Podcast appearances and mentions of deirdre mccloskey

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

Will you be in Washington, D.C. on Wednesday July 15? I will be interviewing Francis Fukuyama about how liberalism should respond to the postliberal threat. Find out more and get your free ticket here! —Yascha Yascha Mounk and Deirdre McCloskey discuss why ideas, not capital accumulation, made the modern world rich. Deirdre Nansen McCloskey, sometimes described as “the conscience of economics,” holds the Isaiah Berlin Chair in Liberal Thought at the Cato Institute in Washington, D.C. In this week's conversation, Yascha Mounk and Deirdre McCloskey discuss why liberalism drives economic growth, how the gradual erosion of inherited hierarchy unleashed centuries of innovation, and what liberals should think about the trans debate. We're delighted to feature this conversation as part of our series on Liberal Virtues and Values. That liberalism is under threat is now a cliché—yet this has done nothing to stem the global resurgence of illiberalism. Part of the problem is that liberalism is often considered too “thin” to win over the allegiance of citizens, and that liberals are too afraid of speaking in moral terms. Liberalism's opponents, by contrast, speak to people's passions and deepest moral sentiments. This series, made possible with the generous support of the John Templeton Foundation, aims to change that narrative. In podcast conversations and long-form pieces, we feature content making the case that liberalism has its own distinctive set of virtues and values that are capable not only of responding to the dissatisfaction that drives authoritarianism, but also of restoring faith in liberalism as an ideology worth believing in—and defending—on its own terms. If you have not yet signed up for our podcast, please do so now by following ⁠this link on your phone⁠. Email: leonora.barclay@persuasion.community Podcast production by Jack Shields and Leonora Barclay. Connect with us! ⁠Spotify⁠ | ⁠Apple⁠ X: ⁠@Yascha_Mounk⁠ & ⁠@JoinPersuasion⁠ YouTube: ⁠Yascha Mounk⁠, ⁠Persuasion⁠ LinkedIn: ⁠Persuasion Community Learn more about your ad choices. Visit megaphone.fm/adchoices

Deirdre McCloskey, premiada por el Juan de Mariana, sostiene que el despegue de Europa se explica por el triunfo de las ideas de libertad. ¿Por qué Europa? Es la gran pregunta de la historia económica. En el año 1000, China y el mundo árabe aventajaban ampliamente a un continente fragmentado y relativamente atrasado. Sin embargo, fue Europa la que en los siglos siguientes protagonizó el mayor salto en prosperidad que ha conocido la humanidad. Esta semana, en Economía para Quedarte sin Amigos, nos adentramos en la obra de Deirdre McCloskey, ganadora del Premio Juan de Mariana, para explorar su respuesta a esa pregunta junto con el subdirector del Juan de Mariana, Juan Navarrete, y el profesor de la Universidad Francisco Marroquín, Eduardo Fernández Luiña. La respuesta que da McCloskey no es la geografía, ni los recursos naturales, ni siquiera las instituciones: fueron las ideas.Música Esta semana, la protagonista de nuestra selección musical es el grupo español Nosoträsh. Y estos son los temas que hemos escuchado: "Dando Vueltas" "Voy a Aterrizar" "Completamente Sola" "Arte"

Welcome to our new series, The Hayekian Triangle. This series will feature a range of conversations between our hosts: Virgil Storr, Chris Coyne, and Peter Boettke. On this episode, the three sit down to mark the 250th anniversary of Adam Smith's The Wealth of Nations — and to ask a deceptively simple question: why are we still reading a book written a quarter-millennium ago?From the invisible hand to the division of labor, Smith's ideas have become so embedded in how we think about markets and society that it's easy to forget just how radical they originally were. Virgil, Chris, and Pete dig into what Smith actually said, why the standard takes on laissez-faire and self-interest so often miss the mark, and what a Scottish moral philosopher writing in 1776 still has to teach us about wealth, poverty, and the institutions that make human flourishing possible.Whether you're coming to Smith for the first time or returning to him with fresh eyes, this conversation is a reminder that the greatest works in political economy aren't monuments to be admired from a distance — they remain living inputs into the science of today.**This episode was recorded on April 3, 2026**Show Notes:Adam Smith, The Wealth of Nations (Liberty Fund, 1982)Adam Smith, The Theory of Moral Sentiments (Liberty Fund, 1982)Kenneth Boulding, "After Samuelson, Who Needs Adam Smith?" (History of Political Economy, 1971)Kenneth Boulding, "Economics as a Moral Science" (The American Economic Review, 1969)Daron Acemoglu and James Robinson, The Narrow Corridor: States, Societies, and the Fate of Liberty (Penguin Press, 2019)Raghuram Rajan, The Third Pillar: How Markets and the State Leave the Community Behind (Penguin Press, 2019)Deirdre McCloskey, The Bourgeois Virtues: Ethics for an Age of Commerce; Bourgeois Dignity: Why Economics Can't Explain the Modern World; Bourgeois Equality: How Ideas, Not Capital or Institutions, Enriched the World (University of Chicago Press, 2006, 2010, 2016)Martha Nussbaum, The Cosmopolitan Tradition: A Noble but Flawed Ideal (Belknap Press/Harvard University Press, 2019)Ludwig von Mises, “Why Read Adam Smith Today?” (FEE, 2015)Richard Ebeling, "Celebrating Adam Smith's Wealth of Nations at 250 Years" (Future of Freedom, 2026)If you like the show, please subscribe, leave a 5-star review, and tell others about the show! We're available on Apple Podcasts, Spotify, Amazon Music, and wherever you get your podcasts.Check out our other podcast from the Hayek Program! Virtual Sentiments is a podcast in which political theorist Kristen Collins interviews scholars and practitioners grappling with pressing problems in political economy with an eye to the past. Subscribe today!Follow the Hayek Program on Twitter: @HayekProgramFollow the Mercatus Center on Twitter: @mercatusCC Music: Twisterium

durée : 00:58:58 - Entendez-vous l'éco ? - par : Aliette Hovine - La loi de programmation militaire 2024-2030 doit doter le budget de la Défense de 36 milliards d'euros supplémentaires. Pourquoi ? La défense peut-elle réindustrialiser la France ? Après avoir répondu à ces questions, place au portrait d'une économiste iconoclaste, Deirdre McCloskey. - réalisation : Tina Iung, Sorj Leroy, Louise Morfouace - invités : Élie Tenenbaum Directeur du Centre des Études de Sécurité de l'IFRI, Fanny Coulomb Maîtresse de conférences HDR en économie à Sciences Po Grenoble., François Facchini Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

durée : 00:27:51 - Entendez-vous l'éco ? - par : Aliette Hovine - Voix singulière de la pensée libertarienne contemporaine, Deirdre McCloskey a développé une réflexion sur le langage de la science économique. En la définissant comme rhétorique, elle en interroge la scientificité. - réalisation : Tina Iung, Sorj Leroy - invités : François Facchini Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

La economía estudia la asignación de unos recursos escasos y en la resolución de este problema ético, la filosofía es de mayor utilidad que las matemáticas. María Blanco quiere saber cómo se ha estudiado la economía a lo largo de los siglos. Esta ciencia se matematizó en la segunda mitad del siglo XX por culpa de Paul Samuelson, quien representaría mediante complejas ecuaciones las funciones de la oferta y la demanda. No siempre fue este el método de estudio, los primeros economistas se asemejaban más a los filósofos. Antes de escribir La riqueza de las naciones, Adam Smith había publicado La teoría de los sentimientos morales, un tratado sobre la moral. Por suerte, todo río regresa a su cauce. La economía del siglo XXI será filosófica, no matemática.Kapital es posible gracias a sus colaboradores:⁠Thenomba⁠. La escuela que te hará encontrar tu propósito.Thenomba es la escuela que te prepara para encontrar un propósito, no un trabajo.Me han hecho embajador del máster y puedo ofrecerte un descuento especial en el precio. Si quieres matricularte, utiliza el código KAPITAL20 para llevarte una rebaja del 20%. 42 oyentes de este podcast ya utilizaron el código en la exitosa edición de diciembre. Si te preguntas si esto encaja contigo, te recomiendo simplemente escuchar los episodios de hace unas semanas con Higinio Marín y Ricardo Piñero. Higinio y Ricardo son dos de los profesores del máster y esas dos entrevistas reflejan la vocación humanista de su programa. Si resuenan en tu cabeza algunas de las ideas de esas conversaciones, entonces Thenomba es para ti.Patrocina Kapital. Toda la información en este link.Índice:0:32 Un empresario paga costes antes de conocer beneficios.8:18 Valor contraintuitivo de Mercadona.14:23 Compañía de las Indias Orientales.21:04 Todos somos empresarios.32:24 Economistas con vidas de película.39:55 Batalla de gallos entre Keynes y Hayek.46:46 Innecesaria matematización de la economía.49:50 Las movidas de Veblen.54:02 Académicos multidisciplinares.1:06:20 ¿Es un historiador optimista sobre el futuro?1:15:54 Escuela de Salamanca.1:24:04 Solo los judíos podían prestar con interés.1:29:43 Tigres, leones, todos quieren ser los campeones.1:38:26 Es más fácil contar pobres que contar ricos.Apuntes:La riqueza de las naciones. Adam Smith.La teoría de los sentimientos morales. Adam Smith.Risk, uncertainty and profit. Frank Knight.The capitalist and the entrepreneur. Peter Klein.Start-up nation. Dan Senor & Saul Singer.Tratado de economia politica. Jean Baptiste Say.La acción humana. Ludwig von Mises.Teoría general de la ocupación, el interés y el dinero. John Maynard Keynes.Principios de economía política y tributación. David Ricardo.Hacia la estación de Finlandia. Edmund Wilson.Teoría de la clase ociosa. Thorstein Veblen.Economical writing. Deirdre McCloskey.Armonías económicas. Frédéric Bastiat.Bastiat as an economist. María Blanco & Carlos Rodríguez Braun.

In the name of material progress, the West has sought to develop and frequently exploit the less-developed “rest.” William Easterly will draw from 400 years of history—ranging from the conquest of the Americas and the Atlantic slave trade to colonization in Asia and Africa and the invention of the Third World—to show how the West has justified different forms of intervention in the societies it has purportedly intended to improve. Easterly will explain why development based on consent, choice, and human agency is superior to an approach that neglects dignity, focuses narrowly on material improvements, and too often justifies various degrees of coercion. Deirdre McCloskey will comment on the fundamental role of freedom in development. Hosted on Acast. See acast.com/privacy for more information.

Deirdre McCloskey argues the world's jump from $2 to $50 per day in average income came from a radical 18th-century shift: equality of permission, or letting ordinary people have a go at bettering themselves. She traces how liberating human creativity through what she calls the "bourgeois deal" sparked innovation from Holland to Scotland to America, while state control stifled it elsewhere. McCloskey critiques modern economics for reducing humans to "vending machines" and argues we need "humanomics" that recognizes love, ethics, and human complexity alongside mathematical models. She challenges the field's statist turn, defends Adam Smith's complete vision beyond self-interest, and explains why India may become the next great creative economy while Europe's trillion-dollar spending plans repeat the old mistake of top-down investment instead of unleashing individual creativity.

Economia Underground, um podcast institucionalista.Neste episódio discutimos a recente coluna de Deirdre McCloskey no Jornal Folha de São Paulo intitulada "um conto histórico de esquerda". O texto argumenta que a Revolução Industrial e a riqueza moderna não vieram primariamente do acúmulo de capital ou da expulsão violenta de camponeses pelos cercamentos após 1700, mas sim de uma mudança ética que elevou a dignidade do burguês e da inovação.Nos siga no Instagram: @economiaunderground

In this conversation from 2023, Alex speaks with Professor Jacob Levy about the concept of neutrality within the history of liberalism and how many historical thinkers have approached the subject within that tradition.  Episode Notes: Michael Oakeshott on “adverbial rules” https://lawliberty.org/forum/michael-oakeshott-on-the-rule-of-law-and-the-liberal-order/  John Locke's religious beliefs https://rb.gy/1yg43  Heresy of Americanism https://en.wikipedia.org/wiki/Americanism_(heresy)  Deirdre McCloskey's Bourgeois Virtues Thesis https://www.deirdremccloskey.com/docs/bv_selection.pdf  Ronald Dworkin “Liberalism” https://www.scribd.com/document/313373358/Ronald-Dworkin-Liberalism#  Stephanie Slade, "Must Libertarians Care About More Than the State?" https://reason.com/2022/03/19/two-libertarianisms/  Alexis De Toqueville's concerns about the rising liberal democratic order https://www.economist.com/schools-brief/2018/08/09/de-tocqueville-and-the-french-exception  John Stuart Mill “On Liberty” https://en.wikipedia.org/wiki/On_Liberty 

You remember your fourth grade history textbook: The British Empire unfairly taxed the American colonies. Tea was dumped in the Boston Harbor. Colonists refused taxation without representation. Therefore, the American Revolution was driven by economics, right? Well, maybe not.Today on Political Economy, I'm talking with Deirdre McCloskey about the core ideas that drove the Revolution. We explore American capitalism and the idea of equal opportunity as America grows closer to its 250th birthday.Deirdre is a senior fellow at the Cato Institute. She is also a distinguished professor emerita of economics and history at the University of Illinois at Chicago, as well as a professor emerita of English and communication. She is the author of some two dozen books, including the Bourgeois trilogy, and has a wonderful article, “Economic Causes and Consequences of the American Revolution,” published in AEI's recent book, Capitalism and the American Revolution, part of our America at 250 series.

Caleb O. Brown has hosted the Cato Daily Podcast since 2007, CatoAudio since 2008, and all told has created several thousand interviews, videos, and other pieces for the Cato Institute. On his final episode, he is interviewed by Cato's Deirdre McCloskey about the art of the interview and his pending move to head Kentucky's Bluegrass Institute. Hosted on Acast. See acast.com/privacy for more information.

Quale è stato l'ingrediente segreto per lo straordinario aumento della ricchezza mondiale degli ultimi secoli?Nell'intervista LeoniFiles di questa settimana, Carlo Stagnaro indaga con Deirdre McCloskey, professoressa emerita presso l'Università di Chicago e distinguished scholar presso il Cato Institute, le cause profonde e meno apparenti del progresso economico e culturale del genere umano, con un occhio di riguardo anche alla situazione attuale.Preferisci seguire su YouTube?

On this episode of the Hayek Program Podcast, Deirdre McCloskey delivers a keynote lecture at the 2022 Markets & Society conference. She argues that the "great enrichment"—a 30-fold rise in global income per capita since 1776—was driven by liberal economic ideas that champion individual freedom and equality of permission. McCloskey also critiques government intervention, emphasizing the transformative power of removing barriers to foster innovation, prosperity, and human flourishing, and more.Deirdre McCloskey is a Distinguished Professor Emerita of Economics and of History and Professor of English and of Communication at the University of Illinois at Chicago. McCloskey is also a Distinguished Affiliated Fellow with the F. A. Hayek Program for Advanced Study in Philosophy, Politics, and Economics at the Mercatus Center at George Mason University. She has published numerous books including Why Liberalism Works: How True Liberal Values Produce a Freer, More Equal, Prosperous World for All(2019) and her trilogy “The Bourgeois Era”: The Bourgeois Virtues: Ethics for a Commercial Society (2006), Bourgeois Dignity: Why Economics Can't Explain the Modern World (2010), and Bourgeois Equality: How Ideas, Not Capital or Institutions, Enriched the World (2016).This lecture has been published in the Markets & Society Journal, Volume 1 Issue 1, as "Humanomics." Learn more about the Markets & Society conference and journal here.If you like the show, please subscribe, leave a 5-star review, and tell others about the show! We're available on Apple Podcasts, Spotify, Amazon Music, and wherever you get your podcasts.Virtual Sentiments, a podcast series from the Hayek Program, is streaming! Subscribe today and listen to seasons one and two.Follow the Hayek Program on Twitter: @HayekProgramLearn more about Academic & Student ProgramsFollow the Mercatus Center on Twitter: @mercatusCC Music: Twisterium

Send us a textAnti-cheat economics, web3 property rights, Deirdre McCloskey, institutional incentives, Halo UGC, and the if single player games have a natural advantage outside the West. Oh my.Dr.Jason Arentz finally guest stars, and he's bringing the econ juice, finally striking a 50/50 web3 split on the case. Zynga Car Price Experiment: https://www.gamesindustry.biz/zynga-apologizes-for-random-dlc-pricing-experiment

An old and common law on many cities' books was meant to crack down on houses of prostitution. Today those same laws are used to effectively ban boarding houses or college student housing. Deirdre McCloskey and Art Carden tell the tale. Hosted on Acast. See acast.com/privacy for more information.

Send us a textGrowth is essential to human life. Always has been, always will be. From the moment we are born, we grow, and we continue to throughout our lives, whether that is physically, mentally, or otherwise. Societies grow too.But what is growth? Real growth is replicable, durable, and sustainable (and not in the sense that immediately comes to mind). Your seven-year-old doesn't shrink back down after she grows an inch. It might happen when she's ninety, but that's gravity (and don't you think she's had a good run at this point? We should accept that it's ok to have a growth recession every now and again). So how have intellectuals conceptualized the growth of societies, environments, and economies over time? And how should we think about growth? The wonderful Henry C. Clark joins us on the podcast today to answer these questions and more. He is the program director of the Political Economy Project at Dartmouth College and the author of several books including the newly released The Moral Economy We Have Lost: Life Before Mass Abundance. Go check it out!Want to explore more?Henry Clark on the Enlightenments, a Great Antidote podcast.Pierre Desrochers, From Prometheus to Arcadia: Liberals, Conservatives, the Environment, and Cultural Cognition, at Econlib.Robert Pindyck on Averting and Adapting to Climate Change, an EconTalk podcast.Sandra Peart and David Levy, Happiness and the Vanity of the Philosopher: Part1, at Econlib.Deirdre McCloskey and Economists' Ideas About Ideas, a Liberty Matters forum at the Online Library of Liberty.Never miss another AdamSmithWorks update.Follow us on Facebook, Twitter, and Instagram.

La Fundación Rafael del Pino organizó, el 22 de mayo de 2024, el diálogo «Dinámicas económicas y demográficas en perspectiva. ¿El crecimiento de la población genera una mayor o menor abundancia de recursos?» en el que participaron Marian L. Tupy, Deirdre McCloskey, Ian Vasquez y Gabriel Calzada (moderador) con motivo de la presentación del libro titulado Superabundancia de los autores Marian L. Tupy y Gale L. Pooley, editado por Deusto.

La Fundación Rafael del Pino organizó, el 22 de mayo de 2024, el diálogo «Dinámicas económicas y demográficas en perspectiva. ¿El crecimiento de la población genera una mayor o menor abundancia de recursos?» en el que participaron Marian L. Tupy, Deirdre McCloskey, Ian Vasquez y Gabriel Calzada (moderador) con motivo de la presentación del libro titulado Superabundancia de los autores Marian L. Tupy y Gale L. Pooley, editado por Deusto.

In May 2022, Alex spoke with Deirdre McCloskey in a wide-ranging conversation that addresses the economic, philosophical, and political reasons why liberalism just works. We're reposting that important conversation today on The Curious Task. 

What explains the wealth of the modern age? Was it capital? Institutions? Slave-holding? Why do some countries seem to have an economic advantage over others? Are the fears of progressives about wealth inequality worth paying attention to? Economist, historian, and prolific author Deirdre McCloskey joins us to talk about the key factor that precipitated the wild success of the modern world.(Re-Mastered for Re-Issue.)Show Notes:Deirdre's WebsiteBourgeois VirtuesThe Bourgeois DealAudio Production by Podsworth Media - https://podsworth.com

Introduction: Caleb O. BrownScott Lincicome and Deirdre McCloskey on Cato's Defending Globalization projectWilliam Ruger and Jason Sorens on the seventh edition of Freedom in the 50 StatesAmy Caiazza and Daniel Gorfine on the SEC's proposed rules on predictive data analyticsAlexandra Hudson on The Soul of CivilityExclusive: Ian Vasquez on the Human Freedom Index 2023 report Hosted on Acast. See acast.com/privacy for more information.

Are the teachings of Christianity compatible with libertarianism? Economist Deirdre McCloskey thinks so. At the Mont Pelerin Society conference in Bretton Woods, New Hampshire, she sits down with Matt Kibbe to lay out her vision of a Christian libertarianism that values the individual over the collective, embraces markets, and demands that we treat each other with kindness, humility, and love. These lessons are more important than ever in a time when politics is dominated by division and hatred.

El capitalismo está bajo ataque y necesita quien lo defienda. Bajo esa premisa, el sociólogo e historiador Rainer Zitelmann ha adoptado una tarea que quizá no es muy popular: defender un sistema que hoy en día es asociado con los problemas que enfrenta la humanidad. Zitelmann, alineado con la también historiadora Deirdre McCloskey, ha venido haciendo una fuerte defensa del capitalismo, asegurando que es el principal causante del progreso de los países. El autor está en Colombia promocionando su libro “En defensa del libre mercado”, con el que busca desmontar las 10 críticas comunes del llamado anticapitalismo.

Johan Norberg's work revolves primarily around economic and intellictual history and attempting to learn lessons from past financial systems. In this episode of Faster, Please! — The Podcast, Johan takes us through his version of capitalism, giving an especially interesting perspective on the economic system of his home country. Johan is a senior fellow at the Cato Institute and the author of several books. His latest is The Capitalist Manifesto: In Defense of Global Capitalism, available now. In This Episode* “Capitalism” and its meanings (0:55)* The state of contemporary capitalism (2:34)* Coordination in capitalism (7:59)* The cyclical nature of economic systems (13:54)* Swedish capitalism  (16:56)* The case for capitalism (21:48)Below is a lightly edited transcript of our conversationJames Pethokoukis: Let's begin with a little definitional work here. Capitalist Manifesto: “Capitalist” is a word people assign a variety of meanings to. What is the capitalism that you're talking about here?Johan Norberg: Yeah, it's not a great word. Quite often it's misunderstood; people think it's all about capital. It's not. We can have capital in many different economic systems. To me, free-market capitalism is about a decentralized economic system with private property where decisions are made locally, decentralized, not command and control, and the prices and wages and things are set in voluntary negotiations rather than top-down.The economist Deirdre McCloskey hates the word "capitalism." She prefers "innovism" or "trade-tested progress." Should we insist on using a different word to describe the world's dominant socio-economic system?Deirdre McCloskey is right. Capitalism is a bad word. I would much prefer “innovism” or something like that. But I've realized that in order to communicate with people, I'd better use some of the words that they are using. And I've realized that we're stuck with the word “capitalism” and the whole concept of capitalism, and if we don't fill it with meaning, those of us who like free markets and free trade, I've realized that somebody else is going to fill it with meaning, and in that case, we are losing the debate. Go to where the sinners are. That's my take.Twenty years ago, it seemed like markets had won. Capitalism was changing the world and bringing people out of poverty. President Clinton declared "the era of big government is over." China was opening its economy. What happened? Why did you feel the need to write this book in this moment?That's exactly why I wrote this book, because nowadays it seems like nobody likes free markets and free trade anymore. I've realized that, in the US, and that should be a place where people appreciate some of this, fewer people believe in capitalism than believe in ghosts nowadays. And there's this lack among politicians and governments everywhere in belief in global capitalism. There's this whole, repatriate stuff, subsidize specific businesses and sectors back home, rather than having global supply chains. So that's why I wrote this.I think this is all based on a complete misunderstanding of what has happened in the world in the past 20 years. It's not that markets have failed. On the contrary, despite the fact that we've had 20 rough years with financial crises and wars and the Great Pandemic and stuff like that, and yet we've seen, when you look at objective indicators of human living standards, more progress than ever before over these 20 years. When it comes to the reduction in poverty, more than 130,000 people lifted out of extreme poverty every day over the past 20 years. We've seen an increase in global GDP per capita of roughly a third. We've reduced child mortality by almost half, which means that four million fewer children died last year than in 2002. And this is because entrepreneurs and innovators, they keep innovating ourselves out of problems all the time — if we give them some freedom to do that. And that's what I'm worried about: that they'll have less freedom in the future if we do not keep on pounding and keep on explaining this.Those are some pretty impressive statistics. But people don't seem to notice. We keep hearing the same narrative of "late-stage, failed capitalism.” Why is that?I think the financial crisis is a very important part of this. If some capitalists do bad stuff, people lose faith in capitalism and I think we saw this in the US but also around the world. There's this sense that perhaps we shouldn't imitate what America is doing if these are the consequences. And I don't think that the financial crisis was a result of unleashed market forces. And I even wrote a book on this a couple of years back, Financial Fiasco. I think there were massive regulatory failures and central banks and ministers of finance trying to make capitalism very safe by implementing a very homogenous structure on everybody, telling everybody to go into the same way, searching for the same AAA-rated securities and stuff like that. And if everybody behaves in the same way, if that fails, there's massive disaster. We need decentralization partly to minimize risks like that. But — doesn't matter, we don't have to go into history. I think this partly explains why we're in this lack of trust in capitalism right now.But also other things. People, when they're afraid of the world, they tend to retreat. They don't want to explore. They don't want to innovate. It triggers their fight-or-flight mechanism and sometimes the societal fight-or-flight mechanism. You want to hide behind walls and tariff barriers and strong, big governments that protect you, and that is a misunderstanding of how we get out of crises. And this is what I think we've learned from these past 20 years. Yes, lots of bad stuff happened. It makes us afraid. It triggers some sort of evolutionary tendency to get away from openness and learning and discovery processes and instead we want just one instant solution to all the problems.But what we're learning is, how did we get out of the pandemic? We did it by having thousands of entrepreneurs constantly finding new ways to rebuild supply chains and find replacements for the resources they couldn't get. And innovators who were looking for new treatments and coming up with a vaccine in a record period of time. It didn't take a thousand years as it usually does, coming up with a vaccine against polio, but more like three months. But try to tell that to our reptilian brains. When we're fearful, we want one simple solution. And as H.L. Mencken once put it, there is always a solution to every problem: it is “neat, plausible, and wrong.” And it's so dangerous because it involves replacing all that discovery, all that learning and wisdom of millions with just the preferences of a few people at the top.Let me read a brief tweet by the right-wing populist writer, Sohrab Ahmari: “We are entering a new age of industrial war. The ‘California ideology,' neoliberalism, Reagan-Clintonism — whatever you want to call it, it's kaput. We're going to see close coordination between state, enterprise, labor. It took security threats to bring us here. I'll take it.” Why won't you take it?That's a scary prospect to me. There is a reason why he's talking about this Silicon Valley thing, because that worked splendidly, and one of the reasons it succeeded was that the outcomes weren't decided in advance by any kind of command-and-control thing. It was, as some criticized it in the ‘70s, it looks more like the Wild West, allowing entrepreneurs and innovators to experiment with crazy ideas, even in garages. And that's the way to … if you want to explore all possible avenues and ideas, we have to let everybody go out and look for it. I think the reason why Sohrab Ahmari is wrong is that he thinks that there is one solution to all the problems we face. Perhaps there is, but I don't know one and he doesn't know it. We have to allow more eyeballs to look at the problems and more brains to go out thinking hard about these things, and that involves not starting geopolitical divisions and nationalist temptations, but it involves having lots of people in other places helping us to find the solutions in a division of labor where we learn from what they're doing.Why has America been so successful so far? When people say that it's failing, this American, this Washington consensus thing, please keep in mind that just 15 years ago, the American economy was slightly smaller than the European one. Now it's almost a third bigger. It's not entirely broken, but some of the fixes might break it, I'm afraid, if we continue doing things like this. Why is it successful? Well, look at different areas. Look at AI. Why is America so successful? We thought that China would come up with it. Well, one reason is that the Chinese have to teach machines not just what to say, but also what not to say, but also the fact that America is learning from others. More than half of America's top AI experts have education or background in other countries and almost a third come from China. So if we want to win against China and everybody else, we also have to allow lots of Chinese to do the work for us.This notion of close coordination between state and business and labor, where does that work well? Is there a model? Is there an example of that kind of formula working elsewhere?A leading European economist just published a book called, I think it's some 50 of them, called Questioning the Entrepreneurial State, where they evaluate this whole idea that we would have this close coordination between governments and businesses, and what they say is that the history of it, at least in Europe but they look around the world as well, is that it's usually a full employment program for lobbyists and for attorneys who just reformulate everything that businesses would usually do as something that fits with this new industrial policy thing. If it was successful, you would look up stuff on the internet by using Quaero, because that's the close coordination stuff in Europe with the European and German and French governments heavily funded a “European Google.” The whole idea was that we will own the digital future by heavily subsidizing this one project. It doesn't work, because you lose some of the trial and error, you lose some of the mechanisms whereby we understand what's a success and what's not.It's okay to fail. Industrial policies fail all the time, but so does big tech. Entrepreneurial capitalism as well. But the great thing with free markets and not having the governments investing heavily in one particular model is that you replace this trial-and-error, constant experimentation and feedback and adaptation that comes when you work on markets and you're risking your own resources. Once you do that by having the government picking a winner, then, when you lose out, you spend more money on these projects instead. And you lose this learning process whereby we're constantly channeling capital and labor to more successful ones. What people would tell you is that China is the most successful place where we've had this…Yes, there seems to be a cyclical component to this belief. I mean, I'm old enough to have seen the version where Japan had figured it out. That didn't turn out so well. And then I think you have people who looked at China. If you have a natural inclination to like the idea of central planning and you eschew the kind of natural chaos of capitalism, you could point to China So that's why I wonder if this is a passing phase, because China doesn't seem like they're able to pull it off either.Yeah, but that'll keep on moving, then, and find another example where it seems to be working. Because it's always easy to find out in retrospect that something seemed to be working. And if the government is involved somewhere, they try to give it credit. But until recently, I think 49 American states tried to spend heavily to create a biotech cluster in their own state to attract businesses from other states. And if one of them succeeded, people would've said, “Look, this is because of this top-down government intervention,” but probably not, right?And it's the same thing with China. Yes, China has been tremendously successful for 30 years, but in which sectors? In the sectors that the government didn't plan for it, in places where we saw grassroots capitalism, farmers secretly privatizing their land, starting village enterprises. And then, and only then, did the Communist Party see that, “This seems to be more successful than what we've been doing recently, so allow them to continue to experiment,” experiment in export processing and stuff like that. But they wanted to keep it elsewhere so that it wouldn't spread throughout the rest of the economy. But it was so successful that it did. That's what succeeded: when people experimented. Entrepreneurs were allowed to innovate. What was it that failed? The large, state-owned enterprises. They were less productive. They were wasting cheap credit and ruining, destroying resources over the years. And once the government gets involved, there's plenty of research into this, they find less productive businesses and they become even less productive if they get access to this cheap credit and cheap land. And I think people are coming around to that now as they're seeing that China has many problems, some of them related to demography, as well. But they would need innovation, strange new business ideas, crazy people in garages coming up with new ideas. That's exactly the thing that top-down governments don't really like, and what they've been doing over the past few years is just destroying tech businesses, [education] businesses, and the gaming industry in China because authoritarians aren't good at spotting where the true potential lies.I wonder if you could clear up a question that confuses many Americans. Do you come from, and are you currently living in, a capitalist country?Yes, I am.We don't know. We're not sure. We're very confused about Sweden.Yes, I know, and that's because lots of perceptions, just like the ideas, are stuck in the 1970s. Sweden had a brief period of some 20 years when we really experimented with socialist ideas, but this was also the moment — the only moment in modern economic history — when Sweden lagged behind other countries. So up until the early 1970s, we had a very limited government, low taxes, free markets, and free trade — that made us rich. It made us so rich in Sweden that we thought that we could experiment with these ideas. Just stop thinking about how to create wealth, just spend it, redistribute it. And that resulted in an awful 20, 25 years when companies like Ikea and Tetra Pak and the greatest entrepreneurs, they just left Sweden because it wasn't possible to do business in Sweden.This is what people still remember: the 1970s. We did all these things: doubled the size of the government, jacking up taxes and so on. At the same time, it looked like a fairly successful place, it's a rich place. But it's like that old joke: How do you end up with a small fortune? Well, you start with a large fortune and then you waste most of it. And that's what we did. This is actually why, since that terrible economic financial crisis that we had in the early 1990s, Sweden has once again liberalized markets quite drastically compared to other places, and we're now back to a system which many Americans would actually think of as more free market in many ways than the US system.As you know, people think of Sweden and Scandinavia more generally as big government with a giant welfare [system], cradle-to-grave welfare, all the welfare you would ever want. So in what ways is Sweden maybe more market friendly than the United States, and perhaps some ways which would greatly surprise many Americans as well as Bernie Sanders?Yeah, I'm trying to tell the Bernie Sanders of the world that if they want to be like Sweden, they would have to do plenty of things. They would have to become more free trade-oriented in many ways. They would have to reform social security, partially privatize it with individual accounts, they would have to introduce a national school voucher system so private schools get the same funding as the public ones. They would actually have to lower taxes in many ways on the rich, and they would have to abolish taxes on property wealth inheritance and lower the corporate tax, and instead put most of the tax burdens on low- and middle-income households, because that's the dirty little secret of the Swedish welfare state. We learned in the 1970s that if you want to have a big universal welfare state that's very generous, in that case, everybody is going to have to pay for it.You have to redistribute over people's life cycle, rather than trying to get the rich to pay for it all, because we realized that the rich are too few and the economy is too dependent on them. So if we are trying to get them to pay for it all, they will flee Sweden, they will move to other places, leave their resources elsewhere, and we won't get the new businesses, the new successful ones that we all depend upon. So for 30 years, we didn't create a single net job in the private sector, the ‘70s, ‘80s, and the ‘90s. So instead, you have to move towards more taxing consumption, 25 percent value-added tax, and making sure that the poor and middle income households pay the bulk of income taxes. So, counterintuitively — and this is something that people really don't get—Sweden has a much less-progressive tax system than the United States does, less-progressive tax system than almost any other rich country because we've learned that the poor are loyal taxpayers. They don't move, they don't dodge taxes, and they don't have tax attorneys.What is the quick pitch for capitalism? If you're on an airplane next to someone who's heard a lot about inequality and wage stagnation and losing to the Chinese, how do you make the case for market capitalism?It's much, much better than you think, but it could be even better. It is much better because we can see, look at the long-term indicators and the data, and perhaps this is where I lose my fellow passenger. But wage stagnation was a phenomenon in the ‘70s and ‘80s, partly because we had to rebuild the economy because it was at risk of becoming much less competitive and we were about to lose jobs everywhere. Once we did that, from the ‘90s and onwards, we've had a tremendous increase in wages, and we can measure this in wages and total compensation and increase in 60 percent. I'd say if you look at the best indicators, but even more interesting is what can you do with those resources? And then you see that all those amenities and goods and technologies that we all considered luxuries in the ‘70 and ‘80s, we're getting close to 100 percent possession in American households.The poor people who fall below the poverty line in the US now own more amenities like that — washing machines, television sets, dryers, clothes washers, and of course cell phones and computers — than the rich did in 1970. That tells you something. If you look around the world, we've actually had the best era ever when it comes to poverty reduction, and we've even, since the turn of the millennium, reduced global inequality for the first time since the Industrial Revolution. So it's much better than the headlines. If you look at the trend lines, they're much better. Yeah, tell me about that. Give me a little of that “could be even better.” Give me a little flavor of that.Yeah. I think that we've lost — you know this and you just wrote a book on this — we've entered a period where we've thought that things cannot be better. We've tried to protect old business models and old ways of doing things, and often in a low interest rate environment, I think protected many businesses that should have been put out of their misery so that capital and labor could go to the new sectors, to the frontiers of the economy. We are seeing some of that happening now with everything from mRNA technology to the new space race to AI, but we're in a mindset and a regulatory situation where we don't want to experiment with the new weird stuff. But we have to do that because that's the only way where we'll get the new goods and services and jobs in the future. So here's to the crazy ones, as Steve Jobs would put it. And in that case, we can't be too protective of our old, safe ways of doing things. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe

Deirdre McCloskey is Distinguished Professor Emerita of Economics and of History and Professor Emerita of English and of Communication at the University of Illinois at Chicago. She is also Isaiah Berlin Chair in Liberal Thought at the Cato Institute. Over the span of her career, Deirdre has written on economic theory, history, rhetoric, feminism, ethics, law, and more. In this episode, she and Robinson discuss her political philosophy—classical liberalism. They begin by discussing her training before delving into liberalism's roots in the eighteen and nineteenth centuries as a celebration of freedom of speech and innovation, as well as its doctrine of equality under the law. They then compare it to competing views, such as conservatism, and address common criticisms of classical liberalism, such as its alleged inability to respond to crises like global warming or that the free market will concentrate wealth in the hands of a few. Why Liberalism Works: https://a.co/d/hvUAtnk Deirdre's Website: https://www.deirdremccloskey.com OUTLINE 00:00 In This Episode… 00:59 Introduction 04:09 Deirdre's Background in Economics 17:36 What is Classical Liberalism? 33:28 The Beginning of Liberalism 51:50 The Great Enrichment 01:05:43 Free Speech 01:17:31 Conservatism and Libertarianism 01:28:36 Criticisms of Liberalism 01:43:00 Climate Change and the Free Market 01:49:57 Liberalism and Queers Robinson's Website: http://robinsonerhardt.com Robinson Erhardt researches symbolic logic and the foundations of mathematics at Stanford University. Join him in conversations with philosophers, scientists, weightlifters, artists, and everyone in-between.  --- Support this podcast: https://podcasters.spotify.com/pod/show/robinson-erhardt/support

What makes for good rules? Good rules are often "discovered," according to Cato's Deirdre McCloskey. Hosted on Acast. See acast.com/privacy for more information.

Deirdre McCloskey explains how freedom and bourgeoise dignity enriched the world.Follow @IdeasHavingSexx on TwitterThe Bourgeoise Era Trilogy Vol. 1 - The Bourgeois Virtues: Ethics for an Age of CommerceVol. 2 - Bourgeois Dignity: Why Economics Can't Explain the Modern WorldVol. 3 - Bourgeois Equality: How Ideas, Not Capital or Institutions, Enriched the WorldShorter summary volume: Leave Me Alone and I'll Make You Rich: How the Bourgeois Deal Enriched the World, by Deirdre McCloskey & Art CardenDiscussed and Recommended: Four Essays on Liberty by Isaiah Berlin; The Conservative Sensibility by George Will; Kathleen Stock & Deirdre McCloskey Debate Issues of Sex, Gender, & IdentityProfessor McCloskey's email, website, Twitter, & author page

Pedro Bial conversa com a economista Deirdre McCloskey, professora emérita de Economia, História e Comunicação.

One of the biggest misconceptions that drives mischief in the economy is the widespread belief that entrepreneurship is easy, and if it's not easy, it's at least formulaic. Deirdre McCloskey explains why that attitude can be so destructive. Hosted on Acast. See acast.com/privacy for more information.

Alex speaks with Professor Jacob Levy about the concept of neutrality within the history of liberalism and how many historical thinkers have approached the subject within that tradition.  Episode Notes: Michael Oakeshott on “adverbial rules” https://lawliberty.org/forum/michael-oakeshott-on-the-rule-of-law-and-the-liberal-order/  John Locke's religious beliefs https://rb.gy/1yg43  Heresy of Americanism https://en.wikipedia.org/wiki/Americanism_(heresy)  Deirdre McCloskey's Bourgeois Virtues Thesis https://www.deirdremccloskey.com/docs/bv_selection.pdf  Ronald Dworkin “Liberalism” https://www.scribd.com/document/313373358/Ronald-Dworkin-Liberalism#  Stephanie Slade, "Must Libertarians Care About More Than the State?" https://reason.com/2022/03/19/two-libertarianisms/  Alexis De Toqueville's concerns about the rising liberal democratic order https://www.economist.com/schools-brief/2018/08/09/de-tocqueville-and-the-french-exception  John Stuart Mill “On Liberty” https://en.wikipedia.org/wiki/On_Liberty 

There are many competing theories that purport to explain the dramatic and sustained increase in wealth and well-being for humans these last two centuries. Cato's Deirdre McCloskey discusses why she believes liberty is the secret sauce of growing prosperity. Hosted on Acast. See acast.com/privacy for more information.

Read a transcript of this episode.Deirdre McCloskey is probably my favorite contemporary liberal scholar. Her work ranges widely across disciplines, is always fascinating, and builds its defense of free markets and the open society in a deeply humane and compassionate fashion.I've talked with her on podcasts before, but today's a little different. Our topic isn't economics, but religion. Deirdre is a committed Anglican, and her next book sets out the case that religious faith is an important component of a thriving liberal society—and that those who think Christianity points in a more reactionary, illiberal direction get Christianity wrong.ReImagining Liberty is a project of The UnPopulist, and is produced by Landry Ayres. Podcast art by Sergio R. M. Duarte. Music by Kevin MacLeod. Get full access to Aaron Ross Powell at www.aaronrosspowell.com/subscribe Hosted on Acast. See acast.com/privacy for more information.

The economic historian and Magatte Wade, Alex Gladstein, Mohamad Machine-Chian, Tony Woodlief, and Tom Palmer are challenging authoritarians everywhere.

En este episodio hablamos con Deirdre McCloskey, una destacada economista y autora de varios libros sobre la economía mundial. Además de su impresionante carrera, McCloskey es también una mujer transgénero que ha tenido que enfrentar una serie de desafíos y obstáculos en su vida personal y profesional. En esta entrevista, McCloskey comparte su perspectiva única como mujer trans en el mundo de la economía y cómo ha influido en su forma de ver y analizar los fenómenos económicos globales. Compra tus boletos para el evento de El Billetazohttps://morisdieck.com/elbilletazoSígueme en todas mis redes sociales:

When policymakers pursue “equality,” which equality should they pursue? Deirdre McCloskey believes neither "equality of outcome" nor "equality of opportunity" is a great option. Hosted on Acast. See acast.com/privacy for more information.

In the fifth panel of the 2nd Ibero-American Congress of Cultural Liberalism - organized by Fundación Libertad and the Fundación Internacional para la Libertad - Yaron Brook, economist and writer, David Boaz, senior fellow of the Cato Institute, and Deirdre McCloskey, economist and writer, discuss the state of authoritarianism in the world and what liberals can do to counter it. Moderator: Marcos Falcone.This virtual panel was recorded on December 15, 2022 as part of the 2nd Ibero-American Congress of Cultural Liberalism - organized by Fundación Libertad and the Fundación Internacional para la Libertad 00:00 Intro01:30 David Boaz Intro10:55 Deirdre McCloskey Intro23:05 Yaron Brook Intro36:20 Are far right and left equaly threatening to liberalism?53:10 Has the US become less of an example on freedom?1:08:00 China1:16:53 Should we leave autocrats alone?1:20:40 Where should we focus our efforts? Join this channel to get access to perks:https://www.youtube.com/@YaronBrook/joinLike what you hear? Like, share, and subscribe to stay updated on new videos and help promote the Yaron Brook Show: https://bit.ly/3ztPxTxBecome a sponsor to get exclusive access and help create more videos like this: https://yaronbrookshow.com/support-members/support-the-show/Or make a one-time donation: https://bit.ly/2RZOyJJContinue the discussion by following Yaron on Twitter (https://bit.ly/3iMGl6z) and Facebook (https://bit.ly/3vvWDDC )Want to learn more about Ayn Rand and Objectivism? Visit the Ayn Rand Institute: https://bit.ly/35qoEC3

Editors' Pick:Rich: Luther Abel's piece “What Happens When a Regular Guy Mishandles Classified Information”Charlie: Douglas Murray's magazine piece “Right and Wrong on Ukraine”Jim: NR's editorial, “Biden's Transparency Claims Have Lost Credibility”Phil: Dan McLaughlin's piece “The 2022 Turnout Puzzle”Light Items:Rich: The Weirdest People on the World by Joseph Henrich, Bourgeois Dignity by Deirdre McCloskey, and Escape from Rome by Walter ScheidelCharlie: His Kansas City tripJim: A good physicalPhil: Accidental shopliftingSponsors:Dividend CafeThis podcast was edited and produced by Sarah Colleen Schutte.

Dr. Matson's lecture explored how in the British tradition, political economy, which partly emerged out of discourses in natural theology, ethics and jurisprudence, casts some light on the content of our moral obligations. Drawing on Hutcheson, Hume, and Smith, he desicussed how commerce in the eighteenth century came to be depicted as a mode of cooperation—either literally with God or metaphorically with our fellow human beings—through which we serve the common good. That depiction energized the emerging authorization of commercial enterprise, helping to illustrate the virtue of what Deirdre McCloskey calls the “bourgeois virtues,” an understanding which contributed to the Great Enrichment. The depiction continues to edify business as a calling and elaborate how freedom serves the good of humankind.Erik W. Matson is a Senior Research Fellow at the Mercatus Center and the Deputy Director of the Adam Smith Program in George Mason University's Department of Economics. He serves as an Online Course Lecturer at The King's College, New York. Previously he was a Postdoctoral Fellow at New York University. He earned a Ph.D. in Economics from George Mason University in 2017.Subscribe to our podcastsRegister Now for Business Matters 2023Apply Now for Acton University 2023 (Early Bird Pricing) Hosted on Acast. See acast.com/privacy for more information.

Daniel Klein is professor of economics and JIN Chair at the Mercatus Center at George Mason University, where he co-leads a program in Adam Smith. There's been renewed interest in the role Christianity has played in liberalism since Larry Siedentop's 2014 book, Inventing the Individual: The Origins of Western Liberalism. Today, Dan Churchwell, Acton's Director of Programs and Education, sits down with Klein to discuss Adam Smith and his enlightenment vision. Building on Siedentop, Klein says universal benevolent monotheism, and Christianity in particular, has led to the articulation of a specific social grammar and corresponding rights—in short Adam Smith's “liberal plan.” Subscribe to our podcastsDr. Klein's faculty pageFull discussion of Larry Siedentop's book:Full set of notes on SiedentopKlein published interview on Siedentop:Klein replies to Deirdre McCloskey on Siedentop: Hosted on Acast. See acast.com/privacy for more information.

What is the proper way for Christians to engage with the world around them? Many theologians believe Christians are called upon to be socialists. Deirdre McCloskey disagrees. Her forthcoming book is God in Commerce. Hosted on Acast. See acast.com/privacy for more information.

“It's tough to be alive now,” the actor Timothée Chalamet recently said. “I think societal collapse is in the air — it smells like it.”It's one of those lines that got picked up by dozens of media outlets. Because those outlets know it's one of those headlines people can't resist clicking on. As the economist Deirdre McCloskey once put it, “For reasons I have never understood, people like to hear that the world is going to hell.”✉️ Want Stoic wisdom delivered to your inbox daily? Sign up for the FREE Daily Stoic email at https://dailystoic.com/dailyemail

Deirdre McCloskey is an economist, the author of more than 20 books, and is one of America's most prominent trans academics. During our conversation, Deirdre talks about growing up in the 1940's and 1950's, knowing from an early age that she wanted to be a woman, her marriage of more than 30 years to the "love of her life" and fathering two children, and her epiphany in the 1990's, at more than 50 years of age, that she wanted to transition from a man to a woman.Deirdre also details the reaction of her family to her desire to transition, how she was twice institutionalized, progress in trans rights in America, and her disagreements with positions taken by individuals like Kathleen Stock and Helen Joyce, who have publicly voiced concerns about allowing children to go through hormone therapy and insist that the majority of kids who transition later regret their decision.As I note during the conversation, I think most people are trying to form their views on this sensitive issue, to best determine what is true and what is decent. A free society should allow adults to do what they want, provided they aren't harming others. I try to understand the concerns of people on both sides of this debate around children, and no matter how one might come down on it, I admire Deirdre's courage in authentically living her life, in being true to herself, and in her commitment to free speech, to allow open and important moral conversations to happen.------------Support via Venmo------------Show notesSocial media and all episodes------------(00:00) Introduction(02:50) “Crossing: A Memoir” quote: boyhood(09:49) Early life and sexuality(14:29) Gender conversations with her ex-wife(16:53) Concealments from her ex-wife(17:44) Being sexually different in the 50s and 60s(20:23) Cross-dressing(21:31) Gender transition after decades of marriage(23:09) 50 years as a male(25:45) Her resistance towards gender change(28:10) Praying to be a woman(29:19) Lived experience as a man identifying as a woman(31:19) The moment of epiphany to transition(35:26) Clarity on the epiphany(36:33) Loved ones' reactions to the gender transition(38:54) Being institutionalized against her will(41:25) Classical liberalism and freedom(43:13) The experience of being institutionalized(45:59) Changing cultural views on gender transitions(50:09) Life post gender transition(53:50) Self-actualization and gender transition(58:25) The best part about being a female(01:01:16) Living doubt-free post gender transition(01:05:00) Freedom of speech being paramount(01:06:26) Is gender change irreversible?(01:11:41) Do children often regret gender transition?(01:16:29) Are claims of children regretting their gender change fabricated?(01:19:19) The state's involvement in personal decisions(01:23:00) Removing the state from personal decisions(01:27:47) Courage, and being a public example

The role of literature in economics, Chinese totalitarianism, levelling-up and trans rights. In this fascinating IEA In Conversation, Matthew Lesh, IEA Head of Public Policy, sits down with Deirdre McCloskey to discuss all of this and much more! Deirdre N. McCloskey has been since 2000 UIC Distinguished Professor of Economics, History, English, and Communication at the University of Illinois at Chicago. Trained at Harvard as an economist, she has written twenty books and edited seven more, and has published some four hundred articles on economic theory, economic history, philosophy, rhetoric, feminism, ethics, and law. She taught for twelve years in Economics at the University of Chicago, and describes herself now as a “postmodern free-market quantitative Episcopalian feminist Aristotelian.”   FOLLOW US: TWITTER - https://twitter.com/iealondon​​  INSTAGRAM - https://www.instagram.com/ieauk/​​  FACEBOOK - https://www.facebook.com/ieauk​​  WEBSITE - https://iea.org.uk/ 

Alex speaks with Deirdre McCloskey in a wide-ranging conversation that addresses the economic, philosophical, and political reasons why liberalism just works. 

Matt Kibbe is joined by Deirdre McCloskey, distinguished professor emeritus of economics and of history at the University of Illinois in Chicago, to talk about the language we use as advocates of free market economics. McCloskey points out that economics is essentially a series of metaphors, and that we can best explain its concepts through stories rather than the dry language of academia. Meanwhile, certain jargon or stigmatized terms like “capitalism” are doing more harm than good when it comes to helping people understand why the freedom to innovate makes life better for everyone.

Why did gross domestic product suddenly start to skyrocket just a few hundred years ago after centuries of only gradual, marginal improvements? Dr. Deirdre McCloskey, Distinguished Professor Emerita of Economics, History, English, and Communication atUniversity of Illinois at Chicago, has written extensively on why it was not the accumulation of capital that caused what she has dubbed "The Great Enrichment"—and eventually enabled the Industrial Revolution—but rather the spread of the great ideas of liberty. On today's episode of Borderless, she joins Vale Sloane to discuss how ideas changed the world and allowed the standard of living to advance so dramatically for millions and billions of people.Dr. McCloskey prefers the term "innovism" to describe the source of this enrichment, rather than the more familiar "capitalism." Innovation, enabled by institutions such as the free market and free speech, was far more important to the economic transformation of the last few centuries than capital alone. Take a deep dive into what this means for our understanding of liberalism, history, and even the future on this episode of Borderless.Stay in the know by following us on social media:https://twitter.com/AtlasNetworkhttps://www.instagram.com/atlasnetwork/https://www.facebook.com/atlasnetwork/Support the Atlas Network Mission Today: https://www.atlasnetwork.org/donate

Kevin Williamson, the Remnant's cheeriest regular, is back for another voyage through the strange realm of contemporary America. A free society is messy, and life is all about contradictions, inconsistencies, and trade-offs. But this can be an uncomfortable truth for many to face. In a conversation that will send you scrambling for your bingo cards, Kevin and Jonah explore the problem with social homogeneity. They also touch on the weaknesses of autocratic regimes, realistic climate change solutions, and Kevin's hatred of Ohio. Plus, tune in to hear Kevin give a rousing reading of “The Love Song of J. Alfred Prufrock.” Show Notes:- Kevin's page at National Review- Kevin: “Autocracy's Fatal Flaws”- Kevin: “Make Putin Pay”- Hayek: “Why the Worst Get on Top”- Christopher Caldwell's The Age of Entitlement- Yuval Levin's The Great Debate- Kevin's The Smallest Minority- The Remnant with Brian Rield- Jonah and Deirdre McCloskey at the Cato Institute- The Books of Jacob, by Olga Tokarczuk- The Remnant with Shadi Hamid- Jonah: “Rise of the Underminers”- Unintentional confession