Podcast appearances and mentions of Tyler Cowen

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

Is Islam Compatible with the West? Get your discounted early bird tickets now before they sell out: https://winstonmarshall.eventbrite.co.uk I cannot wait for you to join us in London for one of the most important debates of our time.In this episode of The Winston Marshall Show, I sit down with economist, author, and columnist Tyler Cowen for a conversation on artificial intelligence, the race between America and China, cyber warfare, and why the AI revolution will reshape every aspect of modern life.We explore the growing battle between the Trump administration and leading AI companies such as Anthropic and OpenAI, the risks of AI-driven cyber attacks, national security, effective altruism, and why Cowen believes the world is entering the most significant technological transition since the Industrial Revolution. We also discuss whether AI represents a greater geopolitical challenge than nuclear weapons, how governments should regulate it, and why the coming years could be both extraordinarily dangerous and extraordinarily prosperous.The conversation also examines the future of work, economic growth, surveillance, healthcare, longevity, education, and whether AI will deepen state control or instead empower individuals. Cowen explains why he believes AI could eradicate many diseases, transform productivity, and fundamentally alter the relationship between governments, corporations, and ordinary citizens.Finally, we turn to Britain's economic decline, immigration, productivity, energy policy, debt, and why Cowen believes the UK urgently needs a new economic direction before its long-term decline becomes irreversible.WATCH THE EXTENDED CONVERSATION HERE: https://www.winstonmarshall.co.uk/Chapters 00:00 Introduction02:10 AI Cyber Warfare & Why The Next Few Years Matter05:00 Trump, Anthropic & Who Controls AI?10:20 Can Britain Defend Itself In The AI Era?15:19 Why AI Is Like World War II19:20 Effective Altruism & The Future Of AI23:23 Is AI More Dangerous Than Nuclear Weapons?27:04 Will AI Cure Disease & Extend Human Life?30:00 AI, Surveillance & The Risk Of Totalitarianism35:00 Jobs, Education & How AI Will Change Work40:31 AI, Space & The Next Global Arms Race45:00 AI, Religion & The Future Of Faith49:07 Is Britain Already In A Debt Crisis?

Welcome to PGX Ideas. #4PGX Ideas is where I sit with people who think for a living — scientists, philosophers, builders, and the occasional genius who doesn't fit any category.These conversations are dense. They're long. They won't give you a shortcut.If you're looking for something light, that's PGX: Raw & Real → https://www.youtube.com/playlist?list=PLa6DgTttATAc0hftp0aZtUvCgVSKO8hbxIn this episode, I spoke to Tyler Cowen — American economist and author.Timestamps:0:00 - Welcome0:44 - AI: smarter but still slow5:23 - Who really has good timing?9:07 - How AI changes news forever12:53 - Will AI become too expensive?17:05 - Can governments actually stop AI?20:03 - Will AI kill our jobs?25:27 - Why people are having fewer kids29:52 - What will be rare and precious?34:34 - AI as the perfect therapist?39:09 - Does AI actually think like us?45:03 - AI, war, love and relationships49:56 - Journalism, status and AI future56:23 - The world is a big mess1:04:47 - BRICS, NATO and new world order1:09:23 - Immigration, China and India explained1:15:49 - Rapid fire: overrated or underrated?1:21:36 - Where could Tyler be wrong?Enjoy.— Prakhar

Dans cet épisode, je reçois Fabien Pinckaers, CEO et fondateur d'Odoo, pour une discussion autour de la construction d'un éditeur de logiciels de gestion mondial, des crises traversées en chemin, et de la vision pour l'avenir du secteur.Nous avons parlé :du premier pivot en 2010, quand Fabien est passé d'une société de services à un modèle d'éditeur pur, en levant 3 millions d'euros pour redistribuer ses revenus à des intégrateurs partenairesde la crise de trésorerie qui a suivi : deux semaines de cash restantes, une tentative de prise de contrôle par les investisseurs sur la base d'une valorisation inférieure à celle d'il y a deux ans, et la décision de licencier 30% des équipes en une nuit, liste écrite à la mainde la découverte tardive d'un principe juridique clé : en Belgique, la loi prime sur le pacte d'actionnaires, et celui qui détient la majorité des parts peut révoquer un administrateur, même nommé par les investisseursde l'étude "Effectuation", qui distingue deux types de profils de dirigeants : les CEO de formation financière qui partent d'un objectif pour en dérouler un plan, et les CEO entrepreneurs qui partent de ce qu'ils ont déjà pour maximiser la valeur à chaque étape, sans prédire le futurde la transparence radicale comme outil de management : graphiques de trésorerie communiqués publiquement pendant la quasi-faillite, moyennes de salaires publiées en ligne et configurables avant même de postulerdu deuxième pivot vers un modèle Open Core en 2014, avec 80% des applications restées open source et 20% devenues payantes, qui a stabilisé le business model mais fracturé temporairement la confiance de la communautéde l'impact de l'IA sur les équipes : 40% des tickets support traités automatiquement, temps d'upgrade des bases de données divisé par deux, et pourtant Odoo recrute 3 500 personnes sur les 12 prochains mois, convaincu que plus ses équipes sont productives, plus il en fautUn échange dense et sans filtre, où un fondateur revient sur ses erreurs de gouvernance et de communication autant que sur ses réussites, avec une vision claire sur la consolidation à venir du marché des logiciels de gestion.Recommandations de Fabien : "Modern Principles of Economics" de Alex Tabarrok et Tyler Cowen https://www.macmillanlearning.com/ed/uk "Never Split the Difference" de Chris Voss https://www.blackswanltd.com/never-split-the-difference Liens utiles: Fabien Pinckaers : https://www.linkedin.com/in/fpodoo/Odoo : https://www.odoo.com/Finscale est aussi disponible sur YouTube: https://www.youtube.com/@finscale.***************************Finscale est bien plus qu'un podcast. Cet épisode est produit et animé par Solenne Niedercorn, fondatrice de Finscale.

Wyatt Thomson of OpenAI speaks with economists Tyler Cowen and Alex Tabarrok about AI, labor markets, and the future of economic growth. The conversation explores one of the most common fears surrounding AI: that increasingly capable systems will eliminate jobs. Cowen and Tabarrok argue instead that economic growth remains the key variable. Throughout history, productivity-enhancing technologies have transformed work, created new industries, and expanded living standards, even as they disrupted existing jobs and institutions. They discuss automation, comparative advantage, inequality, education, healthcare, energy, and the kinds of work that may become more valuable in an AI-driven economy. Along the way, they examine longer-term questions about abundance, ownership, AI agents, and how societies can adapt to rapid technological change.   Resources: Follow Tyler on X: https://x.com/tylercowen Follow Alex on X: https://x.com/ATabarrok Follow Wyatt on X: https://x.com/dataWyatt   Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Matt speaks with Matt Dinan about why AI is not so much ruining liberal education as exposing it's main shortcoming: treating education as a system of credentials rather than a challenging process of reading, writing, discussing, and learning how to learn. Dinan argues that the best response is not to become an "AI cop", but to design courses that incentivize students to learn skills they will need in any scenario where AI has an impact on our society - for better or for worse. References “Permission Structures” — Matt Dinan https://mattdinan.substack.com/p/the-ai-skeptical-professors-guide Matt Dinan's viral “honest B or C student” post on X https://x.com/second_sailing/status/1912857896599105564 “Tyler Cowen's AI Campus” — Marginal Revolution https://marginalrevolution.com/marginalrevolution/2026/01/tyler-cowens-ai-campus.htm Thanks to Our Patrons Thanks to our patrons, including Kris Rondolo, Amy Willis, and Christopher McDonald. To support The Curious Task, visit: https://patreon.com/curioustask

Join renowned economist Tyler Cowen for a live taping of his hit podcast Conversations with Tyler, featuring special guest Craig Newmark, founder of Craigslist and Craig Newmark Philanthropies. Together, they'll explore trust, cybersecurity, and the building blocks of resilient civic institutions. Newmark has become a deeply consequential civic leader of the internet age. Best known for creating Craigslist—a platform celebrated for its radical simplicity and user-first ethos—he has spent the last two decades supporting philanthropy that strengthens democracy. His work spans cybersecurity ("cyber civil defense"), veterans and military families, trustworthy journalism, and combating misinformation. In an era of growing polarization and distrust, Newmark's approach is guided by one pragmatic question: what actually works? Returning to the stage at 92NY after two sold-out programs, Cowen will talk with Newmark about the pivotal choices behind Craigslist and the lessons he has drawn from years of public-interest work, exploring how to safeguard civic trust and apply solutions-driven principles in today's digital world.

The "Death of Woke" Narrative Is a Lie Cardio Miracle, Learn More! - https://cardiomiracle.com/?ref=t4Hpzrm3 Alive and Intelligent Substack - https://aliveandintelligent.substack.com Warriors Of Teancum Men's Retreat- https://www.cwicmedia.com/warriors-of... Greg reacts to political violence, campus radicalism, anti-Semitism, and the next stage of cultural revolution Why progressive movements always retreat, regroup, and push further the next time Tyler Cowen says something worse is coming. Greg argues it's the same revolution wearing a new mask Wokeism Was Never About Equity The Dialectic Never Stops Cwic Media Website: http://www.cwicmedia.com  

“La diferencia entre un chaval de Harvard o del MIT y alguien de la Politécnica que está haciendo Física y Mates es dónde han nacido. El talento está distribuido equitativamente. Las oportunidades no”. Esta frase de Guli esconde una de las verdades sobre la meritocracia. No es el talento, es el entorno. La beca Exponential, un proyecto fantástico, intenta reducir esta brecha ofreciendo a los más jóvenes la oportunidad de trabajar unos meses en una empresa tecnológica de Estados Unidos.Kapital es posible gracias a sus colaboradores:⁠⁠TaxDown⁠⁠. Tus impuestos bien hechos.⁠¿Declaras bien tus inversiones? Este año, si tienes inversiones, hay nuevos cambios y regulaciones que tienes que saber (DAC8, modelo 721, normativa europea), así que es clave hacerlo bien. Si inviertes, yo te recomiendo TaxDown por ser la forma más fácil de presentar la Renta. TaxDown se integra con la mayoría de brókers, te lo calculan todo, y además cuentan con expertos fiscales en inversiones que revisan tu caso. Así evitas líos y cálculos raros. Si quieres probarlo, puedes usar mi código KAPITAL para obtener descuento. O puedes entrar directamente desde este enlace.La Cartera K⁠. Invierte en lo que no cambia.La Cartera K es la evolución lógica de El Proyecto K. Pablo González Vidal y yo abrimos el taller de inversión para que los pequeños ahorradores tomaran el control de sus finanzas. El curso ha sido un éxito (¡nueva edición en junio!) y por eso queremos dar ahora la oportunidad de invertir directamente en una cartera automatizada que siga esos principios K. Lo hacemos de la mano de la plataforma de inversión inbestMe. Con el fin de proteger tu capital en estos tiempos inciertos, la Cartera K sigue una estrategia indexada de bajas comisiones con una diversificación sectorial. Si estás interesado escríbeme a joan@elproyectok.comPatrocina Kapital. Toda la información en este link.Índice:0:32 Subir el listón con el proyecto Sputnik.8:34 La riqueza de la sociedad americana.13:21 No trabajan más horas, pero sí están más horas pensando en el trabajo.16:05 El magnífico proyecto de Exponential.28:20 En el deporte vemos bien la hiperespecialización.36:27 Ley de potencias en el emprendimiento.46:45 Tomarse en serio tu trabajo.1:04:37 Manufacturar la serendipia.1:12:36 Los ricos tienen más balas.1:19:42 Nunca ha sido tan fácil llegar al 1%, nunca ha sido tan difícil llegar al 10%.1:31:50 Twitter, Reddit, YouTube y enterarte de todo unos días antes.Apuntes:Average is over. Tyler Cowen.Bullshit jobs: A theory by David Graeber. Eliane Graser.Las posibilidades económicas de nuestros nietos. John Maynard Keynes.El cisne negro. Nassim Nicholas Taleb.3Blue1Brown. Grant Sanderson.Veritasium. Derek Muller.

The complex intersection of technological progress, economic stability, and human well-being across history and into the year 2026. Data from Our World in Data reveals a dramatic 150-year decline in working hours, while economist Tyler Cowen argues that modern institutional bottlenecks will prevent AI from triggering a rapid growth explosion. Contrasting this perspective, OpenAI's 2026 industrial policy proposes radical measures like "robot taxes" and a four-day workweek to manage the transition to superintelligence. Will Manidis critiques these corporate proposals as disconnected from the violent labor struggles of the past and reflects on the spiritual and cultural anxieties of a society obsessed with technical optimization. Together, the texts debate whether humanity is entering an age of unprecedented leisure or profound displacement as machines begin to outpace human productivity. These narratives suggest that the ultimate challenge of the intelligence age is not just economic efficiency, but redefining the human social contract.

Tyler Cowen is bullish on the integration of AI into higher education. He's also not worried about its effects on the future workplace. Listen as Cowen speaks with EconTalk's Russ Roberts about the reasons for his optimism, and argues that college classes should devote significant time to learning how to use AI. They discuss the future of writing (and thinking) in an academic context, and Cowen's solution to dealing with worries about cheating. Cowen also shares how he personally has adapted to AI, and whether he thinks there's value to a college education designed not to ensure mastery of a subject, but instead to help students become the kind of people they want to be.

This week, Tyler Cowen joins the show. A true polymath, he answers everything on Coleman Hughes's mind about our world and its future. In this rapid-fire exchange, Tyler weighs in on whether AI is a bubble, the minimum wage, Mexican wokeness, and the Donald Trump administration's approach to foreign aid. He also touches on travel, new religions, the UN, and even his three favorite films.  Learn more about your ad choices. Visit megaphone.fm/adchoices

The reception to our recent post on Code Reviews has been strong. Catch up!Amid a maelstrom of discussion on whether or not AI is killing SaaS, one of the top publicly listed SaaS companies in the world has just reported record revenues, clearing well over $1.1B in ARR for the first time with a 28% margin. As we comment on the pod, Aaron Levie is the rare public company CEO equally at home in both worlds of Silicon Valley and Wall Street/Main Street, by day helping 70% of the Fortune 500 with their Enterprise Advanced Suite, and yet by night is often found in the basements of early startups and tweeting viral insights about the future of agents.Now that both Cursor, Cloudflare, Perplexity, Anthropic and more have made Filesystems and Sandboxes and various forms of “Just Give the Agent a Box” cool (not just cool; it is now one of the single hottest areas in AI infrastructure growing 100% MoM), we find it a delightfully appropriate time to do the episode with the OG CEO who has been giving humans and computers Boxes since he was a college dropout pitching VCs at a Michael Arrington house party.Enjoy our special pod, with fan favorite returning guest/guest cohost Jeff Huber!Note: We didn't directly discuss the AI vs SaaS debate - Aaron has done many, many, many other podcasts on that, and you should read his definitive essay on it. Most commentators do not understand SaaS businesses because they have never scaled one themselves, and deeply reflected on what the true value proposition of SaaS is.We also discuss Your Company is a Filesystem:We also shoutout CTO Ben Kus' and the AI team, who talked about the technical architecture and will return for AIE WF 2026.Full Video EpisodeTimestamps* 00:00 Adapting Work for Agents* 01:29 Why Every Agent Needs a Box* 04:38 Agent Governance and Identity* 11:28 Why Coding Agents Took Off First* 21:42 Context Engineering and Search Limits* 31:29 Inside Agent Evals* 33:23 Industries and Datasets* 35:22 Building the Agent Team* 38:50 Read Write Agent Workflows* 41:54 Docs Graphs and Founder Mode* 55:38 Token FOMO Culture* 56:31 Production Function Secrets* 01:01:08 Film Roots to Box* 01:03:38 AI Future of Movies* 01:06:47 Media DevRel and EngineeringTranscriptAdapting Work for AgentsAaron Levie: Like you don't write code, you talk to an agent and it goes and does it for you, and you may be at best review it. That's even probably like, like largely not even what you're doing. What's happening is we are changing our work to make the agents effective. In that model, the agent didn't really adapt to how we work.We basically adapted to how the agent works. All of the economy has to go through that exact same evolution. Right now, it's a huge asset and an advantage for the teams that do it early and that are kinda wired into doing this ‘cause you'll see compounding returns. But that's just gonna take a while for most companies to actually go and get this deployed.swyx: Welcome to the Lane Space Pod. We're back in the chroma studio with uh, chroma, CEO, Jeff Hoover. Welcome returning guest now guest host.Aaron Levie: It's a pleasure. Wow. How'd you get upgraded to, uh, to that?swyx: Because he's like the perfect guy to be guest those for you.Aaron Levie: That makes sense actually, for We love context. We, we both really love context le we really do.We really do.swyx: Uh, and we're here with, uh, Aaron Levy. Welcome.Aaron Levie: Thank you. Good to, uh, good to be [00:01:00] here.swyx: Uh, yeah. So we've all met offline and like chatted a little bit, but like, it's always nice to get these things in person and conversation. Yeah. You just started off with so much energy. You're, you're super excited about agents.I loveAaron Levie: agents.swyx: Yeah. Open claw. Just got by, got bought by OpenAI. No, not bought, but you know, you know what I mean?Aaron Levie: Some, some, you know, acquihire. Executiveswyx: hire.Aaron Levie: Executive hire. Okay. Executive hire. Say,swyx: hey, that's my term. Okay. Um, what are you pounding the table on on agents? You have so many insightful tweets.Why Every Agent Needs a BoxAaron Levie: Well, the thing that, that we get super excited by that I think is probably, you know, should be relatively obvious is we've, we've built a platform to help enterprises manage their files and their, their corporate files and the permissions of who has access to those files and the sharing collaboration of those files.All of those files contain really, really important information for the enterprise. It might have your contracts, it might have your research materials, it might have marketing information, it might have your memos. All that data obviously has, you know, predominantly been used by humans. [00:02:00] But there's been one really interesting problem, which is that, you know, humans only really work with their files during an active engagement with them, and they kind of go away and you don't really see them for a long time.And all of a sudden, uh, with the power of AI and AI agents, all of that data becomes extremely relevant as this ongoing source of, of answers to new questions of data that will transform into, into something else that, that produces value in your organization. It, it contains the answer to the new employee that's onboarding, that needs to ramp up on a project.Um, it contains the answer to the right thing to sell a customer when you're having a conversation to them, with them contains the roadmap information that's gonna produce the next feature. So all that data. That previously we've been just sort of storing and, and you know, occasionally forgetting about, ‘cause we're only working on the new active stuff.All of that information becomes valuable to the enterprise and it's gonna become extremely valuable to end users because now they can have agents go find what they're looking for and produce new, new [00:03:00] value and new data on that information. And it's gonna become incredibly valuable to agents because agents can roam around and do a bunch of work and they're gonna need access to that data as well.And um, and you know, sometimes that will be an agent that is sort of working on behalf of, of, of you and, and effectively as you as and, and they are kind of accessing all of the same information that you have access to and, and operating as you in the system. And then sometimes there's gonna be agents that are just.Effectively autonomous and kind of run on their own and, and you're gonna collaborate and work with them kind of like you did another person. Open Claw being the most recent and maybe first real sort of, you know, kind of, you know, up updating everybody's, you know, views of this landscape version of, of what that could look like, which is, okay, I have an agent.It's on its own system, it's on its own computer, it has access to its own tools. I probably don't give it access to my entire life. I probably communicate with it like I would an assistant or a colleague and then it, it sort of has this sandbox environment. So all of that has massive implications for a platform that manage that [00:04:00] enterprise data.We think it's gonna just transform how we work with all of the enterprise content that we work with, and we just have to make sure we're building the right platform to support that.swyx: The sort of shorthand I put it is as people build agents, everybody's just realizing that every agent needs a box. Yes.And it's nice to be called box and just give everyone a box.Aaron Levie: Hey, I if I, you know, if we can make that go viral, uh, like I, I think that that terminology, I, that's theswyx: tagline. Every agentAaron Levie: needs a box. Every agent needs a box. If we can make that the headline of this, I'm fine with this. And that's the billboard I wanna like Yeah, exactly.Every agent needs a box. Um, I like it. Can we ship this? Like,swyx: okay, let's do it. Yeah.Aaron Levie: Uh, my work here is done and I got the value I needed outta this podcast Drinks.swyx: Yeah.Agent Governance and IdentityAaron Levie: But, but, um, but, but, you know, so the thing that we, we kind of think about is, um, is, you know, whether you think the number 10 x or a hundred x or whatever the number is, we're gonna have some order of magnitude more agents than people.That's inevitable. It has to happen. So then the question is, what is the infrastructure that's needed to make all those agents effective in the enterprise? Make sure that they are well governed. Make sure they're only doing [00:05:00] safe things on your information. Make sure that they're not getting exposed. The data that they shouldn't have access to.There's gonna be just incredibly spectacularly crazy security incidents that will happen with agents because you'll prompt, inject an agent and sort of find your way through the CRM system and pull out data that you shouldn't have access to. Oh, weJeff Huber: have God,Aaron Levie: right? I mean, that's just gonna happen all over the place, right?So, so then the thing is, is how do you make sure you have the right security, the permissions, the access controls, the data governance. Um, we actually don't yet exactly know in many cases how we're gonna regulate some of these agents, right? If you think about an agent in financial services, does it have the exact same financial sort of, uh, requirements that a human did?Or is it, is the risk fully on the human that was interacting or created the agent? All open questions, but no matter what, there's gonna need to be a layer that manages the, the data they have access to, the workflows that they're involved in, pulling up data from multiple systems. This is the new infrastructure opportunity in the era of agents.swyx: You have a piece on agent identities, [00:06:00] which I think was today, um, which I think a lot of breaking news, the security, security people are talking about, right? Like you basically, I, I always think of this as like, well you need the human you and then there you need the agent. YouAaron Levie: Yes.swyx: And uh, well, I don't know if it's that simple, but is box going to have an opinion on that or you're just gonna be like, well we're just the sort of the, the source layer.Yeah. Let's Okta of zero handle that.Aaron Levie: I think we're gonna have an opinion and we will work with generally wherever the contours of the market end up. Um, and the reason that we're gonna have an opinion more than other topics probably is because one of the biggest use cases for why your agent might need it, an identity is for file system access.So thus we have to kind of think about this pretty deeply. And I think, uh, unless you're like in our world thinking about this particular problem all day long, it might be, you know, like, why is this such a big deal? And the reason why it's a really big deal is because sometimes sort of say, well just give the agent an, an account on the system and it just treats, treat it like every other type of user on the system.The [00:07:00] problem is, is that I as Aaron don't really have any responsibility over anybody else's box account in our organization. I can't see the box account of any other employee that I work with. I am not liable for anything that they do. And they have, I have, I have, you know, strict privacy requirements on everything that they're able to, you know, that, that, that they work on.Agents don't have that, you know, don't have those properties. The person who creates the agent probably is gonna, for the foreseeable future, take on a lot of the liability of what that agent does. That agent doesn't deserve any privacy because, because it's, you know, it can't fully be autonomously operated and it doesn't have any legal, you know, kind of, you know, responsibility.So thus you can't just be like, oh, well I'll just create a bunch of accounts and then I'll, I'll kind of work with that agent and I'll talk to it occasionally. Like you need oversight of that. And so then the question is, how do you have a world where the agent, sometimes you have oversight of, but what if that agent goes and works with other people?That person over there is collaborating with the agent on something you shouldn't have [00:08:00] access to what they're doing. So we have all of these new boundaries that we're gonna have to figure out of, of, you know, it's really, really easy. So far we've been in, in easy mode. We've hit the easy button with ai, which is the agent just is you.And when you're in quad code and you're in cursor, and you're in Codex, you're just, the agent is you. You're offing into your services. It can do everything you can do. That's the easy mode. The hard mode is agents are kind of running on their own. People check in with them occasionally, they're doing things autonomously.How do you give them access to resources in the enterprise and not dramatically increased the security risk and the risk that you might expose the wrong thing to somebody. These are all the new problems that we have to get solved. I like the identity layer and, and identity vendors as being a solution to that, but we'll, we'll need some opinions as well because so many of the use cases are these collaborative file system use cases, which is how do I give it an agent, a subset of my data?Give it its own workspace as well. ‘cause it's gonna need to store off its own information that would be relevant for it. And how do I have the right oversight into that? [00:09:00]Jeff Huber: One thing, which, um, I think is kind interesting, think about is that you know, how humans work, right? Like I may not also just like give you access to the whole file.I might like sit next to you and like scroll to this like one part of the file and just show you that like one part and like, you know,swyx: partial file access.Jeff Huber: I'm just saying I think like our, like RA does seem to be dead, right? Like you wanna say something is dead uhhuh probably RA is dead. And uh, like the auth story to me seems like incredibly unsolved and unaddressed by like the existing state of like AI vendors.ButAaron Levie: yeah, I think, um, we're, I mean you're taking obviously really to level limit that we probably need to solve for. Yeah. And we built an access control system that was, was kind of like, you know, its own little world for, for a long time. And um, and the idea was this, it's a many to many collaboration system where I can give you any part of the file system.And it's a waterfall model. So if I give you higher up in the, in the, in the system, you get everything below. And that, that kind of created immense flexibility because I can kind of point you to any layer in the, in the tree, but then you're gonna get access to everything kind of below it. And that [00:10:00] mostly is, is working in this, in this world.But you do have to manage this issue, which is how do I create an agent that has access to some of my stuff and somebody else's stuff as well. Mm-hmm. And which parts do I get to look at as the creator of the agent? And, and these are just brand new problems? Yeah. Crazy. And humans, when there was a human there that was really easy to do.Like, like if the three of us were all sharing, there'd be a Venn diagram where we'd have an overlapping set of things we've shared, but then we'd have our own ways that we shared with each other. In an agent world, somebody needs to take responsibility for what that agent has access to and what they're working on.These are like the, some of the most probably, you know, boring problems for 98% of people on, on the internet, but they will be the problems that are the difference between can you actually have autonomous agents in an enterprise contextswyx: Yeah.Aaron Levie: That are not leaking your data constantly.swyx: No. Like, I mean, you know, I run a very, very small company for my conference and like we already have data sensitivity issues.Yes. And some of my team members cannot see Yes. Uh, the others and like, I can't imagine what it's like to run a Fortune 500 and like, you have to [00:11:00] worry about this. I'm just kinda curious, like you, you talked to a lot like, like 70, 80% of your cus uh, of the Fortune 500, your customers.Aaron Levie: Yep. 67%. Just so we're being verySEswyx: precise.So Yeah. I'm notAaron Levie: Okay. Okay.swyx: Something I'm rounding up. Yes. Round up. I'm projecting to, forAaron Levie: the government.swyx: I'm projecting to the end of the year.Aaron Levie: Okay.swyx: There you go.Aaron Levie: You do make it sound like, like we, we, well we've gotta be on this. Like we're, we're taking way too long to get to 80%. Well,swyx: no, I mean, so like. How are they approaching it?Right? Because you're, you don't have a, you don't have a final answer yet.Why Coding Agents Took Off FirstAaron Levie: Well, okay, so, so this is actually, this is the stark reality that like, unfortunately is the kinda like pouring the water on the party a little bit.swyx: Yes.Aaron Levie: We all in Silicon Valley are like, have the absolute best conditions possible for AI ever.And I think we all saw the dke, you know, kind of Dario podcast and this idea of AI coding. Why is that taken off? And, and we're not yet fully seeing it everywhere else. Well, look, if you just like enumerated the list of properties that AI coding has and then compared it to other [00:12:00] knowledge work, let's just, let's just go through a few of them.Generally speaking, you bring on a new engineer, they have access to a large swath of the code base. Like, there's like very, like you, just, like new engineer comes on, they can just go and find the, the, the stuff that they, they need to work with. It's a fully text in text out. Medium. It's only, it's just gonna be text at the end of the day.So it's like really great from a, from just a, uh, you know, kinda what the agent can work with. Obviously the models are super trained on that dataset. The labs themselves have a really strong, kind of self-reinforcing positive flywheel of why they need to do, you know, agent coding deeply. So then you get just better tooling, better services.The actual developers of the AI are daily users of the, of the thing that they're we're working on versus like the, you know, probably there's only like seven Claude Cowork legal plugin users at Anthropic any given day, but there's like a couple thousand Claude code and you know, users every single day.So just like, think about which one are they getting more feedback on. All day long. So you just go through this list. You have a, you know, everybody who's a [00:13:00] developer by definition is technical so they can go install the latest thing. We're all generally online, or at least, you know, kinda the weird ones are, and we're all talking to each other, sharing best practices, like that's like already eight differences.Versus the rest of the economy. Every other part of the economy has like, like six to seven headwinds relative to that list. You go into a company, you're a banker in financial services, you have access to like a, a tiny little subset of the total data that's gonna be relevant to do your job. And you're have to start to go and talk to a bunch of people to get the right data to do your job because Sally didn't add you to that deal room, you know, folder.And that that, you know, the information is actually in a completely different organization that you now have to go in and, and sort of run into. And it's like you have this endless list of access controls and security. As, as you talked about, you have a medium, which is not, it's not just text, right? You have, you have a zoom call that, that you're getting all of the requirements from the customer.You have a lot of in-person conversations and you're doing in-person sales and like how do you ever [00:14:00] digitize all of that information? Um, you know, I think a lot of people got upset with this idea that the code base has all the context, um, that I don't know if you follow, you know, did you follow some of that conversation that that went viral?Is like, you know, it's not that simple that, that the code base doesn't have all the knowledge, but like it's a lot, you're a lot better off than you are with other areas of knowledge work. Like you, we like, we like have documentation practices, you write specifications. Those things don't exist for like 80% of work that happens in the enterprise.That's the divide that we have, which is, which is AI coding has, has just fully, you know, where we've reached escape velocity of how powerful this stuff is, and then we're gonna have to find a way to bring that same energy and momentum, but to all these other areas of knowledge work. Where the tools aren't there, the data's not set up to be there.The access controls don't make it that easy. The context engineering is an incredibly hard problem because again, you have access control challenges, you have different data formats. You have end users that are gonna need to kind of be kind of trained through this as opposed to their adopting [00:15:00] these tools in their free time.That's where the Fortune 500 is. And so we, I think, you know, have to be prepared as an industry where we are gonna be on a multi-year march to, to be able to bring agents to the enterprise for these workflows. And I think probably the, the thing that we've learned most in coding that, that the rest of the world is not yet, I think ready for, I mean, we're, they'll, they'll have to be ready for it because it's just gonna inevitably happen is I think in coding.What, what's interesting is if you think about the practice of coding today versus two years ago. It's probably the most changed workflow in maybe the history of time from the amount of time it's changed, right? Yeah. Like, like has any, has any workflow in the entire economy changed that quickly in terms of the amount of change?I just, you know, at least in any knowledge worker workflow, there's like very rarely been an event where one piece of technology and work practice has so fundamentally, you know, changed, changed what you do. Like you don't write code, you talk to an agent and it goes and [00:16:00] does it for you, and you may be at best review it.And even that's even probably like, like largely not even what you're doing. What's happening is we are changing our work to make the agents effective. In that model, the agent didn't really adapt to how we work. We basically adapted to how the agent works. Mm-hmm. All of the economy has to go through that exact same evolution.The rest of the economy is gonna have to update its workflows to make agents effective. And to give agents the context that they need and to actually figure out what kind of prompting works and to figure out how do you ensure that the agent has the right access to information to be able to execute on its work.I, you know, this is not the panacea that people were hoping for, of the agent drops in, just automates your life. Like you have to basically re-engineer your workflow to get the most out of agents and, uh, and that, that's just gonna take, you know, multiple years across the economy. Right now it's a huge asset and an advantage for the teams that do it early and that are kinda wired into doing this.‘cause [00:17:00] you'll see compounding returns, but that's just gonna take a while for most companies to actually go and get this deployed.swyx: I love, I love pushing back. I think that. That is what a lot of technology consultants love to hear this sort of thing, right? Yeah, yeah, yeah. First to, to embrace the ai. Yes. To get to the promised land, you must pay me so much money to a hundred percent to adopt the prescribed way of, uh, conforming to the agents.Yes. And I worry that you will be eclipsed by someone else who says, no, come as you are.Aaron Levie: Yeah.swyx: And we'll meet you where you are.Aaron Levie: And, and, and and what was the thing that went viral a week ago? OpenAI probably, uh, is hiring F Dees. Yeah. Uh, to go into the enterprise. Yeah. Yeah. And then philanthropic is embedded at Goldman Sachs.Yeah. So if the labs are having to do this, if, if the labs have decided that they need to hire FDE and professional services, then I think that's a pretty clear indication that this, there's no easy mode of workflow transformation. Yeah. Yeah. So, so to your point, I think actually this is a market opportunity for, you know, new professional services and consulting [00:18:00] firms that are like Agent Build and they, and they kind of, you know, go into organizations and they figure out how to re-engineer your workflows to make them more agent ready and get your data into the right format and, you know, reconstruct your business process.So you're, you're not doing most of the work. You're telling agents how to do the work and then you're reviewing it. But I haven't seen the thing that can just drop in and, and kinda let you not go through those changes.swyx: I don't know how that kind of sales pitch goes over. Yeah. You know, you're, you're saying things like, well, in my sort of nice beautiful walled garden, here's, there's, uh, because here's this, here's this beautiful box account that has everything.Yes. And I'm like, well, most, most real life is extremely messy. Sure. And like, poorly named and there duplicate this outdated s**tAaron Levie: a hundred percent. And so No, no, a hundred percent. And so this is actually No. So, so this is, I mean, we agree that, that getting to the beautiful garden is gonna be tough.swyx: Yeah.Aaron Levie: There's also the other end of the spectrum where I, I just like, it's a technical impossibility to solve. The agent is, is truly cannot get enough context to make the right decision in, in the, in the incredibly messy land. Like there's [00:19:00] no a GI that will solve that. So, so we're gonna have to kind of land in somewhere in between, which is like we all collectively get better at.Documentation practices and, and having authoritative relatively up-to-date information and putting it in the right place like agents will, will certainly cause us to be much better organized around how we work with our information, simply because the severity of the agent pulling the wrong data will be too high and the productivity gain of that you'll miss out on by not doing this will be too high as well, that you, that your competition will just do it and they'll just have higher velocity.So, uh, and, and we, we see this a lot firsthand. So we, we build a series of agents internally that they can kind of have access to your full box account and go off and you give it a task and it can go find whatever information you're looking for and work with. And, you know, thank God for the model progress, but like, if, if you gave that task to an agent.Nine months ago, you're just gonna get lots of bogus answers because it's gonna, it's gonna say, Hey, here's, here are fi [00:20:00] five, you know, documents that all kind of smell like the right thing. And I'm gonna, but I, but you're, you're putting me on the clock. ‘cause my assistant prompt says like, you know, be pretty smart, but also try and respond to the user and it's gonna respond.And it's like, ah, it got the wrong document. And then you do that once or twice as a knowledge worker and you're just neverswyx: again,Aaron Levie: never again. You're just like done with the system.swyx: Yeah. It doesn't work.Aaron Levie: It doesn't work. And so, you know, Opus four six and Gemini three one Pro and you know, whatever the latest five 3G BT will be, like, those things are getting better and better and it's using better judgment.And this sort of like the, all of these updates to the agentic tool and search systems are, are, we're seeing, we're seeing very real progress where the agent. Kind of can, can almost smell some things a little bit fishy when it's getting, you know, we, we have this process where we, we have it go fan out, do a bunch of searches, pull up a bunch of data, and then it has to sort of do its own ranking of, you know, what are the right documents that, that it should be working with.And again, like, you know, the intelligence level of a model six months ago, [00:21:00] it'd be just throwing a dart at like, I'm just, I'm gonna grab these seven files and I, I pray, I hope that that's the right answer. And something like an opus first four five, and now four six is like, oh, it's like, no, that one doesn't seem right relative to this question because I'm seeing some signal that is making that, you know, that's contradicting the document where it would normally be in the tree and who should have access.Like it's doing all of that kind of work for you. But like, it still doesn't work if you just have a total wasteland of data. Like, it's just not, it's just not possible. Partly ‘cause a human wouldn't even be able to do it. So basically if a, if a really, really smart human. Could not do that task in five or 10 minutes for a search retrieval type task.Look, you know, your agent's not gonna be able to do it any better. You see this all day long. SoContext Engineering and Search Limitsswyx: this touches on a thing that just passionate about it was just context engineering. I, I'm just gonna let you ramble or riff on, on context engineering. If, if, if there's anything like he, he did really good work on context fraud, which has really taken over as like the term that people use and the referenceAaron Levie: a hundred percent.We, we all we think about is, is the context rob problem. [00:22:00]Jeff Huber: Yeah, there's certainly a lot of like ranking considerations. Gentech surgery think is incredibly promising. Um, yeah, I was trying to generate a question though. I think I have a question right now. Swyx.Aaron Levie: Yeah, no, but like, like I think there was this moment, um, you know, like, I don't know, two years ago before, before we knew like where the, the gotchas were gonna be in ai and I think someone was like, was like, well, infinite context windows will just solve all of these problems and ‘cause you'll just, you'll just give the context window like all the data and.It's just like, okay, I mean, maybe in 2035, like this is a viable solution. First of all, it, it would just, it would just simply cost too much. Like we just can't give the model like the 5,000 documents that might be relevant and it's gonna read them all. And I've seen enough to, to start believing in crazy stuff.So like, I'm willing to just say, sure. Like in, in 10 years from now,swyx: never say, never, never.Aaron Levie: In, in 10 years from now, we'll have infinite context windows at, at a thousandth of the price of today. Like, let's just like believe that that's possible, but Right. We're in reality today. So today we have a context engineering [00:23:00] problem, which is, I got, I got, you know, 200,000 tokens that I can work with, or prob, I don't even know what the latest graph is before, like massive degradation.16. Okay. I have 60,000 tokens that I get to work with where I'm gonna get accurate information. That's not a lot of tokens for a corpus of 10 million documents that a knowledge worker might have across all of the teams and all the projects and all the people they work with. I have, I have 10 million documents.Which, you know, maybe is times five pages per document or something like that. I'm at 50 million pages of information and I have 60,000 tokens. Like, holy s**t. Yeah. This is like, how do I bridge the 50 million pages of information with, you know, the couple hundred that I get to work with in that, in that token window.Yeah. This is like, this is like such an interesting problem and that's why actually so much work is actually like, just like search systems and the databases and that layer has to just get so locked in, but models getting better and importantly [00:24:00] knowing when they've done a search, they found the wrong thing, they go back, they check their work, they, they find a way to balance sort of appeasing the user versus double checking.We have this one, we have this one test case where we ask the agent to go find. 10 pieces of information.swyx: Is this the complex work eval?Aaron Levie: Uh, this is actually not in the eval. This is, this is sort of just like we have a bunch of different, we have a bunch of internal benchmark kind of scenarios. Every time we, we update our agent, we have one, which is, I ask it to find all of our office addresses, and I give it the list of 10 offices that we have.And there's not one document that has this, maybe there should be, that would be a great example of the kind of thing that like maybe over time companies start to, you know, have these sort of like, what are the canonical, you know, kind of key areas of knowledge that we need to have. We don't seem to have this one document that says, here are all of our offices.We have a bunch of documents that have like, here's the New York office and whatever. So you task this agent and you, you get, you say, I need the addresses for these 10 offices. Okay. And by the way, if you do this on any, you know, [00:25:00] public chat model, the same outcome is gonna happen. But for a different kind of query, you give it, you say, I need these 10 addresses.How many times should the agent go and do its search before it decides whether or not, there's just no answer to this question. Often, and especially the, the, let's say lower tier models, it'll come back and it'll give you six of the 10 addresses. And it'll, and I'll just say I couldn't find the otherswyx: four.It, it doesn't know what It doesn't know. ItAaron Levie: doesn't know what It doesn't know. Yeah. So the model is just like, like when should it stop? When should it stop doing? Like should it, should it do that task for literally an hour and just keep cranking through? Maybe I actually made up an office location and it doesn't know that I made it up and I didn't even know that I made it up.Like, should it just keep, re should it read every single file in your entire box account until it, until it should exhaust every single piece of information.swyx: Expensive.Aaron Levie: These are the new problems that we have. So, you know, something like, let's say a new opus model is sort of like, okay, I'm gonna try these types of queries.I didn't get exactly what I wanted. I'm gonna try again. I'm gonna, at [00:26:00] some point I'm gonna stop searching. ‘cause I've determined that that no amount of searching is gonna solve this problem. I'm just not able to do it. And that judgment is like a really new thing that the model needs to be able to have.It's like, when should it give up on a task? ‘cause, ‘cause you just don't, it's a can't find the thing. That's the real world of knowledge, work problems. And this is the stuff that the coding agents don't have to deal with. Because they, it just doesn't like, like you're not usually asking it about, you're, you're always creating net new information coming right outta the model for the most part.Obviously it has to know about your code base and your specs and your documentation, but, but when you deploy an agent on all of your data that now you have all of these new problems that you're dealing withJeff Huber: our, uh, follow follow-up research to context ride is actually on a genetic search. Ah. Um, and we've like right, sort of stress tested like frontier models and their ability to search.Um, and they're not actually that good at searching. Right. Uh, so you're sort of highlighting this like explore, exploit.swyx: You're just say, Debbie, Donna say everything doesn't work. Like,Aaron Levie: well,Jeff Huber: somebody has to be,Aaron Levie: um, can I just throw out one more thing? Yeah. That is different from coding and, and the rest [00:27:00] of the knowledge work that I, I failed to mention.So one other kind of key point is, is that, you know, at the end of the day. Whether you believe we're in a slop apocalypse or, or whatever. At the end of the day, if you, if you build a working product at the end of, if you, if you've built a working solution that is ultimately what the customer is paying for, like whether I have a lot of slop, a little slop or whatever, I'm sure there's lots of code bases we could go into in enterprise software companies where it's like just crazy slop that humans did over a 20 year period, but the end customer just gets this little interface.They can, they can type into it, it does its thing. Knowledge work, uh, doesn't have that property. If I have an AI model, go generate a contract and I generate a contract 20 times and, you know, all 20 times it's just 3% different and like that I, that, that kind of lop introduces all new kinds of risk for my organization that the code version of that LOP didn't, didn't introduce.These are, and so like, so how do you constrain these models to just the part that you want [00:28:00] them to work on and just do the thing that you want them to do? And, and, you know, in engineering, we don't, you can't be disbarred as an engineer, but you could be disbarred as a lawyer. Like you can do the wrong medical thing In healthcare, you, there's no, there's no equivalent to that of engineering.Like, doswyx: you want there to be, because I've considered softwareJeff Huber: engineer. What's that? Civil engineering there is, right? NotAaron Levie: software civil engineer. Sure. Oh yeah, for sure. But like in any of our companies, you like, you know, you'll be forgiven if you took down the site and, and we, we will do a rollback and you'll, you'll be in a meeting, but you have not been disbarred as an engineer.We don't, we don't change your, you know, your computer science, uh, blameJeff Huber: degree, this postmortem.Aaron Levie: Yeah, exactly. Exactly. So, so, uh, now maybe we collectively as an industry need to figure out like, what are you liable for? Not legally, but like in a, in a management sense, uh, of these agents. All sorts of interesting problems that, that, that, uh, that have to come out.But in knowledge work, that's the real hostile environments that we're operating in. Hmm.swyx: I do think like, uh, a lot of the last year's, 2025 story was the rise of coding agents and I think [00:29:00] 2026 story is definitely knowledge work agents. Yes. A hundredAaron Levie: percent.swyx: Right. Like that would, and I think open claw core work are just the beginning.Yes. Like it's, the next one's gonna just gonna be absolute craziness.Aaron Levie: It it is. And, and, uh, and it's gonna be, I mean, again, like this is gonna be this, this wave where we, we are gonna try and bring as many of the practices from coding because that, that will clearly be the forefront, which is tell an agent to go do something and has an access to a set of resources.You need to be responsible for reviewing it at the end of the process. That to me is the, is the kind of template that I just think goes across knowledge, work and odd. Cowork is a great example. Open Closet's a great example. You can kind of, sort of see what Codex could become over time. These are some, some really interesting kind of platforms that are emerging.swyx: Okay. Um, I wanted to, we touched on evals a little bit. You had, you had the report that you're gonna go bring up and then I was gonna go into like, uh, boxes, evals, but uh, go ahead. Talk about your genetic search thing.Jeff Huber: Yeah. Mostly I think kinda a few of the insights. It's like number one frontier model is not good at search.Humans have this [00:30:00] natural explore, exploit trade off where we kinda understand like when to stop doing something. Also, humans are pretty good at like forgetting actually, and like pruning their own context, whereas agents are not, and actually an agent in their kind of context history, if they knew something was bad and they even, you could see in the trace the reason you trace, Hey, that probably wasn't a good idea.If it's still in the trace, still in the context, they'll still do it again. Uhhuh. Uh, and so like, I think pruning is also gonna be like, really, it's already becoming a thing, right? But like, letting self prune the con windowsswyx: be a big deal. Yeah. So, so don't leave the mistake. Don't leave the mistake in there.Cut out the mistake but tell it that you made a mistake in the past and so it doesn't repeat it.Jeff Huber: Yeah. But like cut it out so it doesn't get like distracted by it again. ‘cause really, you know, what is so, so it will repeat its mistake just because it's been, it's inswyx: theJeff Huber: context. It'sAaron Levie: in the context so much.That's a few shot example. Even if it, yeah.Jeff Huber: It's like oh thisAaron Levie: is a great thing to go try even ifJeff Huber: it didn't work.Aaron Levie: Yeah,Jeff Huber: exactly.Aaron Levie: SoJeff Huber: there's like a bunch of stuff there. JustAaron Levie: Groundhogs Day inside these models. Yeah. I'm gonna go keep doing the same wrongJeff Huber: thing. Covering sense. I feel like, you know, some creator analogy you're trying like fit a manifold in latent space, which kind is doing break program synthesis, which is kinda one we think about we're doing right.Like, you know, certain [00:31:00] facts might be like sort of overly pitting it. There are certain, you know, sec sectors of latent space and so like plug clean space. Yeah. And, uh, andswyx: so we have a bell, our editor as a bell every time you say that. SoJeff Huber: you have, you have to like remove those, likeswyx: you shoulda a gong like TPN or something.IfJeff Huber: we gong, you either remove those links to like kinda give it the freedom, kind of do what you need to do. So, but yeah. We'll, we'll release more soon. That'sAaron Levie: awesome.Jeff Huber: That'll, that'll be cool.swyx: We're a cerebral podcast that people listen to us and, and sort of think really deep. So yeah, we try to keep it subtle.Okay. We try to keep it.Aaron Levie: Okay, fine.Inside Agent Evalsswyx: Um, you, you guys do, you guys do have EVs, you talked about your, your office thing, but, uh, you've been also promoting APEX agents and complex work. Uh, yeah, whatever you, wherever you wanna take this just Yeah. How youAaron Levie: Apex is, is obviously me, core's, uh, uh, kind of, um, agent eval.We, we supported that by sort of. Opening up some data for them around how we kind of see these, um, data workspaces in, in the, you know, kind of regular economy. So how do lawyers have a workspace? How do investment bankers have a workspace? What kind of data goes into those? And so we, [00:32:00] we partner with them on their, their apex eval.Our own, um, eval is, it's actually relatively straightforward. We have a, a set of, of documents in a, in a range of industries. We give the agent previously did this as a one shot test of just purely the model. And then we just realized we, we need to, based on where everything's going, it's just gotta be more agentic.So now it's a bit more of a test of both our harness and the model. And we have a rubric of a set of things that has to get right and we score it. Um, and you're just seeing, you know, these incredible jumps in almost every single model in its own family of, you know, opus four, um, you know, sonnet four six versus sonnet four five.swyx: Yeah. We have this up on screen.Aaron Levie: Okay, cool. So some, you're seeing it somewhere like. I, I forget the to, it was like 15 point jump, I think on the main, on the overall,swyx: yes.Aaron Levie: And it's just like, you know, these incredible leaps that, that are starting to happen. Um,swyx: and OP doesn't know any, like any, it's completely held out from op.Aaron Levie: This is not in any, there's no public data which has, you know, Ben benefits and this is just a private eval that we [00:33:00] do, and then we just happen to show it to, to the world. Hmm. So you can't, you can't train against it. And I think it's just as representative of. It's obviously reasoning capabilities, what it's doing at, at, you know, kind of test time, compute capabilities, thinking levels, all like the context rot issues.So many interesting, you know, kind of, uh, uh, capabilities that are, that are now improvingswyx: one sector that you have. That's interesting.Industries and Datasetsswyx: Uh, people are roughly familiar with healthcare and legal, but you have public sector in there.Aaron Levie: Yeah.swyx: Uh, what's that? Like, what, what, what is that?Aaron Levie: Yeah, and, and we actually test against, I dunno, maybe 10 industries.We, we end up usually just cutting a few that we think have interesting gains. All extras, won a lot of like government type documents. Um,swyx: what is that? What is it? Government type documents?Aaron Levie: Government filings. Like a taxswyx: return, likeAaron Levie: a probably not tax returns. It would be more of what would go the government be using, uh, as data.So, okay. Um, so think about research that, that type of, of, of data sets. And then we have financial services for things like data rooms and what would be in an investment prospectus. Uhhuh,swyx: that one you can dog food.Aaron Levie: Yeah, exactly. Exactly. Yes. Yes. [00:34:00] So, uh, so we, we run the models, um, in now, you know, more of an agent mode, but, but still with, with kinda limited capacity and just try and see like on a, like, for like basis, what are the improvements?And, and again, we just continue to be blown away by. How, how good these models are getting.swyx: Yeah, I mean, I think every serious AI company needs something like that where like, well, this is the work we do. Here's our company eval. Yeah. And if you don't have it, well, you're not a serious AI company.Aaron Levie: There's two dimensions, right?So there's, there's like, how are the models improving? And so which models should you either recommend a customer use, which one should you adopt? But then every single day, we're making changes to our agents. And you need to knowswyx: if you regressed,Aaron Levie: if you know. Yeah. You know, I've been fully convinced that the whole agent observability and eval space is gonna be a massive space.Um, super excited for what Braintrust is doing, excited for, you know, Lang Smith, all the things. And I think what you're going to, I mean, this is like every enter like literally every enterprise right now. It's like the AI companies are the customers of these tools. Every enterprise will have this. Yeah, you'll just [00:35:00] have to have an eval.Of all of your work and like, we'll, you'll have an eval of your RFP generation, you'll have an eval of your sales material creation. You'll have an eval of your, uh, invoice processing. And, and as you, you know, buy or use new agentic systems, you are gonna need to know like, what's the quality of your, of your pipeline.swyx: Yeah.Aaron Levie: Um, so huge, huge market with agent evals.swyx: Yeah.Building the Agent Teamswyx: And, and you know, I'm gonna shout out your, your team a bit, uh, your CTO, Ben, uh, did a great talk with us last year. Awesome. And he's gonna come back again. Oh, cool. For World's Fair.Aaron Levie: Yep.swyx: Just talk about your team, like brag a little bit. I think I, I think people take these eval numbers in pretty charts for granted, but No, there, I mean, there's, there's lots of really smart people at work during all this.Aaron Levie: Biggest shout out, uh, is we have a, we have a couple folks at Dya, uh, Sidarth, uh, that, that kind of run this. They're like a, you know, kind of tag tag team duo on our evals, Ben, our CTO, heavily involved Yasha, head of ai, uh, you know, a bunch of folks. And, um, evals is one part of the story. And then just like the full, you know, kind of AI.An agent team [00:36:00] is, uh, is a, is a pretty, you know, is core to this whole effort. So there's probably, I don't know, like maybe a few dozen people that are like the epicenter. And then you just have like layers and layers of, of kind of concentric circles of okay, then there's a search team that supports them and an infrastructure team that supports them.And it's starting to ripple through the entire company. But there's that kind of core agent team, um, that's a pretty, pretty close, uh, close knit group.swyx: The search team is separate from the infra team.Aaron Levie: I mean, we have like every, every layer of the stack we have to kind of do, except for just pure public cloud.Um, but um, you know, we, we store, I don't even know what our public numbers are in, you know, but like, you can just think about it as like a lot of data is, is stored in box. And so we have, and you have every layer of the, of the stack of, you know, how do you manage the data, the file system, the metadata system, the search system, just all of those components.And then they all are having to understand that now you've got this new customer. Which is the agent, and they've been building for two types of customers in the past. They've been building for users and they've been building for like applications. [00:37:00] And now you've got this new agent user, and it comes in with a difference of it, of property sometimes, like, hey, maybe sometimes we should do embeddings, an embedding based, you know, kind of search versus, you know, your, your typical semantic search.Like, it's just like you have to build the, the capabilities to support all of this. And we're testing stuff, throwing things away, something doesn't work and, and not relevant. It's like just, you know, total chaos. But all of those teams are supporting the agent team that is kind of coming up with its requirements of what, what do we need?swyx: Yeah. No, uh, we just came from, uh, fireside chat where you did, and you, you talked about how you're doing this. It's, it's kind of like an internal startup. Yeah. Within the broader company. The broader company's like 3000 people. Yeah. But you know, there's, there's a, this is a core team of like, well, here's the innovation center.Aaron Levie: Yeah.swyx: And like that every company kind of is run this way.Aaron Levie: Yeah. I wanna be sensitive. I don't call it the innovation center. Yeah. Only because I think everybody has to do innovation. Um, there, there's a part of the, the, the company that is, is sort of do or die for the agent wave.swyx: Yeah.Aaron Levie: And it only happens to be more of my focus simply because it's existential that [00:38:00] we get it right.swyx: Yeah.Aaron Levie: All of the supporting systems are necessary. All of the surrounding adjacent capabilities are necessary. Like the only reason we get to be a platform where you'd run an agent is because we have a security feature or a compliance feature, or a governance feature that, that some team is working on.But that's not gonna be the make or break of, of whether we get agents right. Like that already exists and we need to keep innovating there. I don't know what the right, exact precise number is, but it's not a thousand people and it's not 10 people. There's a number of people that are like the, the kind of like, you know, startup within the company that are the make or break on everything related to AI agents, you know, leveraging our platform and letting you work with your data.And that's where I spend a lot of my time, and Ben and Yosh and Diego and Teri, you know, these are just, you know, people that, that, you know, kind of across the team. Are working.swyx: Yeah. Amazing.Read Write Agent WorkflowsJeff Huber: How do you, how do you think about, I mean, you talked a lot about like kinda read workflows over your box data. Yep.Right. You know, gen search questions, queries, et cetera. But like, what about like, write or like authoring workflows?Aaron Levie: Yes. I've [00:39:00] already probably revealed too much actually now that I think about it. So, um, I've talked about whatever,Jeff Huber: whatever you can.Aaron Levie: Okay. It's just us. It's just us. Yeah. Okay. Of course, of course.So I, I guess I would just, uh, I'll make it a little bit conceptual, uh, because again, I've already, I've already said things that are not even ga but, but we've, we've kinda like danced around it publicly, so I, yeah, yeah. Okay. Just like, hopefully nobody watches this, um, episode. No.swyx: It's tidbits for the Heidi engaged to go figure out like what exactly, um, you know, is, is your sort of line of thinking.Sure. They can connect the dots.Aaron Levie: Yeah. So, so I would say that, that, uh, we, you know, as a, as a place where you have your enterprise content, there's a use case where I want to, you know, have an agent read that data and answer questions for me. And then there's a use case where I want the agent to create something.And use the file system to create something or store off data that it's working on, or be able to have, you know, various files that it's writing to about the work it's doing. So we do see it as a total read write. The harder problem has so far been the read only because, because again, you have that kind of like 10 [00:40:00] million to one ratio problem, whereas rights are a lot of, that's just gonna come from the model and, and we just like, we'll just put it in the file system and kinda use it.So it's a little bit of a technically easier problem, but the only part that's like, not necessarily technically hard, it is just like it's not yet perfected in the state of the ecosystem is, you know, building a beautiful PowerPoint presentation. It's still a hard problem for these models. Like, like we still, you know, like, like these formats are just, we're not built for.They'reswyx: working on it.Aaron Levie: They're, they're working on it. Everybody's working on it.swyx: Every launch is like, well, we do PowerPoint now.Aaron Levie: We're getting, yeah, getting a lot, getting a lot of better each time. But then you'll do this thing where you'll ask the update one slide and all of a sudden, like the fonts will be just like a little bit different, you know, on two of the slides, or it moved, you know, some shape over to the left a little bit.And again, these are the kind of things that, like in code, obviously you could really care about if you really care about, you know, how beautiful is the code, but at the end, user doesn't notice all those problems and file creation, the end user instantly sees it. You're [00:41:00] like, ah, like paragraph three, like, you literally just changed the font on me.Like it's a totally different font and like midway through the document. Mm-hmm. Those are the kind of things that you run into a lot of in the, in the content creation side. So, mm-hmm. We are gonna have native agents. That do all of those things, they'll be powered by the leading kind of models and labs.But the thing that I think is, is probably gonna be a much bigger idea over time is any agent on any system, again, using Box as a file system for its work, and in that kind of scenario, we don't necessarily care what it's putting in the file system. It could put its memory files, it could put its, you know, specification, you know, documents.It could put, you know, whatever its markdown files are, or it could, you know, generate PDFs. It's just like, it's a workspace that is, is sort of sandboxed off for its work. People can collaborate into it, it can share with other people. And, and so we, we were thinking a lot about what's the right, you know, kind of way to, to deliver that at scale.Docs Graphs and Founder Modeswyx: I wanted to come into sort of the sort of AI transformation or AI sort of, uh, operations things. [00:42:00] Um, one of the tweets that you, that you wanted to talk about, this is just me going through your tweets, by the way. Oh, okay. I mean, like, this is, you readAaron Levie: one by one,swyx: you're the, you're the easiest guest to prep for because you, you already have like, this is the, this is what I'm interested in.I'm like, okay, well, areAaron Levie: we gonna get to like, like February, January or something? Where are we in the, in the timelines? How far back are we going?swyx: Can you, can you describe boxes? A set of skills? Right? Like that, that's like, that's like one of the extremes of like, well if you, you just turn everything into a markdown file.Yeah. Then your agent can run your company. Uh, like you just have to write, find the right sequence of words toAaron Levie: Yes.swyx: To do it.Aaron Levie: Sorry, isthatswyx: the question? So I think the question is like, what if we documented everything? Yes. The way that you exactly said like,Aaron Levie: yes.swyx: Um, let's get all the Fortune five hundreds, uh, prepared for agents.Yes. And like, you know, everything's in golden and, and nicely filed away and everything. Yes. What's missing? Like, what's left, right? LikeAaron Levie: Yeah.swyx: You've, you've run your company for a decade. LikeAaron Levie: Yeah. I think the challenge is that, that that information changes a week later. And because something happened in the market for that [00:43:00] customer, or us as a company that now has to go get updated, and so these systems are living and breathing and they have to experience reality and updates to reality, which right now is probably gonna be humans, you know, kinda giving those, giving them the updates.And, you know, there is this piece about context graphs as as, uh, that kinda went very viral. Yeah. And I, I, I was like a, i, I, I thought it was super provocative. I agreed with many parts of it. I disagree with a few parts around. You know, it's not gonna be as easy as as just if we just had the agent traces, then we can finally do that work because there's just like, there's so much more other stuff that that's happening that, that we haven't been able to capture and digitize.And I think they actually represented that in the piece to be clear. But like there's just a lot of work, you know, that that has to, you just can't have only skills files, you know, for your company because it's just gonna be like, there's gonna be a lot of other stuff that happens. Yeah. Change over time.Yeah. Most companies are practically apprenticeships.swyx: Most companies are practically apprenticeships. LikeJeff Huber: every new employee who joins the team, [00:44:00] like you span one to three months. Like ramping them up.Aaron Levie: Yes. AllJeff Huber: that tat knowledgeAaron Levie: isJeff Huber: not written down.Aaron Levie: Yes.Jeff Huber: But like, it would have to be if you wanted to like give it to an Asian.Right. And so like that seems to me like to beAaron Levie: one is I think you're gonna see again a premium on companies that can document this. Mm-hmm. Much. There'll be a huge premium on that because, because you know, can you shorten that three month ramp cycle to a two week ramp cycle? That's an instant productivity gain.Can you re dramatically reduce rework in the organization because you've documented where all the stuff is and where the answers are. Can you make your average employee as good as your 90th percentile employee because you've captured the knowledge that's sort of in the heads of, of those top employees and make that available.So like you can see some very clear productivity benefits. Mm-hmm. If you had a company culture of making sure you know your information was captured, digitized, put in a format that was agent ready and then made available to agents to work with, and then you just, again, have this reality of like add a 10,000 person [00:45:00] company.Mapping that to the, you know, access structure of the company is just a hard problem. Is like, is like, yeah, well, you just, not every piece of information that's digitized can be shared to everybody. And so now you have to organize that in a way that actually works. There was a pretty good piece, um, this, this, uh, this piece called your company as a file is a file system.I, did you see that one?swyx: Nope.Aaron Levie: Uh, yes. You saw it. Yeah. And, and, uh, I actually be curious your thoughts on it. Um, like, like an interesting kind of like, we, we agree with it because, because that's how we see the world and, uh,swyx: okay. We, we have it up on screen. Oh,Aaron Levie: okay. Yeah. But, but it's all about basically like, you know, we've already, we, we, we already organized in this kind of like, you know, permission structure way.Uh, and, and these are the kind of, you know, natural ways that, that agents can now work with data. So it's kind of like this, this, you know, kind of interesting metaphor, but I do think companies will have to start to think about how they start to digitize more, more of that data. What was your take?Jeff Huber: Yeah, I mean, like the company's probably like an acid compliant file system.Aaron Levie: Uh,Jeff Huber: yeah. Which I'm guessing boxes, right? So, yeah. Yes.swyx: Yeah. [00:46:00]Jeff Huber: Which you have a great piece on, but,swyx: uh, yeah. Well, uh, I, I, my, my, my direction is a little bit like, I wanna rewind a little bit to the graph word you said that there, that's a magic trigger word for us. I always ask what's your take on knowledge graphs?Yeah. Uh, ‘cause every, especially at every data database person, I just wanna see what they think. There's been knowledge graphs, hype cycles, and you've seen it all. So.Aaron Levie: Hmm. I actually am not the expert in knowledge graphs, so, so that you might need toswyx: research, you don't need to be an expert. Yeah. I think it's just like, well, how, how seriously do people take it?Yeah. Like, is is, is there a lot of potential in the, in the HOVI?Aaron Levie: Uh, well, can I, can I, uh, understand first if it's, um, is this a loaded question in the sense of are you super pro, super con, super anti medium? Iswyx: see pro, I see pros and cons. Okay. Uh, but I, I think your opinion should be independent of mine.Aaron Levie: Yeah. No, no, totally. Yeah. I just want to see what I'm stepping into.swyx: No, I know. It's a, and it's a huge trigger word for a lot of people out Yeah. In our audience. And they're, they're trying to figure out why is that? Because whyAaron Levie: is this such aswyx: hot item for them? Because a lot of people get graph religion.And they're like, everything's a graph. Of course you have to represent it as a graph. Well, [00:47:00] how do you solve your knowledge? Um, changing over time? Well, it's a graph.Aaron Levie: Yeah.swyx: And, and I think there, there's that line of work and then there's, there's a lot of people who are like, well, you don't need it. And both are right.Aaron Levie: Yeah. And what do the people who say you don't need it, what are theyswyx: arguing for Mark down files. Oh, sure, sure. Simplicity.Aaron Levie: Yeah.swyx: Versus it's, it's structure versus less structure. Right. That's, that's all what it is. I do.Aaron Levie: I think the tricky thing is, um, is, is again, when this gets met with real humans, they're just going to their computer.They're just working with some people on Slack or teams. They're just sharing some data through a collaborative file system and Google Docs or Box or whatever. I certainly like the vision of most, most knowledge graph, you know, kind of futuristic kind of ways of thinking about it. Uh, it's just like, you know, it's 2026.We haven't seen it yet. Kind of play out as as, I mean, I remember. Do you remember the, um, in like, actually I don't, I don't even know how old you guys are, but I'll for, for to show my age. I remember 17 years ago, everybody thought enterprises would just run on [00:48:00] Wikis. Yeah. And, uh, confluence and, and not even, I mean, confluence actually took off for engineering for sure.Like unquestionably. But like, this was like everything would be in the w. And I think based on our, uh, our, uh, general style of, of, of what we were building, like we were just like, I don't know, people just like wanna workspace. They're gonna collaborate with other people.swyx: Exactly. Yeah. So you were, you were anti-knowledge graph.Aaron Levie: Not anti, not anti. Soswyx: not nonAaron Levie: I'm not, I'm not anti. ‘cause I think, I think your search system, I just think these are two systems that probably, but like, I'm, I'm not in any religious war. I don't want to be in anybody's YouTube comments on this. There's not a fight for me.swyx: We, we love YouTube comments. We're, we're, we're get into comments.Aaron Levie: Okay. Uh, but like, but I, I, it's mostly just a virtue of what we built. Yeah. And we just continued down that path. Yeah.swyx: Yeah.Aaron Levie: And, um, and that, that was what we pursued. But I'm not, this is not a, you know, kind of, this is not a, uh, it'sswyx: not existential for you. Great.Aaron Levie: We're happy to plug into somebody else's graph.We're happy to feed data into it. We're happy for [00:49:00] agents to, to talk to multiple systems. Not, not our fight.swyx: Yeah.Aaron Levie: But I need your answer. Yeah. Graphs or nerd Snipes is very effective nerd.swyx: See this is, this is one, one opinion and then I've,Jeff Huber: and I think that the actual graph structure is emergent in the mind of the agent.Ah, in the same way it is in the mind of the human. And that's a more powerful graph ‘cause it actually involved over time.swyx: So don't tell me how to graph. I'll, I'll figure it out myself. Exactly. Okay. All right. AndJeff Huber: what's yours?swyx: I like the, the Wiki approach. Uh, my, I'm actually

A few weeks ago, Stripe launched their [new site (https://stripe.com/) and reminded everyone who the

Artificial intelligence is moving fast, with new tools changing how people work, create and compete. Whether you're an AI doomer or AI boomer, it's hard to ignore what's coming. Economist and professor Tyler Cowen has spent years analyzing how these developments could reshape the economy and everyday life. He joins host Megan McArdle to talk through how AI could transform talent, human capital and competition — and how to make sure you don't get left behind.Subscribe to The Washington Post here.

If you want to understand food – and eat better – economics is a good place to start. How do immigration patterns shape a country's cuisine? How do labour laws make our working lunches worse? And why do strip malls serve such good grub? To find out, Soumaya Keynes talks to Tyler Cowen, economics professor at George Mason University and chair of the Mercatus Center think-tank. Cowen has written about food for more than two decades, including in his 2012 book An Economist Gets Lunch.Read Soumaya's columns here: https://www.ft.com/soumaya-keynesSubscribe to The Economics Show on Apple, Spotify, Pocket Casts or wherever you listen. Presented by Soumaya Keynes. Produced by Mischa Frankl-Duval. Manuela Saragosa is the executive producer. Cheryl Brumley is the FT's global head of audio. Original music and sound design by Breen Turner.Read a transcript of this episode on FT.com Hosted on Acast. See acast.com/privacy for more information.

Donald Trump's drop-in at the World Economic Forum and the ensuing kerfuffle between the American president and the attending globalist elites raises the question: Who is winning on the world stage, Trump or his foes—or do they have more in common than is commonly recognized? Tyler Cowen, an economist, blogger, and Free Press columnist, joins GoodFellows regulars Niall Ferguson, John Cochrane, and H.R. McMaster to discuss Trump's third presidential visit to the Davos, Switzerland, lion's den, plus the rise of “democratic socialism” and “affordability politics” embodied in the ethos of New York Mayor Zohran Mamdani. After that: the three fellows discuss lessons from Minneapolis in the aftermath of two protestors shot to death by federal immigration agents; the odds of American military strikes against Iran; the significance of China's latest military purge; plus whether the show's resident historians are comfortable with the (over)use of phrase “the right side of history.” Subscribe to GoodFellows for clarity on today's biggest social, economic, and geostrategic shifts — only on GoodFellows.

Today, we have Editor-in-Chief Shikha Dalmia in conversation with two of the foremost thinkers of our time, Frank Fukuyama, an American political theorist and public intellectual best known for The End of History and the Last Man who is now a senior fellow at Stanford University's Freeman Spogli Institute, where his work focuses on political order, governance, and democratic backsliding. And Tyler Cowen, an economist, author, and public intellectual who has written books on innovation, talent and cultural change. A professor at George Mason University and director of the Mercatus Center, he writes the highly influential blog Marginal Revolution and hosts the long-running podcast Conversations with Tyler.One reason for the populist revolt in America is the notion of the “deep state”—that an unaccountable bureaucracy is secretly ruling the country. Frank and Tyler come from very different intellectual traditions. Frank, a centrist, is a student of Max Weber and Tyler is a limited government libertarian. Yet they have both argued that liberal states in complex modern societies need a functional bureaucracy— aka state capacity—to deliver public goods and solve collective action problems. But they also have a ton of disagreements, especially on just how broken American governance is—and they duke it out in a spirited discussion.We hope you enjoy.***Thanks for checking out The UnPopulist! Subscribe to support our project.Follow us on Bluesky, Threads, YouTube, TikTok, Facebook, Instagram, and X.© The UnPopulist, 2026 Get full access to The UnPopulist at www.theunpopulist.net/subscribe

American college students can't do basic math – and the problem didn't start in college. In this episode of The Deep, Erika breaks down shocking new data from UC San Diego, exposes how grade inflation and dishonest standards hollowed out education, and explores whether the “Mississippi Miracle” is the solution to America's math crisis.Timestamps:0:00 - Intro: college freshman lack high-school math skills2:33 - What the UCSD report uncovered4:18 - Standardized tests and grade inflation7:18 - The system is broken10:07 - Getting at the root to solve the math crisis13:35 - Conclusion: putting the soul back in educationSources:Bloom, Allan. The Closing of the American Mind. New York: Simon & Schuster, 1987.Horowitch, Rose. 2025. “American Kids Can't Do Math Anymore.” The Atlantic, November 19, 2025. Accessed December 5, 2025. https://www.theatlantic.com/ideas/2025/11/math-decline-ucsd/684973/.Piper, Kelsey. 2025. “When Grades Stop Meaning Anything.” The Argument, November 18, 2025. Accessed December 5, 2025. https://www.theargumentmag.com/p/when-grades-stop-meaning-anything. theargumentmag.comRawat, Saannidhya, and Vikram K. Suresh. 2024. GPT Takes the SAT: Tracing Changes in Test Difficulty and Students' Math Performance. SSRN Working Paper, August 3, 2024. Accessed December 5, 2025. https://papers.ssrn.com/abstract=4915452. SSRNSalzman, Matthew, and Tyler Cowen. 2024. “Math, SAT Scores May Be Doing Worse Than We Had Thought.” Marginal Revolution, August 2024. Accessed December 5, 2025. https://marginalrevolution.com/marginalrevolution/2024/08/math-sat-scores-may-be-doing-worse-than-we-had-thought.html.

Economist and public thinker Tyler Cowen joins host Molly Wood to explore why AI adoption is so challenging for many employees, organizations, and educational institutions. As he puts it, "This may sound counterintuitive, but under a lot of scenarios, the more unhappy people are, the better we're doing, because that means a lot of change." Listen in for unvarnished insights on what leaders can expect as they push their organizations to reinvent how they operate and create value with AI.  Show Notes: WorkLab Subscribe to the WorkLab Newsletter

Economist and public thinker Tyler Cowen joins host Molly Wood to explore why AI adoption is so challenging for many employees, organizations, and educational institutions. As he puts it, "This may sound counterintuitive, but under a lot of scenarios, the more unhappy people are, the better we're doing, because that means a lot of change." Listen in for unvarnished insights on what leaders can expect as they push their organizations to reinvent how they operate and create value with AI.  Show Notes: WorkLab Subscribe to the WorkLab Newsletter

