Podcast appearances and mentions of Vinod Khosla

This week: Silicon Valley legend Vinod Khosla's family is leading a group that's buying the Seattle Seahawks for a record $9.6 billion. We dug into hours of his talks and interviews to answer the big questions: Who is this guy, why does he want an NFL team, and what does his track record tell us about the kind of owner he'll be? Plus, the blind spot that could get him into trouble. Featuring highlights from his 2015 talk at the Stanford Graduate School of Business. Also: A mystery trove of aerospace artifacts is rescued from a Seattle-area electronics recycler, and we want to hear about your coolest tech history find. Send your comments, voice memos and photos to todd@geekwire.com. With GeekWire co-founders Todd Bishop and John Cook.See omnystudio.com/listener for privacy information.

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

Legendary writer Larry Stone joins Dick Fain to talk about the latest on NBA expansion with new comments yesterday from Adam Silver, the Seahawks sale to Vinod Khosla and his family, the Jody Allen leadership tenure, if Dan Wilson’s job is in jeopardy with the Mariners continued struggles, Dom Canzone’s new everyday spot, and Luke Raley.See omnystudio.com/listener for privacy information.

Shane and Ethan are back, and the World Cup jerseys are out. Episode 195 covers the Jersey Mike's IPO and the Forbes disclosures revealing that family members collected around thirty million dollars in compensation ahead of the company going public, Jeff Bezos co-leading an AI startup called Prometheus and declaring that AI will make single income households viable again, and Vinod Khosla's ten billion dollar purchase of the Seattle Seahawks plus the 20-year beach access lawsuit he has been fighting with the state of California. They also break down New York City's new pied-à-terre tax targeting non-resident property owners and what it actually means for people like Ken Griffin who own a $238 million penthouse but live in Florida, and close with a Reddit question from a business owner going from $500K to three million dollars in income who wants to know what to do with his money and why Reddit keeps telling everyone not to hire a wealth advisor. Topics covered: Jersey Mike's IPO valuation and the family compensation disclosures Bezos is co-leading an AI startup, and his case for AI optimism Vinod Khosla is buying the Seattle Seahawks for $10 billion The 20-year beach access lawsuit and how it went to the Supreme Court New York City's pied-à-terre tax explained and who it actually affects Reddit question: business owner going from $500K to $3M in income needs a plan Why Reddit keeps telling people not to hire a wealth advisor and what to do instead Timestamps: 00:00 Intro, World Cup jerseys, and the Argentina vs England matchup 03:15 Jersey Mike's IPO and the family nepo baby compensation disclosures 07:00 Bezos co-leads AI startup Prometheus and says AI will fix the economy 09:30 Vinod Khosla buys the Seattle Seahawks for $10 billion 11:45 The 20-year California beach access lawsuit that went to the Supreme Court 14:00 Khosla Ventures and the investments that made him fantastically wealthy 15:10 New York City's pied-à-terre tax and Ken Griffin's $238 million penthouse 20:15 Reddit question: $3M income S corp owner needs a financial plan 24:00 Why Reddit tells everyone not to hire a wealth advisor and what to do instead

In the second hour, Dick Fain and Alyssa Charlston-Smith chat with Hugh Millen about the sale of the Seattle Seahawks to the family of Vinod Khosla, Neeru Khosla, and Neal Khosla, including his nervousness about the change, then they listen and react to audio from Chuck Powell, who this morning called for the firing of Dan Wilson by the Mariners.See omnystudio.com/listener for privacy information.

In the first hour, Dick Fain and Alyssa Charlston-Smith discuss the sale of the Seattle Seahawks to the family of Vinod Khosla, Neeru Khosla, and Neal Khosla and feelings involved with the move, then midday host Gregg Bell joins the show to talk more about the sale and the new owners of the franchise, then they react to some Fun with Audio.See omnystudio.com/listener for privacy information.

What happens to intimacy, nostalgia, and creative power when a world-class filmmaker decides to step away from the camera and help reimagine their masterpiece for the live stage?  In this episode of TRUST ME I KNOW WHAT I'M DOING, I sit down with award-winning filmmaker, writer, and director Ritesh Batra. Celebrated globally for his masterpiece debut feature film, The Lunchbox, Ritesh has spent the last decade crafting incredibly delicate, human-centered stories. Now, he has brought that same quiet magic to the theater stage, serving as the book writer and co-lyricist for the highly successful musical adaptation of The Lunchbox.Ritesh shares unique process of rewriting a beloved indie film for a live theatrical medium. He shares why he deliberately avoided rewatching his own movie to allow a fresh, organic confluence of creative energy to take over with his collaborators. He also shares his personal philosophy on "artistic fingerprints"—explaining why true success in any creative field requires leaving your own distinct, irreplaceable mark rather than chasing past commercial formulas or letting executive gatekeepers dictate the narrative.  Ritesh shares insights on:• Nostalgia & Friction: Why he prefers focusing his storytelling on the compelling friction of characters "left behind" by rapid societal changes rather than those keeping up with them.  • Stage vs. Screen Intimacy: How the live stage provides a unique canvas to showcase unspoken connection and stillness in ways the cinema camera cannot.  • The Fingerprint Philosophy: Why elite artists must own their distinct perspective, and how to protect your creative power in a system obsessed with past commercial formulas.  • Songs Hidden in the Seams: The organic process of discovering musical numbers hidden in the quiet, empty spaces between film scenes.  • Overcoming the Fear of Collapse: Navigating the intense vulnerability of a production's first week and learning to trust your internal compass.  Whether you're a filmmaker, a theater lover, a creative professional navigating a highly public career, or someone looking to practice deeper mindfulness and trust in your everyday life, Ritesh's thoughts on artistic conviction provides an invaluable blueprint.  If you enjoyed this, please hit that subscribe button, leave a comment, and share this video with someone looking to realize and transform their own vision! --------------------------Chapters-00:00 Introductions02:40 Nostalgia, Change, and Telling Stories of Those Left Behind05:41 Translating the Intimacy of The Lunchbox to the Stage08:04 An Organic Transformation: Working with the Lazours10:09 Finding Songs in Between the Seams of the Story11:43 The Writer's Medium: Curating the Confluence of Creative Energy14:35 Discovering New Skills and Instincts in Musical Theater16:32 Sponsor Breaks - TRAVELOPOD and LOTUS LANE COFFEE17:31 Managing a Public Career: Owning Successes and Failures21:14 Leaving Your Fingerprints Over Everything You Create25:10 Wielding Artistic Power and Collaborating with Actors27:56 Thriving on the Unknown: Ritesh's Operating Mantra29:55 Lessons from Parenting and the Cost of Creative Focus31:42 Quick Hitters: Greatest Fears, Mingling, and Hamnet33:07 Cultivating Trust in Yourself and the Unseen End Goal35:18 Conclusions & Shoutouts#RiteshBatra #TheLunchbox #IndieFilmmaking #SouthAsianRepresentation #Storytelling #TrustMeIKnowWhatImDoingShoutouts:✉️ Mitra Kalita for making this incredible connection happen!

Scott and Eben discuss the Khosla family—led by tech billionaire Vinod Khosla and his wife, Neeru Khosla—agreeing to buy the Seattle Seahawks for an NFL record $9.61 billion. They talk about the Khosla family's background, the deal price and the yet-to-be-announced financing. Learn more about your ad choices. Visit megaphone.fm/adchoices

Hugh Millen joins Dick Fain and Alyssa Charlston-Smith to talk about the sale of the Seahawks to the family of Vinod Khosla, Neeru Khosla, and Neal Khosla including the feelings of nervousness that come with this major leadership change, a math equation that computes his level of nervousness, and what the future of the organization may be.See omnystudio.com/listener for privacy information.

1% Bonus und eine Beteiligung an Anthropic. Das gibt's grad beim BlackRock Private Equity Fund im Broker von Scalable Capital. Mehr Infos hier: https://partner.scalable-capital.de/go.cgi?pid=655&wmid=986&cpid=1&prid=1&subid=&target=private-equity-DE Trump will 20% Gebühr auf Schiffe durch Straße von Hormus. Öl steigt. Meta steckt 250 Milliarden in Rechenzentren. SK Hynix & Co. schwach. TSMC wächst. VW will 50.000 Stellen streichen. Helsing wertvollstes Startup. Minimax eher mini. Vinod Khosla hat Hobby. BASF (WKN: BASF11) will seine Agrarsparte 2027 an die Börse bringen. Bewertung: 20 bis 30 Mrd. €. Der ganze Konzern ist nur 42 Mrd. wert. Chance auf Neubewertung? Toast (WKN: A3C3Y4) ist Weltmarktführer für Restaurantsoftware. 20% Wachstum, steigende Margen, 17 Mrd. $ Börsenwert. Aber DoorDash könnte zum Problem werden. Diesen Podcast vom 14.07.2026, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung. Learn more about your ad choices. Visit megaphone.fm/adchoices

Salk reacts to the sale of the Seahawks by the Allen Estate to Vinod Khosla, a tech investor with a long track record of success. He has more details on the Khosla family that bought the Hawks, the Mariners disgusting road trip, and more in Need To Know. And Maura catches you up on everything you missed from Around The Weekend.

Brock was boots on the ground for the Mariners series in Tampa and has a story to tell the show. Brock and Salk react to the M’s brutal road trip, the Seahawks new ownership, and more in Need To Know. They do a deep dive on the Hawks new owner Vinod Khosla, his family, what they need to do to earn the trust and respect of the fans, and much more. Brock talks about Coach Mike at the American Century golf tournament, the Patriots insider that Coach Mike might have talked to ahead of the Super Bowl, and more in Blue-88.

Brock and Salk talk about the struggles Colt Emerson has been having and what the Mariners should do about it. They talk more about new Seahawks owner Vinod Khosla and his family, the M’s brutal road trip, and more in Need To Know. And ESPN’s Seth Wickersham joins the show to give more details on the Seahawks sale, new owners, and much more.

