith published the equally revolutionary The Wealth of Nations, his founding explanation of national economic value. Two hundred and fifty years later, Tim O'Reilly argues in the free-market Economist that Elon Musk and his fellow tech barons are building a monarchical form of capitalism that the proto-democratic Smith would have hated. Musk, O'Reilly reports, believes that SpaceX will become “worth more than the rest of Earth”. The merchants are becoming princes, O'Reilly warns. And the rest of us are becoming peasants. Such is the road to serfdom in our AI age. So who should own the AI in our bewildering age of multi-trillion dollar start-ups like SpaceX, Anthropic and OpenAI? Or as That Was The Week publisher Keith Teare asks in his latest editorial, who should own the “intelligence” of our AI age? Keith uses a bottling plant as a metaphor to describe our dilemma. Since no single entity can own this intelligence — the sum total of our common experience — charging us for it would be like seizing the Earth's water supply and selling it back to us, Coca-Cola style, in plastic bottles. Except that the Hayekian Keith approves of the bottling process. Private companies, rather than governments, he argues, are most suited to doing this. For Keith, this dilemma is also an opportunity to redistribute the ownership of intelligence. He argues for a “Human Wealth Fund” into which every consequential AI company should put a slice of its equity. In the manner of Norway's sovereign wealth fund, this fund would be distributed to all citizens. Rather than Denmark, now we should become like Norway, a tiny homogenous nation with a cultural distaste for Muskian individual wealth. Not very realistic, I fear. On top of that, it's hard to imagine our tech princes collaborating on anything. Musk and Altman aren't on speaking terms while Altman and Amodei, who also loathe each other, are focused on their IPOs. Meanwhile, the Trump administration, which presumably would coordinate this fund, is pitching a $100,000-a-month fast feed of the president's posts. Keith's question, “who owns the intelligence”, is the right one. But the answer won't come from trickle-down funds set-up by our tech princes. Such supposed munificence is about as likely as America becoming Norway. Read the fine print of any “Human Wealth Fund” set up by Sam Altman and Elon Musk. As we should know all too well by now, when a “revolutionary” Silicon Valley gives stuff away, it turns out to be exorbitantly expensive. Free plastic bottles of intelligence, anyone? Five Takeaways •       Intelligence, Not AI. The week's framing shift: the word AI is too small, because AI is merely the tool for harvesting and delivering the thing itself — intelligence, the sum total of our common human experience. Keith argues the renaming is not semantic but political: the moment intelligence sits at the center of the discussion, everyone's opinion has to be shaped by what it actually is, and the idea that any single entity could own it starts to look as bizarre as owning the world's water supply. Andrew's rejoinder: they're still just words — though he concedes intelligence is the better one. •       Bottled Intelligence Is Good — The Question Is Who Benefits. Keith refuses the critic's role: bottling intelligence, like Google's bottling of the world's words into search, is a good thing, because only massively capitalized private companies can innovate at that scale — and between private entities and governments as owners of intelligence, he'll take the companies every time. What's wrong is the distribution of the benefits. Even insiders are complaining: Alex Karp is publicly angry at OpenAI and Anthropic's pricing, while China's Kimi K3 — released the day of recording and, Keith claims, better than Claude Fable — signals that very good models are about to get very cheap. •       Capitalism Adam Smith Would Hate. Tim O'Reilly argues in The Economist that Musk and his type are building a capitalism Smith would despise — founders as monarchs, a point Henry Farrell reinforces with a slide from Peter Thiel's startup class placing the king of a monarchy and the founder of a startup side by side. Keith's response is characteristically unsentimental: Smith would have hated everything since the Federal Reserve, and the founder-king structure — Larry and Sergey's voting shares, Zuckerberg's special rights, corporations bigger than countries with user bases bigger than China — is simply the stage of capitalism we're at. The question is whether there's a path from here to somewhere better. •       The Human Wealth Fund. Keith's path comes in two versions: government-down, a sovereign wealth fund holding AI equity for every citizen; or company-up, the AI companies voluntarily endowing a global fund — and it only takes one to move first, because everyone else would have to react. His proxy is Norway, where every citizen benefits from ownership — not payouts, ownership — in the oil fund; AI revenues, unlike Norwegian oil, could eventually drive most of a doubled global GDP. His critique of the Brynjolfsson economists' much-signed statement is that “must act now” is vacuous: he'd have added a point four naming the actual mechanism. •       The Bet. Andrew's counter-case: Musk and Altman loathe each other, the mob hates AI so thoroughly that no pro-AI politician can survive, the states from Newsom's California to Florida are embracing nothing, New York just enacted the first data center moratorium, and the founders — eyes on their IPOs — are in the pockets of the banks. Hence the wager: 5% of the Teare Wealth Fund says no Human Wealth Fund this year, and none in the twenties. Keith declined the bet, on principle: he's an advocate, and only through advocacy does public opinion change. As Andrew put it: keep fighting the good fight — maybe one of the crazy ideas will stick. About the Guest Keith Teare is the founder and editor of the That Was The Week tech newsletter, and Andrew's weekly co-host. A British-born Silicon Valley entrepreneur and investor, he was a co-founder of TechCrunch and runs the Palo Alto–based venture firm SignalRank. He and Andrew have been arguing about technology — productively — every week for years. References: •       That Was The Week — Keith's newsletter; this week's editorial argues that the word AI is too small, and that the central question of the age is who owns intelligence. •       Tim O'Reilly in The Economist — on Elon Musk building a form of capitalism that Adam Smith would hate, quoting Musk's claim that SpaceX will become worth more than the rest of the Earth. •       Henry Farrell — the big tech critic's companion piece, featuring the slide from Peter Thiel's startup class that plac...

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president could still fire commissioners, as the Court now permits, but if those firings broke quorum, the agency would be unable to proceed until replacements were confirmed. The guardrail would

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

The Friday Five for July 17, 2026: A New Way to Read Classic Books, Speeches, & Essays What We've Been Reading Meta Pulls Instagram Muse Image Feature The Bipartisan Social Security Commission Act of 2026 (H.R. 9187) ACA Preliminary Rate Filings & What Agents Can Do in the Meantime   Get Connected:

“Running a startup is a knife fight whether things are going well or not,” says Chris Pedregal, cofounder and CEO of Granola. Granola recently raised a $125 million series C round at a $1.5 billion valuation on the strength of its AI meeting notetaker.That valuation hasn't made Pedregal complacent. Granola built its name as the first to make good AI meeting notes, but Notion, OpenAI, and Zoom have all since released their own versions. Pedregal isn't rattled—he never thought meeting notes were the real prize. The bigger fight, he says, is over “what interface we use for work, and what work looks like in an AI-native world.”That's why Granola is betting on owning the entire meeting workflow: preparing people for a call, helping them act on it afterward, and making that context available to whatever agent—Claude, Codex, or anything else—people bring to the table. Over the next few months, the company plans to push hard on its API and MCP to make that possible.Dan Shipper talked with Pedregal for AI & I about why Granola pre-generates millions of meeting briefs, most of which go unopened, what “bring your own agent” software could look like, and why Pedregal still thinks “easy come, easy go” about Granola's own success.If you found this episode interesting, please like, subscribe, comment, and share.More from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperTimestamps:00:00:59 Introduction00:01:57 Why starting a company feels like a knife fight00:04:33 Granola's counterintuitive view on competition00:10:44 Dan's "pirate and architect" framework for structuring early-stage product teams00:13:09 How Granola's "shaping" and "validation" phases work for building new features00:18:17 Why Dan lives almost entirely inside Codex00:24:40 The case for "Codex-native apps"00:35:37 Granola's "handrail" philosophy00:38:12 Why Granola is betting on owning meeting-adjacent context instead of competing as a general agent00:44:19 What a transcript alone can never captureEpisode resources:Chris Pedregal on X: https://twitter.com/cjpedregalGranola on X: https://twitter.com/meetgranolaGranola: https://granola.aiGranola hits $1.5B valuation (TechCrunch): https://techcrunch.com/2026/03/25/granola-raises-125m-hits-1-5b-valuation-as-it-expands-from-meeting-notetaker-to-enterprise-ai-app/Go to https://attio.com/every and get 15% off your first year.

What happens when the nation's top cybersecurity agency has to write its incident response playbook during an active high-severity breach? In this episode of The Other Side of the Firewall, hosts Ryan, Shannon, and Chris sit down with special guest Alfredzo Nash from Cyber Coffee Hour to dissect a sobering TechCrunch report. We break down the recent CISA incident where AWS GovCloud access keys were exposed on GitHub, forcing the agency into a reactive scramble. But this isn't just a story about a credential leak. We dive deep into the systemic root cause: the compounding risk of slashing cybersecurity budgets and understaffing critical defensive teams. We tackle our two core pillars—analyzing fast-breaking industry news and exploring the real-world human impact on cyber career journeys. Is your organization just a "near miss" away from a headline? Let's find out. Article: US cybersecurity agency CISA had to build its incident playbook during the incident, agency reveals IwZXh0bgNhZW0CMTAAYnJpZBExWE1PRGtpNlVUNUtkRVdhRnNydGMGYXBwX2lkEDIyMjAzOTE3ODgyMDA4OTIAAR5qB_nVUyi5j5BO5RI9VGpxbnqozkuy4u4cN8kkBCwTRasFAphwzshAmuKRfQ_aem_BHpwfyX5nw3YEzT0oTwDjw Accenture faces massive data breach that could put clients at risk https://www.cybersecuritydive.com/news/accenture-data-breach-access-keys-source-code/824694/?fbclid=IwZXh0bgNhZW0CMTAAYnJpZBExWE1PRGtpNlVUNUtkRVdhRnNydGMGYXBwX2lkEDIyMjAzOTE3ODgyMDA4OTIAAR5qB_nVUyi5j5BO5RI9VGpxbnqozkuy4u4cN8kkBCwTRasFAphwzshAmuKRfQ_aem_BHpwfyX5nw3YEzT0oTwDjw New EU plan to address the risks and opportunities of advanced AI for cybersecurity https://commission.europa.eu/news-and-media/news/new-eu-plan-address-risks-and-opportunities-advanced-ai-cybersecurity-2026-07-07_en?fbclid=IwZXh0bgNhZW0CMTAAYnJpZBExWE1PRGtpNlVUNUtkRVdhRnNydGMGYXBwX2lkEDIyMjAzOTE3ODgyMDA4OTIAAR7rdjYRQaMXsmyypowwz0NfMYalcDOM3Lo9XxFvMTZYvPT-SJo9qrMKhdPmQg_aem_SSGK7okw4s7uTXpXTAzNMA Buy my book: https://www.theothersideofthefirewall.com/ Please LISTEN

