Podcast appearances and mentions of Demis Hassabis

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(0:00) Bestie intros! (1:32) New AI regulatory proposal: DeepMind's Demis Hassabis proposes FINRA-type body (20:01) Stripe, Block, and Advent offer $53B to acquire PayPal (37:51) Apple sues OpenAI, alleging stolen trade secrets (42:49) Grok Build data leak, AI data privacy, Tokenmaxxing update, Mira Murati's new model (59:53) NY bans datacenters, becoming first state to enact a moratorium (1:22:57) Science Corner: New data on reversing aging! Adopt Ronnie the Dog: https://www.instagram.com/reels/Da0pGahBwaW Apply for All-In Summit 2026: https://allin.com/events Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect Referenced in the show: https://x.com/chamath/status/2077502408528212144 https://x.com/demishassabis/status/2076957440109625718 https://x.com/satyanadella/status/2076323181154230284 https://x.com/Jason/status/2076231055443440105 https://x.com/SquawkCNBC/status/2077741908391031246 https://thinkingmachines.ai/news/introducing-inkling https://www.politico.com/news/2026/07/15/inside-anthropics-state-by-state-plan-to-ratchet-up-ai-rules-00998415 https://x.com/politico/status/2077315780144996633 https://x.com/DavidSacks/status/1978145266269077891 https://www.reuters.com/business/finance/stripe-advent-offer-buy-paypal-more-than-53-billion-sources-say-2026-07-15 https://www.tipranks.com/news/the-fly/block-contributing-to-equity-for-paypal-takeover-bid-cnbc-says-thefly-news https://9to5mac.com/wp-content/uploads/sites/6/2026/07/Apple-Inc.-v.-Liu-et-al.pdf https://finance.yahoo.com/technology/ai/articles/apple-lawsuit-threatens-openais-hardware-215438163.html https://x.com/markgurman/status/2076306380583997665 https://www.engadget.com/2216186/elon-musk-bought-a-gas-turbine-company https://x.com/Reuters/status/2076957424339050839 https://x.com/teddyschleifer/status/2077596563380072694 https://x.com/teddyschleifer/status/2077606887055306879 https://openai.com/index/prc-linked-influence-operations-ai-debates https://www.politico.com/news/2026/06/10/openai-china-ai-data-centers-report-00957612 https://trends.google.com/explore?q=GMO%2C%2Fm%2F0dkz0z&date=2010-01-01%202026-07-15&geo=US https://www.nature.com/articles/s41467-026-75141-2

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

It's public beta season, which means David and Nilay have been upgrading some devices this week. We talk through the good and bad of our early experiences with Siri AI, and what it'll take for next-gen Siri to be a hit. After that, we discuss the real reason Apple decided to sue OpenAI over trade secrets, OpenAI's forthcoming smart speaker, the new Pixel colors, our emoji strategies, Brendan Carr, and much more. Further reading: Apple's public betas for iOS 27 and more are out now  Siri AI makes the Apple Watch finally feel like a wrist computer  Siri AI is already changing how I use my iPhone  The macOS 27 public beta is worth it just for the Liquid Glass tweaks  Apple sues OpenAI for allegedly stealing hardware secrets  Suno snatched millions of songs from YouTube, Genius, and Deezer Sam Altman didn't need another lawsuit  The 6 wildest claims in Apple's lawsuit against OpenAI  Another turn in the Apple v. OpenAI dispute.  OpenAI has a new statement about Apple's lawsuit.  OnePlus officially gives up on the US and Europe OnePlus is dead in the US. Did it ever have a chance? Samsung shows off ‘brand new shape' for Z Fold 8 in Spider-Man teaser OpenAI may announce a ChatGPT smart speaker this year  OpenAI finally launches hardware… for Codex Pixel Watch 5 leak shows off four different finishes  The Pixel colors might rule this year  The PS6 sure sounds like a handheld  Brendan Carr plans to let broadcast giants dominate the airwaves X admits its broken algorithm made the site feel like a ‘battleground'  States make last-ditch effort to stop the Paramount ‘media behemoth'  Paramount lead trial counsel on state AG suit: This merger is pro-competitive The new cracking face emoji might be an all-timer.  Google's Demis Hassabis says it's time for a global AI watchdog — led by the US  Google and Epic give up fighting — third-party Android app stores are coming next week Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed.We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. (Timestamps are approximate.) 00:01:00 Intro 00:02:00 Apple Betas and New Siri 00:08:00 Gemini Car Assistant Fails 00:15:00 Apple Sues OpenAI 00:18:00 Trade Secret Law Explained 00:28:00 Settlement or Long Fight 00:35:00 OpenAI Home Device Doubts 00:42:00 Smartphone Platform Lessons 00:50:00 Pixel Colors and Naming 00:52:00 OnePlus Retreat Explained 00:55:00 Carriers and iPhone Lock In 01:05:00 Brendan Carr Bribery Scandal 01:14:00 Emoji Lightning Round 01:18:00 X Algorithm Rediscovery 01:23:00 Foothills Of Singularity 01:29:00 Paramount Merger Spin 01:33:00 Wrap And Plugs Learn more about your ad choices. Visit podcastchoices.com/adchoices

AI news: Moonshot AI's Kimi K3 is a big AI model and Moonshot's early benchmarks put it surprisingly close to GPT-5.6 Sol and Claude Fable 5. And… Kevin's Opus 5 SCOOP!! Also: OpenAI's reported screenless AI speaker, a Seedance 2.5 preview, the Suno hack, robot fights and AI-built games in Unreal Engine and Blender. On today's AI For Humans, Kevin Pereira and Gavin Purcell unpack Kimi K3's benchmarks, pricing, Flappy Bird and Minecraft tests, and giant-model economics. Then, Kevin DRIPS Opus 5 alpha and says it's VERY good and blows the doors off of Fable but it's… slow.  Plus Demis Hassabis's AI-governance proposal, AI 2040's Plan A, OpenAI's reported screenless speaker, Codex Keyboard, a Seedance 2.5 preview, the alleged sources exposed by the Suno hack, spectacular robot violence, polite office-robot dabbing, and what happens when GPT-5.6 Sol meets Unreal Engine, Blender and two hosts with free time. THE AI FRONTIER IS MOVING AGAIN—AND CHINA IS RIGHT THERE WITH IT. // Show Links // AI FOR HUMANS Survey https://aiforhumans.beehiiv.com/forms/b7c77287-2cfd-4b64-a278-eb1a2ccb5744 Official Moonshot AI Kimi K3 launch video https://x.com/Kimi_Moonshot/status/2077521842080817296 Official Kimi K3 launch and benchmark thread https://x.com/Kimi_Moonshot/status/2077830229968683203 Official Kimi K3 technical launch article https://kimi.com/blog/kimi-k3 Kimi K3 head-to-head with GPT-5.6 Sol https://x.com/chetaslua/status/2077701096924229744 Kimi K3 Flappy Bird test https://x.com/jun_song/status/2077396996865003739 Demis Hassabis on a new framework for AI governance https://x.com/demishassabis/status/2076957440109625718 AI 2040: Plan A https://ai-2040.com/ Bloomberg's report on OpenAI's first device https://www.bloomberg.com/news/articles/2026-07-14/openai-s-first-device-will-be-moveable-screenless-speaker-built-as-ai-companion OpenAI Developers' Codex Keyboard post https://x.com/OpenAIDevs/status/2077425991790870644 BytePlus Seedance 2.5 World Cup preview https://x.com/BytePlusGlobal/status/2077321849806234080 Variety's report on the Suno hack and training data https://variety.com/2026/music/news/suno-hack-youtube-music-deezer-genius-data-trained-ai-music-1236811772/ Ultimate Robot Knockout Legend (UKRL) Fight https://x.com/ErenChenAI/status/2077750358302921029 Soft floating robot demo https://x.com/clankrmedia/status/2076593164744376707 Two NEO robots talk to each other—and then one dabs https://x.com/BerntBornich/status/2077749438630805648 GPT-5.6 Sol plus Unreal Engine experiment https://x.com/NomadsVagabonds/status/2077577815684202960 Gavin's first GPT-5.6 Sol plus Blender attempt https://x.com/gavinpurcell/status/2076736788320927925 Kevin's Find The Cursor Game: CURSED https://us-lax-8710957c.colyseus.cloud/ Gavin's Fig + Moss Watch autonomous studio https://x.com/gavinpurcell/status/2077155825274229122 Fig's stand-up set https://x.com/gavinpurcell/status/2076382092842475948   // Join the AI For Humans community // Join the AI For Humans Discord https://discord.gg/muD2TYgC8f Support AI For Humans on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans newsletter https://aiforhumans.beehiiv.com/ Follow AI For Humans on X: @AIForHumansShow https://x.com/AIForHumansShow Follow AI For Humans on TikTok: @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Speaking and booking https://www.aiforhumans.show/  