Brock and Salk continue to react to the Seahawks sale and talk about new owner Vinod Khosla, his family, and what their plans with the organization might be. They talk more about the new Seahawks owners, the MLB Draft, and more in Need To Know. And they recap the Mariners disappointing road trip and highlight the things holding this team back from their potential.

Group Chat News is back with the hottest stories of the week. Team USA is out of the World Cup and the guys are done with soccer  but not before breaking down why American sports franchises are worth more than every soccer team on Earth. Then they get into the wealth-building conversation nobody wants to hear, why nearly half of Americans under 30 live with their parents, and the $12 water that broke the internet. This week's Group Chat covers: Team USA is out — the World Cup fallout, the Argentina conspiracy, and soccer's broken rules Vinod Khosla's group buys the Seattle Seahawks for $9.65 billion Why one NFL team is worth more than almost every soccer club in the world Thousands of hourly Costco workers now have over $1 million in their 401(k)s The boomer math nobody talks about — save 5-10% since 1990 and you'd have $6 million today Why the S&P 500 is the "Premier League" of investing and still available to everyone Is the system actually broken, or are we just wired for instant gratification? Nearly half of Americans under 30 now live with their parents — coddling or smart money? The participation trophy generation and how parenting changed the game Sleepaway camp, self-sufficiency, and raising kids who can take care of themselves Erewhon's $12 water sells out — genius marketing or peak status symbol? And much more!

Stacy and Curtis break down what they want to know following the Seahawks’ $9.6 billion sale to Vinod Khosla, they discuss the Mariners avoiding the sweep in the final series before the All-Star Break, they give you their thoughts on the Mariners drafting Ace Reese in Headline Rewrites, and they discuss the latest instant of disrespect for Super Bowl winning Seahawks quarterback Sam Darnold. 

The Seattle Seahawks are officially being sold! The Estate of Paul G. Allen has entered into a formal agreement to sell the Seahawks to an ownership group led by the Khosla family, with Vinod Khosla at the helm. The Khosla family will become the team's controlling owners. The deal is still subject to NFL approval in the coming months.This marks the end of an era for the Seahawks, who were owned by Paul Allen since 1997. What do you think about the new ownership group? Will the Khosla family be good stewards of the franchise? Link to my YouTube Channel. Live on Wed and Sunday, 5PM PST...https://www.youtube.com/@TheHawksNest12thman?sub_confirmation=1 Link to my Patreon....https://www.patreon.com/thehawksnest Twitter...@SeahawksNester Twitch...@TheSeahawksNest Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Headlines and GREGG BELL joins the show with the lowdown on the new Seahawks owners. Vinod Khosla may be the name we've heard, but the majority of the shares will actually go to his wife, who is she? What does the Khosla family know about football? What should we be concerned about? :30- Vinod Khosla and his family are the new owners of the Seahawks and we don't even know how to pronounce his name! :45- The World Cup semifinals are set and all four teams remaining were our first round picks in our green jacket draft… talk about chalk! See omnystudio.com/listener for privacy information.

It's the best moments of a bold show!See omnystudio.com/listener for privacy information.

El episodio 119 llegó con historia, escándalo y datos que no te podés perder.Arrancamos con el hito más grande del momento: Elon Musk se convirtió en el primer trillonario de la historia. SpaceX salió pública a 2.3 trillones de dólares y lo puso en una categoría donde nadie más existe. Lucas lo dijo mejor que nadie: él está más cerca de Larry Page que Larry Page de Elon. Un número que no tiene sentido hasta que lo escuchás.Después hablamos de por qué SpaceX no es solo una empresa espacial. Es la única compañía verticalmente integrada de principio a fin para la era AI: tiene datos, centros de cómputo, energía solar, telecomunicaciones satelitales y exploración espacial bajo el mismo techo. El bull case es simple y brutal.Luego viene el escándalo de la semana. El fundador de Cloudflare contó públicamente que Vinod Khosla, uno de los VCs más respetados del mundo, intentó convencerlo de echar a sus co-founders a cambio de quedarse con todo el equity. Y cuando lo negó, publicó el term sheet como prueba. Una historia que debería leer todo founder antes de levantar su primera ronda.También hablamos de un caso cercano que sirve de advertencia real: un co-founder que quiso salirse de una startup que no funcionaba y recibió amenazas legales de sus socios y los VCs para que perdiera su vesting. La lección es clara — antes de arrancar una compañía, hay que tener las conversaciones incómodas.En el frente de IPOs, Anthropic y OpenAI hicieron sus filings privados casi al mismo tiempo y están obligando a los banqueros a elegir bando. Lucas se queda con Anthropic. Cristóbal también, aunque reconoce que la flexibilidad moral de Sam Altman en el mundo de Trump puede ser una ventaja.Después analizamos a Bending Spoons, los italianos que compraron Evernote, Vimeo y WeTransfer entre otros, y los están exprimiendo con AI desde Milano a una fracción del costo americano. Un modelo de negocio que muchos subestiman y que ya factura más de un billón de dólares al año.Cerramos con tres temas que tocan directamente la vida cotidiana. AI destruyó el modelo de las grandes consultoras — Accenture cayó 18% en un solo día y los puestos entry-level están desapareciendo, lo que pone en jaque el ROI del MBA. Los smartphones y la caída global de la natalidad tienen una correlación que empieza a asustarnos a todos. Y los propios CEOs de las redes sociales no le dan pantallas a sus hijos, que es la señal más honesta que existe sobre lo que realmente piensan de sus productos.

Legendary venture capitalist Vinod Khosla joins Mukesh Bansal on SparX, for one of the most wide-ranging and provocative conversations on the future of technology, investing, and humanity. Vinod makes bold claims: AI will replace doctors within 5 years, colleges as we know them are obsolete, space-based data centers don't make sense, India's IT and BPO industry faces an existential threat - and yet, AI may be the greatest opportunity India has ever seen. And ultimately, AI will free humanity from servitude to survival.In this episode, Vinod shares his contrarian investment philosophy, including why he wrote a $50M check to OpenAI in 2019 when it had no product, no revenue, and no business plan — and why he sent an apology letter to his LPs along with it.

Sam Harris speaks with Vinod Khosla about AI, economic disruption, and political risk. They discuss the prospect of mass job displacement, a trillion-dollar policy framework to redistribute AI's gains, the failure of the California wealth tax, the corporate capitulation to Trump, Elon Musk's embrace of white nationalist rhetoric, US-China competition, semiconductor dependence, and other topics. If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe.

Vinod Khosla on Why AI Could End Human Labor and Change Capitalism ForeverVinod Khosla joins Newcomer to discuss AI, capitalism, freedom, education, religion, and why he believes technology will fundamentally reshape how humans live and work.The legendary venture capitalist behind Sun Microsystems and early bets on OpenAI shares his thoughts on why AI could eliminate the need for traditional jobs, how capitalism may evolve in an AI-driven world, and why future generations may no longer need careers purely for survival.He also discusses Silicon Valley's biggest blind spots, the future of education and creativity, human purpose after AI, why institutions are failing to adapt, and what comes next for entrepreneurship and innovation.Vinod also explains why he thinks most people underestimate the speed of AI progress, and what happens when intelligence becomes effectively unlimited.Subscribe for more conversations with the people shaping technology, media, startups, and culture.

Vinod Khosla on Why AI Could End Human Labor and Change Capitalism ForeverVinod Khosla joins Newcomer to discuss AI, capitalism, freedom, education, religion, and why he believes technology will fundamentally reshape how humans live and work.The legendary venture capitalist behind Sun Microsystems and early bets on OpenAI shares his thoughts on why AI could eliminate the need for traditional jobs, how capitalism may evolve in an AI-driven world, and why future generations may no longer need careers purely for survival.He also discusses Silicon Valley's biggest blind spots, the future of education and creativity, human purpose after AI, why institutions are failing to adapt, and what comes next for entrepreneurship and innovation.Vinod also explains why he thinks most people underestimate the speed of AI progress, and what happens when intelligence becomes effectively unlimited.Subscribe for more conversations with the people shaping technology, media, startups, and culture.

Episode #256 features Shernaz Daver, one of Silicon Valley's most respected executive advisors and communications strategists, who has worked alongside leaders including Steve Jobs, Netflix co-founder Reed Hastings, Khosla Ventures founder Vinod Khosla and Waymo/Udacity founder Sebastian Thrun. In conversation with Vidit Agarwal, Shernaz reflects on a remarkable journey shaped across India, Japan and the United States — from growing up between cultures as part of the small Zoroastrian community to navigating the inner circles of Silicon Valley's most influential founders and companies. She shares stories from Motorola, Electronic Arts and Sun Microsystems, the rejection that changed her trajectory, the unforgettable moment Steve Jobs told her she had done a “terrible job” marketing a product, and the lessons she learned working alongside elite founders and operators. The conversation also explores insecurity, ambition, storytelling, AI, burnout, hype versus reality in Silicon Valley, and what separates visionary leaders from merely successful ones. Please enjoy exploring your curiosity. ________ Get in touch with us via email at contact@curiositycentre.com Join our stable of commercial partners including the Australian Government, Google, KPMG, Vanta, Allens, Macquarie Capital, City of Sydney and more.  Show notes and more episodes here Follow us on LinkedIn, Twitter, Instagram, or YouTube Get in touch with our Founder and Host, Vidit Agarwal directly here Contact us via our website ________ The High Flyers Podcast features in-depth interviews with the world's most influential figures in business, tech, finance, government and sport. Launched in 2020, it has ranked in the global top ten for past three years, with listeners in 27 countries and over 200+ episodes released, and featured in Forbes, Daily Telegraph, and at SXSW. Our guests include -- Malcolm Turnbull (Prime Minister of Australia), Anil Sabharwal (Global VP, Product at Google), Jason Collins (Head of BlackRock, Asia Pacific), Jodie Auster (Uber's Global Head of Travel), Stevie Case (Chief Revenue Officer, Vanta), Brad Banducci (CEO, Woolworths),  David Haber (GP, a16z), Rob Giglio (CCO, Canva), Jean-Michel Lemieux (CTO, Shopify + Atlassian), Sweta Mehra (EGM, NAB; ex CMO, ANZ), Bowen Pan (Creator, Facebook Marketplace), Sam Sicilia (Chief Investment Officer, Hostplus), Craig Tiley (CEO, US Tennis), John Haddock (CBO, Harvey), Niki Scevak (Co-Founder, Blackbird Ventures), Mike Schneider (CEO, Bunnings), Trent Cotchin (3x Premiership Winning Captain, Richmond FC), Peter Varghese (Secretary of Foreign Affairs, Australian Government), Jack Zhang (CEO, Airwallex), Matteo Franceschetti (CEO, Eight Sleep), Vivek Bhatia (CEO, MUFG), Sanjeev Gandhi (CEO, Orica) and more. 