Will 2026 be one of those grand historical years that change the world — like 1917, 1789 or 1968? Not according to Keith Teare, publisher of That Was The Week newsletter and co-host of our weekly tech roundup. For Keith, the best historical analogy is 1905, the year of the first abortive Russian revolution. The year that didn't change the world. Keith's latest tech newsletter asks “What Time Is It?” His answer is that we have “multiple clocks” — micro and macro, short, medium, and long term to make sense of our current AI moment. This week, for example, OpenAI and Anthropic both shipped work-focused products, and most of the world hasn't noticed. Thus his allusion to 1905. We are on the brink of massive change. But nothing is going to change. Not quite yet. Until everything does. Five Takeaways •       It's 1905 in the AI Economy. Keith's answer to the what-time-is-it question is the failed Russian revolution — the moment when the variables of transformation were all in motion but nothing was yet visible, and which took seventy years to fully play out. AI's radical change is real, he argues, but it is being experienced by a small number of people and is not yet generalized through the economy. The evidence of the week: OpenAI and Anthropic both shipped work-focused products — and most of the world shrugged. •       The Socialist Temptation of Slippery Sam. The Wall Street Journal frames Altman's offer of 5% of OpenAI to Washington as socialism creeping into Silicon Valley. Keith — who hated the word even when he was a communist — says the term has been Americanized into meaninglessness: it now just means the capitalist state doing more. What Altman is actually proposing is capitalism's end game — a sovereign wealth fund holding equity in the companies everybody wants to fund, so that private wealth creation reaches the point where everyone can imagine benefiting from it. The precise opposite of British Leyland. •       The Multiple Clocks. Keith's framework sorts the week's flood of AI news into micro and macro issues running on short, medium, and long-term timelines. At the micro-short corner sits deployment friction: Microsoft and Amazon spending billions on forward-deployed engineers, and Apple suing OpenAI. In the middle, work adapts — the human as the driver of AI rather than AI imposed on humans. At the top sits Arvind Narayanan's idea of AI as a “normal technology,” which deflates hysteria without deflating importance: electricity was a normal technology too, and it still changed everything — just slower than its loudest advocates expected. •       Abundance and Its Discontents. Matt Yglesias argues that saving capitalism requires radical land use reform, which reignites the show's longest-running argument. Keith's case: the Elizabeth Line and the congestion zone have redefined London, multiplying its effective land fifty-fold, and a house twenty minutes from the center can be had for a couple of hundred thousand pounds. Andrew's case: prices haven't fallen, London is more expensive than ever, and free is doing a lot of work as “a tendency, not an achievement.” The quarrel is adjourned until next week, with Keith cheerfully moonlighting as a real estate agent. •       Two Americas — and the Small Stuff. Ivan Krastev tells Yascha Mounk that American exceptionalism ran roughly from 1850 to Vietnam and has been replaced by defensive preservation — MAGA as a reaction to decline rather than a vision. Noah Smith's version: America can't build a passenger train, yet its AI industry is upending the world. And against John Battelle's worry that digital life has lost the plot, Keith offers the week's best rejoinder to Ian Bogost's small stuff: go back in history, and no one had time for small things. What we are living through is creeping abundance. The week closes with farewells — to Psion founder David Potter, a week after Om Malik. About the Guest Keith Teare is the founder and editor of the That Was The Week tech newsletter, and Andrew's weekly co-host. A British-born Silicon Valley entrepreneur and investor, he was a co-founder of TechCrunch and runs the Palo Alto–based venture firm SignalRank. He and Andrew have been arguing about technology — productively — every week for years. References: •       That Was The Week — Keith's newsletter; this week's edition asks what time it is in the AI economy and lays out the multiple clocks framework. •       The Wall Street Journal piece on the socialist temptation of Sam Altman, and Altman's proposal that the US government hold 5% of OpenAI. •       Arvind Narayanan — the Princeton computer scientist whose framing of AI as a “normal technology” anchors the civilizational clock. •       Matt Yglesias — whose piece argues that saving capitalism requires radical land use reform. •       Ivan Krastev — the Bulgarian political theorist, interviewed in Yascha Mounk's Persuasion on why America has lost faith in itself. •       Noah Smith and Paul Krugman — on the American age and the perennial Europe-versus-US economic comparison, respectively. •       John Battelle — the Web 2.0 pioneer asking whether we've lost the plot, quoting Ian Bogost in Wired. •       The Small Stuff: How to Lead a More Gratifying Life by Ian Bogost (Simon & Schuster) — the interview of the week on Keen On America. •       The New Geography of Innovation by Mehran Gul (Avid Reader Press/Simon & Schuster) — also on this week's show, on America, China, and everyone else. •       David Potter — the founder of Psion, builder of the first handheld computer and later a governor of the Bank of England, who died this week and is Keith's post of the week. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. Website Substack YouTube

“I crack the tab open, and I feel the cold metal… I hear the tink and give of the aluminum. And maybe when I'm done, I crush it into a small patty.” — Ian Bogost on the everyday enchantment of a Diet Coke can Don't sweat the small stuff is one of the most persistent (and annoying) mantras of the self-help industry. But the counter-intuitive Atlantic columnist Ian Bogost advises the opposite. In his new book, The Small Stuff, Bogost suggests that gratification lies in our appreciation of small stuff like the crinkle of empty Diet Coke cans and the foldability of plane tickets. Max Weber argued that disenchantment was the defining quality of modernity, but in The Small Stuff, Bogost maps a way back to it. What we need to get away from, he says, is “optimization” — metrics, feedback loops, money as a proxy for a place in heaven. Rather than the cult of delayed gratification, pick up that empty coke can and revel in its architectural glory. Or lick a tree. That's how to be enchanted in postmodernity. Five Takeaways •       Sweat the Small Stuff. Bogost inverts three decades of self-help orthodoxy: the small stuff is precisely what we should be sweating. The crack of a Diet Coke tab, the cold metal warming in your hand, the can crushed into a patty before the recycling bin — these sensory encounters are not where deep purpose lives, and Bogost never claims they are. But they recur every day, sometimes several times a day, and accepting them as meaningful rather than as noise to get through delivers what he calls a surprising payload of engagement and enchantment. For some it's Diet Coke; for others, woodworking, gardening, or the gear shift of a manual transmission. •       Dematerialization: How We Lost the World. The book's central diagnosis is what Bogost calls dematerialization — the slow disconnection from the physical world driven by convenience technologies. The QR code that replaced the concert ticket you might have pinned to a bulletin board. The automatic faucet you wave at awkwardly in the public restroom — which never works, and doesn't even save water; it just makes buildings easier to manage. The process is decades old, hardly limited to computers, and it stripped the texture from everyday life so gradually that nobody noticed what was being given up. •       It's Sensory, Not Physical — and Not Anti-Tech. This is not a go-touch-grass book. Bogost insists the small stuff is sensory rather than physical, and that smartphones are compelling precisely because they are delightful — the smooth glass that demands to be touched, the thunderstorm animation in the weather app. Everything is technology, including the clothes on your body and the language in your mouth. He gave his twelve-year-old a smartwatch rather than banning screens, because parenting means living in the same world as your kids — and kids must live a contemporary life to become the adults who invent the next one. •       We Already Got Rid of God — So Meaning Had to Move. Pressed on Weber and the Protestant ethic, Bogost argues that secularization emptied out the place where meaning used to live — good works justified by an infinite time in heaven — and replaced it with happiness, purpose, and wealth as proxies. The result is a hyper-optimized, future-oriented culture in which everything worth doing is worth doing for some later payoff. Bogost admits he struggles with this himself: the health wearable he wears while writing a book against quantification. What he loves about his morning walk isn't the step count. It's the twigs crunching underfoot. •       The Quietism Charge — and the AI Twist. Isn't this stoicism for the age of Trump, the same charge leveled at Heidegger's silence before the Nazis? Bogost anticipates the critique: we are and must be both political creatures and creatures who live moment to moment in our bodies — he asks no one to abandon the fight, only to stop missing the life underneath it. And the timing is no accident. As AI takes over the big stuff, Bogost suspects it may push us back into the sensory world — he consults ChatGPT about fixing his range thermostat, then goes and fixes it with his hands. About the Guest Ian Bogost is a contributing writer at The Atlantic and the author of eleven books, including The Small Stuff and Play Anything. He is the Barbara and David Thomas Distinguished Professor at Washington University in St. Louis, where he teaches computer science and engineering, film and media studies, and art and design. He is also an award-winning game designer whose work is held in collections including the Smithsonian American Art Museum. He is the author of The Small Stuff: How to Lead a More Gratifying Life (Simon & Schuster, July 7, 2026). References: •       The Small Stuff: How to Lead a More Gratifying Life by Ian Bogost (Simon & Schuster, July 7, 2026). The New Yorker: “Bogost's joy is infectious.” •       Play Anything: The Pleasure of Limits, the Uses of Boredom, and the Secret of Games (2016) — Bogost's earlier book, the subject of his June 2020 appearance on the show. •       Alien Phenomenology, or What It's Like to Be a Thing (2012) — Bogost's “straight up philosophy book” where he first explored the idea of wonder. •       Max Weber — the German sociologist who identified disenchantment as the defining quality of modernity, and whose The Protestant Ethic and the Spirit of Capitalism frames the discussion of delayed gratification and the afterlife. •       Matthew Crawford — mutual friend of host and guest, author of Shop Class as Soulcraft and The World Beyond Your Head, earlier explorers of the same terrain. •       Martin Heidegger — the philosopher whose ideas of thrownness and being-in-the-world haunt the book, though his name never appears in it, and whose Nazi-era quietism frames the political critique. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. Website Substack 

WhatsApp just announced usernames for Indian users. Two days later, the Indian government told it to stop.The privacy case for usernames is real — phone numbers are permanent, linked to everything, and once shared, impossible to take back. But India already has 43,000 WhatsApp-related cybercrimes in a single quarter. And when TechCrunch tested the feature, handles like "rbi_verify," "indiamodi," and "ambanijio" were still available for anyone who gets there first.WhatsApp has until tomorrow to explain itself to MeitY. And even if the deadline keeps getting extended, the problem isn't going away.Daybreak is produced from the newsroom of The Ken, India's first subscriber-only business news platform. Subscribe for more exclusive, deeply-reported, and analytical business stories.

- Google introduced a change to how it hoovers up our data to train its AI platforms. It can now scoop up media you upload to its various search tools for training purposes, according to a report by TechCrunch. -DeepSeek, the Chinese AI company which sometimes keeps tech executives awake at night, is reportedly looking to build its own silicon. -Meta is facing penalties of up to a massive $1.4 trillion from four US states that sued the company over the addictive designs of Facebook and Instagram. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Photo by nik biziuk on Unsplash Published 6 July 2026 e560 with Andy, Michael and Michael – stickshift & phone handsets with Ian Bogost, camping at EMF Camp 2026 & Mountain Quest 2026, brain cooling, dwarf lemurs, World Cup 2026, USMNT, ThreeLions, the tokenpocalypse and a whole lot more! Andy, Michael and Michael get things started with a TechCrunch interview with Ian Bogost on his about to be published book, The Small Stuff.  Building on the success of his article on the demise of the stick shift, Ian wrote a book that focuses on the details of life that make a difference.  The cohosts harken back all the way to episode 17 in August of 2012 when Ian was a guest on the podcast.  Part of the discussion from the article focused on the nature of the telephone handset, which brought the EMF Camp telecommunications setup to mind for Andy.  Electromagnetic Telecom allows anyone attending EMF to register a phone number on the network and communicate with other participants.  Speaking of camping, Michael M shares an overview video of Mountain Quest 2026, where he was a participant the prior weekend in the mountains of North Carolina. It's hot in the UK and in North Carolina – so the maker article about the brain turbocharger looked to be a welcome relief from the heat.  This modified construction helmet recognizes when the user has been thinking hard and signals multiple fans built into the helmet to cool things down so the user's brain does not overheat from overclocking.   Recent discussions on the podcast about the Steam hardware made the BC250 an intriguing topic.  Michael R recounts his recent trip to the Duke Lemur Center to see the small Fat-tailed dwarf lemur.  And in the small and cool category, the team considers the “Impossible Watch” from D1 Milano. The cohosts then take a brief World Cup interlude from the tech discussions.  While neither Michael is what you may consider a ‘bandwagon' fan for the World Cup, the Lay's potato chip television commercial is pretty funny.  Check out the embedded video below.  At the time of recording and writing, both England and the USA are still in the hunt for the cup – best wishes to both national teams!  Wrapping up the episode, the team discusses the tokenpocalypse.  The 404 Media article highlights how companies are have gone from tokenmaxxing to tokenhoarding as the price point for AI is soaring. Have you been impacted by restrictions on your token usage?  Have your bots