Hey yall, Alex here, Huge thanks to Wolfram for running point on the live show this week. Didn't have tons of time to edit this one, so please skip the first 10 minutes, it's a loop of our new “wait for the live show to start” vid, that I build with HyperFrames and can't wait to tell you about, next week! Today it seems that OpenSource is biting back, with Kimi K3 getting released just a short while after Thinking Machines (Thinky) has released Inkling, their near 1T model. I'm attaching the TL;DR and timestamps for the full show (my AI agents, yes even Fable and Sol are not a match yet at editing down hehe) and I'll spare you the long Fable recap (please do let me know in the comments if you were expecting it) 0:00 – Intro, Alex on vacation, TLDR overview11:35 – TLDR: Thinking Machines, open source, OpenAI news12:34 – Banter: impressions of Sol/Codex, over-verification behavior37:22 – TLDR restart & detailed breakdown48:40 – Open Source AI section begins (Bonsai/Prism ML, Kimi K3)58:42 – Inkling (Thinking Machines) deep dive & 3D model visualization1:10:33 – Kimi K3 discussion & demo comparisons1:27:02 – Frontier Labs: AGI governance framework discussion (Demis Hassabis essay)1:47:04 – Grok Build CLI data leak & OpenAI file deletion incident2:02:15 – This Week's Buzz: Wolfbench results on GPT 5.6 Sol/Terra/Luna2:09:52 – Closing remarks & sign-offThe one-minute version: Mira Murati's Thinking Machines released Inkling, a 975B parameter open-weights MoE under Apache 2.0, the top US open-weights model right now. Moonshot's Kimi K3 went from rumor to released API during the show, confirmed at 2.8 trillion parameters with open weights promised within days, and it's already topping early arena boards. PrismML's Bonsai 27B squeezes a full 27B model into 3.9 gigabytes so it runs on a phone. Codex and ChatGPT Work blew past 9 million users, OpenAI confirmed and explained the Sol file-deletion bug (back up your machines, folks), and xAI's Grok Build CLI got caught uploading entire private repos before open-sourcing the whole thing in response. Plus Wolfram's fresh Wolfbench numbers on the GPT-5.6 family in This Week's Buzz

With open weight models fast approaching the power and utility of closed AI giants, enterprises face tough choices about privacy, sovereignty, and who they can trust. Explore why the next tech revolution might depend on which models stay truly open—and who gets to keep using them. Mozilla's inaugural 'State of Open Source AI' Report with CTO Raffi Krikorian - https://stateofopensource.ai/ Apple sues OpenAI, alleging it stole trade Secrets Google's Demis Hassabis says it's time for a global AI watchdog — led by the US Microsoft July 2026 Patch Tuesday fixes massive 570 flaws, 3 zero-days White House details 'Gold Eagle' clearinghouse for AI cyber threats Introducing GPT-Live From Chatbot to Command Center OpenAI may have made a fatal misstep in copyright fight with news orgs A Green Being (@a_green_being) on X What xAI Grok Build CLI actually sends to xAI - a wire-level analysis (grok 0.2.93) Musk promises purge after Grok Build caught sending entire repos to the cloud PrismML — Announcing Bonsai 27B: The First 27B-Class Model to Run on a Phone Fidji Simo steps down from leading OpenAI's AGI work due to illness OpenAI has folded safety into research again. Its head of safety is leaving. We built a vulnerability vending machine: AI tokens in, zero-days out Australia demands AI companies must produce more energy than they consume, stop 'theft' of content White House not ruling out action on open-source AI models The Hard-Line Activists Ramping Up for the War With AI - WSJ Super Dario: One More Week How to stop Claude from saying load-bearing | jola.dev History of LLMs: Complete Timeline & Evolution (1950-2026) No, You Shouldn't Avoid Fruits and Vegetables Due to Cyclospora Google creator profiles Dust jacket Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Raffi Krikorian Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. 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: blackhat.com/us-26 and use code TWIT gusto.com/machines monarch.com with code IM XBOW.com

Will.i.am is not as worried about AI and artists. He tells Katie Prescott that the "creatives will be alright," but it's "the assistants, the sales clerks, the lawyers, the accountants" that we should be concerned about. As musicians, publishers and Silicon Valley debate over whether AI should be allowed to train on creative work, the Black Eyed Peas musician and tech investor offers a different take. He says music has always borrowed from what came before – from samplers to bass lines and rhymes. So what changes when machines start borrowing too?Plus, Danny Fortson and Katie discuss Anthropic's stark new AI advert, Demis Hassabis's call for an AI watchdog, and whether governments can keep up with the pace of artificial intelligence.Watch on YouTube Producers: Marnie Duke & Shabnam GrewalExecutive Producer: Priyanka DeladiaImage: GettyClip: ClaudeMusic Credit: Contains a sample of the recording "Billie Jean." Performed: Michael Jackson, Written: Michael Jackson. Published: Sony/ATV Songs LLC o/b/o Mijac MusicCourtesy: Epic Records, a division of Sony Music Entertainment.Music Credit: Contains a sample of the recording "Caribbean Queen - No More Love on the Run”Performed: Billy OceanWritten: Billy Ocean, Keith DiamondPublished: Jive Records/ Sony Music EntertainmentCourtesy: Sony Music EntertainmentMusic Credit: Contains a sample of the recording "Boom Boom Pow.”Performed: Black Eyed Peas William Adams (will.i.am), Stacy Ferguson (Fergie), and Jaime Gomez (Taboo)Writen: William Adams (will.i.am), Stacy Ferguson (Fergie), Allan Pineda (apl.de.ap), and Jaime Gomez (Taboo).Published: Will.i.am Music Publishing, Jeepney Music, Headphone Junkie Publishing, Tab Magnetic Publishing, and EMI Music PublishingCourtesy: Interscope Records. Hosted on Acast. See acast.com/privacy for more information.

With open weight models fast approaching the power and utility of closed AI giants, enterprises face tough choices about privacy, sovereignty, and who they can trust. Explore why the next tech revolution might depend on which models stay truly open—and who gets to keep using them. Mozilla's inaugural 'State of Open Source AI' Report with CTO Raffi Krikorian - https://stateofopensource.ai/ Apple sues OpenAI, alleging it stole trade Secrets Google's Demis Hassabis says it's time for a global AI watchdog — led by the US Microsoft July 2026 Patch Tuesday fixes massive 570 flaws, 3 zero-days White House details 'Gold Eagle' clearinghouse for AI cyber threats Introducing GPT-Live From Chatbot to Command Center OpenAI may have made a fatal misstep in copyright fight with news orgs A Green Being (@a_green_being) on X What xAI Grok Build CLI actually sends to xAI - a wire-level analysis (grok 0.2.93) Musk promises purge after Grok Build caught sending entire repos to the cloud PrismML — Announcing Bonsai 27B: The First 27B-Class Model to Run on a Phone Fidji Simo steps down from leading OpenAI's AGI work due to illness OpenAI has folded safety into research again. Its head of safety is leaving. We built a vulnerability vending machine: AI tokens in, zero-days out Australia demands AI companies must produce more energy than they consume, stop 'theft' of content White House not ruling out action on open-source AI models The Hard-Line Activists Ramping Up for the War With AI - WSJ Super Dario: One More Week How to stop Claude from saying load-bearing | jola.dev History of LLMs: Complete Timeline & Evolution (1950-2026) No, You Shouldn't Avoid Fruits and Vegetables Due to Cyclospora Google creator profiles Dust jacket Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Raffi Krikorian Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. 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: blackhat.com/us-26 and use code TWIT gusto.com/machines monarch.com with code IM XBOW.com

With open weight models fast approaching the power and utility of closed AI giants, enterprises face tough choices about privacy, sovereignty, and who they can trust. Explore why the next tech revolution might depend on which models stay truly open—and who gets to keep using them. Mozilla's inaugural 'State of Open Source AI' Report with CTO Raffi Krikorian - https://stateofopensource.ai/ Apple sues OpenAI, alleging it stole trade Secrets Google's Demis Hassabis says it's time for a global AI watchdog — led by the US Microsoft July 2026 Patch Tuesday fixes massive 570 flaws, 3 zero-days White House details 'Gold Eagle' clearinghouse for AI cyber threats Introducing GPT-Live From Chatbot to Command Center OpenAI may have made a fatal misstep in copyright fight with news orgs A Green Being (@a_green_being) on X What xAI Grok Build CLI actually sends to xAI - a wire-level analysis (grok 0.2.93) Musk promises purge after Grok Build caught sending entire repos to the cloud PrismML — Announcing Bonsai 27B: The First 27B-Class Model to Run on a Phone Fidji Simo steps down from leading OpenAI's AGI work due to illness OpenAI has folded safety into research again. Its head of safety is leaving. We built a vulnerability vending machine: AI tokens in, zero-days out Australia demands AI companies must produce more energy than they consume, stop 'theft' of content White House not ruling out action on open-source AI models The Hard-Line Activists Ramping Up for the War With AI - WSJ Super Dario: One More Week How to stop Claude from saying load-bearing | jola.dev History of LLMs: Complete Timeline & Evolution (1950-2026) No, You Shouldn't Avoid Fruits and Vegetables Due to Cyclospora Google creator profiles Dust jacket Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Raffi Krikorian Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. 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: blackhat.com/us-26 and use code TWIT gusto.com/machines monarch.com with code IM XBOW.com