What does 2026 hold for indie authors and the publishing industry? I give my thoughts on trends and predictions for the year ahead. In the intro, Quitting the right stuff; how to edit your author business in 2026; Is SubStack Good for Indie Authors?; Business for Authors webinars. If you'd like to join my community and support the show every month, you'll get access to my growing list of Patron videos and audio on all aspects of the author business — for the price of a black coffee (or two) a month. Join us at Patreon.com/thecreativepenn. Joanna Penn writes non-fiction for authors and is an award-winning, New York Times and USA Today bestselling thriller author as J.F. Penn. She's also an award-winning podcaster, creative entrepreneur, and international professional speaker. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. (1) More indie authors will sell direct through Shopify, Kickstarter, and local in-person events (2) AI-powered search will start to shift elements of book discoverability (3) The start of Agentic Commerce (4) AI-assisted audiobook narration will go mainstream (5) AI-assisted translation will start to take off beyond the early adopters (6) AI video becomes ubiquitous. ‘Live selling' becomes the next trend in social sales. (7) AI will create, run, and optimise ads without the need for human intervention (8) 1000 True Fans becomes more important than ever You can find all my books as J.F. Penn and Joanna Penn on your favourite online store in all the usual formats, or order from your local library or bookstore. You can also buy direct from me at CreativePennBooks.com and JFPennBooks.com. I'm not really active on social media, but you can always see my photos at Instagram @jfpennauthor. 2026 Trends and Predictions for Indie Authors and Book Publishing (1) More indie authors will sell direct through Shopify, Kickstarter, and local in-person events — and more companies like BookVault will offer even more beautiful physical books and products to support this. This trend will not be a surprise to most of you! Selling direct has been a trend for the last few years, but in 2026, it will continue to grow as a way that independent authors become even more independent. The recent Written Word Media survey from Dec 2025 noted that 30% of authors surveyed are selling direct already and 30% say they plan to start in 2026. Among authors earning over $10,000 per month, roughly half sell direct. In my opinion, selling direct is an advanced author strategy, meaning that you have multiple books and you understand book marketing and have an email list already or some guaranteed way to reach readers. In fact, Kindlepreneur reports that 66% of authors selling direct have more than 5 books, and 46% have more than 10 books. Of course, you can start with the something small, like a table at a local event with a limited number of books for sale, but if you want to consistently sell direct for years to come, you need to consider all the business aspects. Selling direct is not a silver bullet. It's much harder work to sell direct than it is to just upload an ebook to Amazon, whether you choose a Kickstarter campaign, or Shopify/Payhip or other online stores, or regular in-person sales at events/conferences/fairs. You need a business mindset and business practices, for example, you need to pay upfront for setup as well as ongoing management, and bulk printing in some cases. You need to manage taxes and cashflow. You need to be a lot more proactive about marketing, as you won't sell anything if you don't bring readers to your books/products. But selling direct also brings advantages. It sets you apart from the bulk of digital only authors who still only upload ebooks to Amazon, or maybe add a print on demand book, and in an era of AI rapid creation, that number is growing all the time. If you sell direct, you get your customer data and you can reach those customers next time, through your email list. If you don't know who bought your books and don't have a guaranteed way to reach them, you will more easily be disrupted when things change — and they always change eventually. Kindlepreneur notes that “45% of the successful direct selling authors had over 1,000 subscribers on their email lists,” with “a clear, positive correlation between email list size and monthly direct sales income — with authors having an email list of over 15,000 subscribers earning 20X more than authors with email lists under 100 subscribers.” Selling direct means faster money, sometimes the same day or the same week in many cases, or a few weeks after a campaign finishes, as with Kickstarter. And remember, you don't have to sell all your formats directly. You can keep your ebooks in KU, do whatever you like with audiobooks, and just have premium print products direct, or start with a very basic Kickstarter campaign, or a table at a local fair. Lots more tips for Shopify and Kickstarter at https://www.thecreativepenn.com/selldirectresources/ I also recommend the Novel Marketing Podcast on The Shopify Trap: Why authors keep losing money as it is a great counterpoint to my positive endorsement of selling direct on Shopify! Among other things, Thomas notes that a fixed monthly fee for a store doesn't match how most authors make money from books which is more in spikes, the complexity and hassle eats time and can cost more money if you pay for help, and it can reduce sales on Amazon and weaken your ranking. Basically, if you haven't figured out marketing direct to your store, it can hurt you.All true for some authors, for some genres, and for some people's lifestyle. But for authors who don't want to be on the hamster wheel of the Amazon algorithm and who want more diversity and control in income, as well as the incredible creative benefits of what you can do selling direct, then I would say, consider your options in 2025, even if that is trying out a low-financial-goal Kickstarter campaign, or selling some print books at a local fair. Interestingly, traditional publishers are also experimenting with direct sales. Kate Elton, the new CEO of Harper Collins notes in The Bookseller's 2026 trend article, “we are seeing global success with responsive, reader-driven publishing, subscription boxes and TikTok Shop and – crucially – developing strategies that are founded on a comprehensive understanding of the reader.” She also notes, “AI enables us to dramatically change the way we interact with and grow audiences. The opportunities are genuinely exciting – finding new ways to help readers discover books they will love, innovating in the ways we market and reach audiences, building new channels and adapting to new methods of consuming content.” (2) AI-powered search will start to shift elements of book discoverability From LinkedIn's 2026 Big Ideas: “Generative engine optimization (GEO) is set to replace search engine optimization (SEO) as the way brands get discovered in the year ahead. As consumers turn to AI chatbots, agentic workflows and answer engines, appearing prominently in generative outputs will matter more than ranking in search engines.” Google has been rolling out AI Mode with its AI Overviews and is beginning to push it within Google.com itself in some countries, which means the start of a fundamental change in how people discover content online. I first posted about GEO (Generative Engine Optimisation) and AEO (Answer Engine Optimisation) in 2023, and it's going to change how readers find books. For years, we've talked about the long tail of search. Now, with AI-powered search, that tail is getting even longer and more nuanced. AI can understand complex, conversational queries that traditional search engines struggled with. Someone might ask, “What's a good thriller set in a small town with a female protagonist who's a journalist investigating a cold case?” and get highly specific recommendations. This means your book metadata, your website content, and your online presence need to be more detailed and conversational. AI search engines understand context in ways that go far beyond simple keywords. The authors who win in this new landscape will be those who create rich, authentic content about their books and themselves, not just promotional copy. As economist Tyler Cowen has said, “Consider the AIs as part of your audience. Because they are already reading your words and listening to your voice.” We're in the ‘organic' traffic phase right now, where these AI engines are surfacing content for ‘free,' but paid ads are inevitably on the way, and even rumoured to be coming this year to ChatGPT. By the end of 2026, I expect some authors and publishers to be paying for AI traffic, rather than blocking and protesting them. For now, I recommend checking that your author name/s and your books are surfaced when you search on ChatGPT.com as well as Google.com AI Mode (powered by Gemini). You want to make sure your work comes up in some way. I found that Joanna Penn and J.F. Penn searches brought up my Shopify stores, my website, podcast, Instagram, LinkedIn, and even my Patreon page, but did not bring up links to Amazon. If you only have an author presence on Amazon, does it appear in AI search at all? Do you need to improve anything about what the AI search brings up? Traditional publishers are also looking at this, with PublishersWeekly doing webinars on various aspects of AI in early 2026, including sessions on GEO and how book sales are changing, AI agents, and book marketing. In a 2026 predictions article on The Bookseller, the CEO of Bloomsbury Publishing noted, “The boundaries of artificial intelligence will become clearer, enabling publishers to harness its benefits while seeking to safeguard the intellectual property rights of authors, illustrators and publishers.” “AI will be deeply embedded in our workflows, automating tasks such as metadata tagging, freeing teams to focus on creativity and strategy. Challenges will persist. Generative AI threatens traditional web traffic and ad revenue models, making metadata optimisation and SEO critical for visibility as we adjust to this new reality online.” (3) The start of Agentic Commerce AI researches what you want to buy and may even buy on your behalf. Plus, I predict that Amazon does a commerce deal with OpenAI for shopping within ChatGPT by the end of 2026. In September 2025, ChatGPT launched Instant Checkout and the Agentic Commerce Protocol, which will enable bots to buy on websites in the background if authorised by the human with the credit card. VISA is getting on board with this, so is PayPal, with no doubt more payment options to come. In the USA, ChatGPT Plus, Pro, and Free users can now buy directly from US Etsy sellers inside the chat interface, with over a million Shopify merchants coming soon. Shopify and OpenAI have also announced a partnership to bring commerce to ChatGPT. I am insanely excited about this as it could represent the first time we have been able to more easily find and surface books in a much more nuanced way than the 7 keywords and 3 categories we have relied on for so long! I've been using ChatGPT for at least the last year to find fiction and non-fiction books as I find the Amazon interface is ‘polluted' by ads. I've discovered fascinating books from authors I've never heard of, most in very long tail areas. For example, Slashed Beauties by A. Rushby, recommended by ChatGPT as I am interested in medical anatomy and anatomical Venuses, and The Macabre by Kosoko Jackson, recommended as I like art history and the supernatural. I don't think I would have found either of these within a nuanced discussion with ChatGPT. Even without these direct purchase integrations, ChatGPT now has Shopping Research, which I have found links directly to my Shopify store when I search for my books specifically. Walmart has partnered with OpenAI to create AI-first shopping experiences, and you have to wonder what Amazon might be doing? In Nov 2025, Amazon signed a “strategic partnership” with OpenAI, and even though it's focused on the technical side of AI, those two companies in a room together might also be working on other plans … I'm calling it for 2026. I think Amazon will sign a commerce agreement with OpenAI sometime before the end of the year. This will enable at least recommendation and shopping links into Amazon stores (presumably using an OpenAI affiliate link), or perhaps even Instant Checkout with ChatGPT for Amazon. It will also enable a new marketing angle, especially if paid ads arrive in ChatGPT, perhaps even integrating with Amazon Ads in some way as part of any possible agreement, since ads are such a good revenue stream for Amazon anyway. The line between discovery, engagement, and purchase is collapsing. Someone could be having a conversation with an AI about what to read next, and within that same conversation, purchase a bookwithout ever leaving the chat interface. This already happens within TikTok and social commerce clearly works for many authors. It's possible that the next development for book discoverability and sales might be within AI chats. This will likely stratify the already fragmented book eco-system even more. Some readers will continue to live only within the Amazon ecosystem and (maybe) use their Rufus chatbot to buy, and others will be much wider in their exploration of how to find and discover books (and other products and services). If you haven't tried it yet, try ChatGPT.com Shopping Research for a book. You can do this on the free tier. Use the drop down in the main chat box and select Shopping Research. It doesn't have to be for your book. It can be any book or product, for example, our microwave died just before Christmas so I used it to find a new one. But do a really nuanced search with multiple requirements. Go far beyond what you would search for on Amazon. In the results, notice that (at the time of writing) it does not generally link to Amazon, but to independent sites and stores. As above, I think this will change by the end of 2026, as some kind of commerce deal with Amazon seems inevitable. (4) AI-assisted audiobook narration will go mainstream I've been talking about AI narration of audiobooks since 2019, and over the years, I've tried various different options. In 2025, the technology reached a level of emotional nuance that made it much easier to create satisfying fiction audio as well as non-fiction. It also super-charges accessibility, making audio available in more languages and more accents than ever before. Of course, human narration remains the gold standard, but the cost makes it prohibitive for many authors, and indeed many small traditional publishers, for all books. If it costs $2000 – $10,000 to create an audiobook, you have to sell a lot to make a profit, and the dominance of subscription models have made it harder to recoup the costs. Famous narrators and voice artists who have an audience may still be worth investing in, as well as premium production, but require an even higher upfront cost and therefore higher sales and streams in return. AI voice/audio models are continuing to improve, and even as this goes out, there are rumours on TechCrunch that OpenAI's new device, designed by Jony Ive who designed the iPhone, will be audio first and OpenAI are improving their voice models even more in preparation for that launch. In 2026, I think AI-narrated audio will go mainstream with far-reaching adoption across publishing and the indie author world in many different languages and accents. This will mean a further stratification of audiobooks, with high quality, high production, high cost human narrated audio for a small percentage of books, and then mass market, affordable AI-narrated audio for the rest. AI-narrated audiobooks will make audio ubiquitous, and just as (almost) every print book has an ebook format, in 2026, they will also have an audio format. I straddle both these worlds, as I am still a human audiobook narrator for my own work. I human-narrated Successful Self-Publishing Fourth Edition (free audiobook) and The Buried and the Drowned, my short story collection. I also use AI narration for some books. ElevenLabs remains my preferred service and in 2025, I used my J.F. Penn voice clone for Death Valley and also Blood Vintage, while using a male voice for Catacomb. I clearly label my AI-narration in the sales description and also on the cover, which I think is important, although it is not always required by the various services. You can distribute ElevenLabs narrated audiobooks on Spotify, Kobo Writing Life, YouTube, ElevenReader, and of course your own store if you use Shopify with Bookfunnel. There are many other services springing up all the time, so make sure you check the rights you have over the finished audio, as well as where you can sell and distribute the final files. If they are just using ElevenLabs models in the back-end, then why not just do that directly? (Most services will be using someone's model in the back-end, since most companies do not train their own models.) Of course, you can use Amazon's own narration. While Amazon originally launched Audible audiobooks with Virtual Voice (AVV) in November 2023, it was rolled out to more authors and territories in 2025. If your book is eligible, the option to create an audiobook will appear on your KDP dashboard. With just a few clicks, you can create an audiobook from a range of voices and accents, and publish it on Amazon and Audible. However, the files are not yours. They are exclusive to Amazon and you cannot use them on other platforms or sell them direct yourself. But they are also free, so of course, many authors, especially those in KU, will use this option. I have done some for my mum's sweet romance books as Penny Appleton and I will likely use them for my books in translation when the option becomes available. Traditional publishers are experimenting with AI-assisted audiobook narration as well. MacMillan is selling digital audiobooks read by AI directly on their store. PublishersWeekly reports that PRH Audio “has experimented with artificial voice in specific instances, such as entrepreneur Ely Callaway's posthumous memoir The Unconquerable Game,” when an “authorized voice replica” was created for the audiobook. The article also notes that PRH Audio “embrace artificial intelligence across business operations—my entire department [PRH Audio] is using AI for business applications.” And while indie authors can't use AI voices on ACX right now, Audible have over 100 voices available to selected publishing partnerships, as reported by The Guardian with “two options for publishers wishing to make use of the technology: “Audible-managed” production, or “self-service” whereby publishers produce their own audiobooks with the help of Audible's AI technology.” In 2026, it's likely that more traditional publishers — as well as indie authors — will get their backlist into audio with AI narration. (5) AI-assisted translation will start to take off beyond the early adopters Over the years, I've done translation deals with traditional publishers in different languages (German, French, Spanish, Korean, Italian) for some fiction and non-fiction books. But of course, to get these kinds of deals, you have to be proactive about pitching, or work with an agent for foreign rights only, and those are few and far between! There are also lots of languages and territories worldwide, and most deals are for the bigger markets, leaving a LOT of blue water for books in translation, even if you have licensed some of the bigger markets. I did my first partially AI-translated books in 2019 when I used Deepl.com for the first draft and then worked with a German editor to do 3 non-fiction books in German. While the first draft was cheap, the editing was pretty expensive, so I stopped after only doing a couple. I have made the money back now, but it took years. In 2025, AI Translation began to take off with ScribeShadow, GlobeScribe.ai, and more recently, in November 2025, Kindle Translate boosting the number of translated books available. Kindle Translate is (currently) only available to US authors for English into Spanish and also German into English, but in 2026, this will likely roll out to more languages and more authors, making it easier than ever to produce translations for free. Of course, once again, the gold standard is human translation, or at least human-edited translations, but the cost is prohibitive even just for proof-reading, and if there is a cheap or even free option, like Kindle Translate, then of course, authors are going to try it. If the translation gets bad reviews, they can just un-publish. There are many anecdotal stories of indie success in 2025 with AI-translated genre fiction sales (in series) in under-served markets like Italian, French, and Spanish, as well as more mainstream adoption in German. I was around in the Kindle gold-rush days of 2009-2012 and the AI-translation energy right now feels like that. There are hardly any Kindle ebooks in many of these languages compared to how many there are in English, so inevitably, the rush is on to fill the void, especially in genres that are under-served by traditional publishers in those markets. Yes, some of these AI translated books will be ‘AI-slop,' but readers are not stupid. Those books will get bad reviews and thus will sink to the bottom of the store, never to be seen again. The AI translation models are also improving rapidly, and Amazon's Kindle Translate may improve faster than most, for books specifically, since they will be able to get feedback in terms of page reads. Amazon is also a major investor in Anthropic, which makes Claude.ai, widely considered the best quality for creative writing and translation, so it's likely that is used somewhere in the mix. Some traditional publishers are also experimenting with AI-assisted translation, with Harlequin France reportedly using AI translation and human proofreaders, as reported by the European Council of Literary Translators' Associations in December 2025. Academic publisher Taylor and Francis is also using AI for book translation, noting: “Following a program of rigorous testing, Taylor & Francis has announced plans to use AI translation tools to publish books that would otherwise be unavailable to English-language readers, bringing the latest knowledge to a vastly expanded readership.” “Until now, the time and resources required to translate books has meant that the majority remained accessible only to those who could read them in the original language. Books that were translated often only became available after a significant delay. Today, with the development of sophisticated AI translation tools, it has become possible to make these important texts available to a broad readership at speed, without compromising on accuracy.” (6) AI video becomes ubiquitous. ‘Live selling' becomes the next trend in social sales. In 2025, short form AI-generated video became very high quality. OpenAI released Sora 2, and YouTube announced new Shorts creation tools with Veo 3, which you can also use directly within Gemini. There are tons of different AI video apps now, including those within the social media sites themselves. There is more video than ever and it's much easier to create. I am not a fan of short form video! I don't make it and I don't consume it, but I do love making book trailers for my Kickstarter campaigns and for adding to my book pages and using on social media. I made a trailer for The Buried and the Drowned using Midjourney for images and then animation of those images, and Canva to put them together along with ElevenLabs to generate the music. But despite the AI tools getting so much easier to use, you still have to prompt them with exactly what you want. I can't just upload my book and say, “Make a book trailer,” or “Make a short film.” This may change with generative video ads, which are likely to become more common in 2026, as video turns specifically commercial. Video ads may even be generated specifically for the user, with an audience of one, maybe even holding your book in their hands (using something like Cameos on Sora), in the same way that some AI-powered clothing stores do virtual try-ons. This might also up-end the way we discover and buy things, as the AI for eCommerce and Amazon Sellers newsletter says about OpenAI's Sora app, “OpenAI isn't just trying to build a TikTok competitor. They're building a complete reimagining of how we discover and buy things …” “The combination of ChatGPT's research capabilities and Sora's potential for emotional manipulation—I mean, “engagement”—could create something we've never seen before: an AI ecosystem that might eventually guide you through every type of purchase, from the most considered to the most impulsive.” In 2026, there will be A LOT more AI-generated video, but that also leads to the human trend of more live video. While you can use an AI avatar that looks and sounds like you using tools like HeyGen or Synthesia, live video has all the imperfect human elements that make it stand-out, plus the scarcity element which leads to the purchase decision within a countdown period. Live video is nothing new in terms of brand building and content in general, but it seems that live events primarily for direct sales might be a thing in 2026. Kim Kardashian hosted Kimsmas Live in December 2025 with a 45 minute live shopping event with special guests, described as entertainment but designed to be a sales extravaganza. Indie authors are doing a similar thing on TikTok with their books, so this is a trend to watch in 2026, especially if you feel that live selling might fit with your personality and author business goals. It's certainly not for everyone, but I suspect it will suit a different kind of creator to those who prefer ‘no face' video, or no video at all! On other aspects of the human side of social media, Adam Mosseri the CEO of Instagram put a post on Threads called Authenticity after Abundance. He said, “Everything that made creators matter—the ability to be real, to connect, to have a voice that couldn't be faked—is now suddenly accessible to anyone with the right tools.” “Deepfakes are getting better and better. AI is generating photographs and videos indistinguishable from captured media. The feeds are starting to fill up with synthetic everything. And in that world, here's what I think happens.Creators matter more.” It's a long article so just to pick a few things from it: “We like to talk about “AI slop,” but there is a lot of amazing AI content … we are going to start to see more and more realistic AI content.” I've talked to my Patreon Community about this ‘tsunami of excellence' as these tools are just getting better and better and the word ‘slop' can also be applied to purely human output, too. If you think that AI content is ‘worse' than wholly human content, in 2026, you are wrong. It is now very very good, especially in the hands of people who can drive the AI tools. Back to Adam's post: “Authenticity is fast becoming a scarce resource, …The creators who succeed will be those who figure out how to maintain their authenticity [even when it can be simulated] …” “The bar is going to shift from “can you create?” to “can you make something that only you could create?” He talks about how the personal content on Instagram now is: “unpolished; it's blurry photos and shaky videos of people's daily experiences … flattering imagery is cheap to produce and boring to consume. People want content that feels real… Savvy creators are going to lean into explicitly unproduced and unflattering images of themselves. In a world where everything can be perfected, imperfection becomes a signal. Rawness isn't just aesthetic preference anymore—it's proof. It's defensive. A way of saying: this is real because it's imperfect.” While I partially love this, and I really hope it's true, as in I hope we don't need to look good for the camera anymore I would also challenge Adam on this, because pretty much every woman I know on social media has been sent sexual messages, and/or told they are ugly and/or fat when posting anything unflattering. I've certainly had both even for the same content, but I don't expect Adam has been the target for such posting! But I get his point. He goes on:“Labeling content as authentic or AI-generated is only part of the solution though. We, as an industry, are going to need to surface much more context about not only the media on our platforms, but the accounts that are sharing it in order for people to be able to make informed decisions about what to believe. Where is the account? When was it created? What else have they posted?” This is exactly what I've been saying for a while under my double down on being human focus. I use my Instagram @jfpennauthor as evidence of humanity, not as a sales channel. You can do both of course, but increasingly, you need to make sure your accounts at places have longevity and trust, even by the platforms themselves. Adam finishes: “In a world of infinite abundance and infinite doubt, the creators who can maintain trust and signal authenticity—by being real, transparent, and consistent—will stand out.” For other marketing trends for 2026, I recommend publicist Kathleen Schmidt's SubStack which is mostly focused on traditional publishing but still interesting for indies. In her 2026 article, she notes: “We have reached a social media saturation point where going viral can be meaningless and should not be the goal; authenticity and creativity should. She also says, “In-person events are important again,” and, “Social media marketing takes a nosedive… we have reached a saturation point … What publishers must figure out is how to make their social media campaigns stand out. If they remain somewhat uninspired, the money spent on social ads won't convert into book sales.” I think this is part of the rise of live selling as above, which can stand out above more ‘produced' videos. Kathleen also talks about AI usage. “AI can help lighten the burden of publicity and marketing.” “A lot of AI tools are coming to market to lessen the load: they can write pitches, create media lists for you, send pitches for you, and more. I know the industry is grappling with all things AI, but some of these tools are huge time savers and may help a book more than hurt it.” On that note … (7) AI will create, run, and optimise ads without the need for human intervention Many authors will be very happy about this as marketing is often the bane of our author business lives! As I noted in my 2026 goals, I would love to outsource more marketing tasks to AI. I want an “AI book marketing assistant” where I can upload a book and specify a budget and say, ‘Go market this,' then the AI will action the marketing, without me having to cobble together workflows between systems. Of course, it will present plans for me to approve but it will do the work itself on the various platforms and monitor and optimize things for me. I really hope 2026 is the year this becomes possible, because we are on the edge of it already in some areas. Amazon Ads launched a new agentic AI tool in September 2025 that creates professional-quality ads. I've also been working with Claude in Chrome browser to help me analyse my Amazon Ad data and suggest which keywords/products to turn off and what to put more budget into. I'll do a Patreon video on that soon. Meta announced it will enable AI ad creation by the end of 2026 for Facebook and Instagram. For authors who find ad creation overwhelming or time-consuming, this could be a game-changer. Of course, you will still need a budget! (8) 1000 True Fans becomes more important than ever Lots of authors and publishers are moaning about the difficulty of reaching readers in an era of ‘AI slop' but there is no shortage of excellent content created by humans, or humans using AI tools. As ever, our competition is less about other authors, or even authors using AI-assisted creation, we're competing against everything else that jostles for people's attention, and the volume of that is also growing exponentially. I've never been a fan of rapid release, and have said for years that you can't keep up with the pace of the machines. So play a different game. As Kevin Kelly wrote in 2008, If you have 1000 true fans, (also known as super fans), “you can make a living — if you are content to make a living but not a fortune.” [Kevin Kelly was on this show in 2023 talking about Excellent Advice for Living.] Many authors and the publishing industry are stuck in the old model of aiming to sell huge volumes of books at a low profit margin to a massive number of readers, many of them releasing ever faster to try and keep the algorithms moving. But the maths can work for the smaller audience of more invested readers and fans. If you only make $2 profit on an ebook, you need to sell 500 ebooks to make $1000, and then do it again next month. Or you can have a small community like my patreon.com/thecreativepenn where people pay $2 (or more) a month, so even a small revenue per person results in a better outcome over the year, as it is consistent monthly income with no advertising. But what if you could make $20 profit per book? That is entirely possible if you're producing high quality hardbacks on Kickstarter, or bundle deals of audiobooks, or whole series of ebooks. You would only need to sell to 50 people to make $1000. What about $100 profit per sale, which you can do with a small course or live event? You only need 10 people to make $1000, and this in-person focus also amplifies trust and fosters human connection. I've found the intimacy of my live Patreon Office Hours and also my webinars have been rewarding personally, but also financially, and are far more memorable — and potentially transformative — than a pre-recorded video or even another book. From the LinkedIn 2026 Big Ideas article: “In an AI-optimized world, intentional human connection will become the ultimate luxury.” The 1000 True Fans model is about serving a smaller, more personal audience with higher value products (and maybe services if that's your thing). As ever, its about niche and where you fit in the long long long long long tail. It's also about trust. Because there is definitely a shortage of that in so many areas, and as Adam Mosseri of Instagram has said, trust will be increasingly important. Trust takes time to build, but if you focus on serving your audience consistently, and delivering a high quality, and being authentic, this emerges as part of being human. In an echo of what happened when online commerce first took off, we are back to talking about trust. Back in 2010, I read Trust Agents: by Julien Smith and Chris Brogan, which clearly needs a comeback. There was a 10th anniversary edition published in 2020, so that's worth a read/listen. Chris Brogan was also on this show in 2017 when we talked about finding and serving your niche for the long term. That interview is still relevant, here's a quick excerpt, where I have (lightly edited) his response to my question on this topic back in 2017: Jo: The principle of know, like, and trust, why is that still important or perhaps even more important these days? Chris: There are a few things that at play there, Joanna. One is that the same tools that make it so easy for any of us to start and run a business also allow certain elements to decide whether or not they want to do something dubious. And with all new technologies that come, you know, there's nothing unique about these new technologies. In the 1800s, anyone could put anything in a bottle and sell it to you and say, this is gonna cure everything. Cancer — gone. And the bottle could have nothing in. You know, it could be Kool-Aid. And so, the idea of trying to understand what's behind the business though, one beautiful thing that's come is that we can see in much more dimensions who we're dealing with. We can understand better who's the face behind the brand. I really want people to try their best to be a lot clearer on what they stand for or what they say. And I don't really mean a tagline. I mean, humans don't really talk like that. They don't throw some sentence out as often as they can that you remember them for that phrase. But I would say that, we have so many media available to us — the plural of mediums — where we can be more of ourselves. And I think that there's a great opportunity to share the ‘you' behind the scenes, and some people get immediately terrified about this, ‘Ah, the last thing I want is for people to know more about me,' but I think we have such an opportunity. We have such an opportunity to voice our thoughts on something, to talk about the story that goes behind the product. We were all raised on overly produced material, but I think we don't want that anymore. We really want clarity, brevity, simplicity. We want the ability for what we feel is connection and then access. And so I think it's vital that we connect and show people our accessibility, not so that they can pester us with strange questions, but more so that you can say, this person stands with their product and their service and this person believes these things, and I feel something when I hear them and I wanna be part of that.” That's from Chris Brogan's interview here in 2017, and he is still blogging and speaking at writing at ChrisBrogan.com and I'm going to re-listen to the audiobook of Trust Agents again myself as I think it's more relevant than ever. The original quote comes from Bob Burg in his 1994 book, Endless Referrals, “All things being equal, people will do business with, and refer business to, those people they know, like and trust.” That still applies, and absolutely fits with the 1000 True Fans model of aiming to serve a smaller audience. As Kevin Kelly says in 1000 True Fans, “Instead of trying to reach the narrow and unlikely peaks of platinum bestseller hits, blockbusters, and celebrity status, you can aim for direct connection with a thousand true fans.” “On your way, no matter how many fans you actually succeed in gaining, you'll be surrounded not by faddish infatuation, but by genuine and true appreciation. It's a much saner destiny to hope for. And you are much more likely to actually arrive there.” In 2026, I hope that more authors (including me!) let go of ego goals and vanity metrics like ranking, gross sales (income before you take away costs), subscribers, followers, and likes, and consider important business numbers like profit (which is the money you have after costs like marketing are taken out), as well as number of true fans — and also lifestyle elements like number of weekends off, or days spent enjoying life and not just working! OK, that's my list of trends and predictions for 2026. Let me know what you think in the comments. Do you agree? Am I wrong? What have I missed? The post 2026 Trends And Predictions For Indie Authors And The Book Publishing Industry with Joanna Penn first appeared on The Creative Penn.

durée : 00:02:10 - Les 80'' - par : Nicolas Demorand - 80 secondes quant à moi ce matin pour vous parler d'une conversation passionnante entre Sam Altman le fondateur d'Open IA - l'entreprise a l'origine de Chat GPT - et Tyler Cowen un économiste influent aux États-Unis. Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

Anil Varanasi is the co-founder and CEO of Meter, which provides full-stack networking infrastructure as a service for businesses. Since founding Meter with his brother Sunil in 2015, Anil has been playing a distinctly long game in one of the most entrenched markets in technology, betting on vertical integration, business model innovation, and a multi-decade time horizon. In this conversation, he unpacks Meter's origin story, from four-plus years of heads-down R&D, and shares how his unconventional approach to planning, management, and pace keeps him excited to run the company for decades. In today's episode, we discuss: Why Anil thinks in 25-year horizons How operating in a monopolistic market shaped Meter's approach Why Meter scrapped a year of OS work during the R&D phase How Meter is rethinking networking's business model Surviving COVID, Apple's M1 transition, and “a thousand bad days” Anil's contrarian views on planning, OKRs, and management How founders can build companies they'll want to run for decades Where to find Anil: LinkedIn: https://www.linkedin.com/in/anilcv/ Twitter/X: https://x.com/acv Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast References: ADT: ⁠https://www.adt.com⁠ Alex Honnold: ⁠https://www.alexhonnold.com⁠ Alex Tabarrok: ⁠https://x.com/ATabarrok⁠ ⁠alarm.com⁠: ⁠https://www.alarm.com⁠ Andreessen Horowitz (a16z): ⁠https://a16z.com⁠ Apple: ⁠https://www.apple.com⁠ Bloomberg: ⁠https://www.bloomberg.com⁠ Bryan Caplan: ⁠http://www.bcaplan.com/⁠ Cisco: ⁠https://www.cisco.com⁠ Coca-Cola: ⁠https://www.coca-colacompany.com⁠ George Mason University (GMU): ⁠https://www.gmu.edu⁠ Intel: ⁠https://www.intel.com⁠ Julia Galef: ⁠https://x.com/juliagalef⁠ Martin Casado: ⁠https://www.linkedin.com/in/martincasado/⁠ Meraki: ⁠https://meraki.cisco.com⁠ Meter: ⁠https://www.meter.com⁠ Michela Giorcelli: ⁠https://x.com/M_Giorcelli⁠ Nicholas Bloom: ⁠https://www.linkedin.com/in/nick-bloom-stanford/⁠ Raffaella Sadun: ⁠https://www.linkedin.com/in/raffaella-sadun-3a182225/⁠ Sanjit Biswas: ⁠https://www.linkedin.com/in/sanjitbiswas/⁠ Sunil Varanasi: ⁠https://www.linkedin.com/in/sunil-varanasi-662a01253/⁠ Tyler Cowen: ⁠https://www.linkedin.com/in/tyler-cowen-166718/⁠ Twitch: ⁠https://www.twitch.tv⁠ Timestamps: (01:27) Meter's unusual timeframes (04:06) “We don't do OKRs” (06:32) How to plan without planning (08:31) Track your unhappy customers (11:43) How Meter's journey began (15:02) Dissecting the 2010s SaaS boom (17:06) The networking industry trap (21:44) Meter's first roadblock (22:07) Why Shenzhen accelerated Meter's progress (26:29) The process to get a sales-ready product (31:02) Why you should own the full stack (32:45) The surprising thing you should innovate (35:03) Avoiding the one-trick pony trap (37:39) The secret to finding an excellent market (43:48) How COVID's constraints propelled growth (48:25) Why founders need to know their customers (49:34) Why Meter didn't sell via traditional channels (51:44) You need “seller-market fit” (54:51) The danger of meta-work (56:25) Decoupling management from authority (1:02:17) When the person is the problem (1:05:05) The inherent value of going slowly (1:09:41) Running a company for as long as possible

Muslim Brotherhood; Ukraine; China; Mamdani; Deportations; X; AI; Golden Age | Yaron Brook Show

American Scholar has an article about people who "write for AI", including Tyler Cowen and Gwern. It's good that this is getting more attention, because in theory it seems like one of the most influential things a writer could do. In practice, it leaves me feeling mostly muddled and occasionally creeped out. "Writing for AI" means different things to different people, but seems to center around: Helping AIs learn what you know. Presenting arguments for your beliefs, in the hopes that AIs come to believe them. Helping the AIs model you in enough detail to recreate / simulate you later. Going through these in order: https://www.astralcodexten.com/p/writing-for-the-ais

Several weeks ago on October 7th, we hosted our 4th annual Fall Macro Conference at our Columbus Circle office in NYC. This year, the event brought together nearly 500 global investors to discuss some of the top macro topics of the day. The conference also featured in-depth conversations on artificial intelligence. Our colleague Anil Atluri, Head of ICG Americas, hosted a wide-ranging discussion with Tyler Cowen, Professor of Economics at George Mason University, on how AI may reshape the macroeconomy. For our latest Podzept, we listen into that conversation

Tetragrammaton with Rick Rubin ✓ 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 --------- Tyler Cowen is a leading economist, author, and professor, currently holding the Holbert L. Harris Chair of Economics at George Mason University, where he also serves as chairman and faculty director of the Mercatus Center. Widely recognized for his influential economic ideas, Cowen co-authors the long-running blog Marginal Revolution with Alex Tabarrok, and together they have also created Marginal Revolution University, which offers accessible, world-class economics education online. Cowen has also authored several books, including "The Great Stagnation," which analyzes the slowdown in economic growth, and "Average Is Over," exploring the future of work and inequality. ------ Thank you to the sponsors that fuel our podcast and our team: Squarespace https://squarespace.com/tetra Use code 'TETRA' ------ LMNT Electrolytes https://drinklmnt.com/tetra Use code 'TETRA' ------ Athletic Nicotine https://www.athleticnicotine.com/tetra Use code 'TETRA' ------ Sign up to receive Tetragrammaton Transmissions https://www.tetragrammaton.com/join-newsletter

Welcome back to Snafu with Robin Zander. In this episode, I'm joined by Miki Johnson – coach, facilitator, and co-founder of Job Portraits, a creative studio that helped companies tell honest stories about their work and culture. Today, Miki leads Leading By Example, where she supports leaders and teams through moments of change – whether that's a career shift, new parenthood, or redefining purpose. We talk about how to navigate transition with awareness, why enjoying change takes practice, and what it means to lead with authenticity in uncertain times. Miki shares lessons from a decade of coaching and storytelling – from building human-centered workplaces to bringing more body and emotion into leadership. We also explore creativity in the age of AI, and how technology can either deepen or disconnect us from what makes us human. And if you're interested in these kinds of conversations, we'll be diving even deeper into the intersection of leadership, creativity, and AI at Responsive Conference 2026. If you're interested, get your tickets here! https://www.responsiveconference.com/  __________________________________________________________________________________________ 00:00 Start 01:20 Miki's Background and Reservations about AI Miki hasn't used AI and has “very serious reservations.” She's not anti-AI – just cautious and curious. Her mindset is about “holding paradox”, believing two opposing things can both be true. Her background shapes that approach. She started as a journalist, later ran her own businesses, and now works as a leadership coach. Early in her career, she watched digital technology upend media and photography – industries “blown apart” by change. When she joined a 2008 startup building editable websites for photographers, it was exciting but also unsettling. She saw innovation create progress and loss at the same time. Now in her 40s with two sons, her focus has shifted. She worries less about the tools and more about what they do to people's attention, empathy, and connection – and even democracy. Her concern is how to raise kids and stay human in a distracted world. Robin shares her concerns but takes a different approach. He notes that change now happens “day to day,” not decade to decade. He looks at technology through systems, questioning whether pre-internet institutions can survive. “Maybe the Constitution was revolutionary,” he says, “but it's out of date for the world we live in.” He calls himself a “relentless optimist,” believing in democracy and adaptability, but aware both could fail without reform. Both worry deeply about what technology is doing to kids. Robin cites The Anxious Generation by Jonathan Haidt and says, “I don't believe social media is good for children.” He and his fiancée plan to limit their kids' screen time, just as Miki already does. They see it as a responsibility: raising grounded kids in a digital world. Robin sees AI as even more transformative – and risky – than anything before. “If social media is bigger than the printing press,” he says, “AI is bigger than the wheel.” He's amazed by its potential but uneasy about who controls it. He doubts people like Sam Altman act in the public's best interest. His concern isn't about rejecting AI but about questioning who holds power over it. Their difference lies in how they handle uncertainty. Miki's instinct is restraint and reflection – question first, act later, protect empathy and connection. Robin's instinct is engagement with vigilance – learn, adapt, and reform systems rather than retreat. Miki focuses on the human and emotional. Robin focuses on the structural and systemic. Both agree technology is moving faster than people can process or regulate. Miki uses curiosity to slow down and stay human. Robin uses curiosity to move forward and adapt. Together, they represent two sides of the same challenge: protecting what's most human while building what's next. 10:05 Navigating the Tech Landscape Miki starts by describing how her perspective has been shaped by living in two very different worlds. She spent over a decade in the Bay Area, surrounded by tech and startups. She later moved back to her small hometown of Athens, Ohio—a progressive college town surrounded by more rural areas. She calls it “a very small Austin”, a blue dot in a red state. She loves it there and feels lucky to have returned home. Robin interrupts briefly to highlight her background. He reminds listeners that Miki and her husband, Jackson, co-founded an employer branding agency called Job Portraits in 2014, the same year they got married. Over eight years, they grew it to around 15 full-time employees and 20 steady contractors. They worked with major startups like DoorDash, Instacart, and Eventbrite when those companies were still small—under 200 employees. Before that, they had started another venture in Chicago during Uber's early expansion beyond San Francisco. Their co-working space was right next to Uber's local team setting up drivers, giving them a front-row seat to the tech boom. Robin points out that Miki isn't coming at this topic as a “layperson.” She deeply understands technology, startups, and how they affect people. Miki continues, explaining how that background informs how she sees AI adoption today. Her Bay Area friends are all-in on AI. Many have used it since its earliest days—because it's part of their jobs, or because they're building it themselves. Others are executives leading companies developing AI tools. She's been watching it unfold closely for years, even if she hasn't used it herself. From her position outside the tech bubble now, she can see two clear camps: Those immersed in AI, excited and moving fast. And those outside that world—more cautious, questioning what it means for real people and communities. Living between those worlds—the fast-paced tech culture and her slower, more grounded hometown—gives her a unique vantage point. She's connected enough to understand the innovation but distant enough to see its costs and consequences. 16:39 The Cost of AI Adoption Miki points out how strange it feels to people in tech that she hasn't used AI. In her Bay Area circles, the idea is almost unthinkable. Miki understands why it's shocking. It's mostly circumstance—her coaching work doesn't require AI. Unlike consultants who “all tell leaders how to use AI,” her work is based on real conversations, not digital tools. Her husband, Jackson, also works at a “zero-technology” K–12 school he helped create, so they both exist in rare, tech-free spaces. She admits that's partly luck, not moral superiority, just “tiny pockets of the economy” where avoiding AI is still possible. Robin responds with his own story about adopting new tools. He recalls running Robin's Café from 2016 to 2019, when most restaurants still used paper timesheets. He connected with two young founders who digitized timesheets, turning a simple idea into a company that later sold to a global conglomerate. By the time he sold his café, those founders had retired in their 20s. “I could still run a restaurant on paper,” he says, “but why would I, if digital is faster and easier?” He draws a parallel between tools over time—handwriting, typing, dictation. Each serves a purpose, but he still thinks best when writing by hand, then typing, then dictating. The point: progress adds options, not replacements. Miki distills his point: if a tool makes life easier, why not use it? Robin agrees, and uses his own writing practice as an example. He writes a 1,000-word weekly newsletter called Snafu. Every word is his, but he uses AI as an editor—to polish, not to create. He says, “I like how I think more clearly when I write regularly.” For him, writing is both communication and cognition—AI just helps him iterate faster. It's like having an instant editor instead of waiting a week for human feedback. He reminds his AI tools, “Don't write for me. Just help me think and improve.” When Miki asks why he's never had an editor, he explains that he has—but editors are expensive and slow. AI gives quick, affordable feedback when a human editor isn't available. Miki listens and reflects on the trade-offs. “These are the cost-benefit decisions we all make,” she says—small, constant choices about convenience and control. What unsettles her is how fast AI pushes that balance. She sees it as part of a long arc—from the printing press to now—but AI feels like an acceleration. It's “such a powerful technology moving so fast” that it's blowing the cover off how society adapts to change. Robin agrees: “It's just the latest version of the same story, since writing on cave walls.” 20:10 The Future of Human-AI Relationships Miki talks about the logical traps we've all started accepting over time. One of the biggest, she says, is believing that if something is cheaper, faster, or easier – it's automatically better. She pushes further: just because something is more efficient doesn't mean it's better than work. There are things you gain from working with humans that no machine can replicate, no matter how cheap or convenient it becomes. But we rarely stop to consider the real cost of trading that away. Miki says the reason we overlook those costs is capitalism. She's quick to clarify – she's not one of those people calling late-stage capitalism pure evil. Robin chimes in: “It's the best of a bunch of bad systems.” Miki agrees, but says capitalism still pushes a dangerous idea: It wants humans to behave like machines—predictable, tireless, cheap, and mistake-free. And over time, people have adapted to that pressure, becoming more mechanical just to survive within it. Now we've created a tool—AI—that might actually embody those machine-like ideals. Whether or not it reaches full human equivalence, it's close enough to expose something uncomfortable: We've built a human substitute that eliminates everything messy, emotional, and unpredictable about being human. Robin takes it a step further, saying half-jokingly that if humanity lasts long enough, our grandchildren might date robots. “Two generations from now,” he says, “is it socially acceptable—maybe even expected—that people have robot spouses?” He points out it's already starting—people are forming attachments to ChatGPT and similar AIs. Miki agrees, noting that it's already common for people under 25 to say they've had meaningful interactions with AI companions. Over 20% of them, she estimates, have already experienced this. That number will only grow. And yet, she says, we talk about these changes as if they're inevitable—like we don't have a choice. That's what frustrates her most: The narrative that AI “has to” take over—that it's unstoppable and universal—isn't natural evolution. It's a story deliberately crafted by those who build and profit from it. “Jackson's been reading the Hacker News comments for 15 years,” she adds, hinting at how deep and intentional those narratives run in the tech world. She pauses to explain what Hacker News is for anyone unfamiliar. It's one of the few online forums that's still thoughtful and well-curated. Miki says most people there are the ones who've been running and shaping the tech world for years—engineers, founders, product leaders. And if you've followed those conversations, she says, it's obvious that the people developing AI knew there would be pushback. “Because when you really stop and think about it,” she says, “it's kind of gross.” The technology is designed to replace humans—and eventually, to replace their jobs. And yet, almost no one is seriously talking about what happens when that becomes real. “I'm sorry,” she says, “but there's just something in me that says—dating a robot is bad for humanity. What is wrong with us?” Robin agrees. “I don't disagree,” he says. “It's just… different from human.” Miki admits she wrestles with that tension. “Every part of me says, don't call it bad or wrong—we have to make space for difference.” But still, something in her can't shake the feeling that this isn't progress—it's disconnection. Robin expands on that thought, saying he's not particularly religious, but he does see humanity as sacred. “There's something fundamental about the human soul,” he says. He gives examples: he has metal in his ankle from an old injury; some of his family members are alive only because of medical devices. Technology, in that sense, can extend or support human life. But the idea of replacing or merging humans with machines—of being subsumed by them—feels wrong. “It's not a world I want to live in,” he says plainly. He adds that maybe future generations will think differently. “Maybe our grandkids will look at us and say, ‘Okay boomer—you never used AI.'” 24:14 Practical Applications of AI in Daily Life Robin shares a story about a house he and his fiancée almost bought—one that had a redwood tree cut down just 10 feet from the foundation. The garage foundation was cracked, the chimney tilted—it was clear something was wrong. He'd already talked to arborists and contractors, but none could give a clear answer. So he turned to ChatGPT's Deep Research—a premium feature that allows for in-depth, multi-source research across the web. He paid $200 a month for unlimited access. Ran 15 deep research queries simultaneously. Generated about 250 pages of analysis on redwood tree roots and their long-term impact on foundations. He learned that if the roots are alive, they can keep growing and push the soil upward. If they're dead, they decompose, absorb and release water seasonally, and cause the soil to expand and contract. Over time, that movement creates air pockets under the house—tiny voids that could collapse during an earthquake. None of this, Robin says, came from any contractor, realtor, or arborist. “Even they said I'd have to dig out the roots to know for sure,” he recalls. Ultimately, they decided not to buy that house—entirely because of the data he got from ChatGPT. “To protect myself,” he says, “I want to use the tools I have.” He compares it to using a laser level before buying a home in earthquake country: “If I'll use that, why not use AI to explore what I don't know?” He even compares Deep Research to flipping through Encyclopedia Britannica as a kid—hours spent reading about dinosaurs “for no reason other than curiosity.” Robin continues, saying it's not that AI will replace humans—it's that people who use AI will replace those who don't. He references economist Tyler Cowen's Average Is Over (2012), which described how chess evolved in the early 2000s. Back then, computers couldn't beat elite players on their own—but a human + computer team could beat both humans and machines alone. “The best chess today,” Robin says, “is played by a human and computer together.” “There are a dozen directions I could go from there,” Miki says. But one idea stands out to her: We're going to have to choose, more and more often, between knowledge and relationships. What Robin did—turning to Deep Research—was choosing knowledge. Getting the right answer. Having more information. Making the smarter decision. But that comes at the cost of human connection. “I'm willing to bet,” she says, “that all the information you found came from humans originally.” Meaning: there were people who could have told him that—just not in that format. Her broader point: the more we optimize for efficiency and knowledge, the less we may rely on each other. 32:26 Choosing Relationships Over AI Robin points out that everything he learned from ChatGPT originally came from people. Miki agrees, but says her work is really about getting comfortable with uncertainty. She helps people build a relationship with the unknown instead of trying to control it. She mentions Robin's recent talk with author Simone Stolzoff, who's writing How to Not Know—a book she can't wait to read. She connects it to a bigger idea: how deeply we've inherited the Enlightenment mindset. “We're living at the height of ‘I think, therefore I am,'” she says. If that's your worldview, then of course AI feels natural. It fits the logic that more data and more knowledge are always better. But she's uneasy about what that mindset costs us. She worries about what's happening to human connection. “It's all connected,” she says—our isolation, mental health struggles, political polarization, even how we treat the planet. Every time we choose AI over another person, she sees it as part of that drift away from relationship. “I get why people use it,” she adds. “Capitalism doesn't leave most people much of a choice.” Still, she says, “Each time we pick AI over a human, that's a decision about the kind of world we're creating.” Her choice is simple: “I'm choosing relationships.” Robin gently pushes back. “I think that's a false dichotomy,” he says. He just hosted Responsive Conference—250 people gathered for human connection. “That's why I do this podcast,” he adds. “To sit down with people and talk, deeply.” He gives a personal example. When he bought his home, he spoke with hundreds of people—plumbers, electricians, roofers. “I'm the biggest advocate for human conversations,” he says. “So why not both? Why not use AI and connect with people?” To him, the real question is about how we use technology consciously. “If we stopped using AI because it's not human,” he asks, “should we stop using computers because handwriting is more authentic?” “Should we reject the printing press because it's not handwritten?” He's not advocating blind use—he's asking for mindful coexistence. It's also personal for him. His company relies on AI tools—from Adobe to video production. “AI is baked into everything we do,” he says. And he and his fiancée—a data scientist—often talk about what that means for their future family. “How do we raise kids in a world where screens and AI are everywhere?” Then he asks her directly: “What do you tell your clients? Treat me like one—how do you help people navigate this tension?” Miki smiles and shakes her head. “I don't tell people what to do,” she says. “I'm not an advisor, I'm a coach.” Her work is about helping people trust their own intuition. “Even when what they believe is contrarian,” she adds. She admits she's still learning herself. “My whole stance is: I don't know. I don't know. I don't know.” She and her husband, Jackson, live by the idea of strong opinions, loosely held. She stays open—lets new conversations change her mind. “And they do,” she says. “Every talk like this shifts me a little.” She keeps seeking those exchanges—with parents, tech workers, friends—because everyone's trying to figure out the same thing: How do we live well with technology, without losing what makes us human? 37:16 The Amish Approach to Technology Miki reflects on how engineers are both building and being replaced by AI. She wants to understand the technology from every angle—how it works, how it affects people, and what choices it leaves us with. What worries her is the sense of inevitability around AI—especially in places like the Bay Area. “It's like no one's even met someone who doesn't use it,” she says. She knows it's embedded everywhere—Google searches, chatbots, everything online. But she doesn't use AI tools directly or build with them herself. “I don't even know the right terminology,” she admits with a laugh. Robin points out that every Google search now uses an LLM. Miki nods, saying her point isn't denial—it's about choice. “You can make different decisions,” she says. She admits she hasn't studied it deeply but brings up an analogy that helps her think about tech differently: the Amish. “I call myself kind of ‘AI Amish,'” she jokes. She explains her understanding of how the Amish handle new technology. They're not anti-tech; they're selective. They test and evaluate new tools to see if they align with their community's values. “They ask, does it build connection or not?” They don't just reject things—they integrate what fits. In her area of Ohio, she's seen Amish people now using electric bikes. “That's new since I was a kid,” she says. It helps them connect more with each other without harming the environment. They've also used solar power for years. It lets them stay energy independent without relying on outside systems that clash with their values. Robin agrees—it's thoughtful, not oppositional. “They're intentional about what strengthens community,” he says. Miki continues: What frustrates her is how AI's creators have spent the last decade building a narrative of inevitability. “They knew there would be resistance,” she says, “so they started saying, ‘It's just going to happen. Your jobs won't be taken by AI—they'll be taken by people who use it better than you.'” She finds that manipulative and misleading. Robin pushes back gently. “That's partly true—but only for now,” he says. He compares it to Uber and Lyft: at first, new jobs seemed to appear, but eventually drivers started being replaced by self-driving cars. Miki agrees. “Exactly. First it's people using AI, then it's AI replacing people,” she says. What disturbs her most is the blind trust people put in companies driven by profit. “They've proven over and over that's their motive,” she says. “Why believe their story about what's coming next?” She's empathetic, though—she knows why people don't push back. “We're stressed, broke, exhausted,” she says. “Our nervous systems are fried 24/7—especially under this administration.” “It's hard to think critically when you're just trying to survive.” And when everyone around you uses AI, it starts to feel mandatory. “People tell me, ‘Yeah, I know it's a problem—but I have to. Otherwise I'll lose my job.'” “Or, ‘I'd have bought the wrong house if I didn't use it.'” That “I have to” mindset, she says, is what scares her most. Robin relates with his own example. “That's how I felt with TikTok,” he says. He got hooked early on, staying up until 3 a.m. scrolling. After a few weeks, he deleted the app and never went back. “I probably lose some business by not being there,” he admits. “But I'd rather protect my focus and my sanity.” He admits he couldn't find a way to stay on the platform without it consuming him. “I wasn't able to build a system that removed me from that platform while still using that platform.” But he feels differently about other tools. For example, LinkedIn has been essential—especially for communicating with Responsive Conference attendees. “It was our primary method of communication for 2025,” he says. So he tries to choose “the lesser of two evils.” “TikTok's bad for my brain,” he says. “I'm not using it.” “But with LLMs, it's different.” When researching houses, he didn't feel forced into using them to “keep up.” To him, they're just another resource. “If encyclopedias are available, use them. If Wikipedia's available, use both. And if LLMs can help, use all three.” 41:45 The Pressure to Conform to Technology Miki challenges that logic. “When was the last time you opened an encyclopedia?” Robin pauses. “Seven years ago.” Miki laughs. “Exactly. It's a nice idea that we'll use all the tools—but humans don't actually do that.” We gravitate toward what's easiest. “If you check eBay, there are hundreds of encyclopedia sets for sale,” she says. “No one's using them.” Robin agrees but takes the idea in a new direction. “Sure—but just because something's easy doesn't mean it's good,” he says. He compares it to food: “It's easier to eat at McDonald's than cook at home,” he says. But easy choices often lead to long-term problems. He mentions obesity in the U.S. as a cautionary parallel. Some things are valuable because they're hard. “Getting in my cold plunge every morning isn't easy,” he says. “That's why I do it.” “Exercise never gets easy either—but that's the point.” He adds a personal note: “I grew up in the mountains. I love being at elevation, off-grid, away from electricity.” He could bring Starlink when he travels, but he chooses not to. Still, he's not trying to live as a total hermit. “I don't want to live 12 months a year at 10,000 feet with a wood stove and no one around.” “There's a balance.” Miki nods, “I think this is where we need to start separating what we can handle versus what kids can.” “We're privileged adults with fully formed brains,” she points out. “But it's different for children growing up inside this system.” Robin agrees and shifts the focus. Even though you don't give advice professionally,” he says, “I'll ask you to give it personally.” “You're raising kids in what might be the hardest time we've ever seen. What are you actually practicing at home?” 45:30 Raising Children in a Tech-Driven World Robin reflects on how education has shifted since their grandparents' time Mentions “Alpha Schools” — where AI helps kids learn basic skills fast (reading, writing, math) Human coaches spend the rest of the time building life skills Says this model makes sense: Memorizing times tables isn't useful anymore He only learned to love math because his dad taught him algebra personally — acted like a coach Asks Miki what she thinks about AI and kids — and what advice she'd give him as a future parent Miki's first response — humility and boundaries “First off, I never want to give parents advice.” Everyone's doing their best with limited info and energy Her kids are still young — not yet at the “phone or social media” stage So she doesn't pretend to have all the answers Her personal wish vs. what's realistic Ideal world: She wishes there were a global law banning kids from using AI or social media until age 18 Thinks it would genuinely be better for humanity References The Anxious Generation Says there's growing causal evidence, not just correlation, linking social media to mental health issues Mentions its impact on children's nervous systems and worldview It wires them for defense rather than discovery Real world: One parent can't fight this alone — it's a collective action problem You need communities of parents who agree on shared rules Example: schools that commit to being zero-technology zones Parents and kids agree on: What ages tech is allowed Time limits Common standards Practical ideas they're exploring Families turning back to landlines Miki says they got one recently Not an actual landline — they use a SIM adapter and an old rotary phone Kids use it to call grandparents Her partner Jackson is working on a bigger vision: Building a city around a school Goal: design entire communities that share thoughtful tech boundaries Robin relates it to his own childhood Points out the same collective issue — “my nephews are preteens” It's one thing for parents to limit screen time But if every other kid has access, that limit won't hold Shares his own experience: No TV or video games growing up So he just went to neighbors' houses to play — human nature finds a way Says individual family decisions don't solve the broader problem Miki agrees — and expands the concern Says the real issue is what kids aren't learning Their generation had “practice time” in real-world social interactions Learned what jokes land and which ones hurt Learned how to disagree, apologize, or flirt respectfully Learned by trial and error — through millions of small moments With social media and AI replacing those interactions: Kids lose those chances entirely Results she's seeing: More kids isolating themselves Many afraid to take social or emotional risks Fewer kids dating or engaging in real-life relationships Analogy — why AI can stunt development “Using AI to write essays,” she says, “is like taking a forklift to the gym.” Sure, you lift more weight — but you're not getting stronger Warns this is already visible in workplaces: Companies laying off junior engineers AI handles the entry-level work But in 5 years, there'll be no trained juniors left to replace seniors Concludes that where AI goes next “is anybody's guess” — but it must be used with intention 54:12 Where to Find Miki Invites others to connect Mentions her website: leadingbyexample.life Visitors can book 30-minute conversations directly on her calendar Says she's genuinely open to discussing this topic with anyone interested  

This is a free preview of a paid episode. To hear more, visit wisdomofcrowds.live“This is one of the greatest historical eras mankind will ever see.”So says Tyler Cowen, economics professor at George Mason University, renowned author and chairman at the Mercatus Center, a think tank. He is also a writer, and famous podcaster whose books, like The Great Stagnation and Average is Over, which for over a decade have helped readers under…

Tyler Cowen returns to continue his conversation in Part Two. Tyler Cowen is a leading economist, author, and professor, currently holding the Holbert L. Harris Chair of Economics at George Mason University, where he also serves as chairman and faculty director of the Mercatus Center. Widely recognized for his influential economic ideas, Cowen co-authors the long-running blog Marginal Revolution with Alex Tabarrok, and together they have also created Marginal Revolution University, which offers accessible, world-class economics education online. Cowen has also authored several books, including "The Great Stagnation," which analyzes the slowdown in economic growth, and "Average Is Over," exploring the future of work and inequality. ------ Thank you to the sponsors that fuel our podcast and our team: LMNT Electrolytes https://drinklmnt.com/tetra Use code 'TETRA' ------ Athletic Nicotine https://www.athleticnicotine.com/tetra Use code 'TETRA' ------ Squarespace https://squarespace.com/tetra Use code 'TETRA' ------ Sign up to receive Tetragrammaton Transmissions https://www.tetragrammaton.com/join-newsletter

Tyler Cowen is a leading economist, author, and professor, currently holding the Holbert L. Harris Chair of Economics at George Mason University, where he also serves as chairman and faculty director of the Mercatus Center. Widely recognized for his influential economic ideas, Cowen co-authors the long-running blog Marginal Revolution with Alex Tabarrok, and together they have also created Marginal Revolution University, which offers accessible, world-class economics education online. Cowen has also authored several books, including "The Great Stagnation," which analyzes the slowdown in economic growth, and "Average Is Over," exploring the future of work and inequality. ------ Thank you to the sponsors that fuel our podcast and our team: Squarespace https://squarespace.com/tetra Use code 'TETRA' ------ LMNT Electrolytes https://drinklmnt.com/tetra Use code 'TETRA' ------ Athletic Nicotine https://www.athleticnicotine.com/tetra Use code 'TETRA' ------ Sign up to receive Tetragrammaton Transmissions https://www.tetragrammaton.com/join-newsletter

Go to https://surfshark.com/robinsonerhardt and use code robinsonerhardt at checkout to get 4 extra months of Surfshark VPN!Tyler Cowen is the Holbert L. Harris Chair of Economics at George Mason University and serves as chairman and faculty director of the Mercatus Center at George Mason University. A dedicated writer and communicator of economic ideas, Tyler is the author of several bestselling books and is widely published in academic journals and the popular media. In this episode, Robinson and Tyler discuss the economics of artificial intelligence. More particularly, they touch on whether AI will destroy humanity, how it will affect employment, whether there will no longer be a place for art in the marketplace, and more. Tyler's latest book is Talent: How to Identify Energizers, Creatives, and Winners Around the World (St. Martin's Press, 2022).Marginal Revolution: https://marginalrevolution.comTyler's X: https://x.com/tylercowenTalent: https://a.co/d/ftqNWcnOUTLINE00:00:00 Introduction00:01:09 Why Won't AI Destroy Humanity?00:06:39 Will AI Be Good or Bad for Employment?00:08:20 On Optimism00:10:10 It Isn't Inevitable that AI Will Wipe Out Human Life00:19:03 How to Align AI with Human Interests00:24:40 Reid's Interest in Friendship00:32:13 Why AI Can't be Our Friends00:36:33 Could AI Replace Therapists?00:45:18 Using AI to Cure Cancer00:52:04 Will AI Extinguish Humanity with a Virus?01:00:02 How Will AI Make Us More Powerful Agents?01:07:06 Will Academia Be Revolutionized by AI?01:15:10 Are You an AI Native?01:17:36 How to Invest in AIRobinson's Website: http://robinsonerhardt.comRobinson Erhardt researches symbolic logic and the foundations of mathematics at Stanford University, where he is also a JD candidate in the Law School.

Séamus joins us to talk about Trump's proposed “Gaza peace plan” and what horrific policies it would actually entail in practice, as well as the Democratic Party's desperate attempts to triangulate on the issue. We also wade into the increasing possibility of regime change in Venezuela as well as ICE's pillaging of Chicago apartment buildings. On the lighter side, we talk about Bari Weiss being given the keys to CBS news and Tyler Cowen's Humbert Humbert-esque ode to an AI actress. Follow @Turbulence_pod on X for updates about when Séamus's pod drops. NEW MERCH IS OUT NOW! Go to https://chapotraphouse.store/ and buy a new hat or shirt, especially our great new “Carousel Club” design. AND be sure to pre-save the date of October 28 for Will and Hesse's LIVE WATCH PARTY of Re-Animator! Tickets available now – use the promo code CHAPO20 for 20% off! https://checkout.stagepilot.com/collections/chapo-trap-house

Join New York Times columnist David Brooks with renowned economist Tyler Cowen for a conversation about technology, morality, and finding humility in today's fractious political culture — in a live taping of Cowen's hit podcast Conversations with Tyler. David Brooks' explorations of morality in contemporary politics and culture — the cultivation of spiritual and intellectual rigor through compromise and humility — have made him an uncommonly steady voice in an unsteady time. Critiquing the excesses of the right and the left in his bestselling books and New York Times columns, Brooks examines how class, education, and consumer culture have shaped our identities. He is exactly the kind of thinker who Tyler Cowen loves to talk with on Conversations with Tyler — Cowen's hit podcast offering wide-ranging examinations of work, the world, and everything in between: a platform for genuine intellectual curiosity. Returning to 92NY's stage after his sold-out conversation kicking off The Dialogue Project, hear Cowen talk to Brooks about what has shaped their intellectual lives. Take an unscripted tour of Brooks's early Chicago crime-reporting days, how he would redesign his famed Yale “Humility” syllabus for a TikTok-native generation, the evolution of his religious worldview, his latest ideas on “moral capital,” and much more.

In this conversation from 2024, Matt speaks with Tyler Cowen about his recent book "GOAT: Who is the Greatest Economist of all Time and Why Does it Matter?", as they discuss the case for and against each of the top finalists, and the interactive AI features that Tyler has integrated into the book's online release. Episode Notes: The full book plus all interactive AI features can be found for free here: https://goatgreatesteconomistofalltime.ai/en   

Yascha Mounk and Tyler Cowen also discuss AI and the state of the world economy. Tyler Cowen is an American economist, columnist, and blogger. Cowen is the Holbert L. Harris chair in economics at George Mason University, and is the co-author, with Alex Tabarrok, of the blog Marginal Revolution. In this week's conversation, Yascha Mounk and Tyler Cowen discuss the likely economic futures of Europe, Asia, and Africa; how the United States should approach competition with China; and what role young people should ascribe to personal financial advancement in their career choices. ⁠ This transcript has been condensed and lightly edited for clar⁠⁠i⁠⁠t⁠⁠y⁠⁠.⁠ Please do listen and spread the word about The Good Fight. If you have not yet signed up for our podcast, please do so now by following ⁠this link on your phone⁠. Email: podcast@persuasion.community  Website: ⁠http://www.persuasion.community⁠ Podcast production by ⁠Jack Shields⁠, and Brendan Ruberry Connect with us! ⁠Spotify⁠ | ⁠Apple⁠ | ⁠Google⁠ Twitter: ⁠@Yascha_Mounk⁠ & ⁠@joinpersuasion⁠ Youtube: ⁠Yascha Mounk⁠ LinkedIn: ⁠Persuasion Community⁠ Learn more about your ad choices. Visit ⁠megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices

Just two weeks ago, New Yorkers voted en masse for a self-proclaimed socialist—someone who once called for “seizing the means of production.” This is, of course, Zohran Mamdani, who dominated in the Democratic primary for New York City mayor with a definitive victory over Andrew Cuomo. He has called for rent freezes, free buses, and even government-run grocery stores. He won 56 percent of the vote in a campaign fueled by young, highly educated, wealthy people—many of whom believe in reviving socialism here in America, in 2025. According to a Cato Institute poll from May: 62 percent of Americans age 18 to 29 say they hold a “favorable view” of socialism. And 34 percent had a positive view of communism. Polls by Emerson and Marist from May and June had shown Mamdani leading with voters under 45 by as much as a 2:1 ratio against the former governor. This phenomenon has left many people wondering: Why are so many young people embracing a failed economic system? Is it their university education? Is it the influence of social media? Is it just “cool”? Is it a desperate call for anything to fix wealth inequality? Or is it something else? Here to help us understand are Tyler Cowen and Kyla Scanlon. Tyler Cowen is an economist and Free Press columnist who just wrote an important essay for us called “Why Won't Socialism Die?” Kyla Scanlon is a writer, economic commentator, and educator—and, importantly for this conversation, a member of Gen Z. She is 28, and her new book is In This Economy? How Money & Markets Really Work. This conversation was originally a Free Press livestream—and you'll hear throughout this conversation that I take lots of questions from people who joined us live. To make sure that you never miss one of these in the future, you can become a paid subscriber today. Go to groundnews.com/Honestly to get 40% off the unlimited access Vantage plan and unlock world-wide perspectives on today's biggest news stories. Learn more about your ad choices. Visit megaphone.fm/adchoices

Austan Goolsbee is one of Tyler Cowen's favorite economists—not because they always agree, but because Goolsbee embodies what it means to think like an economist. Whether he's analyzing productivity slowdowns in the construction sector, exploring the impact of taxes on digital commerce, or poking holes in overconfident macro narratives, Goolsbee is consistently sharp, skeptical, and curious. A longtime professor at the University of Chicago's Booth School and former chair of the Council of Economic Advisers under President Obama, Goolsbee now brings that intellectual discipline—and a healthy dose of humor—to his role as president of the Federal Reserve Bank of Chicago. Tyler and Austan explore what theoretical frameworks Goolsbee uses for understanding inflation, why he's skeptical of monetary policy rules, whether post-pandemic inflation was mostly from the demand or supply side, the proliferation of stablecoins and shadow banking, housing prices and construction productivity, how microeconomic principles apply to managing a regional Fed bank, whether the structure of the Federal Reserve system should change, AI's role in banking supervision and economic forecasting, stablecoins and CBDCs, AI's productivity potential over the coming decades, his secret to beating Ted Cruz in college debates, and more. Read a full transcript enhanced with helpful links, or watch the full video on the new dedicated Conversations with Tyler channel. Recorded March 3rd, 2025. Help keep the show ad free by donating today! Other ways to connect Follow us on X and Instagram Follow Tyler on X Follow Austan on X Sign up for our newsletter Join our Discord Email us: cowenconvos@mercatus.gmu.edu Learn more about Conversations with Tyler and other Mercatus Center podcasts here.

In this episode, general partner Chris Dixon joins economist and author Tyler Cowen to explore the themes behind Chris's book, Read, Write, Own: Building the Next Era of the Internet.They trace the internet's evolution from open, decentralized beginnings to today's consolidated platforms—and ask: how can we build something better? From stablecoins, tokenized payments, and open blockchains to AI's impact on coding, media, and politics, this wide-ranging conversation dives deep into how technologies like crypto and AI could help redistribute power online and reshape the future of ownership and innovation.The two also debate:Whether banks and legacy institutions will adopt stablecoinsThe long-term role of NFTs and digital property rightsHow AI might rewrite venture capital, education, and economic planningWhether we're heading toward a creative renaissance—or a world of AI-generated monocultureListen to similar conversations, listen to web3 with a16z: https://web3-with-a16z.simplecast.com/ Resources: Listen to Conversations with Tyler: https://conversationswithtyler.com/Find Chris on X: https://x.com/cdixonFind Tyler on X: https://x.com/tylercowenJoin a16z's Crypto Substack:https://a16zcrypto.substack.com/ Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

Tyler Cowen of Marginal Revolution continues to disagree with my Contra MR On Charity Regrants. Going through his response piece by piece, slightly out of order: Scott takes me to be endorsing Rubio's claim that the third-party NGOs simply pocket the money. In reality my fact check with o3 found (correctly) that the money was “channelled through” the NGOs, not pocketed. Scott lumps my claim together with Rubio's as if we were saying the same thing. My very next words (“I do understand that not all third party allocations are wasteful…”) show a clear understanding that the money is channeled, not pocketed, and my earlier and longer post on US AID makes that clearer yet at greater length. Scott is simply misrepresenting me here. The full post is in the image below: https://www.astralcodexten.com/p/sorry-i-still-think-mr-is-wrong-about

The race to develop ever-more-powerful AI is creating an unstable dynamic. It could lead us toward either dystopian centralized control or uncontrollable chaos. But there's a third option: a narrow path where technological power is matched with responsibility at every step.Sam Hammond is the chief economist at the Foundation for American Innovation. He brings a different perspective to this challenge than we do at CHT. Though he approaches AI from an innovation-first standpoint, we share a common mission on the biggest challenge facing humanity: finding and navigating this narrow path.This episode dives deep into the challenges ahead: How will AI reshape our institutions? Is complete surveillance inevitable, or can we build guardrails around it? Can our 19th-century government structures adapt fast enough, or will they be replaced by a faster moving private sector? And perhaps most importantly: how do we solve the coordination problems that could determine whether we build AI as a tool to empower humanity or as a superintelligence that we can't control?We're in the final window of choice before AI becomes fully entangled with our economy and society. This conversation explores how we might still get this right.Your Undivided Attention is produced by the Center for Humane Technology. Follow us on X: @HumaneTech_. You can find a full transcript, key takeaways, and much more on our Substack.RECOMMENDED MEDIA Tristan's TED talk on the Narrow PathSam's 95 Theses on AISam's proposal for a Manhattan Project for AI SafetySam's series on AI and LeviathanThe Narrow Corridor: States, Societies, and the Fate of Liberty by Daron Acemoglu and James RobinsonDario Amodei's Machines of Loving Grace essay.Bourgeois Dignity: Why Economics Can't Explain the Modern World by Deirdre McCloskeyThe Paradox of Libertarianism by Tyler CowenDwarkesh Patel's interview with Kevin Roberts at the FAI's annual conferenceFurther reading on surveillance with 6GRECOMMENDED YUA EPISODESAGI Beyond the Buzz: What Is It, and Are We Ready?The Self-Preserving Machine: Why AI Learns to Deceive The Tech-God Complex: Why We Need to be Skeptics Decoding Our DNA: How AI Supercharges Medical Breakthroughs and Biological Threats with Kevin EsveltCORRECTIONSSam referenced a blog post titled “The Libertarian Paradox” by Tyler Cowen. The actual title is the “Paradox of Libertarianism.” Sam also referenced a blog post titled “The Collapse of Complex Societies” by Eli Dourado. The actual title is “A beginner's guide to sociopolitical collapse.”

Economist and polymath Tyler Cowen challenges Silicon Valley's optimistic projections about AI-driven economic growth. We explore what could slow AI's economic impact, despite its remarkable capabilities – and where humans find the new normal amidst major shifts.Timestamps: (00:00) Episode trailer (01:47)  The problem with Silicon Valley's AI-driven growth projections (06:02) The institutional bottleneck to AI progress (10:49) Markets aren't pricing in a radical AI future (12:53) Are we heading for a great job displacement? (17:02) Is GDP still worth talking about? (19:11) Who does AI benefit most? (21:11) Will AI cause a human identity crisis? (27:11) The education system's failure to adapt (35:34) How the Gulf could become a geopolitical powerhouse (39:10)  Could AI change religion? (46:46)  Closing thoughts Tyler's links: Marginal Revolution Blog: https://marginalrevolution.com/ Twitter/X: https://x.com/tylercowen Azeem's links: Substack: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar Twitter/X: https://x.com/azeemOur new showThis was originally recorded for "Friday with Azeem Azhar", a new show that takes place every Friday at 9am PT and 12pm ET. You can tune in through Exponential View on Substack.Produced by supermix.io and EPIIPLUS1 LTD

Jason, Rob, and Asher are taking out a huge, unaffordable mortgage on the housing crisis. What's behind the shortage in housing? Why is it that no one, except canine Tik Tok influencers with billion-dollar bank accounts, can afford to own a home? While mainstream pundits press for an energy-blind buildout of desert sprawl and gleaming towers of glass and steel, we propose a surprising change of course inspired by little people with hairy feet. Originally recorded on 5/21/25.Warning: This podcast occasionally uses spicy language.Sources/Links/Notes:The story of Gunther, the world's most moneyed canine.You can't make this stuff up: Gunther offers to buy Nicholas Cage's island.David Wessel, "Where do the estimates of a 'housing shortage' come from?," Brookings Institute, October 21, 2024.Alex Fitzpatrick and Alice Feng, "Americans' average daily travel distance, mapped," Axios, March 24, 2024.Jon Gertner, "America Is on Fire, Says One Climate Writer. Should You Flee?," New York Times, March 22, 2024.U.S. News and World Report, "Fastest-Growing Places in the U.S. in 2025-2026."Good Ideas for Addressing the Housing Crisis:Jason Bradford, "Growing the Shire, Not the 'Burb: Facing the Housing Crisis with Ecological Sanity," Resilience, May 27, 2025.Global Ecovillage NetworkNate Hagens, "Alexis Zeigler —  Living Without Fossil Fuels: How Living Energy Farm Created a Comfortable Off-Grid Lifestyle," The Great Simplification, April 9, 2025.Energy-Blind Non-Solutions for the Housing Crisis:Conor Dougherty, "Why America Should Sprawl," New York Times, April 10, 2025.Binyamin Applebaum, "Build Homes on Federal Land," New York Times, April 15, 2025.Ezra Klein, "Abundance and the Left," The Ezra Klein Show, April 29, 2025.Samuel Moyn, "Can Democrats Learn to Dream Big Again?," New York Times, March 18, 2025.Tyler Cowen, "Ezra Klein on the Abundance Agenda (Ep. 236)" Conversations with Tyler, March 7, 2025.Related Episode(s) of Crazy Town:Episode 37. Discounting the Future and Climate Chaos, or… the Story of the Dueling EconomistsSupport the show

This week, Noah Smith and Erik Torenberg explore persistent economic myths and recent developments—from cost disease in services like healthcare and education to stagnating manufacturing productivity, rising higher education costs, drug pricing policies, and student loan debates—while also reflecting on broader intellectual shifts driven by culture wars and foreign aid discussions. – SPONSORS: NetSuite More than 41,000 businesses have already upgraded to NetSuite by Oracle, the #1 cloud financial system bringing accounting, financial management, inventory, HR, into ONE proven platform. Download the CFO's Guide to AI and Machine learning: ⁠⁠⁠⁠⁠https://netsuite.com/102⁠⁠⁠⁠⁠ AdQuick The easiest way to book out-of-home ads (like billboards, vehicle wraps, and airport displays) the same way you would order an Uber. Ready to get your brand the attention it deserves? Visit ⁠⁠⁠⁠⁠https://adquick.com/⁠⁠⁠⁠⁠ today to start reaching your customers in the real world. – SEND US YOUR Q's FOR NOAH TO ANSWER ON AIR: Econ102@Turpentine.co – FOLLOW ON X: @noahpinion @eriktorenberg @turpentinemedia – RECOMMENDED IN THIS EPISODE: Noahpinion: ⁠⁠⁠⁠⁠https://www.noahpinion.blog/⁠⁠⁠⁠⁠  – TAKEAWAYS: Healthcare & Education Cost Trends Reversing: Healthcare price growth has slowed significantly since 2009 and is now growing slower than average costs. Technology's Role in Services: AI potentially solving education through personalized one-on-one tutoring (referencing "The Diamond Age"). Student Loans & Market Dynamics: Marginal students are dropping out of college, reducing demand. Pharmaceutical Pricing: Americans actually pay less on average for pharmaceuticals due to cheaper generics. Cultural Commentary: Discussing intellectual debates between prominent thinkers (Tyler Cowen vs. Scott Alexander on foreign aid, Scott Alexander vs. Curtis Yarvin on governance), emphasizing the importance of not getting trapped in the cultural moment of 2020-2021.

Mississippi is richer than France. No, really. The poorest U.S. state now has a higher GDP per person than France, the U.K., Italy, and Spain. How did that happen? Don't miss this eye-opening episode with George Mason University's Tyler Cowen. 

Nabeel Qureshi is an entrepreneur, writer, researcher, and visiting scholar of AI policy at the Mercatus Center (alongside Tyler Cowen). Previously, he spent nearly eight years at Palantir, working as a forward-deployed engineer. His work at Palantir ranged from accelerating the Covid-19 response to applying AI to drug discovery to optimizing aircraft manufacturing at Airbus. Nabeel was also a founding employee and VP of business development at GoCardless, a leading European fintech unicorn.What you'll learn:• Why almost a third of all Palantir's PMs go on to start companies• How the “forward-deployed engineer” model works and why it creates exceptional product leaders• How Palantir transformed from a “sparkling Accenture” into a $200 billion data/software platform company with more than 80% margins• The unconventional hiring approach that screens for independent-minded, intellectually curious, and highly competitive people• Why the company intentionally avoids traditional titles and career ladders—and what they do instead• Why they built an ontology-first data platform that LLMs love• How Palantir's controversial “bat signal” recruiting strategy filtered for specific talent types• The moral case for working at a company like Palantir—Brought to you by:• WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs• Attio—The powerful, flexible CRM for fast-growing startups• OneSchema—Import CSV data 10x faster—Where to find Nabeel S. Qureshi:• X: https://x.com/nabeelqu• LinkedIn: https://www.linkedin.com/in/nabeelqu/• Website: https://nabeelqu.co/—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 Nabeel S. Qureshi(05:10) Palantir's unique culture and hiring(13:29) What Palantir looks for in people(16:14) Why they don't have titles(19:11) Forward-deployed engineers at Palantir(25:23) Key principles of Palantir's success(30:00) Gotham and Foundry(36:58) The ontology concept(38:02) Life as a forward-deployed engineer(41:36) Balancing custom solutions and product vision(46:36) Advice on how to implement forward-deployed engineers(50:41) The current state of forward-deployed engineers at Palantir(53:15) The power of ingesting, cleaning and analyzing data(59:25) Hiring for mission-driven startups(01:05:30) What makes Palantir PMs different(01:10:00) The moral question of Palantir(01:16:03) Advice for new startups(01:21:12) AI corner(01:24:00) Contrarian corner(01:25:42) Lightning round and final thoughts—Referenced:• Reflections on Palantir: https://nabeelqu.co/reflections-on-palantir• Palantir: https://www.palantir.com/• Intercom: https://www.intercom.com/• Which companies produce the best product managers: https://www.lennysnewsletter.com/p/which-companies-produce-the-best• Gotham: https://www.palantir.com/platforms/gotham/• Foundry: https://www.palantir.com/platforms/foundry/• Peter Thiel on X: https://x.com/peterthiel• Alex Karp: https://en.wikipedia.org/wiki/Alex_Karp• Stephen Cohen: https://en.wikipedia.org/wiki/Stephen_Cohen_(entrepreneur)• Joe Lonsdale on LinkedIn: https://www.linkedin.com/in/jtlonsdale/• Tyler Cowen's website: https://tylercowen.com/• This Scandinavian City Just Won the Internet With Its Hilarious New Tourism Ad: https://www.afar.com/magazine/oslos-new-tourism-ad-becomes-viral-hit• Safe Superintelligence: https://ssi.inc/• Mira Murati on X: https://x.com/miramurati• Stripe: https://stripe.com/• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein• Airbus: https://www.airbus.com/en• NIH: https://www.nih.gov/• Jupyter Notebooks: https://jupyter.org/• Shyam Sankar on LinkedIn: https://www.linkedin.com/in/shyamsankar/• Palantir Gotham for Defense Decision Making: https://www.youtube.com/watch?v=rxKghrZU5w8• Foundry 2022 Operating System Demo: https://www.youtube.com/watch?v=uF-GSj-Exms• SQL: https://en.wikipedia.org/wiki/SQL• Airbus A350: https://en.wikipedia.org/wiki/Airbus_A350• SAP: https://www.sap.com/index.html• Barry McCardel on LinkedIn: https://www.linkedin.com/in/barrymccardel/• Understanding ‘Forward Deployed Engineering' and Why Your Company Probably Shouldn't Do It: https://www.barry.ooo/posts/fde-culture• David Hsu on LinkedIn: https://www.linkedin.com/in/dvdhsu/• Retool's Path to Product-Market Fit—Lessons for Getting to 100 Happy Customers, Faster: https://review.firstround.com/retools-path-to-product-market-fit-lessons-for-getting-to-100-happy-customers-faster/• How to foster innovation and big thinking | Eeke de Milliano (Retool, Stripe): https://www.lennysnewsletter.com/p/how-to-foster-innovation-and-big• Looker: https://cloud.google.com/looker• Sorry, that isn't an FDE: https://tedmabrey.substack.com/p/sorry-that-isnt-an-fde• Glean: https://www.glean.com/• Limited Engagement: Is Tech Becoming More Diverse?: https://www.bkmag.com/2017/01/31/limited-engagement-creating-diversity-in-the-tech-industry/• Operation Warp Speed: https://en.wikipedia.org/wiki/Operation_Warp_Speed• Mark Zuckerberg testifies: https://www.businessinsider.com/facebook-ceo-mark-zuckerberg-testifies-congress-libra-cryptocurrency-2019-10• Anduril: https://www.anduril.com/• SpaceX: https://www.spacex.com/• Principles: https://nabeelqu.co/principles• Wispr Flow: https://wisprflow.ai/• Claude code: https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview• Gemini Pro 2.5: https://deepmind.google/technologies/gemini/pro/• DeepMind: https://deepmind.google/• Latent Space newsletter: https://www.latent.space/• Swyx on x: https://x.com/swyx• Neural networks in chess programs: https://www.chessprogramming.org/Neural_Networks• AlphaZero: https://en.wikipedia.org/wiki/AlphaZero• The top chess players in the world: https://www.chess.com/players• Decision to Leave: https://www.imdb.com/title/tt12477480/• Oldboy: https://www.imdb.com/title/tt0364569/• Christopher Alexander: https://en.wikipedia.org/wiki/Christopher_Alexander—Recommended books:• The Technological Republic: Hard Power, Soft Belief, and the Future of the West: https://www.amazon.com/Technological-Republic-Power-Belief-Future/dp/0593798694• Zero to One: Notes on Startups, or How to Build the Future: https://www.amazon.com/Zero-One-Notes-Startups-Future/dp/0804139296• Impro: Improvisation and the Theatre: https://www.amazon.com/Impro-Improvisation-Theatre-Keith-Johnstone/dp/0878301178/• William Shakespeare: Histories: https://www.amazon.com/Histories-Everymans-Library-William-Shakespeare/dp/0679433120/• High Output Management: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884• Anna Karenina: https://www.amazon.com/Anna-Karenina-Leo-Tolstoy/dp/0143035002—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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe

Tyler Cowen recently wrote an article arguing that spending lots of time online is in fact a good thing. In this episode, Cal looks deeper at Cowen's argument and finds some surprising common ground. The internet can be a major source of good in your life, he argues, but only if you use it in the right way. He then answers listener questions and reviews the books he read in April.Find out more about Done Daily at DoneDaily.com!Below are the questions covered in today's episode (with their timestamps). Get your questions answered by Cal! Here's the link: bit.ly/3U3sTvoVideo from today's episode: youtube.com/calnewportmediaDeep Dive: Making the Internet Good Again [5:06]What are good activities for “deep breaks”? [28:38]How can I approach parenting without resenting the sacrifices to deep work? [31:36]How does the deep life compare to David Epstein's book, “Range”? [38:06]What is the difference between a “winner-take-all” field of work and “auction” field of work? [41:12]Does “following your passion” have any connection to “lifestyle centric planning”? [47:39]CASE STUDY: Implementing the concept of “Eat The Frog” [52:48]CALL: Introducing seasonality and the meetings being the work [55:07]APRIL BOOKS: The 5 books Cal read in April, 2025 [1:06:08]I, Robot (Isaac Asimov)After Disney (Neil O'brien)The Baseball Book of Why (John McCollister)The Technology Republic (Alexander Karp and Nicholas Zamiska)Everything is Tuberculosis (John Green)Links:Buy Cal's latest book, “Slow Productivity” at calnewport.com/slowGet a signed copy of Cal's “Slow Productivity” at peoplesbooktakoma.com/event/cal-newportCal's monthly book directory: bramses.notion.site/059db2641def4a88988b4d2cee4657ba?thefp.com/p/the-case-for-living-onlineThanks to our Sponsors:shopify.com/deepauraframes.com [Use promo code “DEEPQUESTIONS”]indeed.com/deepharrys.com/deepThanks to Jesse Miller for production, Jay Kerstens for the intro music, Kieron Rees for the slow productivity music, and Mark Miles for mastering.

Two weeks ago, America thought it was leading the AI race. Then out of nowhere, an unknown Chinese start-up turned the American stock market—and that assumption—on its head. DeepSeek, a Chinese company founded less than two years ago, released a free AI chatbot that rivals the most advanced available open AI products. And they did it despite America's prohibition on shipping our most advanced microchips to China.  America was caught flat-footed, asking how did this happen? And could we actually lose this tech war?  Now, if your understanding of computers stops at the term hard drive, don't worry. Today on Honestly, Bari has two incredible guests, experts on both AI and China, who are going to break it all down for you. Tyler Cowen is an economics professor, AI expert, and a must-read writer at his blog, Marginal Revolution. He is joined today by Geoffrey Cane, an expert on China and the author of The Perfect Police State: An Undercover Odyssey Into China's Terrifying Surveillance Dystopia of the Future.  Today, how this happened and what it means. And can America win the AI war with China? Header 6: The Free Press earns a commission from any purchases made through all book links in this article. If you liked what you heard from Honestly, the best way to support us is to go to TheFP.com and become a Free Press subscriber today. Learn more about your ad choices. Visit megaphone.fm/adchoices