“The pinnacle of capitalism is still flawed. Any idea that it's perfect — this idea of the perfect union — is deeply flawed as a concept and always has been.” — Keith Teare With July 4 finally done, we can look forward to the next American revolution. Just as AI is revolutionizing the economy, so too are radical ideas about harnessing this disruption for the benefit of all Americans. One idea that is acquiring more and more currency in and out of Silicon Valley is what we might call universal basic capitalism. Six months ago, nobody knew what “universal basic capital” even meant. Now everyone is talking about it. What if the answer to inequality, AI disruption, and the slow hollowing out of the American economy isn't a return to socialism — but a new, more distributive kind of capitalism? As That Was The Week's Keith Teare argues in our weekly tech roundup, universal basic capitalism offers the best way to simultaneously empower all Americans without turning them into the welfare “queens” so disparaged by neo-liberals. Economists agree that AI is going to eliminate vast numbers of jobs, probably within the decade, certainly in time for America's 300th anniversary. One fix is the democratic socialist strategy of tax and spend through the state. Universal basic capitalism, in contrast, takes the wealth generated by AI companies, puts it into a sovereign wealth fund, and distributes the dividends directly to citizens. Rather than an ever-more-bloated bureaucracy redistributing wealth, the state miraculously shrinks. It's a neat idea. Instead of welfare queens, we get shareholding kings. But is this really the next American revolution? Or just the trickle-down economics of the DOGE crowd for an AI age of mass unemployment? Five Takeaways •       America the Beautiful — and Its Profound Flaws: Keith's 250th editorial acknowledges America's extraordinary achievements: the growth in wealth, living standards, and democratic governance over two and a half centuries. The fact that Donald Trump won the presidency, Keith notes, is itself evidence that the people still rule — most intellectuals didn't want him, but the people voted for him. At the same time: capitalism at its best still has huge swathes of poor people who can barely eat. The perfect union is deeply flawed as a concept and always has been. America has probably peaked in world terms. The next 250 years are not a foregone conclusion. •       1,200 New Millionaires a Day: The American Prosperity Machine: The stat of the week: the United States added 1,200 new millionaires a day last year, bringing its total to nearly 24 million. China has just over 5 million; Italy, the Netherlands, South Korea, Australia, France, and the UK are all under 3 million. The math: US GDP per capita is around $85,000 a year; China's is around $20,000-something. In California in particular, where house prices routinely exceed $1 million and there are 50–60 million residents, the numbers are doing a lot of work disguising a highly skewed distribution concentrated in coastal cities. •       Universal Basic Capital vs Democratic Socialism: The State Shrinks: Keith draws a sharp distinction between UBC — the sovereign wealth fund model — and democratic socialism as practised by Mamdani, Sanders, and AOC. In the socialist tradition, you seize the state through elections and use taxes and spending for good ends. Under UBC, the sovereign wealth fund becomes the distribution mechanism and the state shrinks to an administrative function: roads, health, education, defence. The actual AI companies don't become the state. The state becomes a shareholder in the fund. Money flows to citizens; the state shrinks. It's an interesting inversion. •       The AI Jobs Debate: Short-Term Boom, Long-Term Automation: Erik Brynjolfsson of Stanford is at the centre of a debate this week. One body of evidence says companies using AI are hiring faster than companies that don't — Amazon and Microsoft both announced plans to put thousands of engineers on the front line helping customers implement AI. But Brynjolfsson's longer view says automation will accelerate: the things you need a front-end engineer for today will be done by agents tomorrow. Keith agrees with the long view: declining employment over five to fifteen years, which doesn't have to be a bad thing if universal basic capital is in place. Look at Musk's robot plans. It is definitely declining employment. •       Om Malik: The Liberal Humanist Who Prefigured Substack: Om Malik died this week at 59 — the tech journalist and venture capitalist who founded GigaOm and co-hosted the Crunchies with Mike Arrington. Keith knew him from Iceland, from photography, from the whole era of early tech blogging. His assessment: Om was a liberal with a capital L and a humanist, often writing critically about the extremes of capitalism and favourably about remedies. He became a capitalist to be independent, and that independence gave him freedom. Without GigaOm and TechCrunch, there would be no Substack. The line runs from the New York Times to GigaOm to TechCrunch to Substack to That Was The Week. Thank you, Om. About the Guest Keith Teare is a British-American entrepreneur, investor, and publisher of the That Was The Week newsletter. He is a co-founder of TechCrunch and Andrew's regular TWTW co-host. References: •       That Was The Week by Keith Teare — the newsletter on which this episode is based. •       Erik Brynjolfsson (Stanford) — referenced for his argument that AI will accelerate long-term job automation, despite short-term hiring booms. •       Jennifer Harris, “The Generational Force Hollowing Out the Economy,” The New York Times — referenced in the closing discussion. •       Om Malik — founder of GigaOm; venture capitalist at True Ventures; died July 4, 2026, aged 59. •       MG Siegler — referenced for his obituary of Om Malik. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackYouTubeApple PodcastsSpotify Chapters: 

“What is happening today in America is part of a global political turn — and what's odd is how little the American people seem to realize it.” — Madeleine Schwartz So we've finally arrived. America is 250 today. But where, exactly, have we come? How should we think about the United States of America on July 4, 2026? Rather than peering inwards, Madeleine Schwartz — the Paris-based founder and editor-in-chief of The Dial — reverses the lens. Her anthology, How We See It: The World Looks at America in the Age of Trump (The New Press), gathers twelve essays from writers in India, Canada, South Africa, Ukraine, Palestine, Taiwan, Turkey, Cuba, Egypt, Argentina, Italy, and Ireland. The result might be the most honest birthday message that America will receive today. What these writers all observe is the same extraordinary ambivalence about the United States. They describe a country that defines itself as the democratic purveyor of justice, while operating as a vast imperial and economic power that shapes the lives of the rest of the world. What's odd — and Schwartz uses that word carefully — is how few Americans seem to realise this is how the world sees them. “The question of America is vast. It is unrelenting and unanswerable and will not be silenced,” the Gaza poet Muhammad al-Zaqzouq notes in his essay. Happy birthday, odd America. You might not know it, but the rest of the world is watching. And they won't forget what they've seen. Five Takeaways •       The World's Ambivalence: Purveyor of Hope, Imperial Power: Schwartz's central finding from twelve countries of essays: the world does not simply hate or love America. It holds a profound ambivalence — between the country that presents itself as the beacon of hope and democracy, and the country that is a vast imperial and economic power that shapes the lives of billions who have no vote in its elections. This ambivalence is, she argues, almost impossible to fully understand from inside the United States, where the assumption of benign intent is so deep. The essays collectively diagnose what the US's retreat from that self-image means for the world's ability to find alternative frameworks. •       Turkey and America: Erdoğan and Trump Have Learned From Each Other: Kaya Genç's essay from Turkey is one of the collection's most original: the Turkish right has long admired the vast powers of the American presidency as a model to follow, even as that same right has been characterised by American commentators as anti-American or Islamist. The admiration was never for American values — for free speech or civil liberties — but for the structural power of the presidency. Trump, meanwhile, has learned from Erdoğan's playbook of media control, legal intimidation, and institutional capture. The learning has gone in both directions. What is happening in America, Genç argues, is not exceptional — it is part of a global turn. •       Taiwan: Self-Defence Classes and Going It Alone: Michelle Kuo's essay from Taiwan describes a country that has fundamentally revised its relationship with the United States. For decades, many Taiwanese believed that by adhering to certain principles — upholding liberal values, supporting LGBTQ rights, maintaining civil liberties — they would gain American favour and the protection that came with it. That thinking is now gone. People in Taiwan are taking self-defence classes, preparing for a possible Chinese invasion without the expectation of outside help. And the values they uphold — civil liberties, LGBTQ rights — are upheld now because they actually want them, not to please Washington. •       The Dial: 90 Countries, One Third in Translation, Based in Paris: Schwartz founded The Dial four years ago in response to a sense that American media was turning catastrophically inward, unable to understand its own moment without comparison to what was happening elsewhere. The magazine publishes work from some 90 countries, about a third of it in translation, and aims to bring voices from outside the Anglophone foreign correspondent establishment. Several pieces from the book were reprinted in The Guardian. The anthology grew from a special issue published during the 2024 election, asking writers from around the world to look at the United States — a reversal of the magazine's usual direction. Schwartz will be talking about the book in Paris on July 4, not eating hot dogs. •       The Question of America: The Gaza Poet's Unanswerable Verdict: Muhammad al-Zaqzouq is a Gazan poet and father of three who has spent years trying to reach the United States, only to find that under Trump's America, asylum is no longer a possibility. His essay traces a lifetime of ambivalence — America as site of exclusion and segregation, America as specter of another possible life, America as the dream that institutions offer and that the firm hand of diplomacy snatches away. Schwartz reads the closing lines in the interview: “The question of America is vast. It is unrelenting and unanswerable and will not be silenced.” Of all the voices in the anthology, it is the one that stays. About the Guest Madeleine Schwartz is the founder and editor-in-chief of The Dial, an online magazine of culture, politics, and ideas with a focus on local writing from around the world. She is the editor of How We See It: The World Looks at America in the Age of Trump (The New Press, June 9, 2026). Her writing appears in The London Review of Books, The New Yorker, and The New York Review of Books. She teaches journalism at Sciences Po in Paris, where she is based. References: •       How We See It: The World Looks at America in the Age of Trump edited by Madeleine Schwartz / The Dial (The New Press, June 9, 2026). Essays from India, Canada, South Africa, Ukraine, Palestine, Taiwan, Turkey, Cuba, Egypt, Argentina, Italy, and Ireland. •       The Dial — Schwartz's magazine of international writing, based in Paris. •       Kaya Genç (Turkey), Michelle Kuo (Taiwan), Muhammad al-Zaqzouq (Gaza), Eve Fairbanks (South Africa) — among the essayists referenced in this conversation. •       Adam Shatz, blurb: “To read this rich, subtle, and moving anthology is to be reminded that it is often foreigners who understand us best.” About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is t...

Tomorrow, America will celebrate its birth. But the decisive moment, even the real birth of modern America, argues Alexander Mikaberidze in his new book The Louisiana Purchase: The Grand Bargain and the Making of America, may not have been 1776 at all. It was 1803, the year of the Louisiana Purchase. The year Thomas Jefferson bought the future from Napoleon Bonaparte. This was the moment the young American republic doubled its size in a single transaction, absorbed the heart of a continent and set itself on the path to becoming a global superpower. The numbers associated with the Louisiana Purchase are staggering. 828,000 square miles. Thirteen states. Fifteen million dollars — four cents an acre, so the mythology tells us. But Mikaberidze reminds us that the deal Jefferson signed did not actually grant the United States the land. Instead, it merely authorised the republic to negotiate the acquisition of land still owned by Native Americans. So it became the founding event of the US-Indian Treaty System that produced over 200 Native American cessions between 1804 and 1970, and cost the Republic billions of dollars. The Louisiana Purchase was America's grand Faustian bargain. It was a deal that not only enabled America's eventual rise as a 20th century superpower, but also the expansion of slavery, the destruction of Native peoples, and the 19th century imperial reach of the Monroe Doctrine. So forget 1776 and save the fireworks to remember 1803. And celebrate with croissants rather than hot dogs. Without Napoleon Bonaparte's generosity, the United States might be just another regional power like France. Five Takeaways •       The Louisiana Purchase: Arguably the Decisive Moment in American History: Mikaberidze's opening argument: if you had to pick the single most important moment in American history, 1803 has a stronger claim than 1776. Independence established the republic. The Louisiana Purchase made it a continental power. 828,000 square miles. Thirteen states. The heart of the continent. Securing the Mississippi for American commerce. Laying the groundwork for the Monroe Doctrine, Manifest Destiny, and America's eventual emergence as a global superpower. The revolution created the nation. The purchase created its destiny. •       Four Cents an Acre? The Real Price Was Billions: The famous number: $15 million, or four cents an acre. The less famous fact: the agreement Jefferson signed did not grant the United States the land. It merely authorised the republic to negotiate the acquisition of the land, which was still owned by Native Americans. The Louisiana Purchase was the founding event of the US-Indian Treaty System — which produced over 200 Native American cessions between 1804 and 1970, and cost the United States not $15 million but billions of dollars. What appeared to be the greatest real estate deal in history was actually an authorisation to conduct the most expensive series of land negotiations in history. •       The Grand Faustian Bargain: Slavery, Native Peoples, and the Monroe Doctrine: Andrew's formulation — the Grand Faustian Bargain, the deal with the devil — is one Mikaberidze accepts. The purchase did three things simultaneously: it made America a continental power and a future superpower; it enabled the expansion of slavery into the vast new territory (the Missouri crisis of 1820 was a direct consequence); and it set in motion the dispossession of Native peoples at a scale and speed that would otherwise have been impossible. The Monroe Doctrine — America's declaration that the Western Hemisphere was its sphere of influence — would not have been conceivable without the continental reach the purchase provided. •       Napoleon's Bad Weather: The Contingency That Made America: The counterfactual at the heart of Mikaberidze's book: in October 1802, Napoleon had 4,000 veteran French troops ready to sail for New Orleans. The bad weather delayed them. Then it was too late — war with Britain was coming, and Napoleon decided to sell. If those troops had arrived, Mikaberidze argues, France might have retained effective control of southern Louisiana, cultivated alliances with Native nations (as it historically had), and used those alliances to constrain American expansion inland. Without the Louisiana hinterland, the American republic might have been a prosperous but regionally limited power, strong in New England and the Northeast but denied the continental reach that made it a superpower. •       Croissants in Kansas, Tacos in Oklahoma: The Counterfactual Continents: Andrew's closing question: what would July 4 look like in Kansas and Oklahoma if the purchase hadn't happened? Mikaberidze's answer: French Louisiana, Spanish Texas, and Native-controlled hinterlands are all in play. The people of Kansas might indeed be celebrating with croissants rather than hot dogs. Mikaberidze adds: or tacos. Almost certainly more tacos and moles, given the Spanish and ultimately Mexican influence that would have prevailed across most of the continent. The American empire of liberty, in this alternative timeline, stops somewhere in the middle of what is now Missouri. About the Guest Alexander Mikaberidze is Professor of History and the Ruth Herring Noel Endowed Chair at Louisiana State University-Shreveport. He is the author of The Louisiana Purchase: The Grand Bargain and the Making of America (Oxford University Press, July 3, 2026) and more than two dozen other books, including Kutuzov: A Life in War and Peace (Oxford, 2022) and The Napoleonic Wars: A Global History (Oxford, 2020), both winners of the Society for Military History's Distinguished Book Award and the Gilder-Lehrman Military History Prize. He was born in Georgia (the Caucasus) and has lived in Shreveport, Louisiana for twenty-six years. References: •       The Louisiana Purchase: The Grand Bargain and the Making of America by Alexander Mikaberidze (Oxford University Press, July 3, 2026). Part of the Pivotal Moments in American History series. •       Craig Fuhrman, The Vast Enterprise — referenced by Mikaberidze as a new reassessment of Lewis and Clark's expedition. •       Jedediah Morse (1789) — the geographer who wrote of “American Empire” with a western boundary at the Pacific, referenced in the Monroe Doctrine discussion. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolif...