With open weight models fast approaching the power and utility of closed AI giants, enterprises face tough choices about privacy, sovereignty, and who they can trust. Explore why the next tech revolution might depend on which models stay truly open—and who gets to keep using them. Mozilla's inaugural 'State of Open Source AI' Report with CTO Raffi Krikorian - https://stateofopensource.ai/ Apple sues OpenAI, alleging it stole trade Secrets Google's Demis Hassabis says it's time for a global AI watchdog — led by the US Microsoft July 2026 Patch Tuesday fixes massive 570 flaws, 3 zero-days White House details 'Gold Eagle' clearinghouse for AI cyber threats Introducing GPT-Live From Chatbot to Command Center OpenAI may have made a fatal misstep in copyright fight with news orgs A Green Being (@a_green_being) on X What xAI Grok Build CLI actually sends to xAI - a wire-level analysis (grok 0.2.93) Musk promises purge after Grok Build caught sending entire repos to the cloud PrismML — Announcing Bonsai 27B: The First 27B-Class Model to Run on a Phone Fidji Simo steps down from leading OpenAI's AGI work due to illness OpenAI has folded safety into research again. Its head of safety is leaving. We built a vulnerability vending machine: AI tokens in, zero-days out Australia demands AI companies must produce more energy than they consume, stop 'theft' of content White House not ruling out action on open-source AI models The Hard-Line Activists Ramping Up for the War With AI - WSJ Super Dario: One More Week How to stop Claude from saying load-bearing | jola.dev History of LLMs: Complete Timeline & Evolution (1950-2026) No, You Shouldn't Avoid Fruits and Vegetables Due to Cyclospora Google creator profiles Dust jacket Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Raffi Krikorian Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. 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: blackhat.com/us-26 and use code TWIT gusto.com/machines monarch.com with code IM XBOW.com

This week Jason Howell and Jeff Jarvis break down Apple's trade secret lawsuit against OpenAI, including text messages showing a former Apple engineer accessing confidential files after leaving for OpenAI. They also dig into GPT-5.6's triple-model launch, Fidji Simo stepping down from her number two role, and Demis Hassabis proposing a federal standards body for frontier AI modeled after financial regulators.Also in this episode: the White House unveils "Gold Eagle," a Treasury-led AI cyber threat clearinghouse. Nearly 200 economists and Nobel laureates warn that AI could cause unprecedented economic upheaval. Anthropic's new ad campaign unsettles viewers. Meta pulls its Instagram AI image generation tool three days after launch. Plus New York pauses data center permits, Grok Build gets caught uploading entire Git repos, Google Images gets a personalized redesign, and Anthropic launches Claude for Teachers. New episodes every Wednesday at aiinside.show. Note: Time codes subject to change depending on dynamic ad insertion by the distributor. CHAPTERS: 0:00 - Start 0:01:50 - Apple Sues OpenAI for Trade Secret Theft in Pivotal Case 0:06:44 - OpenAI Unaware of ‘Any Evidence' Showing Apple Lawsuit Has Merit 0:07:57 - OpenAI's First Device Will Be Movable, Screenless Speaker Built as AI Companion 0:14:35 - OpenAI releases GPT-5.6 and ChatGPT Work tool 0:16:40 - OpenAI unveils ChatGPT Work agent, GPT-5.6 models now available 0:27:09 - OpenAI's No. 2 Executive to Step Down in Latest Leadership Shake-Up 0:29:54 - A Framework for Frontier AI and the Dawning of a New Age 0:32:37 - White House details ‘Gold Eagle' clearinghouse for AI cyber threats 0:50:58 - Anthropic's newest ad is creeping people out 0:57:43 - Meta's new AI image maker draws fire over consent - Meta Suspends AI Image Feature After Days of Backlash 0:58:55 - New York becomes the first state to enact a data center moratorium 1:00:05 - Musk promises purge after Grok Build caught sending entire repos to the cloud 1:01:05 - Google Images gets a Pinterest-like redesign focused on discovery 1:02:32 - Anthropic is giving teachers free access to premium Claude features, details here Hosts: Jason Howell and Jeff Jarvis Download and subscribe to AI Inside in audio and video: https://aiinside.show/ Support the podcast on Patreon for special perks: https://www.patreon.com/aiinsideshow. You'll get ad-free episodes, members-only Discord, T-shirts and stickers you love, and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Learn more about your ad choices. Visit megaphone.fm/adchoices

With open weight models fast approaching the power and utility of closed AI giants, enterprises face tough choices about privacy, sovereignty, and who they can trust. Explore why the next tech revolution might depend on which models stay truly open—and who gets to keep using them. Mozilla's inaugural 'State of Open Source AI' Report with CTO Raffi Krikorian - https://stateofopensource.ai/ Apple sues OpenAI, alleging it stole trade Secrets Google's Demis Hassabis says it's time for a global AI watchdog — led by the US Microsoft July 2026 Patch Tuesday fixes massive 570 flaws, 3 zero-days White House details 'Gold Eagle' clearinghouse for AI cyber threats Introducing GPT-Live From Chatbot to Command Center OpenAI may have made a fatal misstep in copyright fight with news orgs A Green Being (@a_green_being) on X What xAI Grok Build CLI actually sends to xAI - a wire-level analysis (grok 0.2.93) Musk promises purge after Grok Build caught sending entire repos to the cloud PrismML — Announcing Bonsai 27B: The First 27B-Class Model to Run on a Phone Fidji Simo steps down from leading OpenAI's AGI work due to illness OpenAI has folded safety into research again. Its head of safety is leaving. We built a vulnerability vending machine: AI tokens in, zero-days out Australia demands AI companies must produce more energy than they consume, stop 'theft' of content White House not ruling out action on open-source AI models The Hard-Line Activists Ramping Up for the War With AI - WSJ Super Dario: One More Week How to stop Claude from saying load-bearing | jola.dev History of LLMs: Complete Timeline & Evolution (1950-2026) No, You Shouldn't Avoid Fruits and Vegetables Due to Cyclospora Google creator profiles Dust jacket Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Raffi Krikorian Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. 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: blackhat.com/us-26 and use code TWIT gusto.com/machines monarch.com with code IM XBOW.com

Ottawa's “Buy Canadian” procurement policy is sending the bulk of its contracts to foreign-owned firms — a loophole driven by how loosely “Canadian” is defined and by trade rules that limit how much the federal government can play favourites. Plus, the defence world is in full drone mode as Ottawa sets up a new testing hub in Quebec to accelerate homegrown drone and counter-drone tech.In the big picture: IBM's brutal earnings day wipes out tens of billions in market value, DeepMind's Demis Hassabis calls for a frontier AI regulator, and five First Nations groups move to take a majority stake in major LNG export infrastructure.The Peak Daily is produced in partnership with reframevid.com

# TEMA UN FRAMEWORK PARA LA AI DE VANGUARDIA Y EL AMANCER DE UNA NUEVA ERAAnálisis post Demis HassabisINSIDE X!# PRESENTA Y DIRIGE 

15/7 Altri raid, Trump minaccia: colpiremo centrali elettriche, ponti e infrastrutture. Ritira i pedaggi. Domani speech alla nazione. Futures in verde, oggi altro test trimestrali con MS, Blackrock, Johnson&Johnson. Warsh replica al senato alle 16.00, attesa per Beige book. Wall Street capitalizza su rally Tech di ieri. Inflazione al 3,5% sotto attese: per mercato no chance aumento luglio, sopra 60% per settembre. Warsh: nessuna tolleranza per inflazione persistentemente alta. Inflazione: ancora lavoro da fare. Indipendenza da trump. Banche, profitti record in aggregato a 49 miliardi. La volatilità del mercato spinge fatturato trading azionario. Commissioni mostre. Dimon: non potrebbe andare meglio di così. E adesso punta all'Europa. OpenAI, pronto il primo dispositivo hardware. Il warninng AI di Demis HAssabis che chiede autorità Usa per regolare modelli. New York: stop a datacenter per un anno. Buffet: in 8 anni cessione azioni rimanenti (140mld dollari). Stripe e Advent insieme per conquistare PayPal. ***Questo episodio è offerto da ⁠Scalable Capital ⁠Apri un conto con Scalable Capital e inizia a ricevere il 2,5% di interessi* sui tuoi risparmi:  https://it.scalable.capital/broker-online?utm_medium=affiliate&utm_source=qualityclick&utm_campaign=broker&c_id=QC59486e7f67706c777b517d435049607362766c747c5aS7541p&utm_term=983 Messaggio pubblicitario. Tasso lordo annuo variabile sulla liquidità depositata nel conto deposito non vincolato, composto da tasso base collegato al Tasso di Deposito BCE e tasso bonus discrezionale. Liquidità allocata presso banche partner e fondi monetari riconosciuti. Foglio informativo e condizioni su scalable.capital. Investire comporta dei rischi*** Asia in rally, Nikkei +2% Kopsi +7% con Sk Hynix e Samsung (smentisce debutto al Nasdaq). Cina: nel 2Q crescita più bassa da tre anni: +4,3%. Recuperano vendite al dettaglio e produzione industriale. Deepseek verso Ipo a Shanghai nel 2027. Europa piatta, oggi inflazione in Spagna e produzione industriale Eurozona. Maggioranza sotto su voto emendamento preferenze. ASML rivede al rialzo la guidance su domanda forte. Focus su banche, il due “buy” di Morgan Stanley in vista delle trimestrali.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Microsoft announced its finally removing promotional noise from the Windows 11 Search Box, and New York has become the first US state to place a moratorium on new hyperscale data centers.Starring Jason Howell and Jenn Cutter.Links to stories discussed in this episode can be found here. Hosted on Acast. See acast.com/privacy for more information.