This Week In Startups is made possible by:Render - https://Render.com/TWISTNorthwest Registered Agent - https://NorthwestRegisteredAgent.com/TWISTLinkedIn Jobs - https://LinkedIn.com/TWISTToday's show:A beanie that reads your thoughts and turns them into text — no surgery required? Jason grills the Sabi co-founders on their noninvasive brain-computer interface, backed by Vinod Khosla, and calls a cap on the whole thing (until he doesn't).This episode of This Week in Startups covers a lot of ground: Jason's tactical tip of the day on making everyone the CEO of their domain, a deep dive into Sabi's thought-to-text beanie, a live demo of AI-powered podcast sidebars built by the TWiST audience, the announcement of a new $5K bounty for an annotation tool, and Jason's big five wellness framework.Guests:Sabi: Website, Wired articleCo-founder and CEO Rahul Chhabra: LinkedIn, XCompanies and people discussed:Matt Coffin, founder of LowerMyBills.comVinod Khosla, founder of Khosla VenturesNeuralink, a leading BCI companyElevenLabs, a leading voice AI startupCalm, the popular meditation appTimestamps:0:00 Intro & tactical tip: Make everyone the CEO of their domain1:49 Matt Coffin's "CEO of X" management philosophy3:04 Community ownership: Deputizing Ricky, Lawn, Bianca, Maddie, Kabir4:02 Building the Noti Gang: X group chats and community flywheels5:08 Founder takeaways: Activate your top 1%, make someone the CEO of it6:18 The streamer trick, parasocial dynamics, and creator ethics7:59 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off!9:34 Guest intro: Rahul Chhabra of Sabi10:01 What is Sabi? Noninvasive BCI in a beanie with 100,000 sensors10:13 LinkedIn Jobs - Hire right, the first time. Post your first job and get $100 off towards your job post at https://LinkedIn.com/TWIST11:02 How it works: From fMRI to EEG, from hospital to hat13:46 The brain foundation model and thought-to-text decoding15:55 Vetting the founders: BITS Pilani, Stanford, athlete fatigue AI17:22 Vinod Khosla's investment thesis: BCI must be noninvasive19:30 Jason's challenge: Say "Calacanis," or it doesn't count20:11 Northwest Registered Agent: Get more when you start your business with Northwest. In 10 clicks and 10 minutes, you can form your company and walk away with a real business identity — Learn more at https://northwestregisteredagent.com/twist21:30 Sabi.com: Reserve your device, release by end of 202622:26 Privacy concerns: Does the beanie read everything?25:41 Bounty #1: AI live sidebar contest — demos from the TWiST audience27:04 Oliver's breakdown: What's easy vs. hard about live AI commentary28:40 Demo #1: Armchair (by Mark Colebrook) — fact-checker + troll personas, live30:59 Render: Find out why 5 million developers are already using the all-in-one cloud platform, Render. Go to https://render.com/twist and apply for the Render Startup Program to get $500-$100,000 in free credits, depending on your stage and backers.35:30 Live political violence test: The sidebar in real time on the WHCD shooting38:45 Demo #2: Pod Commentators / SideCast — browser-based, Gemini-powered40:35 Jason's revised Bounty #1 spec: Fact-checker + cynic, public stream or Zoom42:41 Timeline: check-ins May 1, May 8; final winner May 1544:51 Demo #3: BMD Pat (by Patrick Hughes) — instant URL, all-snarky personas46:52 Bounty #2 announced: Annotated.com — a fair-use multimedia annotation tool49:31 Annotated: the delicious.com of media commentary51:11 Contest rules: Jason owns the domain, winner gets $5K + potential ongoing work53:13 Wrap-up: Bounty 1 = AI sidebar, Bounty 2 = AnnotatedSubscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanis

Billionaire investor Vinod Khosla suggested as many as 125 million people should be exempt from paying income taxes in the coming decades as artificial intelligence gets closer to eliminating a mass number of jobs, suggesting the government make up the lost revenue by relying on capital gains tax. Key Facts Khosla wrote on X the AI boom will require a "rethink of capitalism and equity" and suggested increasing capital gains taxes would allow the government to eliminate "the bottom 125 million taxpayers from the tax rolls."  He also suggested eliminating certain tax breaks—like tax-free borrowing against unrealized gains—would help to make up the deficit.  There are only about 160 million taxpaying Americans, and Khosla's plan would exempt almost 80% of them from income taxes.  Last year, Khosla suggested it may be necessary to implement a universal basic income for lower-income Americans whose jobs are eliminated due to AI automation, predicting 80% of jobs will soon be handled by AI.  A video Khosla shared this week highlighted dozens of jobs being replaced by AI in his venture capital firm's portfolio, including personal assistants, financial analysts, doctors, accountants, customer service agents and more.  Big Number 92 million. That's how many jobs will be displaced by AI by 2030, according to the World Economic Forum's Future of Jobs Report 2025. Key Background Economic experts have been warning about the loss of jobs due to artificial intelligence automation for years. More than 50,000 jobs cuts in 2025 were blamed on AI, according to career services firm Challenger, Gray and Christmas, with another 20,000 in 2023 and 2024. Aneesh Raman, LinkedIn's chief economic officer, said in a New York Times op-ed that AI is destroying the “bottom rungs of the career ladder,” eliminating entry level jobs and significantly cutting into the hiring of recent college graduates. Tech billionaires like Elon Musk and Bill Gates, as well as leaders like Microsoft AI CEO Mustafa Suleyman, have all suggested artificial intelligence is on its way to automating a significant portion of white-color work and, in some scenarios, could eliminate the need for traditional jobs altogether. Last May, Anthropic CEO Dario Amodei predicted AI could drive unemployment up 10% to 20% in the next five years. Women and people of color are expected to be disproportionately impacted by AI job automation. Crucial Quote “In my little group chat with my tech CEO friends, there's this betting pool for the first year there is a one-person billion-dollar company, which would've been unimaginable without AI. And now [it] will happen,” OpenAI CEO Sam Altman saidlast year. Read the full story on Forbes: By Mary Whitfill Roeloffs https://www.forbes.com/sites/maryroeloffs/2026/02/17/billionaire-khosla-if-125-million-are-unemployed-by-ai-they-shouldnt-pay-taxes/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Allbirds just pivoted to AI and the stock jumped from $2 to $21 overnight.This week on Bricks, Bucks & Bytes, Mo (founder of Build Crew, backed by Vinod Khosla) joined us to break down what's actually happening in construction tech right now — and Nitin from Planera dropped in to launch their AI scheduling assistant, Manny.Tune in to find out about:✅ Why Allbirds' AI pivot looks like a 1999-style dotcom scam✅ Mo's unfiltered take on why knowledge graphs are the most overhyped term in AEC right now✅ Why Palantir's real construction customer isn't who you think it is✅ How Planera's Manny is codifying decades of scheduling expertise before the industry loses itWatch now on Spotify and YouTube. Link in the comments!#aec #construction #constructiontech #bricksandbytes #bricksbytes #bricksbucksandbytes #ai #vcOur Sponsors:BreadCrumb- 50,000+ projects globally. All running safer, faster, with Breadcrumb. - breadcrumb.coAphex is the multiplayer planning platform where construction teams plan together, stay aligned, and deliver projects faster – check out aphex.coArchdesk - “The #1 Construction Management Software for Growing Companies - Manage your projects from Tender to Handover” check archdesk.comChapters00:00 Intro01:00 Introduction to Mo's Transformation03:57 The Allbirds Pivot: From Sneakers to AI07:00 Technological Breakthroughs in Architectural Design09:50 AI in Construction: The Role of Domain Expertise12:51 BuildCrew: Mo's New Venture and Its Vision15:35 Knowledge Graphs: The Future of Construction Documentation25:11 Integrating Knowledge Graphs in Construction27:39 The Role of Technologists in Construction29:18 Palantir's Impact on Construction Technology31:09 The Future of ERP Systems34:45 Challenging Industry Norms with Optimism38:25 Nemechek's Acquisition of HCSS42:39 Planera's AI Scheduling Assistant53:27 Current Trends in Construction and AI

Lenny's Podcast: Product | Growth | Career ✓ Claim : Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- Keith Rabois was an early executive at PayPal (part of the famous PayPal Mafia), COO at Square, VP of Corporate Development at LinkedIn, and an early investor in Stripe, DoorDash, Airbnb, YouTube, Ramp, and Palantir. Currently he's managing director at Khosla Ventures. Also, he hasn't touched a computer since September 2010 (he does everything from an iPad).In our in-depth conversation, Keith shares:1. The barrels vs. ammunition hiring framework (and how to spot barrels)2. Why talking to customers is actively harmful for consumer products3. How to identify undiscovered talent4. Why the PM role is dying5. The three traits of the best-performing companies right now6. The specific interview question he asks every senior candidate7. Why CMOs (not engineers) are becoming the #1 consumer of tokens—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/hard-truths-about-building-in-the-ai-era—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Keith Rabois:• X: https://x.com/rabois• LinkedIn: linkedin.com/in/keith• Website: https://www.khoslaventures.com—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 Keith Rabois(01:59) Why Keith hasn't used a computer since 2010(04:52) The team you build is the company you build(07:40) How Keith learned to identify talent at PayPal(10:05) Tactics for getting better at hiring(15:31) The barrels vs. ammunition framework(18:52) What makes someone a barrel(22:36) How to attract the best talent(26:18) Building companies on undiscovered talent(27:53) Why better performance requires more pressure(32:36) Career advice in the age of AI(35:14) The future of the product triad(41:03) Why design and code are merging(49:35) What practicing law taught Keith about entrepreneurship(51:22) Contrarian takes on customer feedback(1:02:33) Identifying great AI opportunities(1:05:13) Advice for evaluating statrups (1:12:36) Criticizing in public vs. private(1:15:05) Failure corner(1:17:29) Lightning round—Referenced:• Square: https://squareup.com• Jack Dorsey on X: https://x.com/jack• Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens• Simon Willison's Weblog: https://simonwillison.net• Vinod Khosla on X: https://x.com/vkhosla• Peter Thiel on X: https://x.com/peterthiel• Max Levchin on X: https://x.com/mlevchin• David Sacks on LinkedIn: https://www.linkedin.com/in/davidoliversacks• Tony Xu on X: https://x.com/t_xu• David Sze on X: https://x.com/davidsze• Faire: https://www.faire.com• Max Rhodes on X: https://x.com/MaxRhodesOK• Jeffrey Kolovson on LinkedIn: https://www.linkedin.com/in/jeffreykolovson• Uncapped | Comparative Advantages w/ Keith Rabois: https://www.khoslaventures.com/posts/uncapped-comparative-advantages-w-keith-rabois• Lattice: https://lattice.com• Taylor Francis on LinkedIn: https://www.linkedin.com/in/taylor-francis-4ba49640• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein• The art of hiring: insights from Khosla Ventures, Airbnb, Ramp and Traba: https://ramp.com/velocity/the-art-of-hiring-insights• Eric Glyman: Seek out super individual contributors (ICs): https://ramp.com/velocity/the-art-of-hiring-insights#Eric-Glyman:-Seek-out-super-individual-contributors-(ICs)• Eric Glyman on X: https://x.com/eglyman• Mike Moore on LinkedIn: https://www.linkedin.com/in/mike-moore-802223177• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• Why you should work much harder RIGHT NOW: https://marginalrevolution.com/marginalrevolution/2026/03/why-you-should-work-much-harder-right-now.html• Opendoor: https://www.opendoor.com• The Craft of Early Stage Venture | Peter Fenton, General Partner at Benchmark | Uncapped with Jack Altman: https://www.youtube.com/watch?v=vRiblwiXt-Q• Lovable: https://lovable.dev• The rise of the professional vibe coder (a new AI-era job) | Lazar Jovanovic (Professional Vibe Coder): https://www.lennysnewsletter.com/p/getting-paid-to-vibe-code• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Marc Andreessen: The real AI boom hasn't even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom• Jeremy Stoppelman on X: https://x.com/jeremys• The design process is dead. Here's what's replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead• Andy Warhol: https://en.wikipedia.org/wiki/Andy_Warhol• Curation and Algorithms: https://stratechery.com/2015/curation-and-algorithms• Ernest Hemingway: https://en.wikipedia.org/wiki/Ernest_Hemingway• William Shakespeare: https://en.wikipedia.org/wiki/William_Shakespeare• Evan Moore on X: https://x.com/evancharles• Andrew Mason on X: https://x.com/andrewmason• Read Taylor Swift's Full Viral Speech After Record-Breaking Awards Sweep: https://www.newsweek.com/entertainment/read-taylor-swift-full-acceptance-speech-record-breaking-awards-sweep-11745941• The Chainsmokers: Stories Behind the Songs, AI's Impact on Music, and Venture Investing | Uncapped with Jack Altman: https://www.youtube.com/watch?v=9GMSC-2pYnw&list=PLtpH7YnTL8ihy0nR2BV32n5VkRtqlDAS1&index=16• How to spot a top 1% startup early: https://www.lennysnewsletter.com/p/how-to-spot-a-top-1-startup-early• David Weiden on LinkedIn: https://www.linkedin.com/in/davidweiden• Alfred Lin on LinkedIn: https://www.linkedin.com/in/linalfred• Keith's post about vertical integration on X: https://x.com/rabois/status/870673635375104000• Jon Chu on X: https://x.com/jonchu• Kanu Gulati on X: https://x.com/KanuGulati• Rogo: https://rogo.ai• Profound: https://www.tryprofound.com• Basis: https://www.getbasis.ai• Spellbook: https://www.spellbook.legal• Roelof Botha on X: https://x.com/roelofbotha• Delian Asparouhov on LinkedIn: https://www.linkedin.com/in/delian-asparouhov-87447742• Lessons From Keith Rabois, Essay 1: How to become a Venture Capitalist: https://delian.io/lessons-1• Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product): https://www.lennysnewsletter.com/p/velocity-over-everything-how-ramp• Nuremberg on AppleTV+: https://tv.apple.com/us/movie/nuremberg/umc.cmc.3sg4y0382byupy76bfy7307k4• Eight Sleep: https://www.eightsleep.com• “NO DAYS OFF”—Bill Belichick on X: https://x.com/SNFonNBC/status/829036279069364224—Recommended books:• Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration: https://www.amazon.com/Creativity-Inc-Overcoming-Unseen-Inspiration/dp/0812993012• The Jordan Rules: The Inside Story of One Turbulent Season with Michael Jordan and the Chicago Bulls: https://www.amazon.com/Jordan-Rules-Sam-Smith/dp/0671796666• The Upside of Stress: Why Stress Is Good for You, and How to Get Good at It: https://www.amazon.com/Upside-Stress-Why-Good-You/dp/1101982934—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

Keith Rabois was an early executive at PayPal (part of the famous PayPal Mafia), COO at Square, VP of Corporate Development at LinkedIn, and an early investor in Stripe, DoorDash, Airbnb, YouTube, Ramp, and Palantir. Currently he's managing director at Khosla Ventures. Also, he hasn't touched a computer since September 2010 (he does everything from an iPad).In our in-depth conversation, Keith shares:1. The barrels vs. ammunition hiring framework (and how to spot barrels)2. Why talking to customers is actively harmful for consumer products3. How to identify undiscovered talent4. Why the PM role is dying5. The three traits of the best-performing companies right now6. The specific interview question he asks every senior candidate7. Why CMOs (not engineers) are becoming the #1 consumer of tokens—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/hard-truths-about-building-in-the-ai-era—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Keith Rabois:• X: https://x.com/rabois• LinkedIn: linkedin.com/in/keith• Website: https://www.khoslaventures.com—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 Keith Rabois(01:59) Why Keith hasn't used a computer since 2010(04:52) The team you build is the company you build(07:40) How Keith learned to identify talent at PayPal(10:05) Tactics for getting better at hiring(15:31) The barrels vs. ammunition framework(18:52) What makes someone a barrel(22:36) How to attract the best talent(26:18) Building companies on undiscovered talent(27:53) Why better performance requires more pressure(32:36) Career advice in the age of AI(35:14) The future of the product triad(41:03) Why design and code are merging(49:35) What practicing law taught Keith about entrepreneurship(51:22) Contrarian takes on customer feedback(1:02:33) Identifying great AI opportunities(1:05:13) Advice for evaluating statrups (1:12:36) Criticizing in public vs. private(1:15:05) Failure corner(1:17:29) Lightning round—Referenced:• Square: https://squareup.com• Jack Dorsey on X: https://x.com/jack• Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens• Simon Willison's Weblog: https://simonwillison.net• Vinod Khosla on X: https://x.com/vkhosla• Peter Thiel on X: https://x.com/peterthiel• Max Levchin on X: https://x.com/mlevchin• David Sacks on LinkedIn: https://www.linkedin.com/in/davidoliversacks• Tony Xu on X: https://x.com/t_xu• David Sze on X: https://x.com/davidsze• Faire: https://www.faire.com• Max Rhodes on X: https://x.com/MaxRhodesOK• Jeffrey Kolovson on LinkedIn: https://www.linkedin.com/in/jeffreykolovson• Uncapped | Comparative Advantages w/ Keith Rabois: https://www.khoslaventures.com/posts/uncapped-comparative-advantages-w-keith-rabois• Lattice: https://lattice.com• Taylor Francis on LinkedIn: https://www.linkedin.com/in/taylor-francis-4ba49640• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein• The art of hiring: insights from Khosla Ventures, Airbnb, Ramp and Traba: https://ramp.com/velocity/the-art-of-hiring-insights• Eric Glyman: Seek out super individual contributors (ICs): https://ramp.com/velocity/the-art-of-hiring-insights#Eric-Glyman:-Seek-out-super-individual-contributors-(ICs)• Eric Glyman on X: https://x.com/eglyman• Mike Moore on LinkedIn: https://www.linkedin.com/in/mike-moore-802223177• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• Why you should work much harder RIGHT NOW: https://marginalrevolution.com/marginalrevolution/2026/03/why-you-should-work-much-harder-right-now.html• Opendoor: https://www.opendoor.com• The Craft of Early Stage Venture | Peter Fenton, General Partner at Benchmark | Uncapped with Jack Altman: https://www.youtube.com/watch?v=vRiblwiXt-Q• Lovable: https://lovable.dev• The rise of the professional vibe coder (a new AI-era job) | Lazar Jovanovic (Professional Vibe Coder): https://www.lennysnewsletter.com/p/getting-paid-to-vibe-code• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Marc Andreessen: The real AI boom hasn't even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom• Jeremy Stoppelman on X: https://x.com/jeremys• The design process is dead. Here's what's replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead• Andy Warhol: https://en.wikipedia.org/wiki/Andy_Warhol• Curation and Algorithms: https://stratechery.com/2015/curation-and-algorithms• Ernest Hemingway: https://en.wikipedia.org/wiki/Ernest_Hemingway• William Shakespeare: https://en.wikipedia.org/wiki/William_Shakespeare• Evan Moore on X: https://x.com/evancharles• Andrew Mason on X: https://x.com/andrewmason• Read Taylor Swift's Full Viral Speech After Record-Breaking Awards Sweep: https://www.newsweek.com/entertainment/read-taylor-swift-full-acceptance-speech-record-breaking-awards-sweep-11745941• The Chainsmokers: Stories Behind the Songs, AI's Impact on Music, and Venture Investing | Uncapped with Jack Altman: https://www.youtube.com/watch?v=9GMSC-2pYnw&list=PLtpH7YnTL8ihy0nR2BV32n5VkRtqlDAS1&index=16• How to spot a top 1% startup early: https://www.lennysnewsletter.com/p/how-to-spot-a-top-1-startup-early• David Weiden on LinkedIn: https://www.linkedin.com/in/davidweiden• Alfred Lin on LinkedIn: https://www.linkedin.com/in/linalfred• Keith's post about vertical integration on X: https://x.com/rabois/status/870673635375104000• Jon Chu on X: https://x.com/jonchu• Kanu Gulati on X: https://x.com/KanuGulati• Rogo: https://rogo.ai• Profound: https://www.tryprofound.com• Basis: https://www.getbasis.ai• Spellbook: https://www.spellbook.legal• Roelof Botha on X: https://x.com/roelofbotha• Delian Asparouhov on LinkedIn: https://www.linkedin.com/in/delian-asparouhov-87447742• Lessons From Keith Rabois, Essay 1: How to become a Venture Capitalist: https://delian.io/lessons-1• Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product): https://www.lennysnewsletter.com/p/velocity-over-everything-how-ramp• Nuremberg on AppleTV+: https://tv.apple.com/us/movie/nuremberg/umc.cmc.3sg4y0382byupy76bfy7307k4• Eight Sleep: https://www.eightsleep.com• “NO DAYS OFF”—Bill Belichick on X: https://x.com/SNFonNBC/status/829036279069364224—Recommended books:• Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration: https://www.amazon.com/Creativity-Inc-Overcoming-Unseen-Inspiration/dp/0812993012• The Jordan Rules: The Inside Story of One Turbulent Season with Michael Jordan and the Chicago Bulls: https://www.amazon.com/Jordan-Rules-Sam-Smith/dp/0671796666• The Upside of Stress: Why Stress Is Good for You, and How to Get Good at It: https://www.amazon.com/Upside-Stress-Why-Good-You/dp/1101982934—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

This week, Liz traveled to San Francisco for HumanX, an artificial intelligence conference where she sat down with an all-star lineup for FOX Business exclusive interviews. Vinod Khosla of Khosla Ventures explains how AI-driven startups are currently disrupting the legacy military-industrial complex. Zoom CEO Eric Yuan shares his vision to increase human productivity. Jeffrey Katzenberg and Justin Wexler of WndrCo dive into the future of consumer cybersecurity and creative storytelling. Then, Sequoia Capital partner Konstantine Buhler describes how agentic AI is transforming specialized sectors like the legal industry. Learn more about your ad choices. Visit podcastchoices.com/adchoices

“I don't know if any rational person ever became a billionaire running a disruptive company.” — Keith TeareIs capitalism by permission of democracy, or is democracy by permission of capitalism? That's the question Keith Teare and I have been circling for a while on our weekly tech roundup, and this week it triggered a full-blown discussion of our 21st century economic and political fate.Earlier this week, Vinod Khosla — one of Silicon Valley's most successful venture capitalists — posted on X that “capitalism is by permission of democracy.” Keith agrees. I'm not so sure. My sense is that as AI start-ups approach valuations that rival the GDP of nation states, the old equation inverts. Governments no longer permit capitalism. Capitalism permits government. The Sam Altmans and Elon Musks of the future, running 10 or $15 trillion dollar startups, won't lobby politicians. They'll replace them. Dario Amodei's confrontation with the US government, then, is a sneak preview of the future. Indeed, as what Om Malik calls a “symbolic capitalist”, Amodei is a good example of the type of engaged capitalist who will usurp traditional politicians. That's the good news. The bad news is that other examples of symbolic capitalists include Elon Musk and Peter Thiel. Five Takeaways•       Keith Says OpenAI Will Be Worth $10 Trillion in Five Years: I told him I'd take him to dinner if he's right. He said I'd have to do more than that. His logic: NVIDIA promises $1 trillion in new revenue by the end of next year, Anthropic did $5 billion in new revenue in a single month, and the three expected IPOs — Anthropic, OpenAI, SpaceX — would together raise more money than the entire IPO market of the last decade. The Netscape moment, if it comes, won't be a moment. It'll be an earthquake.•       Fundrise Is the Canary in the Coal Mine: A fund holding private shares in Anthropic, OpenAI, SpaceX, Databricks, and Anduril went public this week at $34 and closed above $100. Retail investors paying three times net asset value for companies that aren't even public yet. Keith says that's not irrational — it's the market pricing the future. I'm less sure. History is littered with futures the market got catastrophically wrong.•       Om Malik Reframes the Entire Debate: His essay on “neo-symbolic capitalism” argues that value in the 21st century derives from symbols, narratives, and reputation rather than products. In that framing, Amodei's fight with the government isn't a miscalculation — it's brand-building. Musk is the master of it. Altman tries to wear every hat simultaneously. Peter Thiel is in Rome talking about the Antichrist. And the billionaires who signed the Giving Pledge now want out.•       Keith and I Disagree on What $10 Trillion Means: Keith says the government retains power regardless of corporate size. Being big doesn't give you political power unless governments are corrupt. I think that's naïve. If AI companies approach valuations that rival the GDP of nation states, the old equation inverts. Government doesn't permit capitalism. Capitalism permits government. The Amodeis and Musks of the future won't lobby politicians. They'll replace them.•       Contrarianism Is at the Very Core of Innovation: The one thing Keith and I agree on this week. Every billionaire is irrational. Musk is on the spectrum. Thiel believes in the Antichrist. Amodei thinks he can fight the US government and win. Keith concedes: no rational person ever became a billionaire running a disruptive company. The question is whether that irrationality is a feature of capitalism or a threat to democracy. We disagree on the answer. About the GuestKeith Teare is a serial entrepreneur, investor, and publisher of That Was The Week, a weekly newsletter on the tech economy. He is co-founder of SignalRank and a regular Saturday guest on Keen On America.References:•       That Was The Week — Keith's editorial on public markets and price outcomes.•       Om Malik on neo-symbolic capitalism — the essay that reframes the Amodei debate.•       Episode 2835: Why Dario Amodei Might Be the 21st Century's First Real Leader — last week's TWTW, where the Amodei debate began.•       Episode 2836: Is Elon Human? — Charles Steel on the curious mind of Elon Musk, referenced in the conversation.•       Fundrise (VCX) — the IPO that triggered this week's discussion, trading at 300% above NAV.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:(00:00) - Introduction: AI and unreason define the world (01:49) - Markets as prediction machines: NVIDIA's $1 trillion promise (04:42) - The three IPOs that would dwarf a decade of IPOs (05:50) - Fundrise (VCX): retail investors paying 300% premium (09:23) - Keith's prediction: OpenAI at $10 trillion in five years (11:44) - The Anthropic debate continues: tactics vs. morals (14:22) - Silicon Valley's behind-the-scenes support for Amodei (16:42) - What happens when an AI company rivals a nation's GDP? (23:05) - Om Malik on neo-symbolic capitalism (28:10) - Musk as the master of symbolic capitalism (30:08) - Bezos, Project Prometheus, and the Prometheuses of AI (32:07) - Peter Thiel, the Antichrist, and the Giving Pledge collapse (35:27) - Vinod Khosla: capitalism by permission of democracy? (38:23) - Or democracy by permission of capitalism?

When tech is at its best, it is a group of people working together to solve hard problems in a way that makes the world a better place. That goal is what motivated so many folks in Silicon Valley to come here. How then did we cede the microphone to a small number of people who espouse an authoritarian, rich get richer algorithm? How can people working inside tech companies grab the bullhorn away from the authoritarians to describe the world we want to create? How we can take action to advocate for our vision of a better future? One recent manifestation of this is the ⁠ICEout.tech⁠ movement. In this episode, Kim speaks with Lisa Conn, founder of Gatheround and former Meta employee, and Anne Wootton, co-founder of Pop Up Archive and current senior engineering manager at Apple, about why they signed the pledge and what they hope it can accomplish. Kim, Lisa and Anne also discuss more generally ideas for people who are frustrated with the state of affairs at their companies or in tech more broadly, but are not sure where to start and how to find a community of similarly civic-minded people to take action. They discuss ways to host meetups for your like-minded co-workers while still working hard at your day job and staying within your company's policies.   They also talk about how important it is to speak respectfully with people who disagree with you. A good goal is to deepen your own thinking, not to change a person's mind. You probably won't change their mind, and you probably won't change yours. That doesn't mean you're wasting your breath. When you invite discussion about your beliefs with people who disagree, two good things can happen. One, you get to know them a bit better. Two, you challenge yourself to think more deeply. JS Mill said that belief without discussion can give way to prejudice. Background on ICEout.tech: ⁠⁠ICEout.tech⁠⁠, started by and for people in tech, wants the tech industry to use its influential position in our economy to stop ICE. The pledge, which was started after Renee Good was murdered in Minneapolis, has more than 2,000 verified signatures from people across major companies including NVIDIA, Anthropic, OpenAI, Google, Microsoft, Amazon, Meta, and dozens more. The call to speak up against ICE in tech gained momentum after Border Patrol agents killed Alex Pretti, an ICU nurse, and has drawn public support from leaders like Dario and Daniela Amodei (Anthropic), Reid Hoffman, and Vinod Khosla. Tech professionals want their CEOs to join them in this effort, to protect our neighbors and communities and stop ICE's terror. Resources:  ⁠⁠ICEout.tech⁠⁠ information and how to get involved. Resist and Unsubscribe Resist and Unsubscribe - movement by Prof. Scott Galloway to encourage individuals to use their economic power by unsubscribing from big tech web services as a way to press these leaders to push for government reforms.   CHAPTERS: (00:00) Introduction to iceOut.tech Movement (02:00) Understanding the Pledge and Its Impact (04:59) Navigating Ethical Dilemmas in Tech (10:02) The Role of Affluence and Courage (15:20) Building Solidarity and Taking Action (20:04) Employee Power and Organizing for Change (22:53) The Role of Technology in Society (26:10) Tactics for Influencing Corporate Decisions (29:51) Building Internal Solidarity and Communication (34:04) Navigating Polarization and Finding Common Ground (39:03) Self-Care and Community Engagement Connect with the Radical Candor team: ⁠⁠⁠⁠⁠⁠Website⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

"He's blundered here. He's trying to set policy for the government on the use of AI through a sales contract." — Keith Teare on Dario AmodeiThere's only one story this week: Dario Amodei's refusal to let the Department of War use Anthropic's best technology for mass domestic surveillance and fully autonomous weapons. Silicon Valley rallied behind him. The New York Times covered it. Sam Altman publicly supported him—while quietly cutting his own deal with the administration. But Keith Teare thinks Anthropic is wrong.Keith's argument is simple: vendors don't set policy. If you want to sell to governments, you can't then dictate what they do with your product. That's not your job. And by trying to do it, Amodei has alienated the entire US administration and created a fake battle that can only damage his company. Andrew is more sympathetic. In his view, Amodei is taking a political position against Trump—and in 2026, with Congress marginalized and corporations increasingly powerful, that's just the nature of things.The debate cuts to something deeper: the power shift between corporations and the state. Oppenheimer couldn't say no to the government because he worked for them. Amodei can say no because he doesn't. These companies now speak to the government as almost equals. Meanwhile, Citruni Research released a white paper predicting AI will collapse the economy and destroy white-collar jobs. Jack Dorsey just cut 40% of Square's workforce. The stock jumped 25%. Five Takeaways●      Keith: Amodei Has Blundered: Vendors don't determine the use of what you buy from them. By trying to set policy through a sales contract, Amodei has alienated the entire US administration and created a fake battle that can only damage his company. He hasn't read the Art of War.●      Andrew: This Is a Political Stand: Amodei isn't naive—he's taking a position against Trump. And in 2026, with Congress marginalized and corporations increasingly powerful, the fact that he's willing to take the government on publicly is astonishing. He's kept his job. The investors are fine with it.●      The Power Has Shifted: Oppenheimer couldn't say no to the government because he worked for them. Amodei can say no because he doesn't. What Anthropic has at its fingertips is not something the government has. These companies now speak to the government as almost equals.●      Silicon Valley Is Split: Right libertarians are small-government supporters of the administration. Left libertarians are bigger-government supporters of welfare. Vinod Khosla is a hybrid—pro-America militarily, fearful of China. Tim Cook does whatever governments tell him. NVIDIA is navigating best.●      Jack Dorsey Cut 40%—Stock Jumped 25%: Citruni Research released a white paper predicting AI will collapse the economy. Noah Smith called it a scary bedtime story. But Dorsey just did it for real at Square. If AI succeeds, lots of white-collar jobs go. The social contract between capital and labor is breaking. About the GuestKeith Teare is a Silicon Valley entrepreneur and publisher of That Was The Week, a weekly tech newsletter. He is a co-founder of TechCrunch and has been a fixture in Silicon Valley for decades.ReferencesThis week's reading:●      Ezra Klein's interview with Jack Clark — Andrew calls it the interview of the week.●      Citruni Research white paper — The AI jobs apocalypse scenario that crashed the software market on Monday.●      Noah Smith's response — Calls the Citruni report a "scary bedtime story."Previous Keen On episodes mentioned:●      Maya Kornberg on Congress being "Stuck" (Episode 2815)●      Arne Westad on pre-WWI parallels (upcoming)About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States—hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:

In this issue of Moneycontrol Editor's Picks, our focus: the India AI Summit where venture capitalist Vinod Khosla predicted AI will make jobs obsolete, Union Minister Ashwini Vaishnaw said India will start commercial production of memory chips soon, Union MoS Pemmasani Chandra Sekhar assured telecom backbone is ready to take on AI workload and more. Also read about tax scrutiny, trade negotiations and property prices as we bring you all the top headlines from the day. Tune in!

In today's Tech3 from Moneycontrol, we bring you a wrap from the India AI Impact Summit in Delhi. Venture Capitalist Vinod Khosla calls Emergent one of the fastest growing startups he has ever seen as it reportedly doubles ARR to $100 million in weeks. AI Coding firm Replit's CEO Amjad Masad spoke to Moneycontrol on India plans, AI bubble and Razorpay integration, Infosys cofounder Nandan Nilekani says AI is an execution risk not an opportunity gap, Zoho's Sridhar Vembu flags affordable AI momentum, Philips CEO Roy Jakobs highlight India's healthcare AI role, and Neysa's plans to use $1.2 billion to expand GPU infrastructure.

Recorded live at Apollo House 2026, this fireside chat captures a candid, in-the-room conversation between StartUp Health's Unity Stoakes and entrepreneur, investor, and technologist Vinod Khosla of Khosla Ventures on AI's accelerating impact on healthcare. Khosla explains why he believes AI is a platform shift larger than the internet or mobile, and how that shift could unlock access to high-quality care for billions. The conversation explores his “AI intern” model for healthcare, why copilots often underperform in complex clinical work, and why trust, supervision, and fit-for-purpose guardrails are essential. A live exchange with Esther Dyson adds perspective on empathy, communication, and the enduring human dimensions of care. As a live recording, the audio reflects the energy of the room rather than a studio setting. Do you want to participate in live conversations with industry luminaries? When you join the StartUp Health Network – a new private community for investors, buyers, and industry leaders to connect year-round with top health entrepreneurs – you are invited to a full calendar of interactive Fireside Chats with the most influential leaders shaping health innovation. Come with questions, learn what is working right now, and connect with industry icons. » Learn more and join today. Want more content like this? Sign up for StartUp Health Insider™ to get funding insights, news, and special updates delivered to your inbox.

Khaby Lame macht den größten Influencer-Exit aller Zeiten. TikTok USA zeigt erste Zensur-Anzeichen: Nutzer berichten, dass Begriffe wie "Epstein" in Privatnachrichten blockiert werden und ICE-kritische Posts null Reichweite bekommen. Der All-In Podcast erreicht einen neuen Tiefpunkt. OpenAI startet sein Werbeprodukt mit $60 TKP. Nvidia investiert weitere 2 Milliarden in CoreWeave – und finanziert damit seinen eigenen Umsatz. Microsoft kontert mit dem Maya 200 Inferenz-Chip. Greg Brockman hat 25 Millionen an die MAGA-Kampagne gespendet. DOGE-Mitarbeiter stehen unter Verdacht, Sozialdaten für politisches Campaigning entwendet zu haben. Vinod Khosla zeigt Rückgrat und distanziert sich von Keith Rabois' rassistischen Aussagen. Die EU eröffnet eine Untersuchung gegen Grok wegen Deepfakes. Und Sepp Blatter ruft zum Boykott des FIFA World Cups in den USA auf. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Intro (00:01:09) Der Deal der Woche: Khaby Lame (00:15:49) TikTok: Stagnation und Übernahme durch die USA (00:24:00) Zensur und Kontrolle auf TikTok USA (00:30:06) OpenAI Werbung TKP (00:40:17) Nvidia und die Konkurrenz im KI-Chip-Markt (00:43:08) Investitionsstrategien in KI-Startups (00:49:14) Zoom hält Anthropic-Anteile (00:51:38) Earnings-Vorschau: Microsoft, Meta, Apple (00:53:08) Verbraucherschutz: Videoident-Scam bei Wohnungssuche (00:57:54) Datensicherheit: DOGE-Mitarbeiter unter Verdacht (01:02:01) Politische Einflüsse: Greg Brockman spendet 25 Mio. an MAGA (01:06:37) Tim Cook Kritik (01:10:01) Vinod Khosla (01:16:40) EU untersucht Grok-Deepfakes (01:19:00) Sepp Blatter ruft zum USA-Boykott auf Shownotes Rich Sparkle Holdings übernimmt Khaby Lames Kernunternehmen - prnewswire.com Donald J Trump freut sich, TikTok gerettet zu haben. - x.com TikTok untersucht, warum Nutzer 'Epstein' nicht schreiben können. - npr.org Senator Scott Wiener (@scott_wiener) auf Threads - threads.com OpenAI Seeks Premium Prices in Early Ads Push - theinformation.com Nvidia CoreWeave - ft.com Microsoft kündigt Maia 200 AI-Chip an - theverge.com Satya Nadella Post - x.com Zoom und Anthropic: Beteiligung, Bewertung und Börsengang - barrons.com Trump und Musk: Dogecoin und Sozialversicherung - politico.com OpenAI-Präsident Brockman Trump Super-PAC - theverge.com tim cook gefangen - spyglass.org Khosla distanziert sich von Rabois' ICE-Kommentaren - techcrunch.com EU xAI- ft.com Post von Sepp Blatter - x.com Greiftrupps, Panik, Straßenkämpfe: Minnesotas Kampf gegen Trumps ICE-Agenten | auslandsjournal - youtube.com whoranks - whoranks.io

From building Medal into a 12M-user game clipping platform with 3.8B highlight moments to turning down a reported $500M offer from OpenAI (https://www.theinformation.com/articles/openai-offered-pay-500-million-startup-videogame-data) and raising a $134M seed from Khosla (https://techcrunch.com/2025/10/16/general-intuition-lands-134m-seed-to-teach-agents-spatial-reasoning-using-video-game-clips/) to spin out General Intuition, Pim is betting that world models trained on peak human gameplay are the next frontier after LLMs.We sat down with Pim to dig into why game highlights are “episodic memory for simulation” (and how Medal's privacy-first action labels became a world-model goldmine https://medal.tv/blog/posts/enabling-state-of-the-art-security-and-protections-on-medals-new-apm-and-controller-overlay-features), what it takes to build fully vision-based agents that just see frames and output actions in real time, how General Intuition transfers from games to real-world video and then into robotics, why world models and LLMs are complementary rather than rivals, what founders with proprietary datasets should know before selling or licensing to labs, and his bet that spatial-temporal foundation models will power 80% of future atoms-to-atoms interactions in both simulation and the real world.We discuss:* How Medal's 3.8B action-labeled highlight clips became a privacy-preserving goldmine for world models* Building fully vision-based agents that only see frames and output actions yet play like (and sometimes better than) humans* Transferring from arcade-style games to realistic games to real-world video using the same perception–action recipe* Why world models need actions, memory, and partial observability (smoke, occlusion, camera shake) vs. “just” pretty video generation* Distilling giant policies into tiny real-time models that still navigate, hide, and peek corners like real players* Pim's path from RuneScape private servers, Tourette's, and reverse engineering to leading a frontier world-model lab* How data-rich founders should think about valuing their datasets, negotiating with big labs, and deciding when to go independent* GI's first customers: replacing brittle behavior trees in games, engines, and controller-based robots with a “frames in, actions out” API* Using Medal clips as “episodic memory of simulation” to move from imitation learning to RL via world models and negative events* The 2030 vision: spatial–temporal foundation models that power the majority of atoms-to-atoms interactions in simulation and the real world—Pim* X: https://x.com/PimDeWitte* LinkedIn: https://www.linkedin.com/in/pimdw/Where to find Latent Space* X: https://x.com/latentspacepodFull Video EpisodeTimestamps00:00:00 Introduction and Medal's Gaming Data Advantage00:02:08 Exclusive Demo: Vision-Based Gaming Agents00:06:17 Action Prediction and Real-World Video Transfer00:08:41 World Models: Interactive Video Generation00:13:42 From Runescape to AI: Pim's Founder Journey00:16:45 The Research Foundations: Diamond, Genie, and SEMA00:33:03 Vinod Khosla's Largest Seed Bet Since OpenAI00:35:04 Data Moats and Why GI Stayed Independent00:38:42 Self-Teaching AI Fundamentals: The Francois Fleuret Course00:40:28 Defining World Models vs Video Generation00:41:52 Why Simulation Complexity Favors World Models00:43:30 World Labs, Yann LeCun, and the Spatial Intelligence Race00:50:08 Business Model: APIs, Agents, and Game Developer Partnerships00:58:57 From Imitation Learning to RL: Making Clips Playable01:00:15 Open Research, Academic Partnerships, and Hiring01:02:09 2030 Vision: 80 Percent of Atoms-to-Atoms AI Interactions Get full access to Latent.Space at www.latent.space/subscribe

From building Medal into a 12M-user game clipping platform with 3.8B highlight moments to turning down a reported $500M offer from OpenAI (https://www.theinformation.com/articles/openai-offered-pay-500-million-startup-videogame-data) and raising a $134M seed from Khosla (https://techcrunch.com/2025/10/16/general-intuition-lands-134m-seed-to-teach-agents-spatial-reasoning-using-video-game-clips/) to spin out General Intuition, Pim is betting that world models trained on peak human gameplay are the next frontier after LLMs. We sat down with Pim to dig into why game highlights are “episodic memory for simulation” (and how Medal's privacy-first action labels became a world-model goldmine https://medal.tv/blog/posts/enabling-state-of-the-art-security-and-protections-on-medals-new-apm-and-controller-overlay-features), what it takes to build fully vision-based agents that just see frames and output actions in real time, how General Intuition transfers from games to real-world video and then into robotics, why world models and LLMs are complementary rather than rivals, what founders with proprietary datasets should know before selling or licensing to labs, and his bet that spatial-temporal foundation models will power 80% of future atoms-to-atoms interactions in both simulation and the real world. We discuss: How Medal's 3.8B action-labeled highlight clips became a privacy-preserving goldmine for world models Building fully vision-based agents that only see frames and output actions yet play like (and sometimes better than) humans Transferring from arcade-style games to realistic games to real-world video using the same perception–action recipe Why world models need actions, memory, and partial observability (smoke, occlusion, camera shake) vs. “just” pretty video generation Distilling giant policies into tiny real-time models that still navigate, hide, and peek corners like real players Pim's path from RuneScape private servers, Tourette's, and reverse engineering to leading a frontier world-model lab How data-rich founders should think about valuing their datasets, negotiating with big labs, and deciding when to go independent GI's first customers: replacing brittle behavior trees in games, engines, and controller-based robots with a “frames in, actions out” API Using Medal clips as “episodic memory of simulation” to move from imitation learning to RL via world models and negative events The 2030 vision: spatial–temporal foundation models that power the majority of atoms-to-atoms interactions in simulation and the real world — Pim X: https://x.com/PimDeWitte LinkedIn: https://www.linkedin.com/in/pimdw/ Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction and Medal's Gaming Data Advantage 00:02:08 Exclusive Demo: Vision-Based Gaming Agents 00:06:17 Action Prediction and Real-World Video Transfer 00:08:41 World Models: Interactive Video Generation 00:13:42 From Runescape to AI: Pim's Founder Journey 00:16:45 The Research Foundations: Diamond, Genie, and SEMA 00:33:03 Vinod Khosla's Largest Seed Bet Since OpenAI 00:35:04 Data Moats and Why GI Stayed Independent 00:38:42 Self-Teaching AI Fundamentals: The Francois Fleuret Course 00:40:28 Defining World Models vs Video Generation 00:41:52 Why Simulation Complexity Favors World Models 00:43:30 World Labs, Yann LeCun, and the Spatial Intelligence Race 00:50:08 Business Model: APIs, Agents, and Game Developer Partnerships 00:58:57 From Imitation Learning to RL: Making Clips Playable 01:00:15 Open Research, Academic Partnerships, and Hiring 01:02:09 2030 Vision: 80 Percent of Atoms-to-Atoms AI Interactions

Alex is an AI recruiter that autonomously handles phone screens, video interviews, and candidate communications at scale for enterprise talent teams and staffing firms. The company rebranded from Apriora after acquiring alex.com for over half a million dollars—a brand investment that immediately increased word-of-mouth referrals and inbound pipeline. In this episode of BUILDERS, we sat down with Aaron Wang, Co-Founder & CEO of Alex, to discuss achieving seven figures in revenue through founder-led sales in staffing, their "respectful zagging" approach to standing out in a crowded AI agent market, and building toward network effects that could fundamentally reshape talent matching. Topics Discussed Justifying a $500K+ domain acquisition to co-founders and investors Building candidate experience that drives engagement rather than rejection Design decisions around AI avatars versus voice-only interactions Differentiation strategy in marketing: zagging without rage baiting Hiring framework based on incentive understanding and first-principles thinking Market segmentation between staffing firms and corporate TA teams Long-term platform vision leveraging cross-company recruiting data GTM Lessons For B2B Founders Quantify intangible asset ROI through pipeline metrics, not brand sentiment: Aaron defended the $500K+ alex.com purchase by tracking "huge increase in word of mouth and inbound, which is obviously directly measurable." The previous name Apriora created friction in sharing and referrals. With enterprise contract sizes, removing pronunciation and memorability barriers has concrete pipeline impact. The domain also functions as a balance sheet asset. Founders should evaluate premium domains against customer acquisition cost and deal velocity, not abstract brand value. Extract vertical-specific insights before horizontal expansion: Alex reached seven figures in staffing revenue exclusively through founder-led sales before entering corporate TA. Aaron noted they had "a few key insights into what made staffing particularly relevant as a market." This concentrated approach allowed them to refine product-market fit and build referenceable customers in one segment. Only after achieving clear traction did they expand strategically to corporate TA. Founders should resist premature market expansion—depth in one vertical provides the learnings needed for successful adjacency moves. Structure interviews to surface first-principles thinking across functions: Aaron described having A-player marketers conduct first rounds, then A-player engineers conduct second rounds for the same candidate. This cross-functional approach tests whether candidates can operate from first principles rather than just applying domain playbooks. The key insight: "A players want to work with A players and A players can identify A players. A B player can't identify an A player." Founders should design interview loops that reveal foundational reasoning ability, not just functional competence. Hire for incentive mapping ability over category experience: Exceptional marketers understand "what is incentivizing someone to share or post or like" and how to create mindshare. Aaron emphasized this matters more than HR tech background, citing Vinod Khosla's gene pool engineering concept. You need domain expertise somewhere in the company, but hiring everyone for it dilutes your ability to think differently. Founders should prioritize candidates who demonstrate deep understanding of human incentives and can identify non-obvious differentiation opportunities. Align brand aesthetic with product philosophy to reinforce positioning: Alex deliberately avoided human avatars, choosing nature imagery and green color schemes to make AI feel "grounded" rather than "abstract." This extends their product belief that "bad AI is worse than no AI"—the brand needed to signal reliability and familiarity. Aaron explicitly contrasted this with rage baiting tactics: "not something we're interested in doing." Founders should ensure visual identity and messaging tactics authentically reflect product values rather than chasing engagement metrics that misalign with positioning. Map product roadmap by studying adjacent verticals with faster adoption curves: When discussing category, Aaron compared Alex to Harvey rather than interview intelligence tools. He noted HR tech "tends to lag others" in technology uptake, making legal AI a better predictive model. Just as Harvey expanded from document review to email automation to client portals, Alex views phone screening as "one important, but only one portion of what a recruiter does today." Founders in slower-adopting categories should analyze product evolution in faster-moving verticals to anticipate feature expansion and avoid getting boxed into point solution positioning. //   Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Aujourd'hui dans Silicon Carne, on parle de :