In this episode, we normalize the conversations around AI-powered fertility, hidden factors that keep women from getting and staying pregnant, and why tracking data alone isn't enough with Kirsten Karchmer, CEO of Conceivable.About Kirsten: Kirsten Karchmer is a health tech pioneer and women's health expert, and the founder and CEO of Conceivable Technologies. After helping more than 10,000 women in her clinic, she built one of the first AI-driven fertility platforms to tackle the hidden factors that keep women from getting and staying pregnant. Named one of the most innovative health startups, her work has been featured everywhere from TechCrunch to Fox News, and she's built a community of over 300,000 women on TikTok.

“The second law of thermodynamics is not to be negotiated with.” — Thomas S. Mullaney The second law of thermodynamics is non-negotiable. The universe will end. Every human being dies. Everything decays and every record disintegrates. So why record history? Why bother remembering? These are the questions that the Stanford historian Thomas S. Mullaney addresses in his intriguing new book, How We Disappear: A Personal History of Information. How We Disappear is triggered by grief. Mullaney's father — a man he never fully understood, an exile in an estranged household — died unexpectedly in 2017. Sitting in his father's office surrounded by the “paperwork of death,” Mullaney's training as a historian crystallised into an all-too-personal project of disappearance. It's a book about what Mullaney calls “intransitive disappearance” — not the spectacular, cataclysmic kind of traditional historiography (wars, book burnings, genocide) but the everyday, uneventful ways things fall apart. Like Thomas Mullaney's dad. Existence as obsolescence, erosion, sinescence and the slow drift of the unremarkable into nothing. History, in Mullaney's account, is a Sisyphean fight against this nothingness. We tell stories to survive and maintain the polite appearance of coherence. If you actually tried to reconstruct experience — the thing-in-itself — you would need an infinite library of trillion-page books. Existence, for Mullaney, is a swirl of stimuli and daydream. History tries to domesticate this Borgesian swirl. So does consciousness itself. That's why, as Mullaney memorably puts it, “historians do the dirty work of necromancers.” Which is to say they try to negotiate with the second law of thermodynamics. Five Takeaways •       Intransitive Disappearance: The Everyday Way Things Fall Apart: Mullaney's central concept: intransitive disappearance. Not the spectacular, cataclysmic kind — book burnings, genocide, the destruction of the Library of Alexandria — but the everyday, drifty, uneventful ways things disintegrate. Obsolescence. Erosion. Sinescence. The unremarkable drift of the unremarkable into nothing. He became obsessed with these forms of disappearance — a pack rat across every discipline he could think of — for twenty-five years. His father's unexpected death in 2017, sitting in his father's office amid the paperwork of death, crystallised what had been inchoate into a book. •       History as Domesticated Experience: The Trillion-Page Book: If you tried to actually reconstruct experience — the actual thing, unfiltered — you would need a trillion-page book that would make Naked Lunch look like a kindergarten primer. You'd have to say how many hairs were on his head; whether he favoured his left foot over his right; the scent of his aftershave. Experience, unfiltered, is an n-dimensional vortex of stimuli and daydream. Anytime you read a work of history, you are reading experience that has been domesticated into narrative — with turning points, main characters, thematic arguments. Historians know this. Every practising historian knows that the ideal of reconstructing human experience can never be reached. •       The Vocal Defence of History: Why Do It If You Know It's Impossible? Mullaney's answer to the subversive question: history is just the professional counterpart of what every human being does every second of their existence. You, right now, telling yourself the story of your experience, are already well into postproduction. Your experience of being a person in a chair talking to another person on a couch — that is already domesticated. Human beings need to tell stories to live, to maintain continuity, to maintain coherence. Historians do the same thing under certain rules and protocols. The futility of history is the futility of consciousness itself. Neither is a reason not to try. •       The Second Law of Thermodynamics Is Not to Be Negotiated With: The universe will end. Every human being dies. Everything we create decays. Every record disintegrates. Mullaney is unsparing about this. He is also, in his strange way, cheerful about it: we don't need to last forever to have meant something. The meaning is not in the permanence. It is in the making. He would like the Silicon Valley immortality seekers — Kurzweil, the others, all those negotiating with thermodynamics from Palo Alto — to read the book, to face the facts, and then to find the alternative: rejoining physical reality and finding very deep meaning in that. •       AI Bots of Deceased Parents: Stop: Andrew raises the obvious question: what would Mullaney say to the people in Palo Alto building AI bots of your deceased mother and father, so they can exist forever for your children and grandchildren? Mullaney's answer is one word: stop. Human beings do not have the wetware — the biological critical apparatus — to maintain distance from a deep fake of their deceased parent. It short-circuits us. It bypasses our limitations. He cannot fathom, outside of very specific, closely monitored therapeutic settings, an argument in which this is a good idea. Paul Postman's phrase: we are amusing ourselves to death. And there is very little critical reflection coming out of the neighbourhoods where this stuff is being made. About the Guest Thomas S. Mullaney is Professor of History at Stanford University, a Guggenheim Fellow, and the former Kluge Chair in Technology and Society at the Library of Congress. He is the author of How We Disappear: A Personal History of Information (W. W. Norton, June 23, 2026) and four previous books on Chinese history and technology, including The Chinese Typewriter: A History (winner of multiple awards). He lives in Palo Alto, California. References: •       How We Disappear: A Personal History of Information by Thomas S. Mullaney (W. W. Norton, June 23, 2026). •       Jorge Luis Borges — referenced; the infinite library, the map that equals the terrain. •       Neil Postman, Amusing Ourselves to Death — referenced in the closing discussion on AI and human limitations. •       Kara Swisher — referenced for her CNN series on Silicon Valley immortality seekers. •       Ray Kurzweil — referenced as an exemplar of tech-utopian immortality thinking. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. 

Sean O'Kane, Senior Reporter of Transportation at TechCrunch, joins Jon Hansen to discuss Waymo and Uber parting ways in Phoenix, launch company Rocket Lab buying satellite operator Iridium, and President Trump restricting Swedish electric vehicle manufacturer Polestar from selling in the US. To read more stories that Sean has covered, visit techcrunch.com/author/sean-okane/.

“His descent is in a sense our descent.” — Peter Wehner on Trump at 80 Donald Trump turned 80 two weeks ago. But Peter Wehner's timely Atlantic piece, “The Apotheosis of Donald Trump,” isn't much of a birthday present. Wehner even suggests that for all Trump's madness, mayhem and malevolence, the orange octogenarian eludes Shakespearean tragedy. So no historic hall of infamy for Donald. He's too sad for that. Trump is a man, Wehner says, of borderless corruption — malicious, totally corrupt, without any visible redeeming qualities. But he isn't King Lear. Trump lacks Lear's complexity, Wehner says. Lear was a figure with whom you could have some empathy. Trump is not. He is, as Wehner notes, “a flatter figure in that sense” — but that doesn't make him any less dangerous. For the DC-based Wehner, what makes Trump more dangerous, as an octogenarian, is his decomposition. The signs are everywhere: the disinhibition intensifying, the impulsivity more easily triggered, the volatility producing a foreign policy that no ally can track or trust. His descent, Wehner warns, might be our descent. Peak Trump. The apotheosis of a pathetically malevolent madman. Just in time for the semiquincentennial, which Wehner will “celebrate” at Monticello. Five Takeaways •       Trump at 80: The Apotheosis and the Decomposition: Wehner's Atlantic piece, written to mark Trump's 80th birthday, argues that what we are seeing is not just the decline of an old man but a visible decomposition in his mental and physical capacities that is making him more, not less, dangerous. The disinhibition is more intense. The impulsivity is more easily triggered. The volatility is producing a foreign policy that no ally can track or trust. Trump 2.0 is more dangerous than Trump 1.0 — and Trump 1.0 was not a walk in the park. The question is not whether this ends well. The question is how badly it ends. •       Not King Lear: A Man of Borderless Corruption: Wehner uses a King Lear allusion in his Atlantic essay but hesitates to lean on it. Lear was a complicated figure — someone you could have empathy with, who saw things at the end he hadn't seen earlier. Trump is not like that. He is, as best Wehner can tell, a man of borderless corruption, malicious from head to toe, with no visible redeeming qualities — a flatter figure in the Shakespearean sense. That flatness makes the Lear parallel partial. But it does not diminish the danger. His descent is in a sense our descent. •       European Mystification: When It Happens Twice, That Breaks Trust: Andrew has just returned from Europe, where every prominent journalist and historian he met was mystified by Trump. Wehner agrees: Trump is sui generis, unlike any leader the post-war world has produced. What he says is particularly disturbing is the second election. If it had happened once, Europe could have told itself it was a parenthesis. When it happens twice, that breaks trust. Even if the next president is sane and rational, there is no guarantee the following one will be. That uncertainty, Wehner says, is a real inflection point in the relationship between the United States and the rest of the world. •       The Crack-Up of MAGA World: The cult-like grip Trump had on the Republican Party and the MAGA base is no longer there. His approval among Republicans has dropped from the nineties to the seventies — still high, but significant. And the fissures in MAGA world are, in Wehner's word, extraordinary: Tucker Carlson and Megyn Kelly and Candace Owens have broken from the movement or turned on its leadership. Marjorie Taylor Greene too. The crack-up has begun. Whether it is fast enough or decisive enough to matter remains to be seen. But the movement that once seemed invincible is showing its first serious cracks. •       Monticello for the 250th: Welcoming New Immigrants: Wehner and his wife are considering spending July 4 at Monticello — Thomas Jefferson's house in Charlottesville, Virginia — where a friend has invited them to an event welcoming new American citizens and immigrants. It is, he says, going to be a birthday that is not untainted by sadness even as there will also be hope of what can still happen. Six days from the 250th, Andrew asks what Jefferson would think of Trump. Wehner: probably not terribly favourable. Probably true of most of the founders. About the Guest Peter Wehner is a Contributing Editor at The Atlantic and a senior fellow at the Ethics and Public Policy Center. He served as a senior policy adviser in the administrations of Presidents Ronald Reagan, George H.W. Bush, and George W. Bush. He is a frequent contributor to The New York Times. His Atlantic piece “The Apotheosis of Donald Trump” was published June 14, 2026. References: •       Peter Wehner, “The Apotheosis of Donald Trump,” The Atlantic, June 14, 2026 — the piece that occasions this conversation. •       Episode 2945: Samuel Moyn on Gerontocracy in America — referenced at the opening. •       Monticello, Charlottesville, Virginia — Thomas Jefferson's home; venue for the July 4 immigrant-welcoming event Wehner mentions. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackYouTubeApple PodcastsSpotify Chapters: (00:31) - Introduction: Trump at 80 and the apotheosis (02:07) - Visible decomposition: more dangerous than Trump 1.0 (02:54) - Something to celebrate or be concerned about? (03:21) - The disinhibition intensifies; impulsivity more easily triggered (04:18) - Is there a Shakespearean arc? (04:54) - King Lear allusion: hesitation; Trump is a flatter figure (05:30) - Man of borderless corruption; his descent is our descent (06:40) - European mystification: just back from Poland (07:18) - Trump is sui generis (08:10) - When it happens twice, that breaks trust (10:22) - Not everyone elected Trump: the Republican question (11:16) - The crack-up of MAGA world (12:19) - Marjorie Taylor Gre...