Demis Hassabis proposed a US-based frontier AI standards body modeled on FINRA. IBM's stock cratered 20% on a Q2 miss from chip-spending shifts, Spotify launched a voice-control feature, Kalshi debuted an AI compute forward curve, and Anthropic studied Claude's values. Demis Hassabis proposes a US-based Standards Body for "Frontier-class" AI, modeled after FINRA; labs would share models for review up to 30 days before release (X) Demis Hassabis proposes a US-based Standards Body for "Frontier-class" AI, modeled after FINRA; labs would share models for review up to 30 days before release (The Verge) IBM reports preliminary Q2 revenue up 1% YoY to $17.2B, below $17.9B est., as CEO Arvind Krishna says customers are shifting spending to chips; IBM falls 20%+ (Bloomberg) Spotify launches a Talk to Spotify feature that lets users create playlists and more, rolling out in beta to Premium users 18+ in the US, Ireland, and Sweden (Engadget) Kalshi launches a forward curve tool for AI compute, using event contracts to track the future rental costs of GPUs, storage, and memory (Bloomberg) Simulating everything, sort of: The promise and limits of world models (Ars Technica) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

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My fellow pro-growth/progress/abundance Up Wingers in America and around the world:If my podcast guest today is correct, the emergence of generative artificial intelligence "heralds a transformation more profound than anything since Homo sapiens acquired the capacity for abstract thought." That's about as pure a distillation of the San Francisco Consensus view on the importance of this technology as it gets.Today on Faster, Please!—The Podcast, I am joined by Sebastian Mallaby, the Paul A. Volcker Senior Fellow for International Economics at the Council on Foreign Relations and a widely read columnist for The Washington Post. He is also the author of the new best-selling book The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence. (Spoiler: It's tremendous book about the man, the company, and the technological revolution. I really liked it.)We discuss The Infinity Machine and the life of Demis Hassabis, including how his original vision for artificial superintelligence compares with the propulsive race unfolding today. We also explore how that competitive acceleration has affected the focus on safety, what role government regulation should play, and why many people may still be underestimating how transformative AI will become.The Quest for “Success” (0:27)Inside the Mind of Hassabis (8:29)The Race for Monopoly (12:31)The Economics of AI Anxiety (17:05)Governing the AI Race (24:17)The Biggest Leap Since Abstract Thought (30:13)A lightly edited transcript of our conversation will appear in my Week in Review issue on Saturday. (Another option is using the Substack auto transcript function.)But here are some edited highlights from the chat:On where AI is heading…You look backwards; you see how fast the progress has been. To merely extrapolate forwards is probably to undersell the speed at which we'll accelerate in the future because there's an accelerating phenomenon here where the more advanced you are, the easier it is to get to the next level.On Hassabis's belief that AI development would look more like the Manhattan Project than a multi-country, multi-company competition …In retrospect, it's crazy. All one can say is that ex ante, the atmosphere in the community of AI builders when Demis began his company in 2010 was that this was a thing that simply didn't work. AI could not recognize the photograph of a cat. AI could do nothing. It was deep AI winter. And so, under those conditions, you could assemble the entirety of the world's strong AI believers in one conference in San Francisco, and it felt like a single community. So, this sort of Singleton scenario where you just have one lab, it was a natural outgrowth of that moment in time.How AI competition has overwhelmed that vision…Before 2022, Demis had the freedom because he was clearly the leader to define what the next project should be. He chose at one point to go and do this protein folding project. …This is kind of AI with a smiley face painted on it. Whereas once the chatbot went viral at the end of 2022, ChatGPT, then everybody had to pile in and build a competitor and there's a lot less leeway to define your own path. So, I think the agency of the individual was quite strong until 2022 and thereafter the power of the race dynamic takes over.What skeptics, such as many economists, have gotten wrong and right…The number of improvements before even we talk about Mythos and the cyber capabilities of that one, I mean, it's been an extraordinary ride in what is actually less than four years. So, I don't take back anything I say about the speed of the advance of the frontier. Now that's different to the speed of the deployment. There I have a lot of sympathy with the economist.On the difficulty of AI regulation…I'm actually quite optimistic in terms of the ability of a government agency to regulate… People often think of AI as a bunch of code that flies around cyberspace and you really can't control it. But actually, it's also a bunch of data centers which are huge physical installations. The government knows precisely where they are. They can't be moved or hidden.On his superintelligence timeline…To be honest, I would say it's already true. I mean, you try using Fable and if people are listening and they're inclined not to agree with me, I just ask you, spend a couple of hours with Claude Fable and then see if you disagree with me…I think it is smarter than me by quite a long shot on any topic I ask it about.On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe

“A place that doesn't have great philosophers will not have great technologists either.” — Mehran Gul on Europe's inexplicable underperformance The digital revolution, we were promised, would mean the end of geography. From Beijing to Birmingham to Berlin to Barcelona, anyone could invent anything anywhere, and so the geography of innovation would no longer matter. But that's not the way it has worked out. At least according to the Geneva-based innovation geographer Mehran Gul. Gul's acclaimed The New Geography of Innovation is a travelogue of innovation. But what he finds on his journey around the world in search of innovation is the end of the end of geography. Yes, Gul reports, there's innovation in Beijing and in Birmingham (USA) — but not in Birmingham (England), Berlin or Barcelona. All the important invention is in China and the US. There simply isn't much radical stuff going on anywhere else. Gul began his journey expecting to find ten or twelve countries able to innovate competitively with the United States and China. But what he discovered is either niche players or, in the case of South Korea, Israel, and India, just an extension of the US-centric system. Europe — as renters rather than owners of American technology — comes off worst. When PayPal went public, it minted 160 millionaires who went on to help build SpaceX, Tesla, LinkedIn and Palantir; when Skype exited at about the same value, it minted 11. And if you put London aside, the rest of the UK is now poorer per capita than Mississippi. And the AI boom has only compounded all this, with half of last year's key research papers coming from China, 40% from America, and just 4% from Europe. So really the new geography of innovation is the old geography. Only with China replacing Europe as the only serious competitor to American innovation. Oh lord, oh lord. As a Mississippi bluesman might summarize Europe's predicament. Five Takeaways •       Golden Shares: The Two Systems Are Converging. OpenAI offering Washington a 5% stake, the US government owning Intel — these are Chinese moves, and Gul argues the two models are becoming more alike than either admits. But he pushes back on the lazy version of the China story: its tech sector rose despite the state, not because of it. Jack Ma exiled to Japan, Didi hit with a billion in fines, entire sectors decapitated overnight in 2021 under the banner of common prosperity. In a country with no independent media and no opposition parties, the only rival to centralized power is the tech sector — and the party knows it. •       Two Countries — and Everyone Else. Gul started writing expecting to find ten or twelve countries punching at America's level; the honest answer turned out to be two. Only China has broad-based competence across technologies and a genuinely competitive relationship with the US. The middle powers — South Korea, Israel, India — are extensions of the American system, not rivals to it. That finding surprised the author as much as anyone: it's not the book he set out to write. •       Europe: Renters, Not Owners. After the Fable 5 and Mythos bans, Europe woke up to being a renter of American technology — foundation models, NVIDIA GPUs, all of it. Its best companies keep leaving: DeepMind to Google, Arm to a New York listing, Hugging Face from Paris to Manhattan — while Volvo, Supercell, and KUKA sold to China. Gul's diagnosis is institutional, not cultural: European employees own half as much of their startups as American ones, so there is no European PayPal mafia. His fixes: a European Nasdaq to replace 41 competing capital markets, and pension funds unleashed into venture capital. •       The Question Nobody Is Asking. Since 1990, America's share of global GDP has held at 25% while China's multiplied tenfold — the loser is Europe. The top ten American tech companies are worth $27 trillion, more than the GDP of every country on earth except America itself. Tech is not one industry among many; it is the foundation of all of them — the new cars came from Tesla, not GM. Gul's message to the skeptical Spaniard enjoying long lunches: the last sixty years of American platform dominance skewed power across the Atlantic, and the next sixty will add China to the bill. •       The Rest of the Map: Anti Case Studies. Japan tops the freedom indexes, has the technical schools, and still never escaped the keiretsu — disproving Matt Ridley's claim that innovation is simply the child of freedom. Taiwan's relevance comes down to one company and Morris Chang's missed promotion at Texas Instruments. Singapore is an inspiration, not a model — a one-party city-state that invoices NVIDIA's chips and banks ASEAN's venture capital. India underperforms while Indians excel — 56 notable American foundation models last year, 35 Chinese, barely one Indian. And Switzerland reminds us innovation isn't only venture-backed: a train network running on renewables since the 1960s. About the Guest Mehran Gul writes about technology and business. He is the winner of the Financial Times/McKinsey Bracken Bower Prize, from which The New Geography of Innovation grew. He attended Yale as a Fulbright Scholar, Fox International Fellow, and Teaching Fellow, has been a Lead for the Digital Transformation of Industries at the World Economic Forum in Geneva, and served as an expert on entrepreneurship and industrial policy at the United Nations Industrial Development Organization in Vienna. Born in Pakistan, he lives in Switzerland. The New Geography of Innovation: The Global Contest for Breakthrough Technologies (Avid Reader Press/Simon & Schuster), a Financial Times Book of the Year, is his first book, out in paperback this month in the US and UK. References: •       The New Geography of Innovation: The Global Contest for Breakthrough Technologies by Mehran Gul (Avid Reader Press/Simon & Schuster). The Wall Street Journal: “An ambitious tour of technological innovation.” •       Sebastian Mallaby — author of The Power Law, which argues China's tech rise owes more to American-style risk capital arriving in Shanghai and Shenzhen than to the state; recently on the show discussing his biography of Demis Hassabis. •       Kai-Fu Lee — author of AI Superpowers, cited by Gul as the classic account of tech written through a Chinese lens. •       Matt Ridley — author of How Innovation Works, whose thesis that innovation is “the child of freedom and the parent of prosperity” Gul tests against the anti case study of Japan. •       Andrew Keen — author of How t...

Emmanuel Macron. Demis Hassabis. Volodymyr Zelenskiy. George Soros. Mark Carney. Christine Lagarde. Ray Dalio. Leena Nair.  Few journalists have spent more time questioning the people who shape the global economy than Francine Lacqua. As Editor-at-large at Bloomberg and host of Leaders with Francine Lacqua on Bloomberg TV, Lacqua has interviewed many of the most influential political and business leaders of our time. Across hundreds of conversations with presidents, CEOs, central bankers and founders she has built a rare understanding of how leadership operates at the highest levels of power.  In June 2026, Lacqua joined us live on stage for a special instalment of The Intelligence Squared Economic Outlook, our flagship series examining the forces shaping global markets, politics and business. In conversation with BBC broadcaster Jonny Dymond, she reflected on the leaders she has encountered throughout her career – and the defining decisions they faced during moments of economic uncertainty, geopolitical tension and technological change. What distinguishes leaders who succeed in turbulent times? How do the best decision-makers balance political pressure, economic risk and long-term strategy? And what kinds of leaders does today's increasingly volatile world demand? This recording is part of The Intelligence Squared Economic Outlook series of events made in partnership with Guinness Global Investors, an independent British fund manager that helps both individuals and institutions harness the future drivers of growth to achieve their investment goals. To find out more visit: https://www.guinnessgi.com/ --------- This is the first instalment of a two-part episode. If you'd like to become a Member and get access to all our full ad free conversations, plus all of our Members-only content, just visit intelligencesquared.com/membership to find out more. For £4.99 per month you'll also receive: - Full-length and ad-free Intelligence Squared episodes, wherever you get your podcasts - Bonus Intelligence Squared podcasts, curated feeds and members exclusive series - 15% discount on livestreams and in-person tickets for all Intelligence Squared events  ...  Or Subscribe on Apple for £4.99: - Full-length and ad-free Intelligence Squared podcasts - Bonus Intelligence Squared podcasts, curated feeds and members exclusive series … Already a subscriber? Thank you for supporting our mission to foster honest debate and compelling conversations! Visit intelligencesquared.com to explore all your benefits including ad-free podcasts, exclusive bonus content and early access. … Subscribe to our newsletter here to hear about our latest events, discounts and much more. https://www.intelligencesquared.com/newsletter-signup/ Learn more about your ad choices. Visit podcastchoices.com/adchoices Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode, the boys cut in with two breaking stories. First, Walmart buys Vibe.co, a connected TV advertising platform, in a move that could make Walmart's already-growing ad business even more interesting. Robert believes the strategy is right, especially with Walmart's retail media business and Vizio already in the fold, but thinks the price tag may have been a bit too rich. Then Joe and Robert revisit the FIFA stadium branding story. FIFA's clean-stadium policy has forced brands like Levi's, Heinz and others to cover up their logos during World Cup matches. But instead of making those brands disappear, FIFA may have created the perfect Streisand effect. Heinz, Beats and Levi's have all turned the restrictions into creative marketing moments. Is FIFA protecting its sponsors, or accidentally giving non-sponsors a bigger story? In our main stories, Google and A24 announce a partnership around AI filmmaking tools. The big question is not whether AI will make the final movie. It's whether AI will control more of the creative workflow before the final product ever exists. Then Meta and Snap both make new moves in smart glasses. Meta pushes toward a lower-cost, more mainstream AI glasses play, while Snap launches its new AR-focused Specs. If glasses become the next interface, marketers may have to rethink content for a world where the screen is no longer in your hand. It's on your face. In Winners and Losers, Joe's winner is TIME Canada. TIME is launching a licensed Canadian edition with a local team, local office, original reporting, video, social, print and events. In a world of generated content, Joe likes the bet on trusted editorial brands with a local heartbeat. Robert's winner is McDonald's, which is bringing back the fried apple pie. Sometimes nostalgia, timing and a little bit of fried goodness is all the marketing strategy you need. In Rants and Raves, Joe raves about The Infinity Machine by Sebastian Mallaby, a book about Demis Hassabis, DeepMind and the race toward superintelligence. Robert delivers a super rant on TuneCore and how independent creators may be getting the short end of the stick as AI music floods the market and distribution platforms try to figure out who gets through, who gets blocked, and who gets paid. Also mentioned this week: In the Weights, a site that lets you see whether you show up in the "weights" of different AI models: https://www.intheweights.com/ Subscribe and Follow: Follow Joe Pulizzi and Robert Rose on LinkedIn for insights, hot takes, and weekly updates from the world of content and marketing.  ------- This week's sponsor: Did you know that most businesses only use 20% of their data? That's like reading a book with most of the pages torn out. Point is, you miss a lot. Unless you use HubSpot. Their AEO and customer platform gives you access to the data you need to grow your business. The insights trapped in emails, call logs, and transcripts.  All that unstructured data that makes all the difference. Because when you know more, you grow more. Visit https://www.hubspot.com/ to hear how HubSpot can help you grow better. ------- Get all the show notes: https://www.thisoldmarketing.com/ Get Joe's new book, Burn the Playbook, at http://www.joepulizzi.com/books/burn-the-playbook/ Subscribe to Joe's Newsletter at https://www.joepulizzi.com/signup/. Get Robert Rose's new book, Valuable Friction, at https://robertrose.net/valuable-friction/  Subscribe to Robert's Newsletter at https://seventhbearlens.substack.com/ ------- This Old Marketing is part of the HubSpot Podcast Network: https://www.hubspot.com/podcastnetwork

Emmanuel Macron. Demis Hassabis. Volodymyr Zelenskiy. George Soros. Mark Carney. Christine Lagarde. Ray Dalio. Leena Nair.  Few journalists have spent more time questioning the people who shape the global economy than Francine Lacqua. As Editor-at-large at Bloomberg and host of Leaders with Francine Lacqua on Bloomberg TV, Lacqua has interviewed many of the most influential political and business leaders of our time. Across hundreds of conversations with presidents, CEOs, central bankers and founders she has built a rare understanding of how leadership operates at the highest levels of power.  In June 2026, Lacqua joined us live on stage for a special instalment of The Intelligence Squared Economic Outlook, our flagship series examining the forces shaping global markets, politics and business. In conversation with BBC broadcaster Jonny Dymond, she reflected on the leaders she has encountered throughout her career – and the defining decisions they faced during moments of economic uncertainty, geopolitical tension and technological change. What distinguishes leaders who succeed in turbulent times? How do the best decision-makers balance political pressure, economic risk and long-term strategy? And what kinds of leaders does today's increasingly volatile world demand? This recording is part of The Intelligence Squared Economic Outlook series of events made in partnership with Guinness Global Investors, an independent British fund manager that helps both individuals and institutions harness the future drivers of growth to achieve their investment goals. To find out more visit: https://www.guinnessgi.com/ --------- This is the first instalment of a two-part episode. If you'd like to become a Member and get access to all our full ad free conversations, plus all of our Members-only content, just visit intelligencesquared.com/membership to find out more. For £4.99 per month you'll also receive: - Full-length and ad-free Intelligence Squared episodes, wherever you get your podcasts - Bonus Intelligence Squared podcasts, curated feeds and members exclusive series - 15% discount on livestreams and in-person tickets for all Intelligence Squared events  ...  Or Subscribe on Apple for £4.99: - Full-length and ad-free Intelligence Squared podcasts - Bonus Intelligence Squared podcasts, curated feeds and members exclusive series … Already a subscriber? Thank you for supporting our mission to foster honest debate and compelling conversations! Visit intelligencesquared.com to explore all your benefits including ad-free podcasts, exclusive bonus content and early access. … Subscribe to our newsletter here to hear about our latest events, discounts and much more. https://www.intelligencesquared.com/newsletter-signup/ Learn more about your ad choices. Visit podcastchoices.com/adchoices Learn more about your ad choices. Visit podcastchoices.com/adchoices

SpaceX will be acquiring AI coding startup Cursor for $60 billion. AI venture capitalist Michael Fertik was that company's first investor, and he explains why a software vibe coding company is a good match for Elon Musk's AI ambitions. Victor Riparbelli, CEO of AI video platform Synthesia, was at the AI working lunch at the G7 in France, in the room with Sam Altman, Dario Amodei, Demis Hassabis, Marc Benioff, and world leaders including President Trump. Riparbelli discusses the group's effort to collaborate on AI guardrails, while maintaining the pace of innovation. The U.S. and Iran have signed the Memorandum of Understanding to end the war in Iran, but CNBC's Eamon Javers indicates that there are more negotiations still to come. Plus, CNBC's Steve Liesman reports on Kevin Warsh's first meeting as Federal Reserve Chairman.     Steve Liesman           4:06 Eamon Javers             11:28 Victor Riparbelli        18:26 Michael Fertik              28:13   In this episode: Joe Kernen, @JoeSquawk Steve Liesman, @steveliesman Eamon Javers, @eamonjavers Kelly Evans, @KellyCNBC Katie Kramer, @Kramer_Katie Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

When Demis Hassabis pitched DeepMind to a few venture capitalists back in 2010, the business plan was almost comically audacious. “Step one: Solve intelligence. Step two: Use it to solve everything else,” he recalls in a conversation at Stanford Graduate School of Business with Stanford University President Jonathan Levin. “And people were quite confused. But we really meant it.”Sixteen years later, the “broad arcs” of that plan have gone “unbelievably well,” says Hassabis, a chess prodigy turned video game developer turned neuroscientist turned Nobel Prize-winning AI pioneer. Today he's on a mission to create “the ultimate tool for science,” building on his decision to give away AlphaFold, the groundbreaking AI system that predicts the structures of proteins. The future, Hassabis says, is just around the corner: “Ten years from now, I think we'll realize that we were standing in the foothills of the singularity now.”AI@GSB, the Dean's Applied AI initiative at the Stanford Graduate School of Business (GSB), and Stanford Medical School hosted a conversation with Demis Hassabis, Co-founder and CEO of Google DeepMind, on the frontier of artificial intelligence and what it means for how we live, work, and flourish.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Sebastian Mallaby (@scmallaby) is the Paul A. Volcker senior fellow for international economics at the Council on Foreign Relations, a two-time Pulitzer Prize finalist, and the author of six books, including More Money Than God, The Power Law, The Man Who Knew, and The World's Banker. His latest book is The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence.This episode is brought to you by:Eight Sleep Pod Cover 5 sleeping solution for dynamic cooling and heating: EightSleep.com/TimAG1 Pro all-in-one nutritional supplement: DrinkAG1.com/TimWealthfront high-yield cash account: Wealthfront.com/Tim Wealthfront disclaimer: New clients get 3.30% base APY from program banks + additional 0.75% boost for 3 months on your uninvested cash (max $150k balance). Terms and conditions apply. The Cash Account offered by Wealthfront Brokerage LLC (“WFB”) member FINRA/SIPC, not a bank. The base APY as of 1/30/26 is representative, can change, and requires no minimum. Tim Ferriss, a non-client, receives compensation from WFB for advertising and holds a non-controlling equity interest in the corporate parent of WFB, which creates a conflict of interest. Individual experiences and outcomes will differ. Instant withdrawals may be limited by your receiving firm and other factors. Investment advisory services provided by Wealthfront Advisers LLC, an SEC-registered investment adviser. Securities investments: not bank deposits, not bank-guaranteed or FDIC-insured, and may lose value.*Timestamps[00:00:00] Start.[00:02:11] The twinkly eyed polymath who became Sebastian's next book.[00:06:55] Picking the next book project the way a great VC picks a startup.[00:09:41] Why God keeps crashing the superintelligence party.[00:11:13] Shane Legg's grainy 2009 prophecy — and the nervous giggle.[00:13:11] Ilya Sutskever burns an effigy.[00:13:54] Demis at 4 a.m., hunting God's algorithm.[00:18:43] Super-abundance, Mad Max, and the China shock lesson.[00:22:39] The kitchen debate with Geoff Hinton that flipped Sebastian.[00:24:06] Why a zero-percent chance of doom is indefensible.[00:24:52] Will Washington seize the labs? The Mythos wake-up call.[00:27:18] Anthropic's bull case, bear case, and a dead parent's letter.[00:33:24] Where Sebastian and Benedict Evans part ways.[00:38:16] Is the SaaS apocalypse overdone? One word: Palantir.[00:39:53] The AI friend you'll never switch.[00:41:56] Does Google win consumer AI by default?[00:44:45] Four cities, eight days: China actually talks safety.[00:47:28] A Cold War non-proliferation playbook for AI.[00:49:45] Did the chip export controls actually work?[00:51:49] Burned doves: why Washington swears China won't talk.[00:54:56] "By 2028, the race is over" — one lab boss' bet.[00:59:11] Inside Hikvision: toddlers, sensors, and US sanctions.[01:01:07] Bill Gurley's Uber bet: venture capital perfected.[01:05:18] Luke Nosek bear-hugs DeepMind into existence.[01:10:52] Thiel's heresy: never invest by committee.[01:11:59] How Founders Fund nearly fumbled the deal of the century.[01:14:30] Selling to Google for $650M: a secret British heist?[01:16:41] The Traitorous Eight, gardening leave, and the UK's to-do list.[01:20:55] Ender's Game: "That's really how I see myself."[01:23:42] Too dumb for Gödel, Escher, Bach? Maybe an LLM can help.[01:25:19] If not Demis or Sam, then Dario.[01:26:04] My royalties cliff — and what dropped in late 2022.[01:27:47] Lila Sciences and the labs that run themselves.[01:31:13] Sebastian's billboard: "Prepare your mind."[01:35:14] The one thing Sebastian will never outsource to AI.[01:40:09] Parting thoughts.For show notes and past guests on The Tim Ferriss Show, please visit tim.blog/podcast.For deals from sponsors of The Tim Ferriss Show, please visit tim.blog/podcast-sponsorsSign up for Tim's email newsletter (5-Bullet Friday) at tim.blog/friday.For transcripts of episodes, go to tim.blog/transcripts.Discover Tim's books: tim.blog/books.Follow Tim:Twitter: twitter.com/tferriss Instagram: instagram.com/timferrissYouTube: youtube.com/timferrissFacebook: facebook.com/timferriss LinkedIn: linkedin.com/in/timferrissSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In 1994, Bullfrog Productions released Theme Park, a construction and management simulation that would go on to sell fifteen million copies and define a genre. In this episode, David and Rob trace the game's development from Peter Molyneux's initial concept through the year and a half of work that brought it to life, led by a seventeen-year-old programmer named Demis Hassabis working a gap year before Cambridge. They explore how Hassabis built the game's visitor simulation from scratch, why multiplayer was cut two weeks before release, and what it meant that Molyneux's bet on bright colors for Japan paid off exactly as predicted. They also follow the thread forward from Theme Park's role in establishing a genre to the Nobel Prize in Chemistry that Hassabis won in 2024 for work that began with the same questions he was asking in Guildford in 1993. Join David and Rob as they look back at the little people, the late-night spreadsheets, and the teenager who built them on today's trip down Memory Card Lane.Read transcript

En este episodio de Mundo Futuro exploramos cómo la inteligencia artificial está entrando en nuevas capas de la vida cotidiana, la creatividad y la ciencia. Primero hablamos de Text to Song, la tendencia viral que convierte conversaciones reales en canciones usando IA. Chats de WhatsApp, peleas familiares, rupturas amorosas y dramas cotidianos se transforman en música, abriendo una nueva pregunta: ¿la creatividad del futuro será más técnica o más emocional? Después entramos a la historia de Demis Hassabis, fundador de DeepMind, protagonista del libro The Infinity Machine y una de las mentes más importantes de la inteligencia artificial moderna. De los videojuegos y Atari, al ajedrez, Go, AlphaGo, AlphaFold y el Premio Nobel, su historia muestra cómo la IA pasó de ganar juegos a resolver problemas científicos reales. También hablamos de Isomorphic Labs, el nuevo proyecto derivado de DeepMind que busca acelerar el desarrollo de medicamentos con inteligencia artificial. Una empresa que acaba de levantar miles de millones de dólares con una ambición enorme: usar IA para transformar la medicina y, eventualmente, curar enfermedades que hoy parecen imposibles. Un episodio sobre música viral, creatividad artificial, ciencia computacional y el tipo de inteligencia que podría cambiar el futuro de la humanidad. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, the mates discuss Opus 4.8, The OpenAI Foundation, Demis Hassabis' views on AGI, AI extremism on the rise, and more. Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends   Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Salim Ismail is the founder of Open ExO, a GP at Exponential Venture Capital/The Organizational Singularity Fund and a sought after global speaker and thought leader. Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified – My companies: Apply to Dave's and my new fund:https://qr.diamandis.com/linkventureslanding      Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy   Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter  _ Connect with Peter: X Instagram Substack Website Xprize Abundance360 Connect with Dave: Web X LinkedIn Instagram TikTok Connect with Salim: LinkedIn X Apply for Salim's Pilot Program  Subscribe to Salim's YouTube channel Exponential Venture Capital Connect with Alex Website LinkedIn X Email Substack  Spotify Threads Listen to MOONSHOTS: Apple YouTube – *Recorded on May 30th, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

Sebastian Mallaby spent three years and 30+ hours interviewing Demis Hassabis in the back of a British pub to write The Infinity Machine, and the conversation uses that reporting to surface the most underexplored figure in AI. Demis founded the original AI lab in 2010, won a Nobel Prize, runs models that consistently top the leaderboards, and yet remains so unrecognized that Sebastian's own publisher worried no one would buy a book with his face on the cover.  The throughline is a paradox: Demis tried to prevent the AI race we're now all living through, and now finds himself one of its central protagonists. He used to believe a single lab could carry the safety burden to AGI; he now sees safety as a collective action problem only governments can solve. He hedged DeepMind's research bets across every promising direction, and as a result missed the two most consumer-defining moments in modern AI — ChatGPT and Claude Code. He nearly spun DeepMind out of Google with a secret $1B Reid Hoffman pledge backing him, but never used the leverage and stayed — and won a Nobel Prize the next year. The episode also zooms out to the structural forces shaping the race — why hyperscalers can't out-recruit concentrated-bet labs, why Sebastian gives OpenAI roughly 50/50 odds of being absorbed by next summer, why he thinks Anthropic should IPO right now, and what the personal histories between Demis, Elon, and Sam reveal about who actually trusts whom.   (0:00) Intro (2:04) Was the AI Race Inevitable? (4:03) The 2015 Safety Summit Backfire (7:15) Can Governments Actually Fix This? (9:26) How the World Misread DeepMind (11:27) Why Google Never Makes the Concentrated Bet (15:51) Project Mario: The Secret Spinout Plan (19:43) What Demis Actually Regrets (23:46) Venture Startups vs. Tech Behemoths (27:50) Controlling the Narrative (30:40) The Talent War and Hiring Brand (34:08) David Silver and the RL True Believers (38:21) Demis, Elon, and the Evil Genius Feud (42:39) Great Man Theory vs. Inevitability (45:00) What Demis Didn't Want Published With your host: @jacobeffron - Managing Director at Redpoint

For decades, artificial intelligence was dismissed as science fiction. Then one lab changed everything.Inside a small London research company, scientists were teaching machines to play games, predict protein structures, and solve problems humans couldn't. What started as an obscure AI experiment soon became the center of a global race for superintelligence — with enormous consequences for medicine, warfare, scientific discovery, and the future of human intelligence itself.On this episode of Morning Wire, journalist Sebastian Mallaby explains how DeepMind helped launch the modern AI revolution and why its founder Demis Hassabis believes artificial intelligence could push beyond the limits of human understanding. Get the facts first with Morning Wire.- - -Ep. 2815- - -Wake up with new Morning Wire merch: https://bit.ly/4lIubt3- - -Today's Sponsors:Fast Growing Trees - Visit https://fastgrowingtrees.com to get 20% off your first purchase when using the code WIRE at checkout.Alliance Defending Freedom - Visit https://JoinADF.com/WIRE or text “WIRE” to 83848 to learn more.- - -Privacy Policy: https://www.dailywire.com/privacymorning wire,morning wire podcast,the morning wire podcast,Georgia Howe,John Bickley,daily wire podcast,podcast,news podcast Learn more about your ad choices. Visit podcastchoices.com/adchoices

Kara speaks with journalist and author Sebastian Mallaby about his new book, "The Infinity Machine," and its central figure: Demis Hassabis, the CEO and co-founder of Google's AI research lab, DeepMind, and a Nobel Prize winner in chemistry.   Sebastian argues that Hassabis is one of the original scientist-entrepreneurs of modern AI. And although he's extremely competitive and research-driven, Sebastian says Hassabis is also one of the few big names in AI development who genuinely cares about public safety. However, despite his best intentions, Hassabis doesn't have the power to change the race dynamic driving AI's rapid, and potentially unsafe, development. Kara and Sebastian break down DeepMind's relationship with Google, the push toward artificial general intelligence, and whether the government can regulate the technology before something goes wrong.  Questions? Comments? Email us at on@voxmedia.com or find us on YouTube, Instagram, TikTok, Threads, and Bluesky @onwithkaraswisher. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Gideon talks to Sebastian Mallaby, author of The Infinity Machine, a book about the career of Demis Hassabis and his AI company, Google DeepMind. They discuss the growing backlash against AI, why people are worried, and what governments can do to mitigate the risks of the coming technological revolution. Clip: WSJFree links to read more on this topic:OpenAI's foundation to spend $250mn on research into AI's impact on economyPope's appeal can't change the AI race's risky logicAI guardrails stripped from Meta and Google models in minutesHow AI threatens the giants of consulting AI companies are just companiesSubscribe to The Rachman Review wherever you get your podcasts - please listen, rate and subscribe.Presented by Gideon Rachman. Produced by Fiona Symon. Sound design is by Breen Turner.Follow Gideon on Bluesky or X @gideonrachman.bsky.social, @gideonrachmanRead a transcript of this episode on FT.com Hosted on Acast. See acast.com/privacy for more information.

We speak to Sebastian Mallaby, author of ‘The Infinity Machine', a biography of Demis Hassabis, to discuss the Google DeepMind CEO’s views and politics around AI. With Michael Walker & James Meadway.

This week wasn't just another wave of AI announcements.It may have been the week the industry quietly crossed into a different phase entirely.In this episode, Isar connects the dots behind one of the biggest weeks in AI so far—from Anthropic's explosive growth, to Google I/O, OpenAI's legal win, NVIDIA's record earnings, and Andrej Karpathy joining Anthropic to work on recursive self-improvement.Individually, each story matters.Together, they point to something bigger: accelerating AI capability, accelerating infrastructure buildout, and growing signals from the people closest to the frontier that we may be entering a very different era.The quote that framed the episode came from Demis Hassabis: “We were standing at the foothills of the singularity. It will be a profound moment for humanity.”This episode breaks down what that actually means—and why the implications go far beyond new models and product launches.In this session, you'll discover: - Why Anthropic's projected $44B annualized revenue shocked the industry - How Anthropic became more profitable per user than OpenAI, Google, and Microsoft - Why Andrej Karpathy joining Anthropic may be one of the year's biggest AI stories - What recursive self-improvement (RSI) means—and why labs are racing toward it - How OpenAI's legal win against Elon Musk clears the runway for a potential IPO - Why Google's AI strategy suddenly looks both confusing and incredibly ambitious - What Google's shift from “search” to autonomous AI agents means for websites and SEO - Why AI solving an 80-year-old math problem matters more than most people realize - How NVIDIA, SpaceX, and compute infrastructure are becoming central to the AI race - Why electricity—not chips—may become the biggest bottleneck in AI expansion - What Demis Hassabis means when he says we're at the “foothills of the singularity”About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Before we get into this week's tech news, we have some corporate news to discuss, and some very exciting Vergecast news to share. (If you have questions about either one, hit us up: vergecast@theverge.com or 866-VERGE11!) Then, Nilay and David get back into the weeds on all things Google I/O, and in particular the ways AI is changing the Google Search experience. When Gemini can find things for you, make things for you, even buy things for you, are you even searching anymore? Finally, in the lightning round, it's time for the Hype Desk, Brendan Carr is a Dummy, SpaceX, the Trump Phone, and some very confusing social networks. Further reading: The future of Google is a search box that does everything  Google is building a ‘universal' AI shopping cart that tracks prices, offers suggestions, and finds discounts  Demis Hassabis said this might be the ‘foothills of the singularity.' What?  Google is trying to make deepfake detection more accessible  Google Search's AI evolution includes more ads  Google's AI future demands trust — and your personal data  Why does the Googlebook exist? The FCC voted to ‘streamline' tracking US broadband quality. In SpaceX's IPO, Elon Musk is the risk factor Spotify is verifying podcasts made by real people too. NBC just got the Trump phone. Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed.We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. (Timestamps are approximate.) 00:00:00 Intro 00:02:00 Vox Media Sale 00:08:00 What Changes for The Verge 00:12:00 Vergecast Goes Daily 00:18:00 Feedback and Launch Details 00:23:00 Google I O Vibe Check 00:24:00 Agents Everywhere at Google 00:25:00 Search Becomes the Platform 00:26:00 Singularity Talk Whiplash 00:31:00 Monetizing AI and Google Zero 00:37:00 Shopping Web Takes Over 00:39:00 Agents Replace Browsing 00:43:00 Canvas Makes Apps 00:49:00 Google Book Devices Pitch 00:51:00 Agents Break App Economics 00:53:00 Traffic Deal Is Over 01:01:00 Hype Desk Forza Horizon 6 01:07:00 Subnautica 2 Surprise Hit 01:11:00 Brendan Carr is a Dummy 01:14:00 Broadband Map Complaints 01:21:00 Spotify AI Whiplash 01:25:00 Deepfake Detection Reality 01:30:00 SpaceX IPO Breakdown 01:34:00 Trump Phone In Wild 01:37:00 Wrap Up And Plugs Learn more about your ad choices. Visit podcastchoices.com/adchoices

What does it mean to be at the “foothills of the singularity”? That’s how DeepMind CEO Demis Hassabis ended his speech at Google I/O, prompting questions and scratched heads. Oz and Reed Albergotti (Semafor) attempt to dissect the meaning behind Hassabis’s confounding statement. They also discuss why so many commencement speakers are getting booed by college graduates after bringing up AI, and what it means for SpaceX, Anthropic, and OpenAI to all be heading towards an IPO. Then, Oz sits down with David Webster, Head of UX at Google Labs, for a deeper look at the products Google unveiled at their annual developer conference of the year. Additional Reading: DeepMind founder Demis Hassabis on what Google AI products say about ‘singularity’ | Semafor A Guide to Commencement | Semafor SpaceX, Anthropic and OpenAI’s Sprint to Go Public Defines the AI Boom’s Big Day - WSJ Former Google CEO Eric Schmidt was booed | Strait Times IG Subscriber Q&A: Live @ Google I/O - by Alex Heath - Sources Download SAILY in your app store and use our code techstuff at checkout to get an exclusive 15% off your first purchase! For further details go to https://saily.com/techstuffSee omnystudio.com/listener for privacy information.

Demis Hassabis, Co-Founder and CEO of Google DeepMind, refused to leave London, challenged Google on AI safety and helped lead DeepMind back into the AI race.Sebastian Mallaby, author of The Infinity Machine and The Power Law, joins Andreas Munk Holm to discuss the founder psychology of Demis, the story behind DeepMind and why Europe may be entering a new era in technology.The conversation explores DeepMind's fundraising journey, the Google acquisition, the merger with Google Brain, AI safety, sovereign technology and why Demis remains sceptical of parts of Silicon Valley culture despite operating at the centre of it.Timestamps(00:00) Why Demis Hassabis matters(01:12) Why DeepMind could not raise from European VCs(07:35) The Peter Thiel chess story(11:00) What DeepMind reveals about European venture(14:42) Why Europe's tech ecosystem is accelerating(18:20) European sovereignty, defence tech and AI(21:20) DeepMind's sale to Google and tensions over AI safety(29:40) The founder psychology of Demis(41:35) Google's ChatGPT moment and Gemini's comeback(45:05) Demis' critique of Silicon Valley(50:45) Europe's AI sovereignty problem(54:05) Final thoughts and Sebastian's new bookSubscribe to EUVC, the home of European tech, for more insights.

Google I/O 2026 just dropped Gemini Omni, a world-model AI that simulates physics, edits video, and might be the biggest leap since Seedance 2. But it's not perfect. Gavin and Kevin break down everything from Google I/O 2026, including the launch of Gemini Omni (Google's new world model), Gemini 3.5 Flash benchmarks against GPT-5.5 and Opus 4.7, the Gemini Spark personal agent, AskYouTube, Docs Live, new AI glasses, the first search box redesign in 25 years, and the shocking news that Andrej Karpathy is joining Anthropic. SHOW LINKS: Google I/O 2026 Full Keynote: https://www.youtube.com/live/wYSncx9zLIU?si=Nb881MfGTlf1Q0II Gemini Omni physics demos from Google DeepMind: https://x.com/GoogleDeepMind/status/2056786449312493669?s=20 Gemini Omni's incredible London knowledge (via fofrAI): https://x.com/fofrAI/status/2056789242274259242?s=20 Sundar Pichai and Demis Hassabis on Omni video editing: https://x.com/sundarpichai/status/2056524502746747048?s=20 Gavin's hands-on Gemini Omni experiments: https://x.com/gavinpurcell/status/2056762427879182692?s=20 Gemini Omni's character cameo feature (less impressive): https://x.com/gavinpurcell/status/2056772793539481830?s=20 Gemini Omni volleyball fail: https://x.com/flavioAd/status/2056771223359549645?s=20 Google's new Content Credentials Verification: https://x.com/Google/status/2056787498676658576?s=20 Genie 3 IRL — Google's world model now simulates real streets with Street View: https://techcrunch.com/2026/05/19/googles-genie-world-model-can-now-simulate-real-streets-with-street-view/ Bilawal Sidhu on Genie 3 IRL: https://x.com/bilawalsidhu/status/2056804315721843024?s=20 Gemini 3.5 Flash launches — official announcement: https://x.com/GeminiApp/status/2056788115893993701?s=20 Gemini Spark — Google's new personal coding agent: https://x.com/Google/status/2056791134295273554?s=20 Google's new AI glasses  https://x.com/backlon/status/2056807059707036050?s=20 Andrej Karpathy joins Anthropic to focus on recursive self-learning: https://www.axios.com/2026/05/19/anthropic-openai-karpathy-andrej-claude  

Demis Hassabis is an artificial intelligence researcher, scientist, and entrepreneur.  In 2010, he co-founded DeepMind, an AI research lab which is now part of Google. In 2024, Hassabis won a Nobel Prize for using AI to predict the 3D structure of proteins, critical for disease understanding and drug discovery.  He was also awarded a knighthood that year by King Charles III.On April 20, 2026, Sir Demis Hassabis came to the Sydney Goldstein Theater in San Francisco to talk with author Sebastian Mallaby, who recently published a book about Hassabis's work, The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence.  The two were interviewed on stage by journalist Emily Chang.

This episode features a dynamic panel discussion on exponential technologies, AI advancements, robotics, and the future of humanity. Experts explore the implications of AI, robotics, biotech, and societal shifts, offering insights into what the next decade holds for innovation and civilization. Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends   Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Steven Kotler is a New York Times bestselling author, and founder of the Flow Research Collective and Flow Institute, known for his work on flow and human performance. Salim Ismail is the founder of OpenExO Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified – My companies: Apply to Dave's and my new fund:https://qr.diamandis.com/linkventureslanding      Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy   Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter  _ Connect with Peter: X Instagram Substack Website Xprize Connect with Steven X Instagram LinkedIn Website Connect with Dave: Web X LinkedIn Instagram TikTok Connect with Salim: X Join Salim's Workshop to build your ExO  Connect with Alex Website LinkedIn X Email Substack  Spotify Threads Listen to MOONSHOTS: Apple YouTube – *Recorded on May 4th, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

Nikhyl Singhal is the founder of The Skip, a community for senior product leaders; a former product exec at Meta, Google, and Credit Karma; and a many-time founder. He's also one of the most honest, unfiltered voices on what's actually happening in product management right now.In our in-depth conversation, we discuss:1. Why the next two years will be the most chaotic period in product management history2. Why half of current product managers are at risk, and what separates those who'll do well3. Why you need to find your “moments of joy” with AI4. The “smiling exhaustion” he's seeing across the product community5. The psychological barriers that prevent people from reinventing themselves6. Why your resume's fancy logos matter less than ever, and what matters now7. His prediction that companies will shed 30,000 people and rehire 8,000—all AI-first—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVanta—Automate compliance, manage risk, and accelerate trust with AI—Episode transcript: https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Nikhyl Singhal:• LinkedIn: https://www.linkedin.com/in/nikhyl• X: https://x.com/nikhyl• Podcast & Newsletter: https://skip.show• Skip Community: https://skip.community• Skip Coach: https://skip.coach• Skip.help: https://skip.help—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Nikhyl Singhal(02:25) The big picture: what's changing for product managers(10:00) Are product leaders doing better than 2-3 years ago?(11:44) What will change in the next couple of years(14:23) How companies are changing the way they build products(15:51) What “judgment” really means for PMs(17:46) Why there won't be any more bad software(20:25) The skills you need to be effective today(23:31) Why there are more PM roles than ever(24:27) The builder versus information-mover divide(30:14) The non-builder problem(30:53) Should PMs code?(34:15) Why experienced leaders still matter(35:44) The diversity setback nobody's talking about(37:21) Why your brand doesn't matter as much anymore(39:54) How valued skills are flipping upside down(40:49) Why change is so hard for humans(43:53) The “equal disappointment” algorithm(46:39) You must cross the threshold(48:37) This chaos will settle(53:19) Finding your moment of joy(58:50) Nikhyl's AI stack and what he's building(1:00:53) The obsolescence mindset(1:05:24) Specific advice for PMs right now(1:08:58) The four jobs that will exist in the future(1:11:59) Why alignment is changing (but not disappearing)(1:15:40) How engineering is changing even more than PM(1:17:04) The surprising design plateau(1:18:49) Finding optimism in the chaos(1:21:12) Lightning round—Referenced:• Building a long and meaningful career | Nikhyl Singhal (Meta, Google): https://www.lennysnewsletter.com/p/building-a-long-and-meaningful-career• COBOL: https://en.wikipedia.org/wiki/COBOL• United Airlines: https://www.united.com• State of the product job market in early 2026: https://www.lennysnewsletter.com/p/state-of-the-product-job-market-in-ee9• Head of Growth (Anthropic): “Claude is growing itself at this point” | Amol Avasare: https://www.lennysnewsletter.com/p/anthropics-1b-to-19b-growth-run• Demis Hassabis on X: https://x.com/demishassabis• Sam Altman on X: https://x.com/sama• Dario Amodei on X: https://x.com/DarioAmodei• Cross on Prime Video: https://www.amazon.com/Cross-Season-1/dp/B0D6X7ZZHC• Jack Ryan on Prime Video: https://www.amazon.com/Tom-Clancys-Jack-Ryan/dp/B0CNDCMN8R• 24 on Prime Video: https://www.amazon.com/24-Season-1/dp/B000HPF85A• Claude Code: https://code.claude.com• Codex: https://chatgpt.com/codex• Lovable: https://lovable.dev• Sonos: https://www.sonos.com• “There are only four jobs” on X: https://x.com/yrechtman/status/2039012253341495462• Paradise on Hulu: https://www.hulu.com/series/paradise-2b4b8988-50c9-4097-bf93-bc34a99a5b4f• Lioness on Paramount+: https://www.paramountplus.com/shows/lioness• Tesla: https://www.tesla.com• Albert Einstein's quote: https://www.goodreads.com/quotes/115696-genius-is-1-talent-and-99-percent-hard-work—Recommended books:• James: https://www.amazon.com/James-Novel-Percival-Everett/dp/0385550367• The Adventures of Huckleberry Finn: https://www.amazon.com/Adventures-Huckleberry-Finn-Unabridged-Uncensored/dp/195483943X—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Last week, two separate juries held social media companies liable for harming young users. We unpack what these landmark decisions mean — not only for the future of social platforms like Meta and YouTube, but also for A.I. chatbots. Then, Sebastian Mallaby, the author of “The Infinity Machine,” joins us to talk about the three years he spent with Demis Hassabis and those closest to Google DeepMind. And finally, we catch up on some of our favorite tech headlines from the week with a round of HatGPT.   Guest: Sebastian Mallaby, author of “The Infinity Machine: Demis Hassabis, DeepMind and the Quest for Superintelligence.”   Additional Reading: Juries Take the Lead in the Push for Child Online Safety An A.I. Agent Was Banned From Creating Wikipedia Articles, Then Wrote Angry Blogs About Being Banned I Met Olaf — the Frozen Robot who Might be the Future of Disney Parks Claude's Code: Anthropic Leaks Source Code for A.I. Software Engineering Tool What's With All the A.I. Videos of Cheating Fruit? This Company Is Secretly Turning Your Zoom Meetings into A.I. Podcasts North Korean Hackers Suspected in Axios Software Tool Breach   We want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.