Why are 95% of AI projects struggling to create positive ROI, according to a recent MIT study? Do businesses need help getting those first few AI projects started and driven to success?SHOW: 970SHOW TRANSCRIPT: The Cloudcast #970 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcast[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.SHOW NOTES:MIT Study - The State of AI in Business (2025)The Information - Interview with Vihod Khosla (Oct 2025)WHERE ARE THE BIGGEST AI RISKS?Why are Businesses struggling with AI projects in 2025?What are simple steps to make AI projects successful?FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Khosla Ventures' Vinod Khosla talks with TITV Host Akash Pasricha about AI's high costs, the risks in circular financing deals like those involving NVIDIA and Oracle, and the energy solutions to AI. We also talk with The Information's Ann Gehan about creator reaction to TikTok Shop's new advertising tool. Li Haslett Chen, Founder of Howl, discusses where AI can transform the shopping experience, noting returns as the biggest opportunity. Lastly, we get into rebuilding college for the AI era with Tade Oyerinde, Founder & Chancellor, and Jerome Pesenti, CTO, of Campus.Articles discussed on this episode:https://www.theinformation.com/articles/tiktok-shops-new-ad-policy-risks-alienating-merchantshttps://www.theinformation.com/articles/can-ai-deliver-shoppers-wantTITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Subscribe to: - The Information on YouTube: https://www.youtube.com/@theinformation4080/?sub_confirmation=1- The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agenda

In this episode of Tank Talks, host Matt Cohen is joined by John Ruffolo to dive into a wide range of topics affecting Canada's economy, from energy and tech to blockchain. They discuss the government's announcement of major projects like LNG Canada's expansion and the critical need for nuclear energy. The conversation also touches on Canada's role in the global energy market, especially with the growing importance of renewables and the challenges of balancing carbon-based energy.The episode shifts to AI and blockchain, exploring how Canada can stay competitive in the tech race. They also dive into the rise of stablecoins in Canada with Tetra Digital Group's new Canadian peg stablecoin aimed at transforming B2B payments. Lastly, the episode examines the increasing presence of Canadian executives in U.S. tech companies and the risks of overvaluation in the AI startup space.A Quick Word from our Sponsor, FaskenAt Fasken, our clients don't wait for the future. They build it. As the first and largest dedicated emerging tech practice in Canada, our team is composed of founders, ex in-house counsel, developers and business advisors who have guided clients from startup, to scale-up, to exit. The trust of our clients has enabled us to consistently rank at the top of every major Canadian M&A, Capital Markets and Venture Capital league table. With deep industry knowledge and experience across all areas of emerging and high growth technology including ClimateTech, MedTech, Artificial Intelligence, Fintech, and AgTech we're your partners within the innovation ecosystem as you transform the landscape of what's possible.Tomorrow starts here. Own it with us.For more information, visit fasken.com/emergingtech and follow us on LinkedIn.LNG, Nuclear & Energy Future: Canada's Next Big Steps (00:07:13)Matt and John discuss the announcement of major energy projects in Canada, including LNG Canada's expansion and the importance of investing in nuclear energy for the future.Stablecoin Innovation: The Canadian Peg Stablecoin (00:15:01)Tetra Digital Group in Calgary launches a Canadian peg stablecoin set to revolutionize B2B payments. John and Matt explore how it could impact Canada's financial landscape.Opendoor's New CEO: Canadian Leadership in U.S. Tech (00:18:25)Opendoor appoints a Canadian executive as CEO, sparking a conversation about the growing influence of Canadian tech talent in major U.S. companies.AI Valuations: Are We Heading for a Tech Crash? (00:25:39)With the AI boom in full swing, Matt and John examine the potential for overvaluation and the risks of an AI bubble, echoing insights from Vinod Khosla on market “carnage.”AI's Future: Innovation or Overhype? (00:26:15)Matt and John delve into the power law of investing, discussing how capital is flowing into AI and why only a few companies will win big while many others will fail.Connect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Patrick McKenzie (patio11) is joined by Jim Weisser, a serial entrepreneur and founder of SignTime, for an in-depth exploration of Japan's software market and startup ecosystem. They discuss the unique challenges of building software in a culture that prizes stability over rapid iteration, the dominance of systems integrators, and how Japan's economic stagnation shaped its relationship with technology. The conversation covers everything from Excel-driven development processes to the recent transformation of Japan's VC landscape, offering insights for anyone considering doing business in the world's third-largest economy.–Full transcript: https://www.complexsystemspodcast.com/building-software-in-japan-with-jim-weisser/–Sponsor: BoxWorks 2025Discover hands-on AI data extraction techniques, network with industry experts, and hear featured speakers Vinod Khosla, Jared Kaplan, and Aaron Levie at BoxWorks 2025 in San Francisco (Sept 11-12). Use code BW25-ComplexSystems for 50% off at this link: https://bit.ly/452036h–Links:Signtime:: https://www.signtime.com/en/–Timestamps:(00:00) Intro(00:24) Exploring the Japanese software market(01:25) Challenges in the Japanese startup ecosystem(02:37) Jim Weisser's background and journey(05:14) The evolution of Japan's economy and business practices(10:15) Understanding Japan Inc. and its impact on software(10:28) The role of systems integrators in Japan(19:01) Sponsor: BoxWorks Conference 2025 (*discounted tickets for Complex Systems listeners)(20:29) Cultural differences in software development(26:06) Digital transformation and efficiency in Japan(32:44) Labor market dynamics and employment practices(35:50) Startup ecosystem and equity culture in Japan(42:53) Success stories and market potential in Japan(43:42) Corporate relationships in Japanese companies(44:10) Salesforce's success in Japan(45:44) E-commerce boom in Japan(46:33) Rakuten's evolution and strategy(48:27) Amazon's journey in Japan(49:13) Cloud services and security concerns(51:18) Sansan: Business card management(55:14) Venture capital landscape in Japan(01:05:02) Personal reflections on living in Japan(01:16:21) Wrap

Join our Patreon for extra-long episodes and ad-free content: https://www.patreon.com/techishThis week on Techish, host Michael Berhane teams up with TechCrunch reporter Dominic-Madori Davis to unpack how AI is shaking up the fashion and film industries. They also cover Vinod Khosla's take on ditching traditional careers, Figma's IPO, why former FCC chair Lina Khan's feels vindicated, and why Apple is in need of a shake-up. And for our Patreon listeners: could the UK's new Online Safety Act spell the end of free speech?Chapters00:36 Guess's AI Model in Vogue Sparks Backlash08:17 Billionaire Investor Says Ditch the Idea of a Fixed Career13:21 Figma Goes Public 20:10 Is It Time for Tim Cook to Step Down?27:05 The UK's Online Safety Act: The End of Free Speech? [Patreon-Only]Follow Dom on Instagram (@dominicmadori) and subscribe to her Substack, The Black Cat. Extra Reading & ResourcesThe Brutalist and Emilia Perez's voice-cloning controversies make AI the new awards season battleground [The Guardian] Amazon's Alexa Fund Invests in ‘Netflix of AI' Start-Up Fable, Which Launches Showrunner: A Tool for User-Directed TV Shows [Variety]Vinod Khosla says young people should plan their careers for flexibility instead of one profession [Business Insider, $] Lina Khan points to Figma IPO as vindication of M&A scrutiny [TechCrunch] Support the show————————————————————Join our Patreon for extra-long episodes and ad-free content: https://www.patreon.com/techish Watch us on YouTube: https://www.youtube.com/@techishpod/Advertise on Techish: https://goo.gl/forms/MY0F79gkRG6Jp8dJ2———————————————————— Stay in touch with the hashtag #Techishhttps://www.instagram.com/techishpod/https://www.instagram.com/abadesi/https://www.instagram.com/michaelberhane_/ https://www.instagram.com/hustlecrewlive/https://www.instagram.com/pocintech/Email us at techishpod@gmail.com

Everywhere you look, it seems like bad news for climate tech. Investments are down, the US government has cut incentives and startups are running out of cash. But venture capitalist Vinod Khosla is still bullish, even though the One Big Beautiful Bill cut an estimated $500 billion in green spending. This week on Zero, Akshat Rathi speaks with Khosla to find out when we can expect to see fusion, whether he’s reconsidering investing in the US and why he still thinks the best clean tech is yet to come. Explore further: Trump Immigration Policies Hit Climate Tech Talent Pipeline, Khosla Says Vinod Khosla Says ‘Fusion Will Be Real’ Within the Next Five Years From Brazil to Singapore, Manipulated Mosquitoes Fight Dengue - Bloomberg Zero is a production of Bloomberg Green. Our producer is Oscar Boyd. Special thanks to Brian Kahn, Michelle Ma, Eleanor Harrison Dengate, Jessica Beck, Siobhan Wagner, Meg Szabo, Abby Danzig and Krystal Contreras. Thoughts or suggestions? Email us at zeropod@bloomberg.net. For more coverage of climate change and solutions, visit https://www.bloomberg.com/green.See omnystudio.com/listener for privacy information.