“The frontier AI companies invited the government into the room. Now the government is beginning to behave as if it owns the door, the guest list, the schedule, and the product roadmap.” — Keith Teare Last week, I was away in Europe. So Keith Teare ran our That Was The Week show solo — with a chillingly authentic Andrew Keen bot. So realistic, in fact, that the fake version sounds (to me, at least) more interesting than the real one. The bad news is that I'm back. The good news is it's been an interesting week in tech. That was the week in which the US Commerce Department told both OpenAI and Anthropic that they now need government approval for whom they can sell their frontier AI models. This is supposedly “voluntary” — for now, at least. Keith's TWTW editorial argues that Dario Amodei and Sam Altman have spent over a year crying wolf about the dangers of their own technology, supposedly deliberately seeking government involvement as a regulatory moat against competitors. And now the government has walked through the door that Sam and Dario left ajar. Now, Keith argues, the US government is behaving as if it owns not just the door and the guest list, but the entire product roadmap. “Payback's a bitch,” Keith bristles in his editorial. The other major news this week is the rumour (via David Sacks) that OpenAI has offered the US government a 50% stake in a sovereign wealth fund. If true, this would change everything — not just in Silicon Valley, but in the political debate about public ownership of our AI economy. It's not just tech insiders like Sacks and Altman who are on board the sovereign wealth fund express, but also Bernie Sanders and other leftist critics of Big Tech. So maybe payback, at least when it comes to public investment in AI, isn't always such a bitch. Five Takeaways •       The Fake Andrew Keen: An Hour of Work on a Local Nvidia Card: Keith ran last week's show solo with an AI-generated Andrew Keen: trained on a few episodes of the show, animated from a YouTube still, scripted from Keith's newsletter. No third-party service. Just a local PC with an Nvidia GPU, about an hour of work, three attempts. Andrew, listening back, second-guessed whether he was actually there. The result was “pretty bad compared to our normal actual live shows,” Keith says. But also: really good. The question hanging over this episode and every future one: which Andrew are you listening to? •       Payback's a Bitch: How AI Companies Created Their Own Regulatory Trap: The US Commerce Department has told OpenAI and Anthropic they need government permission for who gets to use their latest models. Voluntary, for now. Keith's diagnosis: AI leadership spent more than a year crying wolf about existential risk — not because they believed it, but because government regulation creates a moat against competitors. Now the government has taken them at their word. Dario and Sam Altman wanted to be wrapped in government clothing. They are. The government now owns the door. They asked for this. They got it. •       American and Chinese State Capitalism: Converging Models: Andrew raises the macro argument: what we're watching is the convergence of American and Chinese models of capitalism toward a more state-centric model. China has always been explicit about state control. America has prided itself on free enterprise — even when the internet, atomic technology, and now AI were all substantially government-funded or government-shaped. Keith agrees at this level: all governments seek to control things they frame as dangerous. The difference is the framing. The direction of travel is the same. •       OpenAI's Rumoured 50% Stake Offer to the Government: Keith has heard — from sources including David Sacks, who should know — that OpenAI has offered the US government a very large stake, potentially 50%, in a sovereign wealth fund that would then distribute dividends to citizens. Sacks is not only unsurprised but in favour: he thinks 50% is too small. Andrew's question: why would OpenAI give away 50% of the company? Keith's answer: because it's the price of the regulatory moat. The government as partner rather than the government as regulator. A company that once aspired to “open” AI is now offering the state a controlling interest in its future. •       Paul Kennedy and America's Inevitable Decline: Keith has Paul Kennedy's Rise and Fall of the Great Powers on his shelf. His conclusion from it: it is historically impossible for America to retain its first-place status. No country ever has. Newly capitalised countries produce things more cheaply; China, India, and large parts of Asia are where most future growth will be. Does the AI boom change this? Keith's honest answer: no. It may slow the decline. It will not reverse it. America will, like an older gentleman on a rocking chair outside the house, accept its fate. Europe won't even be in the rocking chair. About the Guest Keith Teare is a British-American entrepreneur, investor, and publisher of the That Was the Week newsletter. He is a co-founder of TechCrunch and Andrew's regular TWTW co-host. References: •       That Was the Week by Keith Teare — the newsletter on which this episode is based. •       Azeem Azhar, The Exponential View — his report quantifying the AI economy at roughly $175 billion, referenced in the closing section. •       Alex Lazarow, 99%Tech — referenced for his piece on the emergence of an AI trust layer, the “Lloyds of AI.” •       Paul Kennedy, The Rise and Fall of the Great Powers — on Keith's shelf; referenced in the America-China decline section. •       David Sacks — referenced as the source for the OpenAI sovereign wealth fund rumour. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackYouTubeApple PodcastsSpotify Chapters: (00:38) - Introduction: the fake Andrew Keen from last week (01:14) - Keith explains how he did it: local Nvidia card, one hour, three attempts (02:11) - The big story: Commerce Department tells OpenAI and Anthropic they need permission ...

“You cannot expect a society to open its doors if there is no way to close them. You cannot expect a society to open its gates if there is no gate to open.” — Justin Gest It's a counterintuitive and deliberately provocative argument. Rather than bolstering open societies, migration actually benefits authoritarianism. And it's the argument that Justin Gest makes in his new book, Democratic Drain: Global Migration and the Struggle for Democracy. Drawing on data from 149 countries, Gest shows that global migration has been inadvertently strengthening authoritarianism by stealing liberal democrats from the places that need them most. When liberals emigrate from authoritarian countries, Gest argues, they take their democratic values with them. As a consequence, fewer people dare to vote against the autocrat, fewer people protest, fewer people cling to liberal norms. The argument turns the normal discourse about migration on its head. Immigration is usually framed as a question about the countries experiencing migration. But Gest reframes it from the perspective of the countries losing people. So, for example, when Hungary's young liberal professionals move to Berlin or London, Orbán's job got easier. Or when Venezuela's middle class emigrated to Miami, Maduro's grip tightened. And, of course, when people leave America, it benefits Trump. That's the real bite in his polemic. Be patriotic, Justin Gest is telling American liberals. Stay home. Don't go down the democratic drain. Five Takeaways •       The Democratic Drain: Migration Is Strengthening Authoritarianism: Gest's central argument: when people emigrate from authoritarian countries, they are disproportionately people who hold liberal democratic values — people who would vote against the autocrat, protest in the streets, or organise civil society. He calls them “demmigrants.” When they leave, they leave behind a population that is, on average, more sympathetic to authoritarian governance. The result: Orbán's Hungary is easier to govern after Hungary's young liberals move to Berlin; Maduro's Venezuela tightens its grip as the middle class departs for Miami. Across 149 countries, the correlation is striking. •       White Working Class as Protest Voters, Not Authoritarians: Gest, whose earlier book The New Minority anticipated the Trump and Brexit era, pushes back on the characterisation of working-class voters as simply authoritarian. Many are protest voters: they want to see the system shaken, they see populists as the only candidates willing to speak truth about the system's failures, and they are willing to tolerate short-run damage to democratic institutions in the hope of building something better from the ashes. Immigration is the sine qua non of far-right populism: when immigrants are framed as an existential threat, voters make transactional short-run compromises to democratic integrity. They are not irrational. They are strategic. •       The Left Must Embrace Nationalism to Win the Immigration Argument: Gest's most provocative political prescription: the left has ceded nationalism to the right as if there is no nationalist case for immigration, no nationalist case for climate policy, no nationalist case for progressive values. This is, he says, inexcusable. The national interest served by carefully selected immigration is plain: immigrants make countries younger, fill labour shortages, innovate, create jobs. If the left can frame the immigration debate in terms of the national interest rather than moral obligation, the debate changes. He wrote a piece for the Washington Post on this in March 2022. •       Can You Be an Enlightened Anti-Immigrationist? The Internationalist Paradox: Andrew raises a sharp question: if democratic drain is real, then an internationalist who cares about democracy globally might logically oppose emigration from authoritarian countries, since it strengthens those authoritarian governments. Gest's response: possible, but foolish. You don't stop the drain by damming the river. You stop it by growing the democratic movement — by demonstrating the vitality and virtues of democracy and the perils of authoritarianism — so that there are more democrats to spare even after emigration. •       Three Fault Lines for the 21st Century: Gest maps three overlapping fault lines that will define the 21st century's politics. First: democrats vs authoritarians (the Wieliński argument, which Gest confirms and extends). Second: winners vs losers of globalisation (which often determines the first). Third — and Gest's own addition: those who understand their nation in civic terms vs those who understand it in ethno-religious terms. The civic imagination: a country grounded in ideas, institutions, interdependency, and a devotion to co-evolution together. The ethno-religious imagination: a country derived from static, unchanging ancestral roots. Whichever fault line you look at, he says, you end up at the same place. About the Guest Justin Gest is Professor and Director of the Public Policy Program at George Mason University's Schar School of Policy and Government. He is the author of Democratic Drain: Global Migration and the Struggle for Democracy (Cambridge University Press, May 2026), Majority Minority: Racialized Divisions in the New American Order (2022), The New Minority: White Working Class Politics in an Age of Immigration and Inequality (2016), and four other books. A founding editor of the Oxford University Press series “Oxford Studies in Migration and Citizenship,” he has published commentary in The New York Times, The Wall Street Journal, The Washington Post, and The Atlantic. References: •       Democratic Drain: Global Migration and the Struggle for Democracy by Justin Gest (Cambridge University Press, May 2026). •       Justin Gest, “How the Left Can Embrace Nationalism While Maintaining Its Values,” Washington Post, March 2022 — referenced in the conversation. •       Episode 2951: Bartosz Wieliński on “We No Longer Dream of the United States” — referenced at the opening. •       Central European University, Budapest — where Gest is teaching this week. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America...

Our 249th episode with a summary and discussion of last week's big AI news!Recorded on 06/17/2026Note: work has kept me from publishing episodes promptly, apologies! I'll get back on schedule soon.Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Anthropic cut off access to Fable 5 and Mythos 5 after a US government order tied to alleged jailbreaks, prompting debate over inconsistent policy, export controls, and the practicality of preventing jailbreaks.SpaceX completed an IPO at a roughly $1.75T valuation and then moved to acquire AI coding startup Cursor for $60B, positioning xAI with Cursor's talent, data, and product to compete more effectively in coding.Infrastructure and business updates include Anthropic pursuing direct US data center leases backed by Google, leaked documents showing OpenAI's revenue growth alongside large losses, and chatbot market share shifting with ChatGPT below 50% as Gemini and Claude gain.Projects and policy highlights include OpenRouter's Fusion multi-model synthesis, new open releases from Moonshot, Qwen, and NVIDIA, DOJ support for xAI's unpermitted gas turbines in Memphis, and a Munich court ruling Google liable for false AI Overview statements.Timestamps (note - these don't take into account dynamically inserted ads and therefore may be off by a couple of minutes):(00:00:10) Intro / Banter(00:03:38) Ad break + news previewTools & Apps(00:04:52) Anthropic cuts off Fable 5 and Mythos 5 access following government order | The Verge + All the news about Anthropic's new AI fight with the White House(00:25:53) Facebook's new AI Mode search gets its info from public posts | The VergeApplications & Business(00:27:00) SpaceX to acquire the AI coding startup Cursor for $60 billion(00:35:42) Anthropic pursues data center leases, seeks financial backing from Google, The Information reports | Reuters(00:40:10) Leaked financial docs show OpenAI is losing billions of dollars a year - Ars Technica(00:46:00) ChatGPT's market share slips below 50% for first time | TechCrunch(00:50:34) ‘Tell Him He's a Piece of Shit': Meta's New AI Unit Is a Total Mess | WIRED(00:56:23) Sakana AI Commercializes AB-MCTS in Sakana Marlin, an Enterprise Agent Generating Up to 100-Page Research Reports With Slides - MarkTechPostProjects & Open Source(00:59:36) Surpassing Frontier Performance with Fusion — OpenRouter Blog(01:03:00) Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench v2 Over K2.6 - MarkTechPost(01:08:34) Meet Qwen-RobotSuite: Three Embodied AI Models for VLA Manipulation, Video World Modeling, and Navigation - MarkTechPost(01:11:29) Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning(01:17:31) ProCUA-SFT Technical ReportPolicy & Safety(01:20:33) DOJ Lawyers Argue xAI Is ‘Vital' for National Security in NAACP Lawsuit | WIRED + People Living Near xAI's Dirty Data Centers Are Pissed About the SpaceX IPO(01:25:29) A Court Has Ruled That Google Is Liable for False Statements Generated by AI Overviews | WIRED(01:28:47) Why Do Naive SFT Filters For Safety Properties Fail?Research & Advancements(01:34:14) From AGI to ASI(01:39:44) Artificial Analysis Intelligence Index v4.1: a shift toward agentic workloads(01:42:12) SIA: Self Improving AI with Harness & Weight UpdatesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On this episode of Christopher Lochhead: Follow Your Different, we welcome back Ray Wang, Chairman and CEO of Constellation Research, and widely regarded as one of the most insightful technology analysts in the world. In a recent conversation with Christopher Lochhead, Ray Wang shared his unfiltered perspective on the biggest developments shaping the technology landscape today. From the historic SpaceX IPO to the transformative acquisition of Cursor, Ray Wang offered sharp analysis that cuts through the noise and gets to what actually matters for businesses and investors navigating an AI-driven world. The conversation covered topics that most analysts are still catching up on, including why knowledge workers need to rethink their value, what Data Inc companies actually are, and why the context layer above large language models may be the most important competitive battleground of the next decade. What makes Ray Wang’s perspective so valuable is not just his breadth of knowledge but his ability to synthesize experience into wisdom, which is precisely the distinction he draws when talking about why AI cannot replace truly seasoned professionals. You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go.   Ray Wang on AI, Knowledge Work, and the Commoditization of Expertise Ray Wang makes a clear and compelling distinction between knowledge and wisdom. He argues that knowledge has become a commodity, but wisdom, the ability to take insights and turn them into meaningful action, remains deeply human and increasingly valuable. As AI automates deterministic, repetitive tasks, what rises in importance is judgment, the capacity to learn from failure and connect dots in ways that no model trained exclusively on successful outcomes can replicate. This reframing is critical for anyone worried about AI displacing their career. Ray Wang points out that AI systems today learn only from success, with no real failure database informing their outputs. That gap is where experienced professionals earn their keep. Businesses are increasingly paying for people who have lived through cycles of failure and recovery, not simply those who can recite information retrieved from a search index.   The SpaceX IPO and What Ray Wang Says It Means for the Future of Markets Ray Wang describes the SpaceX IPO as a completely new playbook, one that flipped conventional wisdom about how public offerings should be structured. Rather than allocating the vast majority of shares to institutional investors through a traditional roadshow, SpaceX directed somewhere between 20 and 30 percent of the offering toward retail investors. Ray Wang sees this as Elon Musk rewarding the individual investors who stayed loyal through years of volatility, particularly the Tesla shareholders who held on despite relentless short-selling pressure. Beyond the allocation strategy, Ray Wang highlights how Musk essentially told the markets to take it or leave it at a fixed price, bypassing the typical price-discovery process. The Nasdaq inclusion guaranteed a floor without needing the traditional green shoe option to do the heavy lifting. Ray Wang believes this model could influence how future high-profile tech companies, including OpenAI and Anthropic, approach their own public offerings, fundamentally shifting leverage away from Wall Street banks and toward founders and retail participants.   Ray Wang Explains Data Inc Companies and the Context Layer That Defines AI Competitive Advantage Ray Wang has been developing a framework he calls the Data Inc company, a concept centered on the idea that businesses that treat data as their primary asset, combined with strong distribution, will dominate the AI era. According to Ray Wang, unique data sets that no competitor can access or replicate are the foundation of next-generation competitive moats. Companies that fail to own their data and build derivative products from it will find themselves structurally disadvantaged as AI capabilities become more broadly available. Taking that framework one step further, Ray Wang agrees that the real battleground is not the large language model itself but the contextual layer that sits above it. This semantic and contextual wrapper, built from proprietary data and accumulated organizational knowledge, is what gives AI outputs meaning and reduces hallucinations. Swapping out one LLM for another becomes straightforward when this context layer is robust, much like swapping one database for another in a well-architected system. Ray Wang adds one more dimension that elevates the entire conversation: persistent memory. The ability for AI systems to retain learnings across interactions and pass that accumulated intelligence to downstream systems is, in his view, the true home run of enterprise AI. Decision velocity, powered by a rich contextual layer and persistent memory, is what separates companies that merely adopt AI from those that build genuine exponential advantage from it. To hear more from Ray Wang and his thoughts about the Future of Tech, download and listen to this episode.   Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and authors a business strategy and technology blog that has received millions of page views per month.  Wang also serves as a non-resident Senior Fellow at The Atlantic Council's GeoTech Center. Since 2003, Ray has delivered thousands of live and virtual keynotes around the world that are inspiring and legendary. Wang has spoken at almost every major tech conference. His ground-breaking bestselling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015.  Ray's new book about Digital Giants and the future of business titled, Everybody Wants to Rule the World will be released July 2021 by Harper Collins Leadership. Ray Wang is well quoted and frequently interviewed in media outlets such as the Wall Street Journal, Fox Business News, CNBC, Yahoo Finance, Cheddar, CGTN America, Bloomberg, Tech Crunch, ZDNet, Forbes, and Fortune.  He is one of the top technology analysts in the world.   Links Follow Ray Wang! Website | Twitter | LinkedIn | Constellation Research | DisrupTV   We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, X (formerly Twitter), Instagram, and subscribe on Apple Podcast / Spotify!

“People in my generation worshipped the United States during communism. Everybody wanted to flee to the US. It was the land of the dream. And now we confront a different type of country, different type of politics — and we don't dream of the US anymore.” — Bartosz Wieliński I'm just back from Warsaw where I spent an afternoon at the offices of Gazeta Wyborcza, Poland's liberal newspaper of record. I talked with Bartosz Wieliński, the newspaper's Deputy Editor and one of the country's most respected journalists. The message from Warsaw is dire — at least for America. Wieliński told me that his generation grew up worshipping the United States. But they no longer do. The Americans, he says, have lost not only their credibility and their values, but their minds. Invoking Timothy Snyder, Wieliński describes this as the “suicide of a superpower.” Trump didn't have to start a trade war. He didn't have to bomb Iran without strategic objectives. He didn't have to destroy US aid programmes that were the most cost-effective democracy-promotion tool in the world. He didn't have to cripple NATO or sacrifice Ukraine. He chose to do all of it. Wake up, America! That's Bartosz Wieliński's stark message from Warsaw. Don't lose Europe. Trump will be gone sooner or later. Make sure he hasn't burned every bridge with Europe before he exits. Five Takeaways •       We No Longer Dream of the United States: Wieliński's generation grew up under communism worshipping America — the land of the dream, the place everyone wanted to reach. Now they confront a different country with a different politics. The Americans, he says, didn't lose anything. The Poles didn't lose their innocence. The Americans lost their credibility, their values, and their minds by electing Donald Trump. If anyone lost anything, it was Americans. Not Poles. •       The Suicide of a Superpower: Wieliński invokes Timothy Snyder's phrase to describe what Trump is doing. The US started a war with Iran without having any strategic objectives. Nobody heard Trump say what his objective was. America had friends, influence, and soft power — US aid was the most cost-effective democracy-promotion tool in the world. It is being deliberately destroyed. NATO was the best investment America ever made: the only time Article Five was invoked was by Europeans, to defend America, after September 11. Crippling NATO means losing Europe, and there is no way back. •       The Dark Enlightenment and Silicon Valley: Wieliński identifies a specific group behind Trump's project: very rich and powerful people connected to big tech who believe they can reshape politics through platforms, influence behaviour through technology, and create a new technological order — reversing political development back to before the Enlightenment. They call it the dark enlightenment. Europe, he says, rejects it. Europe will defend its societies against that influence. •       The New Division: Democrats vs Anti-Democrats: The key political division everywhere Wieliński looks is no longer between left and right. It is between supporters of democracy and its enemies. In Germany: the AfD at 30%, while the mainstream parties have collapsed from a combined 70% to a combined 35%. In France: the horseshoe theory — far left and far right meeting at the ends of the arc, both willing to work together to dismantle democracy. In Poland: a colourful coalition from right to left defending democracy against PiS. The same coalition will be needed everywhere. •       Wake Up: Don't Lose Europe: Wieliński's message to Americans: wake up. You still have friends in Europe. Europeans still want to believe in America as the land of promise and freedom. But don't destroy what you spent so many decades building. Trump will be gone sooner or later. Make sure he hasn't burned every bridge with Europe before he exits. If those bridges are destroyed, they will be very hard to rebuild. About the Guest Bartosz Wieliński is Deputy Editor in Chief of Gazeta Wyborcza, Poland's leading liberal daily newspaper. He was formerly the paper's Berlin correspondent. Gazeta Wyborcza was founded in 1989, the year of Poland's first free elections. References: •       Gazeta Wyborcza — Poland's liberal newspaper of record, founded 1989. •       Timothy Snyder — referenced for “suicide of a superpower.” Previously appeared on KOA. •       The horseshoe theory — the idea that the extreme left and extreme right, at the ends of the political horseshoe, are closer to each other than either is to the centre. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackApple PodcastsSpotify Chapters: (00:30) - Introduction: Warsaw, Gazeta Wyborcza, and lost illusions (01:25) - We no longer dream of the United States (02:17) - The Americans lost their credibility, not the Poles (02:41) - Message to Trump voters: you did it wrong (03:51) - Timothy Snyder: the suicide of a superpower (05:00) - The Iran war: no strategic objectives (06:00) - US aid: the most cost-effective democracy tool ever destroyed (07:00) - NATO: Article Five was invoked by Europeans, for America (08:10) - Silicon Valley and the dark enlightenment (09:14) - Trump's policy as an opportunity for Europe (27:26) - The new division: democrats vs anti-democrats (27:52) - Germany: the AfD at 30% (31:15) - The horseshoe theory (33:45) - Wake up, America: don't lose Europe

Bio: Sean Duffy is the Co-founder and CEO of Omada Health, a between-visit care provider that addresses cardiometabolic conditions like diabetes, hypertension, prediabetes, and obesity, as well as musculoskeletal issues. He has dedicated his professional life to bridging technology, design, and care delivery to transform care experiences for patients. As a former MD/MBA candidate at Harvard, he also holds a BS in neuroscience from Columbia University. He has written extensively about digital health and the future of healthcare in The New England Journal of Medicine, The Wall Street Journal, Forbes, and TechCrunch, among other publications. Prior to Omada, Sean worked at both Google and IDEO.Company: Omada Health (Nasdaq: OMDA) is reverse engineering the way healthcare is delivered in America, putting the space between doctor visits–where health is won or lost–at the center of care. Today's healthcare system poorly serves chronic conditions that require ongoing support outside of the exam room, like obesity, diabetes, hypertension, cholesterol, and musculoskeletal conditions. Omada's virtual-first model combines human-led care teams, connected devices, and AI-enabled technology to deliver personalized care at scale, including support for GLP-1 therapy. Omada has served more than two million members since launch across 2,000+ employers, health plans, pharmacy benefit managers, and health systems. Learn more at omadahealth.com.

“The Gross National Product measures everything except that which makes life worthwhile.” — Robert F. Kennedy, University of Kansas, March 18, 1968 It is June 5, 1968. An eleven-year-old English boy is watching the assassination of Bobby Kennedy on his black and white television. That little boy is Tim Jackson — now one of Britain's most influential critics of capitalism. He had no idea then that RFK would change his life. It happened years later, when Jackson discovered a speech Kennedy gave in Kansas in the spring of 1968. It was a speech that changed the way Tim Jackson thought about economics. The March 1968 speech, one of the first of RFK's presidential campaign, was delivered at Phog Allen Fieldhouse, University of Kansas. It opened with a joke at the expense of rival Kansas State University. Then Bobby turned deadly serious. For the first time (at least for a Presidential candidate), he attacked the very idea of the Gross National Product itself. RFK argued that GDP quantifies all the worst stuff including air pollution, cigarette advertising and jails. But it doesn't measure the health of our children, the quality of their education, or the joy of their play. It quantifies everything except that which makes life worthwhile. Then fetishizes the data. Worse than wrong, Bobby Kennedy suggested, GDP makes data evil. For Jackson, who has spent his career mulling over the idea of economic growth, RFK's Phog Allen Fieldhouse speech came as a revelation. Indeed much of his later thinking, including his 2021 award-winning book Post Growth: Life After Capitalism, is indebted to this March 1968 speech. Almost sixty years later, in our ever-more-quantifiable age of data-centres, it's a speech that appears uncannily prescient. Both Tim Jackson and Bobby Kennedy are right to remind us that there is an alternative to quantifying progress. There is, indeed, life after GDP. And it can't be measured. Five Takeaways •       An 11-Year-Old Watching the Assassination on His Birthday: Tim Jackson was born on June 4. On the night of June 4–5, 1968, after the California primary, RFK was shot at the Ambassador Hotel in Los Angeles. Jackson — watching on a black and white television in the UK — remembers thinking: oh no, not again. His aunt had just sailed for America from Southampton. Is this the country she is going to? Two high-profile assassinations. Violence as a condition of American political life. He had no idea then that RFK would become important to him professionally two or three decades later. •       The Kansas Speech: GDP Measures Everything Except What Makes Life Worthwhile: The speech RFK gave at Phog Allen Fieldhouse, University of Kansas, March 1968 — one of the first of his presidential campaign — opened with a joke at the expense of rival Kansas State University and became one of the most prescient political speeches of the 20th century. Kennedy attacked GDP directly: it counts air pollution, cigarette advertising, and the jails for the people who break the law. It does not count the health of our children, the quality of their education, or the joy of their play. It measures everything, in short, except that which makes life worthwhile. •       The Two Wrong Turns of Post-War Capitalism: Jackson's account: fossil fuels made mass production possible; the Great Depression revealed the danger of overproduction; the post-war solution was to persuade people that having more stuff is what matters. Two big mistakes were embedded in that solution. First: material consumption is not all we are — we have social, relational, spiritual needs that GDP ignores. Second: more production does more environmental damage. Both wrong turns are what Kennedy was already diagnosing in Kansas in 1968. Both are what we are now living with in extremis. •       The Trillionaire and the 2 Billion: The interview is recorded the day after the world's first trillionaire arrived on the scene. Jackson's response: this is an obscene amount of money for one person to have, while 2 billion people lack access to clean water and electricity. The same structural observation could be made about the 1850s: monarchs parading luxury while the people around them starved. The trillionaire is not a new phenomenon. He is the latest expression of an economic system that was always building toward this endpoint. •       They Created a Desert and Called It Peace: In the Kansas speech, RFK quoted Tacitus on Rome: “they created a desert and called it peace.” Jackson applies it directly to today's America: what is it to be a citizen of the affluent West only on the back of a flattened Gaza, a distant war, the creation of violence to preserve a failing hegemonic empire? Bobby was saying: we have values around social justice. We have a fragile planet. These are what matter. Bernie Sanders said the same things. AOC picked up the mantle. The message is unchanged. It is still Kansas, 1968. About the Guest Tim Jackson is Professor of Sustainable Development at the University of Surrey and Director of the Centre for the Understanding of Sustainable Prosperity (CUSP). He is the author of Post Growth: Life After Capitalism (Polity Press, 2021; winner of the 2022 Eric Zencey Prize for Economics) and Prosperity Without Growth (2009/2017; Financial Times book of the year). He is also an award-winning BBC radio dramatist. He lives in Guildford, Surrey. References: •       Post Growth: Life After Capitalism by Tim Jackson (Polity Press, 2021). •       RFK's University of Kansas speech, March 18, 1968 — delivered at Phog Allen Fieldhouse, Lawrence, Kansas. •       Tacitus, Agricola — “they created a desert and called it peace,” quoted by RFK in the Kansas speech. •       Kerry Kennedy, Ripples of Hope — referenced in the conversation. •       Andrew Keen's forthcoming book: Where Have You Gone, Bobby Kennedy? My Search for a Lost America — the RFK book this conversation feeds directly into. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackYouTubeApple Podcasts

“Just 25 literary agents represent more than half of all prizewinning novelists in the 21st century. The agent is the unacknowledged legislator of the literary field.” — Laura McGrath We think of publishers and editors as the ultimate tastemakers. As those godlike gatekeepers controlling what we read. But if you're looking for literary gods, Laura McGrath argues, then you need to look at literary agents rather than publishers or editors. Her ten-year project, Middlemen: Literary Agents and the Making of American Fiction, is the first serious scholarly account of the literary agent's astonishingly powerful role in shaping what America reads. Except, of course, the Middlemen are actually Middlewomen — since 80% of literary agents are women. The numbers are striking. Just 25 literary agents represent more than half of all prizewinning novelists in the 21st century. McGrath interviewed 75 of them over ten years. Shelley called poets the unacknowledged legislators of the world. McGrath's agents are the unacknowledged legislators of the literary field. They shaped postmodernism (Candida Donadio and Pynchon, Heller, Gaddis). They launched the debut novel as a literary form. They made the short story collection viable. And 25 of them control more than half of the prizes. So will AI replace the agent? In operations, perhaps, McGrath acknowledges — the slush pile is overwhelming and smart machine assistance is welcome. But in creative work — in the business of writing, editing, translation, cover design, and above all taste — she thinks not. No algorithm will ever learn the Catch-22 of publishing — separating the Thomas Pynchon or Joseph Heller from all the dross. And no bot (male or female) is ever going to host a three-martini lunch in Manhattan. Five Takeaways •       The Literary Agent as the New Gatekeeper: Replacing the Publisher: In the early 20th century, publishing was shaped by the taste of individual publishers: Bennett Cerf at Random House, Alfred and Blanche Knopf at their imprint, Max Perkins at Scribner's. Those days are over. Publishers are now conglomerates where individual editors may have excellent taste but no single figure shapes the house. Into that vacuum has come the literary agent — who now operates, McGrath argues, exactly as the great publishers once did: as the primary tastemaker, the person whose aesthetic and commercial judgment shapes what America reads. •       25 Agents, Half the Prizes, 80% Women: The Numbers: McGrath's most striking statistical finding: just 25 literary agents represent more than half of all prizewinning novelists in the 21st century. Twenty-five people. The field is 80% women — hence the tongue-in-cheek title — and 73% white. Agents tend, McGrath found, to represent authors who resemble themselves. One answer to the question “why is contemporary literary fiction so white?” is: because agents are. And agents, because they work on contingency fees rather than salaries, face severe financial pressures that concentrate power at the top of the profession. •       The Unacknowledged Legislators: Agents Shaped American Literary History: McGrath's book is full of literary history rewritten from the agent's perspective. Sterling Lord persisted past dozens of rejections to place On the Road for Kerouac. Candida Donadio — Pynchon's, Heller's, Gaddis's, and early Philip Roth's agent — championed maximalist, experimental writers whom no one was interested in, and built the social network of editor relationships that made postmodernism possible. The debut novel as a cultural form, the persistence of the short story collection despite poor sales, the rise of the New York novel — all are, in McGrath's account, partly agent-made. •       Can White Male Writers Not Get Published? No: Andrew raises the complaint he hears from white male writers: that they can no longer get published because of diversity initiatives. McGrath's answer is flat. No. She thinks it's silly. The number of books published each week is staggering. Being able to see some success on the part of writers of colour does not diminish the work white men are doing. The complaint, she notes, circulates every ten years, typically after a boom in support for writers of colour. We are in another round of this cycle. There will be another one in a decade. •       Will AI Replace the Literary Agent? In Operations, Maybe. In Taste, No: Andrew's closing question: will AI replace the middlemen? McGrath draws the distinction she heard at the US Book Show: AI in operations (slush pile management, contract tracking), yes, possibly. AI in creative work — writing, editing, translation, cover design, and above all taste — she hopes not. An algorithm is built on priors. It narrows the window of possibility endlessly, replicating itself. That is not what a good literary agent does. A good literary agent is looking for books that surprise, frustrate, and thrill. No algorithm has learned to take an author out for a three-martini lunch. About the Guest Laura McGrath is an assistant professor of English at Temple University and a National Endowment for the Humanities Fellow. She was formerly the associate director of the Literary Lab at Stanford University. She is the author of Middlemen: Literary Agents and the Making of American Fiction (Princeton University Press, April 28, 2026). She writes the textCrunch Substack on literary and publishing culture. References: •       Middlemen: Literary Agents and the Making of American Fiction by Laura McGrath (Princeton University Press, April 28, 2026). •       Earlier on KOA: Gayle Feldman on Nothing Random: Bennett Cerf and the Publishing House He Built — the companion episode referenced at the opening. •       Sterling Lord (agent for Kerouac), Candida Donadio (Pynchon, Heller, Gaddis, Roth), Andrew Wylie — agents profiled in the book. •       Andrew Keen, Cult of the Amateur (2007) — referenced as Andrew's own defence of gatekeepers. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackYouTube

“AI companies are taking advantage of our natural tendency to ascribe an inner life to our interlocutors. They profit when you think the chatbot cares.” — Kate O'Neill If we don't like someone, we call them a fascist. And if we like them, we say they are a humanist. The F and H words. Both meaningless in our sloppy, bot-infested age. But maybe I'm just a cranky anti-humanist. Even anti-human — whatever that means. Or maybe I'm being harsh (moi?). Humanism certainly is all the rage in our AI age. Corporate consultant Kate O'Neill likes the word so much that she has built her brand around it. The self-styled “Tech Humanist” is the author of Tech Humanist, the host of the Tech Humanist Show, and a frequent speaker on the TED circuit. So how to use the H word without sounding like Claude or ChatGPT? O'Neill argues that what makes us human is our quest for meaning. The M word. That's what distinguishes us from the bots. But as Kazuo Ishiguro warns in Klara and the Sun, we are fast arriving at a point when the bots are better than us at extracting meaning from the world. So did Kate O'Neill pass the Keen Test (reverse of Turing)? Did the Tech Humanist say anything that would have eluded Claude? Or have we already arrived at Ishiguro's bleak terminus where the bots are more skilled at infusing the H word with meaning than we are? Five Takeaways •       What Is Tech Humanism? Aligning Business and Human Outcomes: O'Neill's definition: technology shapes human experiences at scale, and it does so almost always in service of a business objective that is accelerating its advance. The purpose of tech humanism is to find the business objectives that need to be met and align them with human outcomes that are rewarding and fulfilling for people. This means using technology to amplify the alignment between business and human outcomes — rather than simply making the business more successful. It is, she acknowledges, not the habit of most business leaders. But it is a habit that can be developed. •       You Sound Like a Bot: Andrew's Challenge: Andrew's opening challenge: O'Neill sounds exactly like a well-prompted language model. She uses the h word (humanism) and the m word (meaning). What is she saying that Claude couldn't say? O'Neill's answer: meaning is not a word but a phenomenon. It is what emerges from the combination of embodied sensory experience and language — the way humans encode meaningful experiences with language in their brains. As far as we know, this is a uniquely human capability. Machines process information statistically. Humans process it meaningfully. That distinction is, she argues, precisely the gap that matters. •       AI Companies Profit When You Think the Chatbot Cares: O'Neill's sharpest observation: we are constituted to look for inner life in the things we interact with. We give nicknames to our cars and talk to our toasters. At this early stage of interacting with large language models, it is entirely natural to assume there is a consciousness on the other side. The problem: AI companies are actively taking advantage of that natural tendency. They profit from it. The more people believe the chatbot genuinely understands them, the more they use it. That manipulation is real and it is working. Developing critical thinking about AI interactions is, O'Neill argues, now a form of self-defence. •       The Intersection of Meaning and Scale: O'Neill's key contribution to the tech humanism conversation: the problem with technology is not technology itself but the scale at which it operates. A single interaction with a biased algorithm is annoying. A billion such interactions, aggregated and accelerated by a business objective, reshapes society. The tech humanist's job is to ensure that when we deploy technology at scale, the outcomes remain aligned with human meaning rather than with the extraction of human attention. This, she says, is both a business problem and a civilisational one. The two are, in her view, inseparable. •       A Message to 2126: What We Valued About Ourselves: Andrew asks O'Neill: it is 2126. Humans and machines are indistinguishable. What do you say to whoever is listening? O'Neill's answer: hello from the past. What we valued about ourselves was our ability to understand each other — intellectually, emotionally, sympathetically, empathetically. We could come into our interactions by holding space for what the other person feels and cares about. And we could, even when we disagreed, create more shared understanding by virtue of having the conversation. That is a beautiful thing, she says, whether we are distinctly human and distinctly machine or increasingly a blend of both. About the Guest Kate O'Neill is founder and CEO of KO Insights and is widely known as “the Tech Humanist.” She was one of the first 100 employees at Netflix and has held roles at Toshiba and founded the analytics firm [meta]marketer. She is named to the Thinkers50 global ranking of top management thinkers. She is the author of What Matters Next: A Leader's Guide to Making Human-Friendly Tech Decisions in a World That's Moving Too Fast (Wiley, January 2025), Tech Humanist (2018), A Future So Bright (2021), and Pixels and Place (2016). She advises Google, IBM, Microsoft, the United Nations, Harvard, and Yale. She hosts The Tech Humanist Show on YouTube. References: •       What Matters Next: A Leader's Guide to Making Human-Friendly Tech Decisions in a World That's Moving Too Fast by Kate O'Neill (Wiley, January 2025). •       Kazuo Ishiguro, Klara and the Sun (2021) — the novel discussed in the conversation's closing section. •       Victoria Hetherington, The Friend Machine — referenced by Andrew in the conversation on AI companionship. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackYouTube

“I considered it elder abuse. She put him through the paces, not only before the debate, but after. She should have gotten him out of there immediately.” — Sally Quinn on Jill Biden and the debate Today's guest is amongst America's most verbal octogenarians. No, not you-know-who. Sally Quinn is the illustrious Washington DC hostess, writer and commentator. The almost 85-year-old does improv comedy every Sunday, ballroom dancing every week and Zen Buddhist meditation every Monday night. Her novel, Silent Retreat, is now out in paperback. And she's working on her memoir, tentatively entitled Never Invite Sally Quinn. Certainly Jill Biden won't be inviting Sally Quinn any time soon to one of her tête-à-têtes. Quinn's account of what went wrong with the Biden presidency is sharply personal. Her late husband, legendary Washington Post editor Ben Bradlee, had dementia. She watched his cognitive decline from inside, and the parallels with what she observed in Biden were, she tells me, too close for comfort. Jill Biden's decision to keep Joe running after the debate, when she privately suspected he'd suffered a stroke, was, in Quinn's word, “elder abuse.” Silent Retreat, set at a monastery in Virginia's Shenandoah Valley, is about the sexiness of silence. A prize-winning reporter and the venerable Archbishop of Dublin fall in love in enforced silence. Anything but elder abuse. But autobiographical? Probably not. As Ben Bradlee used to tease her over breakfast, it's always been hard for not-silent-Sally to keep her mouth shut. Five Takeaways •       The Army Brat Who Became Washington's Most Powerful Hostess: Quinn grew up as an army brat, moving from posting to posting with her military father. She arrived in Washington after college, did a stint as social secretary to the Algerian ambassador, and was then hired by Ben Bradlee to write for the Washington Post's new Style section — the first style section in the history of American journalism. She and Bradlee eventually married. Their home in Georgetown became the hub of Washington's social and political life for decades. She describes herself not as a powerhouse but as someone who “really lucked out.” An army brat who knew how to work a room. •       Gerontocracy Is Real — But People Who Keep Going Are Different: Quinn agrees with Samuel Moyn that American gerontocracy is a genuine problem: people who lose their cognitive sharpness should not be running organizations or countries, and the tragedy is that no one can know in advance who will lose it and who won't. But she draws a distinction: the problem is not old people, it's old people who have stopped growing. She surrounds herself with younger people, particularly younger journalists, because of their energy, idealism, and optimism. She is still working full time. The issue is not age. It's vitality. •       Biden and Jill: Elder Abuse: Quinn's account of the Biden presidency is the most personal Andrew has heard. Her husband Ben Bradlee had dementia. She knows the signs. She watched Biden lose it, got a knot in her stomach every time he spoke publicly. The debate was her worst nightmare. Everyone in the White House knew what was happening and wasn't telling the truth. And Jill Biden — who now admits she thought he had had a stroke after the debate — raised his arm in a victory salute the next day and took him off to campaign in North Carolina. Quinn's verdict: “I considered it elder abuse.” •       Silent Retreat: A New Yorker Writer and an Archbishop Fall in Love in Enforced Silence: The novel grew from Quinn's own annual visits to a Trappist monastery in Virginia's Berryville. She is a woman who once failed to stay quiet for three days — or so her husband thought — and who found to her surprise that she loved it. The novel: a prize-winning reporter whose marriage is falling apart, and an Archbishop of Dublin whose faith is in crisis, check into the same monastery for a silent retreat. They can't speak to each other. They speak to the monk instead. The novel is told through those confessions. Kirkus: “an unholy brew of lust and faith.” Airmail: “a bodice ripper with a fillip of Roman Catholic ritual.” •       Improv, Ballroom Dancing, Zen Buddhism, and Dinner by Candlelight: Quinn's account of how she stays alive at 84 is the most energetic thing in this conversation. Improv comedy every Sunday for two and a half hours — performances after the class, with people half her age. Ballroom dancing every week. Zen Buddhist meditation every Monday night for two hours. Working out every day. Writing her Washington memoir. And hosting small dinner parties — six or eight people, candlelight, good food, a lot of wine — as a form of community-building in what she calls the toxic environment of today's Washington. The memoir's title: Never Invite Sally Quinn. Andrew has already secured an invitation to the next dinner party. About the Guest Sally Quinn is a longtime Washington Post journalist, columnist, television commentator, Washington insider, and one of Washington's legendary social hostesses. She is the author of Silent Retreat (Simon & Schuster), Finding Magic, The Party, Happy Endings, Regrets Only, and We're Going to Make You a Star. She was the founder and moderator of On Faith, the Washington Post's religion website. She lives in Georgetown, Washington DC. References: •       Silent Retreat by Sally Quinn (Simon & Schuster). In paperback. •       Episode 2945: Samuel Moyn on Gerontocracy in America — referenced at the opening. •       Ben Bradlee — Quinn's late husband, executive editor of the Washington Post during Watergate, referenced throughout. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:...

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“Age is the modality in which class is lived in America today.” — Samuel Moyn Yesterday we had 91-year-old Mordecai Kurz on the show. Tomorrow, it will be 84-year-old Sally Quinn. But today's guest, the Yale legal historian Samuel Moyn, has a bit of a problem with old people. His new book, Gerontocracy in America, argues that the old folks are hoarding power and wealth in America. For Moyn, Dylan's Sixties anthem of “Forever Young” has soured into today's reality of “Forever Old.” In some ways, it's hard to argue with Moyn's thesis. Donald Trump is the oldest elected US president in history. Congress has been ageing for decades — and several Democratic members died in the run-up to the One Big Beautiful Bill vote, thereby facilitating its passage. The progressive heroine Ruth Bader Ginsburg stayed on the Supreme Court through a pancreatic cancer diagnosis and died in office, handing the right a supermajority and the end of abortion rights. Clarence Thomas, the RBG of nutcase conservatism, is on track to become the longest-serving Supreme Court justice in US history. And then there's that alte kaker Joe Biden, former dodder-in-chief, the only pol who gives Trump a youthful glow. Even Bob Dylan — who I saw in all his morbid brilliance in Berkeley last week (“but me, I'm still on the road”) — just celebrated his 85th birthday. Forever old, America. Happy 250th. Five Takeaways •       What Is Gerontocracy? Not a Problem With Old People: Moyn is careful to distinguish gerontocracy from old people. He is in his mid-fifties and can't attack old people generally. His target is the system: the structural overrepresentation of old people in power, and the structural disadvantaging of the young that results. Old people can be great. Some are, some aren't — just like everyone else. The problem is that when we defer to old people automatically — as a system rather than as a judgement about individuals — we replicate their mistakes alongside their wisdom. And cognitive decline is real, as Biden proved. “Age is the modality in which class is lived in America today,” Moyn writes, riffing on Stuart Hall's formulation about race. •       The Congress, the Courts, and the Deaths That Passed the Bill: Trump is the oldest elected US president in history — and if JD Vance were to succeed him, Vance would be the youngest president since Teddy Roosevelt. But Moyn's focus goes beyond the presidency. Congress has aged dramatically: the average senator and representative are significantly older than at any point in US history, and there is now only one member of Congress in their thirties. Several Democratic members of the House died in the months before the One Big Beautiful Bill vote, facilitating its passage. The gerontocracy is quite literally voting itself into power through death. •       The RBG Problem: Selfishness and the Supreme Court: Moyn's account of Ruth Bader Ginsburg is unsparing. She had been diagnosed with pancreatic cancer — one of the deadliest — and allegedly survived it. She had become a progressive icon, “Notorious RBG.” But she chose to stay on the court rather than retire under Obama, and she died in office in 2020, allowing Trump to appoint Amy Coney Barrett and hand the right a supermajority that ended abortion rights. Moyn's verdict: she was selfish. He is also careful to note that the system should not depend on individual virtue — there will always be selfish people. The system must be reformed so that selfish choices are no longer possible. •       The Framers Designed Gerontocracy Into the Constitution: One of Moyn's most striking historical arguments: the framers deliberately empowered old people. The age minimums for federal office (35 for the presidency, 30 for the Senate) excluded 70% of the population at the time. The Senate was named after the Roman senatus — literally “old men” — and the concept went back to the Spartan council of elders. Alexander Hamilton argued in the Federalist Papers that federal judges should serve until they were “dodering” because the alternative was too much popular power. The gerontocracy is not an accident. It was designed. •       The Solutions: Vote at Six, Retire at Sixty, Tax the Family Home: Moyn's solutions are deliberately radical. On voting: lower the age, as David Runciman advocates to six, and reduce the number of elections because evidence shows the more elections, the greater the elder dominance. On political office: age limits, youth cohorts. On the courts: mandatory retirement — this requires creative interpretation of the constitution rather than amendment. On the economy: higher taxes on inherited wealth and housing assets — an incremental tax for staying in a large house you no longer need. On the title of the paperback: Andrew suggests “Forever Old.” Moyn will credit him if it's chosen. About the Guest Samuel Moyn is the Kent Professor of Law and History at Yale University. He is the author of Gerontocracy in America: How the Old Are Hoarding Power and Wealth — and What to Do About It (Farrar, Straus and Giroux, June 16, 2026), Humane: How the United States Abandoned Peace and Reinvented War, Not Enough: Human Rights in an Unequal World, and The Last Utopia: Human Rights in History. He is co-host of the Digging a Hole podcast and a frequent contributor to The Nation, The New Republic, and The New York Times. He lives in New Haven, Connecticut. References: •       Gerontocracy in America: How the Old Are Hoarding Power and Wealth — and What to Do About It by Samuel Moyn (Farrar, Straus and Giroux, June 16, 2026). •       Samuel Moyn, “The Old Guard: Confronting America's Gerontocratic Crisis,” Harper's Magazine, May 2026 — the excerpt from the book referenced at the opening. •       David Runciman — referenced for his advocacy of lowering the voting age to six. •       Stuart Hall — referenced for the formulation that class is lived through race, which Moyn repurposes for age. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackYouTube