Podcast appearances and mentions of Daron Acemoglu

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

L'IA est aujourd'hui une commodité.Mais avant avant le dévoilement de ChatGPT au grand public, elle n'était qu'une innovation cachée, exploitée seulement par quelques curieux.Pendant des années, Vincent Luciani code, développe des produits et montre à ses clients les capacités de l'intelligence artificielle alors qu'une majorité ignore encore son existence.Puis les LLM sont dévoilés et ils deviennent une évidence pour tout le monde.En 2014, Vincent quitte McKinsey et fonde Artefact avec son associé Guillaume de Roquemaurel. Une entreprise de conseil en data, à une époque où faire du service est mal vu.En 2017, ils fusionnent avec Netbooster, une agence de pub cotée en bourse qui réalise 40 millions d'euros de chiffre d'affaires et compte 600 collaborateurs.Du jour au lendemain le duo se retrouve à la tête d'une structure dix fois plus importante mais le rêve tourne vite au cauchemar.Les dirigeants anglais démissionnent le lendemain du deal, les clients historiques partent un par un, un redressement fiscal en Allemagne vide la trésorerie et le titre de l'entreprise chute de 96 % en bourse.Le directeur financier annonce en 2018 qu'elle ne passera pas l'année mais Vincent ne lâche pas l'affaire et se lance dans une restructuration totale.Il ferme l'Italie et les pays scandinaves. Ouvre les États-Unis et le Moyen-Orient. Et surtout, il trace un cap : tout miser sur l'IA et la data, abandonner le reste et ne jamais en dévier.Aujourd'hui, Artefact emploie 2 500 personnes dans 30 pays.C'est le plus gros acteur indépendant du conseil en data et IA en Europe, en concurrence directe avec McKinsey.Vincent revient sur cette trajectoire hors norme et livre sa lecture du basculement en cours :Pourquoi la différence ne se fait plus sur le modèle utiliséComment l'humain est devenu le goulot d'étranglement de la productivitéLe piège du "token maxing" qui ruine les boîtes de la Silicon ValleyLes trois étapes pour organiser ses données avant de les fournir à son IA préféréeCe qui menace vraiment les grands groupes, au-delà de l'IAUn épisode concret pour comprendre pourquoi à l'ère de l'intelligence illimité et presque gratuite, la pensée profonde coûte de plus en plus cher.Vous pouvez contacter Vincent sur Linkedin.TIMELINE:00:00:00 - La décennie des plateformes SaaS est finie 00:14:31 - Faut-il faire coder ses enfants à l'ère de l'IA ? 00:21:46 - Ce que l'IA va vraiment changer dans la science 00:26:24 - Comment créer la confiance même si on ne contrôle plus rien 00:35:44 - Utiliser de l'IA ne suffira plus 00:44:48 - La vérité derrière les gains de productivité avec l'IA 00:53:18 - L'IA n'est pas un outil mais la nouvelle électricité 01:06:21 - Les IA sont paresseuses, et c'est un problème pour les marques 01:18:26 - La fin des slides marque le début du vrai conseil 01:29:48 - L'IA améliore vos réponses, pas votre cerveau 01:35:55 - Les startups : la plus grande menace des grandes entreprises 01:48:35 - La souveraineté Européenne est-elle encore possible 01:56:21 - Les 3 étapes pour organiser ses données 02:05:16 - Les dérives du Token Maxing 02:13:50 - Les modèles d'IA sont arrivés à maturité 02:22:54 - Pourquoi Google et Amazon se lancent dans le nucléaire ? 02:27:07 - La décision à l'encontre de tous les conseils qui a sauvé son entreprise02:37:59 - La fusion de rêve qui a viré au cauchemar 02:47:57 - Se faire ubériser par sa propre technologie 02:53:29 - Les 3 valeurs qui survivent à l'IA 03:04:31 - Le sport de combat que tout entrepreneur devrait pratiquerLes anciens épisodes de GDIY mentionnés : #543 - Yann Le Cun - AMI Labs - Rendre l'IA plus humaine#542 - VO - Yoni Assia - eToro - "AI Will Replace Most Traders in 18 Months"#542 - VF - Yoni Assia - eToro - "Les traders ont 18 mois avant d'être remplacés par l'IA"#539 - Loïc Hecht - Auteur de « La simulation » - Dix ans d'enquête sur l'hypothèse qui bouleverse la Silicon Valley#531 - Mathias Frachon - The Product Crew - IA et agents, tout part en vrille, il est temps de vous y mettre#500 - VO - Reid Hoffman - LinkedIn, Paypal - How to master humanity's most powerful invention#500 - VF - Reid Hoffman - LinkedIn, Paypal - Comment dompter l'invention la plus puissante de l'humanité#452 - VO - Reid Hoffman - LinkedIn, Paypal - "We are more Homo technicus than Homo sapiens"#452 - VF - Reid Hoffman - LinkedIn, Paypal - L'humanité 2.0 : Homo technicus plus qu'Homo sapiens#418 - Clément Delangue - Hugging Face - 4,5 milliards de valo avec un produit gratuit à 99%#397 - Yann Le Cun - Chief AI Scientist chez Meta - L'Intelligence Artificielle Générale ne viendra pas de Chat GPT#238 - Clément Delangue - Hugging Face - Démocratiser le machine learning pour impacter des milliards d'individusNous avons parlé de :Marketing digital : Artefact engloutit définitivement NetBoosterScratch : pourquoi c'est le meilleur premier langage pour 6-12 ansScratchThe Diary of a CEO avec Mo GawdatPrix Nobel de physique 2025 : félicitations à Michel Devoret !Anthropic - When AI builds itselfOpenClawVotre équipe met-elle à profit le temps économisé grâce à l'IA générative ?AI Doesn't Reduce Work—It Intensifies ItQu'est-ce que le « tokenmaxxing », nouvelle obsession des salariés de la Silicon Valley ?Reachy Mini – The Open-Source Robot for Today's and Tomorrow's AI BuildersLe protocole MCP (Model Context Protocol), qu'est-ce que c'est ?GranolaLe gouvernement américain autorise le retour de Claude Mythos 5, mais Fable 5 reste bloquéEffet Dunning-KrugerLes recommandations de lecture :Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity, by Daron Acemoglu & Simon Johnson

Rapid improvements in AI capabilities and growing corporate adoption have led to predictions that the technology could spark large-scale job losses before the end of the decade. Do these concerns have merit? MIT's Daron Acemoglu and Neil Thompson, and Goldman Sachs Research economist Joseph Briggs discuss with host Allison Nathan. This episode explores the latest Top of Mind report. The opinions and views expressed herein are as of the date of publication, subject to change without notice, and may not necessarily reflect the institutional views of Goldman Sachs or its affiliates. The material provided is intended for informational purposes only, and does not constitute investment advice, a recommendation from any Goldman Sachs entity to take any particular action, or an offer or solicitation to purchase or sell any securities or financial products. This material may contain forward-looking statements. Past performance is not indicative of future results. Neither Goldman Sachs nor any of its affiliates make any representations or warranties, express or implied, as to the accuracy or completeness of the statements or information contained herein and disclaim any liability whatsoever for reliance on such information for any purpose. Each name of a third-party organization mentioned is the property of the company to which it relates, is used here strictly for informational and identification purposes only and is not used to imply any ownership or license rights between any such company and Goldman Sachs. A transcript is provided for convenience and may differ from the original video or audio content. Goldman Sachs is not responsible for any errors in the transcript. This material should not be copied, distributed, published, or reproduced in whole or in part or disclosed by any recipient to any other person without the express written consent of Goldman Sachs. Disclosures applicable to research with respect to issuers, if any, mentioned herein are available through your Goldman Sachs representative or at ⁠http://www.gs.com/research/hedge.html⁠ Goldman Sachs does not endorse any candidate or any political party. Copyright 2026. All rights reserved. Learn more about your ad choices. Visit megaphone.fm/adchoices

Who kept the courts sitting and the streetlights lit when the state had almost no money to pay anyone?Two hundred years ago, British local government ran on unpaid labour. In a parliamentary survey of the boroughs from 1835, two in three of the people doing local government work were not paid at all.James Robinson (University of Chicago, CEPR) explains how this succeeded in this week's episode of VoxTalks Economics. Robinson and his co-authors call this the "embedded state". Members of the elite willingly took the unpaid jobs because the postings carried prestige and led to Parliament, promotion or a paid post. Less glamorous or dead-end postings -- the jailer for example -- had to be paidBut the unpaid officers were more productive than the paid ones.Robinson argues this is not a quirk of England at that time. Rwanda runs a high-capacity state today on much the same basis, without ever raising the taxes the IMF says a proper government needs. The lesson for anyone trying to make government work: start with the society, not the tax code.New episode of VoxTalks Economics. Link in bio.Image: William Benjamin Watkins by George Patten / Manchester Town Hall.The research behind this episode:Heldring, Leander, Davis Kedrosky, James A. Robinson, and Matthias Weigand. 2026. "The Success of the Embedded State in England." CEPR Discussion Paper No. 21460. Centre for Economic Policy Research, London. To cite this episode:Phillips, Tim, and James A. Robinson. 2026. "The success of the embedded state." VoxTalks Economics (podcast). Assign this as extra listening. The citation above is formatted and ready for a reading list or VLE.About the guestJames A. Robinson is University Professor at the Harris School of Public Policy and the Department of Political Science, University of Chicago, and a Research Fellow at the Centre for Economic Policy Research. His research spans comparative political and economic development, state capacity, and the long-run relationship between institutions and prosperity, with fieldwork across sub-Saharan Africa and Latin America. He shared the 2024 Nobel Memorial Prize in Economic Sciences with Daron Acemoglu and Simon Johnson.Research cited in this episodeThe 1835 parliamentary report. After the 1832 Reform Act, Parliament sent lawyers to roughly three hundred of the largest boroughs to record who worked for each borough government, what they did, whether they were paid, how much, and how well the job was done. The commissioners graded public goods directly; whether a jail existed, and if so whether its condition was satisfactory. The 3,500-page report is the factual basis for the paper, and it survives because Parliament itself did not know how these idiosyncratic, often medieval borough governments worked.The fiscal-military state. The dominant account of British state formation comes from John Brewer's The Sinews of Power (1989), which traces the rise of a tax-raising, salaried fiscal state after the Glorious Revolution of 1688. Robinson's point is that this describes 20,000 officials in London; across the rest of the country, where fiscal resources were thin, most government work was done for free.Mark Goldie and the unpaid office-holder. The historian and political theorist Mark Goldie documented the scale of unpaid local office-holding in earlier work; Robinson and his co-authors took that observation and asked how to study it systematically, which led them to the 1835 report.The embedded state. A state has high capacity when it can implement policy and provide public goods. The embedded state does this without the fiscal resources to fund a modern bureaucracy, by drawing on the social structure of the society it governs to motivate people to do government work unpaid. Because that social structure differs from place to place, embedding looks different in 1830s Britain, in modern Rwanda, and in 1970s South Korea; understanding the state means understanding the sociology beneath it.Rwanda's state capacity. Robinson and Leander Heldring also study the organisation of the state in Rwanda, where most government workers are unpaid and the country has never raised the 15% of national income in taxes that the International Monetary Fund treats as the threshold for a functioning state, yet implements policy effectively.Elinor Ostrom and the commons. Elinor Ostrom won the 2009 Nobel Memorial Prize for showing that communities can organise to provide and govern shared resources without the state. Robinson's argument is that the interface between such collective provision and the state is productive rather than antagonistic.Somaliland and the Guurti. Somaliland has an elaborate clan structure, and its upper house, the Guurti, represents the clans. Robinson offers it as a case where anyone trying to improve public good provision should start from the existing social structure rather than from tax reform.The History of British Local Government. Beatrice and Sidney Webb's nine-volume history of English local government documents the medieval charters, inherited land and bequests that determined how much fiscal capacity each borough had. That historically determined variation in whether a borough could afford to pay its officers is what the paper uses to identify the effect of pay on performance.More VoxTalks Economics episodesNobel Special - James Robinson on antisocial norms. The saying “don't be a toad” in Colombia tells people to mind their own business and not to tell on others. The warning that “snitches get stitches” is common to many societies. It's easy to imagine why groups adopt prosocial norms like sharing and volunteering. But what sustains an “antisocial” norm?

(0:00) Intro *Reference to the Boardroom Governance Summit at Limerick Lane Cellars, Healdsburg, California (Aug 26-27, 2026)  (2:12) About the podcast sponsor: The American College of Governance Counsel. (2:59) Start of interview.  (4:00) Origin Story of Emily, and Stewardship  (6:15) From Engineer to CEO  (7:14) Companies that she led: Elo Touch Systems (97-00), Capstone Turbine (02-03), Apexon (04-07) and NovaTorque (09-17). (9:50) Changing geopolitics of manufacturing (10:49) First Boards and Public Company Lessons (first board experience in Japan) "The soft skills are the hard part to do." (15:48) On serving in private VC-backed boards. "If you know one board, you know one board. I mean, they are all so different." (22:43) On serving in non-profit boards. "It's one of the best possible ways to get governance experience." (26:20) CEO Mistakes (32:03) Board Succession for leadership and skills. (35:33) Board Evaluations Done Right  (37:41) What Makes Great Directors. *reference to Leading Edge Stewardship, by Linda Riefler and Mayree Clark (Stanford Women on Boards). "Asking the right question, at the right time, in the right way." (39:57) AI and the Boardroom. (46:16) Innovation Versus Oversight. "The goal is informed oversight without operational interference" (49:34) Teaching Governance to Stanford Students  (52:17) Boards need to have a long-term orientation in this short-term world. (52:34) Books that have greatly influenced her life: The Bible Why Nations Fail: The Origins of Power, Prosperity, and Poverty, by Daron Acemoglu and James A. Robinson (2012) The Count of Monte Cristo by Alexandre Dumas (1846) (54:12) Her mentors. "[T]hey told me things I needed to hear in a way that I could hear them because it's easy to get defensive." (55:38) Quotes that she thinks of often or lives her life by. "Never doubt that a small group of thoughtful, committed, citizens can change the world. Indeed, it is the only thing that ever has.' by Margaret Mead. (56:43) An unusual habit or an absurd thing that she loves.  (57:30) The living person she most admires in governance: Bob Joss. Emily Liggett serves on the boards of Ultra Clean Technology and Materion Corporation. She also serves as Lecturer at Stanford GSB, where she teaches corporate governance and board leadership. You can follow Evan on social media at:X: @evanepsteinLinkedIn: https://www.linkedin.com/in/epsteinevan/ Substack: https://evanepstein.substack.com/__To support this podcast you can join as a subscriber of the Boardroom Governance Newsletter at https://evanepstein.substack.com/__Music/Soundtrack (found via Free Music Archive): Seeing The Future by Dexter Britain is licensed under a Attribution-Noncommercial-Share Alike 3.0 United States License

This is not a typical Adelaide Show episode. For the first time in 434 instalments, Steve Davis opens by confessing he’s not sure how many more episodes there will be because something has broken in him. Not in South Australia’s people, whom he loves unreservedly, but in his trust of the state’s governance. What follows is one of the most honest conversations the show has ever hosted. There is no SA Drink of the Week this episode. The mood didn’t call for it. In the Musical Pilgrimage, Steve closes with Australia Day by Steve Davis & The Virtuosos, a song whose thesis turns out to be the quiet heart of everything discussed: that we’ve retreated into our selfish dwellings, stopped sticking our arms over the fence to say hello, and in doing so have left ourselves vulnerable to exactly the kind of politics this episode is about. You can navigate episodes using chapter markers in your podcast app. Not a fan of one segment? You can click next to jump to the next chapter in the show. We’re here to serve! The Adelaide Show Podcast: Awarded Silver for Best Interview Podcast in Australia at the 2021 Australian Podcast Awards and named as Finalist for Best News and Current Affairs Podcast in the 2018 Australian Podcast Awards. And please consider becoming part of our podcast by joining our Inner Circle. It’s an email list. Join it and you might get an email on a Sunday or Monday seeking question ideas, guest ideas and requests for other bits of feedback about YOUR podcast, The Adelaide Show. Email us directly and we’ll add you to the list: podcast@theadelaideshow.com.au If you enjoy the show, please leave us a 5-star review in iTunes or other podcast sites, or buy some great merch from our Red Bubble store – The Adelaide Show Shop. We’d greatly appreciate it. And please talk about us and share our episodes on social media, it really helps build our community. Oh, and here’s our index of all episode in one concisepage. Running Sheet: Something Has Broken: SA Politics, the Park Lands, and the Politics of Distraction 00:00:00 Intro Introduction 00:00:00 SA Drink Of The Week There is no SA Drink Of The Week this week. 00:03:15 David Olney and Steve Davis Steve opens by describing where he is: not disconnected from South Australia’s people, but from its governance. He says he is earnestly worried, and that there is no performative aspect to the episode. To stress-test his thinking and provide context, he has invited back David Olney, whose academic background covers history, international politics, international security, and complex problem-solving. David notes that colleagues once told him he thought more like a psychologist or neurologist than a political scientist, always searching for the human motivation beneath structural problems. David introduces the work of political theorist Ted Robert Gurr, who studied the conditions preceding revolution across different periods of history. Gurr found two sequential thresholds: first, when people stop believing things will get better; and second, when they become convinced things are actively getting worse. Steve places himself at Gurr’s second threshold, citing the government’s handling of the algal bloom, a secret tower deal at peppercorn rent, tree clearing in the Park Lands for a golf event, and the prospect of further clearing for a motorcycle race. His concern is not with the events or sports themselves but with the irreversible damage to trees that Tourism SA uses to represent Adelaide. Two further things have deepened Steve’s despair. The first is what he reads as a coordinated flood of upbeat ministerial social media videos that do not address the Park Lands issue at all. He sees it as a tactic borrowed from Trump’s playbook. The second is the government’s launch of a media literacy tool to help students decode messaging, at the same time as the government itself, in Steve’s view, avoids transparency, attacks critics personally rather than engaging with their arguments, and operates through private deals. David draws on Rebecca Costa’s book The Watchman’s Rattle to frame this: Costa observed that as civilisations struggle to deal with significant problems, political attention shifts to small and peripheral ones. David’s illustration from literature is the war in Gulliver’s Travels fought over which end of a boiled egg to crack. Steve recommends the book Angertainment by Ed Koper as a guide to recognising this pattern. He uses Koper’s framing to contrast two dystopian visions: Orwell’s 1984, where repression at least provokes resistance, and Huxley’s Brave New World, where a population entertained into passivity never finds cause to push back. David agrees that Huxley’s version is the more troubling of the two. David then explains neoliberalism at Steve’s request: the economic model adopted across the English-speaking world in the early 1980s under Thatcher, Reagan, and Hawke, which replaced mixed economies with market-driven ones. David argues that the mixed economy model of the postwar decades, while imperfect, delivered stable living standards and could absorb shocks. What replaced it produced private monopolies, underinvestment in infrastructure and services, and a political landscape where both major parties operate within the same economic framework. His summary: in Australia, both parties wear one jackboot and one fluffy slipper. David connects this to the growth of parties like One Nation and Britain’s Reform Party, arguing that voters who have seen no meaningful improvement from either major party are reaching for alternatives, not out of ideological conversion but out of exhaustion. Steve raises a related concern: that the same billionaire interests bankrolling One Nation-type parties have no real incentive to disrupt neoliberalism, which raises questions about where that political energy actually leads. Toward the end of the episode, Steve reads from a reply he has just received from his federal member, written in response to a handwritten letter he sent six weeks earlier about a gas tax. The reply is considered and personal, acknowledging hundreds of individual constituent responses and explaining the member’s position. Steve describes it as a strand still holding, though he is careful not to place too much weight on it. David names two economists whose recent books offer some grounds for thinking a better model is possible: Mariana Mazzucato and Daron Acemoglu. Steve closes by naming David Pocock as an example of what a politician in this era can be, and David adds Barbara Pocock to that list. The episode ends with a brief exchange about what Don Dunstan and Malcolm Fraser might have made of where their respective parties have ended up. The following resources were mentioned during the episode. Books Angertainment by Ed KoperThe Watchman’s Rattle by Rebecca CostaBrave New World by Aldous Huxley1984 by George OrwellAmusing Ourselves to Death by Neil PostmanThe Common Good Economy by Mariana Mazzucato Podcasts The Rest is Politics with Alastair Campbell and Rory StewartThe Rest is Politics US featuring Anthony Scaramucci 00:42:34 Musical Pilgrimage In the Musical Pilgrimage this week we listen to Australia Day by Steve Davis & The Virutalosos. Steve introduces Australia Day as a song exploring how Australia lost the social conditions that made postwar migrant integration work. The central argument is that Italians, Greeks, and Vietnamese newcomers were absorbed into communities partly because people had time and proximity, sticking their arms over fences and saying hello. McMansions, mobile phones, and an economic model built on scarcity and anxiety have eroded that. David adds that prime ministers who romanticised the 1950s as a human ideal were simultaneously promoting the economic model that made those conditions impossible to replicate. Steve writes the songs and uses a virtual session band to produce them, with the hope that a live musician will one day take them further.Support the show: https://theadelaideshow.com.au/listen-or-download-the-podcast/adelaide-in-crowd/See omnystudio.com/listener for privacy information.

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

No podcast da MIT Technology Review Brasil desta semana, Rafael Coimbra e Alexandre Roldao Roldão discutem as análises de Daron Acemoglu sobre os impactos da IA na economia, o avanço dos agentes inteligentes nas empresas e os desafios que ainda limitam a adoção dessas tecnologias.A conversa também aborda por que as previsões de demissões em massa ainda não se concretizaram e o que esperar da próxima fase da Inteligência Artificial.Você acredita que a IA vai substituir profissões ou transformar a forma como trabalhamos?

In this Money Talks: MIT professor Daron Acemoglu joins Emily Peck to explain his research into pro-worker technologies and how we can not only avoid the AI job apocalypse but also improve workers' lives by shifting the goal of AI from automation to collaboration. Join Slate Plus to unlock weekly bonus episodes. Plus, you'll access ad-free listening across all your favorite Slate podcasts. You can subscribe directly from the Slate Money show page on Apple Podcasts and Spotify. Or, visit slate.com/moneyplus to get access wherever you listen. Podcast production by Jessamine Molli and Cheyna Roth. Hosted on Acast. See acast.com/privacy for more information.

In this Money Talks: MIT professor Daron Acemoglu joins Emily Peck to explain his research into pro-worker technologies and how we can not only avoid the AI job apocalypse but also improve workers' lives by shifting the goal of AI from automation to collaboration. Join Slate Plus to unlock weekly bonus episodes. Plus, you'll access ad-free listening across all your favorite Slate podcasts. You can subscribe directly from the Slate Money show page on Apple Podcasts and Spotify. Or, visit slate.com/moneyplus to get access wherever you listen. Podcast production by Jessamine Molli and Cheyna Roth. Hosted on Acast. See acast.com/privacy for more information.

In this Money Talks: MIT professor Daron Acemoglu joins Emily Peck to explain his research into pro-worker technologies and how we can not only avoid the AI job apocalypse but also improve workers' lives by shifting the goal of AI from automation to collaboration. Join Slate Plus to unlock weekly bonus episodes. Plus, you'll access ad-free listening across all your favorite Slate podcasts. You can subscribe directly from the Slate Money show page on Apple Podcasts and Spotify. Or, visit slate.com/moneyplus to get access wherever you listen. Podcast production by Jessamine Molli and Cheyna Roth. Hosted on Acast. See acast.com/privacy for more information.

Há uma pergunta que quase ninguém faz quando abre o ChatGPT para resolver um problema: "O que é que eu deixo de aprender ao fazer isto?" A pergunta parece absurda — afinal, estamos a obter uma resposta melhor, mais rápida, mais completa. Mas um grupo de investigadores do MIT acaba de demonstrar, com rigor matemático, que essa pergunta não é apenas pertinente. É urgente.Em fevereiro de 2026, Daron Acemoglu, Dingwen Kong e Asuman Ozdaglar publicaram um working paper no National Bureau of Economic Research (NBER) com um título que merece ser lido devagar: AI, Human Cognition and Knowledge Collapse. O artigo constrói um modelo formal — não uma opinião, não uma especulação — que mostra como a inteligência artificial generativa, e em particular a IA agêntica, pode levar ao desaparecimento progressivo do conhecimento coletivo de uma sociedade. Mesmo quando, paradoxalmente, cada indivíduo toma decisões melhores no curto prazo.

As artificial intelligence continues to integrate into the workforce, Jon is joined by MIT economists David Autor and Daron Acemoglu, recipient of the 2024 Nobel Prize in Economics, to understand what the future might hold for American workers. Together, they explore lessons from past waves of technological change, examine what pro-worker AI could look like, and discuss what policies could help workers navigate an increasingly uncertain economic future -- and whether the incentives exist to achieve them.  This episode is brought to you by: GROUND NEWS - Go to https://groundnews.com/stewart to see all sides of every story. Subscribe for 40% off the Vantage Subscription only for a limited time through my link https://groundnews.com/stewart AVOCADO GREEN MATTRESS - Go to https://AvocadoGreenMattress.com/TWS and check out their mattress and bedding sale! BOLL AND BRANCH - Go to https://BollAndBranch.com/tws with code TWS to unlock 15% off. BOMBAS - Head over to https://Bombas.com/WEEKLY and use code WEEKLY for 20% off your first purchase. Follow The Weekly Show with Jon Stewart on social media for more:  > YouTube: https://www.youtube.com/@weeklyshowpodcast > Instagram: https://www.instagram.com/weeklyshowpodcast > TikTok: https://tiktok.com/@weeklyshowpodcast  > X: https://x.com/weeklyshowpod   > BlueSky: https://bsky.app/profile/theweeklyshowpodcast.com Host/Executive Producer – Jon Stewart Executive Producer – James Dixon Executive Producer – Chris McShane Executive Producer – Caity Gray Lead Producer – Lauren Walker Producer – Brittany Mehmedovic  Producer – Gillian Spear Video Editor & Engineer – Rob Vitolo Audio Editor & Engineer – Nicole Boyce Music by Hansdle Hsu Learn more about your ad choices. Visit podcastchoices.com/adchoices

Jayme sits down with Nobel laureate economist, Daron Acemoglu, a professor at MIT, and one of the leading thinkers about labour, politics and technology. He's the author of the best-selling book “Why Nations Fail” and the forthcoming work “What Happened to Liberal Democracy?”. They talk about the decline of western liberal democracy, the alienation of the working class, AI, and more.This was a live conversation at a summit put on by OCAD and Toronto Metropolitan University called the Democracy Xchange.For transcripts of Front Burner, please visit: https://www.cbc.ca/radio/frontburner/transcripts

Operativo especial en el Estadio Banorte este domingo  SMN prevén altas temperaturas este fin de semana  Daron Acemoglu advierte riesgos laborales por la IA  Más información en nuestro podcast#grc

¿Escuchas noticias sobre indicadores macroeconómicos positivos, pero tu realidad al ir al mercado es otra? En este episodio de Tertulia y Dinero, nuestros tres profesionales apasionados por los negocios analizan la desconexión que existe entre los números del papel y el bolsillo del venezolano común.Asdrúbal Oliveros nos explica por qué, tras una de las crisis más profundas de la historia económica moderna (con una caída del 75% del PIB y años de hiperinflación), la recuperación no puede ser inmediata ni homogénea para todos.En este capítulo conversamos sobre:La crisis estructural: Los tres datos que explican de dónde venimos y por qué la solución tomará años.La economía de las "olas": ¿Por qué el sector petrolero e inmobiliario reaccionan primero mientras el comercio y el consumo se quedan atrás?.El factor Salario: La cruda realidad de por qué los ingresos no se han ajustado y la propuesta de un bono temporal de $200-$250 para la administración pública.- Fases de la recuperación: De la estabilización (ordenamiento del flujo petrolero) a la transición democrática.Inflación y Brecha Cambiaria: ¿Por qué el dólar paralelo sigue separándose de la tasa oficial a pesar de la mayor oferta de divisas?. La Píldora del día:

On this episode of the Energy Security Cubed Podcast, Charles St-Arnaud joins Joe Calnan to unpack the economic consequences of the ongoing energy crisis for Canada. For the intro, Joe explores various oil price benchmarks and what they mean. --- Guest: Charles St-Arnaud is the Chief Economist of Servus Credit Union Joe Calnan is VP Energy and Calgary Operations at the Canadian Global Affairs Institute. Reading recommendation: "Why Nations Fail: The Origins of Power, Prosperity, and Poverty", by Daron Acemoglu and Jakmes Robinson: https://www.amazon.ca/Why-Nations-Fail-Origins-Prosperity/dp/0307719227 --- Interview recording Date: April 2, 2026 // Energy Security Cubed is part of the CGAI Podcast Network. Follow the Canadian Global Affairs Institute on Facebook, Twitter (@CAGlobalAffairs), or on LinkedIn. Head over to our website at www.cgai.ca for more commentary. // Produced by Joe Calnan. Music credits to Drew Phillips.

We're joined by Ian Morris, British historian, archaeologist, and author of Foragers, Farmers, and Fossil Fuels Ian's central argument is both simple and radical: our beliefs about fairness, justice, hierarchy, equality, and even democracy are not timeless moral truths floating above history. They are shaped, constrained, and repeatedly reorganised by the ways societies extract and use energy. Across tens of thousands of years, he argues, there is a pattern beneath the chaos.   We dive into: • Why hunter-gatherer societies tended to enforce radical egalitarianism • How agriculture made hierarchy, inheritance, patriarchy, and forced labor more functional • Why fossil fuel societies unexpectedly shifted back toward equality and democracy • How values evolve like adaptations to changing material conditions • Why the industrial age expanded the moral community • Why inequality has begun rising again in recent decades • Whether we are entering a fourth great shift in human values • What energy transitions, AI, and new technologies could mean for democracy and civilisation   Key Takeaways from the Episode: 1. Human Values Are Not Fixed — They Adapt to Energy Systems Morris argues that values are not random, but nor are they eternal. Over the long run, societies repeatedly develop moral systems that fit the material conditions created by how they capture energy from the world. This is not a metaphor. Morris means it in a nearly biological sense: values that match the prevailing energy regime help societies function, grow, and outcompete their neighbours — while mismatched values lead to stagnation, fragmentation, or collapse. The mechanism is cultural evolution, operating on a civilisational timescale. A foraging band that tried to enforce agrarian-style kingship would fall apart. An industrial economy run on feudal principles would be outproduced by its rivals. Morris draws on decades of archaeological and anthropological data — compiled in his earlier work Why the West Rules — for Now — to show that this pattern holds across every major region and epoch. The implication is unsettling: the values we consider timeless may be temporary artefacts of the energy system we happen to inhabit. 2. Hunter-Gatherer Life Favoured Equality In low-energy societies, people lived in small, mobile groups with little surplus and little material inheritance. Under those conditions, strong egalitarian norms were not idealistic luxuries — they were necessary for survival and cohesion. Morris draws on ethnographic evidence from groups like the Kung San of the Kalahari and the Hadza of Tanzania to show that foraging bands actively enforced equality through what Christopher Boehm calls “reverse dominance hierarchies” — systems in which the group collectively suppresses anyone who tries to accumulate too much power or prestige. The tools were social: ridicule, gossip, ostracism, and in extreme cases, targeted violence. This was not paradise. Per capita rates of violent death among foragers were far higher than in modern states. But it was a system that worked under the constraints of low energy capture. When you cannot store surplus, when anyone can walk away from the group, when survival depends on mutual cooperation, radical equality is not a philosophy — it is an engineering requirement. 3. Agriculture Made Inequality Functional Once farming emerged, people settled, accumulated land, inherited property, and built larger social structures. In that world, hierarchy, patriarchy, kingship, and coercive labour became easier to justify and more useful for organising society. Morris is careful to frame this not as moral decline but as adaptive reorganisation. Agrarian societies that developed clear lines of inheritance, centralised leadership, and mechanisms for extracting surplus labour — whether through serfdom, taxation, or slavery — were able to build irrigation systems, raise armies, and defend territory more effectively than those that did not. The Gini coefficients of agrarian civilisations, from ancient Rome to Qing Dynasty China, consistently clustered between 0.40 and 0.60 — far higher than anything observed in foraging societies. Patriarchy, too, became structurally embedded: when wealth flows through land and land flows through lineage, control of reproduction becomes an economic imperative. As Morris puts it, agrarian societies did not choose hierarchy because they were morally inferior. They chose it — or more precisely, it chose them — because it was the value system that worked at that scale of energy capture. 4. Industrialisation Reversed the Pattern The fossil fuel age created such a dramatic expansion in energy capture that it supported a return toward broader equality. Democracy, women's rights, religious tolerance, and mass political participation became more functional in industrial societies than they had been in agrarian ones. The scale of the shift is difficult to overstate. Drawing on the data compiled in his Social Development Index, Morris shows that Western economies went from capturing roughly 38,000 kilocalories per person per day in 1800 to 230,000 by the 1970s. This explosion of productive capacity required a workforce that was literate, mobile, and motivated — not coerced. Slavery became economically irrational when a free worker operating a power loom could outproduce a plantation of forced labourers. The franchise expanded because industrial states needed buy-in from the populations whose labour and consumption drove growth. The period between 1945 and 1975 — what economists call the Great Compression — saw inequality fall to historic lows across the industrialised world, a pattern Morris attributes directly to the structural demands of fossil-fuel economies rather than to moral awakening alone. 5. Moral Progress May Be Less Moral Than We Think One of the most provocative ideas in the conversation is that what we call moral progress may often be adaptation. Values spread not simply because they are truer or nobler, but because they work better under new productive conditions. Morris is not arguing that moral reasoning is meaningless — he acknowledges the role of philosophers, activists, and reformers in articulating new ethical frameworks. But he insists that these frameworks gain traction only when the material conditions are right. The abolition of slavery is his sharpest example: anti-slavery arguments had existed since antiquity, from Stoic philosophers to medieval theologians. They gained no lasting foothold until the fossil fuel revolution made free industrial labour more productive than coerced agricultural labour. In this reading, the abolitionists were morally right — but they succeeded because the energy regime had shifted in their favour. The danger in this insight, as Princeton philosopher Christine Korsgaard argues in her response to Morris's Tanner Lectures, is that it can erode our confidence in the permanence of our own moral achievements. If democracy rose with fossil fuels, what happens when fossil fuels decline? 6. The Last 40 Years May Mark the Start of a New Shift Morris suggests the egalitarian arc of the fossil fuel age may be weakening. Since the late 20th century, rising inequality and growing acceptance of concentrated power may signal the beginnings of a fourth great transformation in values. The data supports the concern. According to the World Inequality Database, the share of national income captured by the top one per cent in the United States roughly doubled between 1980 and 2020, returning to levels last seen before the Great Depression. Freedom House has documented eighteen consecutive years of global democratic decline. Morris interprets these trends not as policy failures to be corrected but as potential symptoms of a deeper structural shift: as economies move from mass industrial production toward automation, platform monopolies, and AI-driven services, the number of people whose active participation is economically essential may be shrinking. If the fossil fuel age favoured equality because it needed mass labour and mass consumption, an age of intelligent machines may not. The egalitarian values we assumed were permanent may have been contingent on a phase of industrial development that is now passing. 7. Energy Abundance Does Not Automatically Create Equality Cases like Qatar and other resource-rich states show that energy alone is not enough. The social context into which new energy arrives matters enormously; pre-existing structures can allow elites to monopolise wealth and preserve hierarchy. Qatar holds the fourth-highest GDP per capita in the world, yet ranks near the bottom of the V-Dem Electoral Democracy Index. Saudi Arabia, the UAE, Kuwait, and Brunei tell similar stories: vast energy wealth, minimal democratic development. Morris argues this is not a contradiction of his thesis but a refinement. What matters is not merely how much energy a society captures, but how many people must participate in capturing it. In industrial economies, millions of workers were needed — creating structural pressure for education, wages, and political rights. In petrostates, a tiny elite controls extraction, distributes revenue as patronage, and faces no structural need to empower the broader population. The lesson is critical for understanding the current energy transition: if the next energy regime — whether solar, nuclear, or AI-driven — can be controlled by a narrow class of technologists and capital owners, the democratic dividend may not follow. 8. The Future May Be a Contest Between Democratic and Authoritarian Models As energy systems, technology, and AI evolve, Morris sees a real competitive struggle ahead between more egalitarian democratic societies and more centralised, authoritarian ones. The question is not only what kind of world we want — but which kind will prove more effective. Democracy's advantages are significant: distributed innovation, self-correcting institutions, the ability to attract global talent through individual freedom. But authoritarian systems have their own competitive strengths, particularly in an age of AI-enabled surveillance and rapid state-directed investment. China's ability to mobilise resources for infrastructure, energy, and technology development without electoral friction presents a genuine challenge to the democratic model. Morris draws on the framework laid out by Daron Acemoglu and James Robinson in Why Nations Fail — the contest between inclusive and extractive institutions — but adds an energy dimension: the outcome may depend less on which system we prefer and more on which system the next energy regime structurally favours. If renewable energy is distributed and requires broad participation, democracy may thrive. If AI and automation concentrate power, authoritarianism may prove more durable than we hope. Timestamps: (00:00) – Introduction to Ian Morris and the core thesis of Foragers, Farmers, and Fossil Fuels (01:00) – Why values are not random: the pattern across history (02:10) – Hunter-gatherers, equality, and the logic of low-energy societies (03:10) – Agriculture, hierarchy, kingship, and why inequality became moralized (06:00) – Energy capture as the hidden driver of value systems (09:10) – Why farming societies relied on inheritance, patriarchy, and force (15:20) – Rousseau, Hobbes, and why both misunderstood early humans (17:20) – Cultural evolution and how values adapt like biological traits (21:20) – Why fossil fuel societies moved back toward equality (28:20) – Factory labor, capitalism, and the widening of the moral community (34:20) – Are we now moving into a fourth great shift? (36:20) – Inequality, EROI, and the current energy transition (38:00) – Why Morris thinks we are still early in a new energy revolution (44:00) – Elon Musk, elite power, and why democracy is being questioned again (46:10) – Oil-rich states, Qatar, and why history still matters (54:40) – What readers should take from the book for navigating the future (56:00) – China, democracy, and the coming civilizational competition

Robby the chef has lots of endearing qualities. He can make over 5000 dishes, he's a consistent cook, and he's never late for work. But he's not a human. It is a 750 lb. stainless steel robot. With a rotating wok at its center. It's a wok-bot. Automation has changed many industries. But automation only started entering restaurant kitchens in the past couple decades. Which raises the question – what will robots mean for the restaurant industry? How will automation change jobs and how will it change the very food we eat?Today on the show, we talk with a Nobel prize-winning economist, Daron Acemoglu, about when automation is complementing or displacing workers. And we decide to put this wok-bot to the test. We pit a human chef against Robby the wok-bot in a head-to-metalhead smackdown. Further Listening/Reading:How AI could help rebuild the middle class The Big Red Button Check out our AI series: Planet Money makes an episode using AIWhy Nations Fail, America Edition (newsletter)A New Way To Understand Automation (newsletter)Get your book tour tickets here. / Pre-order the Planet Money book and get a free gift.Subscribe to Planet Money+Listen free: Apple Podcasts, Spotify, the NPR app or anywhere you get podcasts.Facebook / Instagram / TikTok / Our weekly Newsletter.This episode was hosted by Erika Beras and Justin Kramon. It was produced by Sam Yellowhorse Kesler. It was edited by Jess Jiang. It was fact-checked by Sierra Juarez and engineered by Robert Rodriguez with help from Cena Loffredo. Interpretation help from Huo Jingnan. Alex Goldmark is Planet Money's executive producer.To manage podcast ad preferences, review the links below:See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy

Economists don't usually talk about “culture.” But Joel Mokyr argues that it's the engine of innovation — and the Nobel Prize committee agreed. Stephen Dubner sits down for a thousand-year conversation (including advice!) with the new Nobel laureate.   SOURCES: Joel Mokyr, economic historian at Northwestern University.   RESOURCES: Two Paths to Prosperity: Culture and Institutions in Europe and China, 1000–2000, by Avner Greif, Joel Mokyr, and, Guido Tabellini (2025). "The Outsize Role of Immigrants in US Innovation," by Shai Bernstein, Rebecca Diamond, Abhisit Jiranaphawiboon, Timothy McQuade, and Beatriz Pousada (NBER, 2023). A Culture of Growth: The Origins of the Modern Economy, by Joel Mokyr (2016). Why Nations Fail: The Origins of Power, Prosperity, and Poverty, by Daron Acemoglu and James Robinson (2012). "The Economics of Being Jewish," by Joel Mokyr (Critical Review, 2011). Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A report released this week lays out a bleak future that comes with artificial intelligence's displacement of white-collar workers. MIT's Daron Acemoglu shares what he predicts AI will lead to in work and the economy. Then, U.S. officials are involved in two rapidly evolving foreign policy situations this week: a firefight where Cuban officials shot at a Florida-registered speedboat, killing four people and injuring six, and negotiating with Iranian officials over the country's nuclear program. Jon Finer, former principal deputy national security advisor during the Biden administration, reacts. And, the rapper Flavor Flav has invited all of the women athletes who medaled in the Olympics and Paralympics to celebrate with him in Las Vegas. He talks about his support of women's sports, the Olympics, and his music career with Public Enemy.To manage podcast ad preferences, review the links below:See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy

In this bonus episode, Nobel Prize-winning economist Daron Acemoglu joins Sam to challenge some of the most common assumptions about artificial intelligence's future. Drawing on his book Power and Progress, Daron argues that technology doesn't have a fixed destiny — and that today's choices will determine whether AI boosts workers or simply accelerates automation and inequality. He makes a case for focusing on new tasks that complement human skills, rather than replacing them, and warns that current incentives push AI toward centralization and automation by default. The conversation tackles productivity myths, reliability risks, and why regulation should proactively steer AI toward social good. Read the episode transcript here. Guest bio: Daron Acemoglu is an institute professor at MIT, faculty codirector of the James M. and Cathleen D. Stone Center on Inequality and Shaping the Future of Work, and a research affiliate at MIT's newly established Blueprint Labs. He is an elected fellow of the National Academy of Sciences, American Philosophical Society, the British Academy of Sciences, the Turkish Academy of Sciences, the American Academy of Arts and Sciences, the Econometric Society, the European Economic Association, and the Society of Labor Economists. He is also a member of the Group of Thirty. He has authored six books, including Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity with Simon Johnson. His work in economics has been recognized around the world, notably with the Nobel Prize in economic sciences, along with co-laureates Johnson and James A. Robinson, in 2024. *Please take our listener survey: ⁠⁠mitsmr.com/podcastsurvey⁠⁠ It's short — we promise! — and all respondents will receive a free MIT SMR article collection, "Maximizing the Value of Generative AI." Me, Myself, and AI is a podcast produced by MIT Sloan Management Review and hosted by Sam Ransbotham. It is engineered by David Lishansky and produced by Allison Ryder. We encourage you to rate and review our show. Your comments may be used in Me, Myself, and AI materials. ME, MYSELF, AND AI® is a federally registered trademark of Massachusetts Institute of Technology. All rights reserved.

【百花新闻信 Baihua Newsletter】 百花 Baihua Newsletter 正式上线了。也希望疲惫娇娃的听众们去订阅这个新闻信。 如果你一直在听《疲惫娇娃》《美轮美换》，或者来过我们的读书会、线下活动，你其实已经在百花的世界里了。只是现在，我们想把这个世界更系统、更持续地建起来。我们想记录中文播客圈正在发生什么，记录中国互联网的公共讨论空间，也记录这一代数字原住民华语创作者如何在"平行互联网"之间生存、创造、发声，这个新闻信是双语的，我们几个女的和其他百花成员会轮流写作。 请点击这里订阅：https://baihua.substack.com/ 如果打不开这个链接，请用以下问卷留下你的邮箱账号：https://wj.qq.com/s2/25773709/59ax/ Baihua Newsletter has officially launched. We warmly invite listeners of CyberPink to subscribe. If you've been listening to CyberPink or Mei Lun Mei Huan, or have joined our book clubs and offline events, you've already been part of the Baihua world. Now, we want to build that world in a more systematic and sustainable way. Through Baihua, we aim to document what's happening in the Chinese-language podcast sphere, trace the evolving public discourse on the Chinese internet, and explore how a generation of digital-native Chinese creators are surviving, creating, and speaking across what we call “parallel internets.” The newsletter is bilingual. We — along with other Baihua contributors — will take turns writing. Subscribe here: https://baihua.substack.com/ If you can't open the URL above, leave your email address here: https://wj.qq.com/s2/25773709/59ax/ 【聊了什么The What】 这期节目是vibe shift三部曲的最后一集——在酝酿了很久以后，我们终于找到了一个适合开启的角度——“匮乏”。这里的匮乏不仅仅指经济指标上的衰退（如好莱坞罢工、票房崩溃、生活成本危机），更指向一种集体心理状态：对美好未来的想象力丧失了。 当“信心”消失，欲望就变成了苦涩的愤怒和嘲讽。我们聊到了韩国电影中为了生存系统性谋杀竞争对手的失业男主，聊到了好莱坞自由主义叙事的全面崩塌，以及 Gen Z 为何不再相信《哈利·波特》式的英雄主义。在这样一个极化、原子化、被算法裹挟的时代，我们该如何寻找解药？答案或许在于做一些“低效”但具体的事情，比如给邻居送菜，比如慢慢地讲一个故事。 This episode is the final installment of our “Vibe Shift” trilogy. After circling the theme for a long time, we finally found the right entry point: scarcity. Scarcity here doesn't only refer to economic downturns — Hollywood strikes, collapsing box office numbers, or the cost-of-living crisis. It points to something deeper: a collective psychological state in which the imagination of a better future has eroded. When confidence disappears, desire curdles into resentment, sarcasm, and anger. We talk about a Korean film in which an unemployed man systemically eliminates his competitors just to survive. We discuss the collapse of Hollywood's liberal narrative framework. We explore why Gen Z no longer believes in Harry Potter–style heroism. In an era defined by polarization, atomization, and algorithmic manipulation, how do we find an antidote? Perhaps the answer lies in doing things that feel inefficient but concrete — bringing vegetables to a neighbor, telling a story slowly and carefully. 【时间轴 The When】 00:00 什么是“匮乏感”？它不只是缺钱，而是对未来失去信心。当希望消失，欲望会转化为愤怒和犬儒。 05:30 从《无路可逃》和《爱丁顿》说起。当资源紧缩，他人变成路障。传统“供养者”男性叙事在现代系统中崩塌。 12:11 自由主义叙事开始失效。观众厌倦好莱坞的说教，《The Studio》揭示的是文化工业的瘫痪与精英脱节。 23:36 代际断裂。为什么 Gen Z 对《哈利波特》那种九十年代式的乐观主义越来越无感，Gen Z 成长于极化与停滞之中，不再相信善恶分明的世界。 36:00 我们开始怀旧：布拉德·皮特和《F1》是一个典型例子，电影构建了一个没有政治争议、只有输赢规则的世界。传统男性气概重新被召唤。你爹还是你爹。这种叙事满足了人们对确定性的渴望，也提供了一种逃避复杂现实的方式。 47:50 性别角色回潮。《爱情盲选》和 tradwife 现象反映了在极化时代中对安全感的表演式追寻。 55: 50 AI 并未带来连接，反而放大分裂，我们开始怀疑“科技救世”的神话。《神奇四侠》中的技术乐观主义不复存在。 1:23:30 西方社会 生活成本危机加剧匮乏感。算法利用焦虑制造争吵，社交网络赛博巴尔干化是什么？ 1:35:30 结语 拒绝犬儒，从具体行动开始。真实的连接或许是对抗虚无的唯一方式。 00:00 What is “scarcity”? It's not just financial lack, but a loss of faith in the future. When hope disappears, desire turns into anger and cynicism. 05:30 We begin with No Other Choice and Eddington. As resources shrink, other people become obstacles. The traditional male “provider” narrative collapses within the modern system. 12:11 The liberal narrative loses its grip. Audiences are exhausted by Hollywood moralizing. The Studio exposes paralysis within the cultural industry and its detachment from ordinary life. 23:36 A generational rupture. Why Gen Z no longer connects with 1990s optimism like Harry Potter. Raised amid polarization and stagnation, they no longer believe in a morally clear world. 36:00 We turn to nostalgia. Brad Pitt and F1 serve as a key example. The film constructs a world without political conflict — only winners and losers. Traditional masculinity is revived. “Daddy is still daddy.” It satisfies a longing for certainty and offers escape from complexity. 47:50 The return of gender roles. Love Is Blind and the tradwife phenomenon reflect a performative search for safety in a polarized age. 55:50 AI did not bring connection; it amplified division. We begin to question the myth of technological salvation. The technological optimism of Fantastic Four no longer holds. 1:23:30 The Western cost-of-living crisis deepens scarcity. Algorithms exploit anxiety and manufacture endless conflict. What does cyber-Balkanization of social networks mean? 1:35:30 Conclusion. Reject cynicism and begin with concrete action. Real human connection may be the only antidote to nihilism. 【拓展链接 The Links】 阿花： Beyond the Machine: Creative agency in the AI landscape Personal canon by Celine Nguyen Remaking Liberalism: The Past, Present, and Future of Freedom by Daron Acemoglu and Simon Johnson 配图：从1998年开始的价格变化 影视/剧集： 电影《无路可逃》(No Other Choice / The AX) 电影《爱丁顿》(Eddington) 电影《F1》 电影《神奇四侠》(Fantastic 4) 剧集《The Studio》 真人秀《爱情盲选》(Love is Blind) 剧集《Abbott Elementary》 书籍/人物/概念： Daron Acemoglu (经济学家) Hanif Abdurraqib (作家/诗人) Tradwife (传统家庭主妇风潮) Cyber-Balkanization (赛博巴尔干化) Identity Politics (身份政治) 【疲惫红书 CyberRed】 除了播客以外，疲惫娇娃的几个女的在小红书上开了官方账号，我们会不定期发布【疲惫在读】、【疲惫在看】、【疲惫旅行】、【疲惫Vlog】等等更加轻盈、好玩、实验性质的内容。如果你想知道除了播客以外我们在关注什么，快来小红书评论区和我们互动。 Apart from the podcast, we have set up an official account on Xiaohongshu. We will periodically post content such as “CyberPink Reading,” “CyberPink Watching,” “CyberPink Traveling,” “CyberPink Vlog,” and more. Those are lighter, more fun and more experimental stuff about our lives. Leave us some comments on Xiaohongshu! 【买咖啡 Please Support Us】 如果喜欢这期节目并愿意想要给我们买杯咖啡： 海外用户：https://www.patreon.com/cyberpinkfm 海内用户：https://afdian.com/a/cyberpinkfm 商务合作邮箱：cyberpinkfm@gmail.com 商务合作微信：CyberPink2022 If you like our show and want to support us, please consider the following: Those Abroad: https://www.patreon.com/cyberpinkfm Those in China: https://afdian.com/a/cyberpinkfm Business Inquiries Email: cyberpinkfm@gmail.com Business Inquiries WeChat: CyberPink2022

AI is reshaping national power and governance. Drawing on India's digital public infrastructure, Jayant Sinha and Vasant Dhar discuss innovation and sovereignty over compute, data consent and privacy by design in Episode 103 of Brave New World. Useful Resources: 1. Jayant Sinha2. Eversource Capital3. India's Green Startups: Jayant Sinha and Sandeep Bhammer4. Nandan Nilekani5. Brave New World Episode 15: Nandan Nilekani on an Egalitarian Internet6. Brave New World Episode 50: Pramod Varma on India's Digital Empowerment 7. iSpirit8. Unique Identification Authority Of India9. Unified Payments Interface10. M-Pesa11. DigiYatra. 12. Australia has banned social media for kids under 16. 13. Data Empowerment and Protection Architecture, DEPA14. Paul Gruenwald, Global Chief Economist, S&P Global15. Daron Acemoglu, Simon Johnson, James A. Robinson. 16. Neeraj Chopra17. Thinking with Machines, The Brave New World of AI: Vasant Dhar18. Battery Smart19. Nutrifresh20. Zero Cow21. RevFin22. Upside Foods23. Brave New World Episode 93: Uma Valeti on Cultivating Meat24. Brave New World Episode 101: Deepak Chopra On Consciousness and Reality25. Geoffrey Hinton26. Asimov's Laws27. Jonathan Haidt28. The Anxious Generation: Jonathan Haidt Check out Vasant Dhar's newsletter on Substack. The subscription is free! Order Vasant Dhar's new book, Thinking With Machines

After a week of significant drops across many AI and tech-related stocks, we speak to Nobel Prize winner Daron Acemoglu, and economist Cary Leahey of Columbia University in New York, to examine whether the tech bubble could be set to burst. With Nike under investigation by Donald Trump's administration over claims it has hidden evidence that the company is using its so-called diversity, equity and inclusion policies to discriminate against white workers, Ed Butler speaks to Stefan Padfield of the Free Enterprise Project. Elsewhere, Beijing says Panama will pay 'a heavy price' for a court ruling against a Hong Kong port owner, and we look at how a growing trend has led to Kenya's central bank banning people from using bank notes to make floral-like bouquets and decorations. The latest business and finance news from around the world, on the BBC. (Picture: A sign marks Wall Street near the New York Stock Exchange in New York, NY, USA. Credit: Sarah Yenesel/EPA-EFE/REX/Shutterstock.)

We live in a world that is in desperate need of peace and wholeness. Communities across the globe are ravaged by violence and instability, but what does it look like to be practitioners that seek to transform conflict into thriving communities. In this conversation, Brandon Stiver is joined by Prashan De Visser, the Founder and CEO of Global Unites. Prashan shares his insights on the impact of colonialism, civil war and poor governance in Sri Lanka and the role of the church can play in conflict transformation. He shares about the work of Global Unites in promoting peace and reconciliation in over 20 countries emphasizing the importance of nonviolence, grassroots movements, and youth leadership in conflict transformation. This conversation dives into the complexities and the unique hope that comes with youth movements for peace. Support the Show Through Venmo - @canopyintl Subscribe to Our New YouTube Channel Podcast Sponsors Take the free Core Elements Self-Assessment from the CAFO Research Center and tap into online courses with discount code 'TGDJ25' Take the Free Core Elements Self-Assessment Resources and Links from the show Global Unites Online Why Nations Fail by Daron Acemoglu and James A. Robinson Conversation Notes (AI Generated) The importance of creating an inclusive Sri Lankan identity and governance structure. The legacy of colonialism continues to affect Sri Lanka's social fabric. Nonviolence is a crucial principle for sustainable change in conflict situations. Grassroots movements are essential for effective peace building. Youth leadership is vital for the future of conflict transformation. Reconciliation involves healing, repairing, and transforming societal structures. Inherited prejudices can be dismantled through personal connections and experiences. The church has a significant role to play in promoting peace and reconciliation. Copy-paste solutions in conflict resolution often lead to more harm than good. Local expertise is invaluable in creating effective interventions for peace. Theme music Kirk Osamayo. Free Music Archive, CC BY License

Daron Acemoglu, Institute Professor in the Department of Economics at the Massachusetts Institute of Technology, talks with Bloomberg's Carol Massar and Tim Steneovec about his Bloomberg Businessweek article detailing his “Unified Theory of Trump”.See omnystudio.com/listener for privacy information.

A special seasonal edition featuring Nobel laureates in economics whose insights inform UBS’s approach to answering complex questions. The 2024 laureate, Daron Acemoglu, reflects on the turbulent times in which we live.See omnystudio.com/listener for privacy information.

The world has reached various inflection points, or so we are often told. Advanced technology, such as artificial intelligence, promises to transform our way of life. In geopolitics, the growing competition between China and the United States heralds an uncertain new era. And within many democracies, the old assumptions that undergirded politics are in doubt; liberalism appears to be in disarray and illiberal forces on the rise.  Few scholars are grappling with the many dimensions of the current moment quite like Daron Acemoglu is. “The world is in the throes of a pervasive crisis,” he wrote in Foreign Affairs in 2023, a crisis characterized by widening economic inequalities and a breakdown in public trust. Acemoglu is a Nobel Prize–winning economist, but his research and writing has long strayed beyond the conventional bounds of his discipline. He has written famously, in the bestselling book Why Nations Fail, about how institutions determine the success of countries. He has explored how technological advances have transformed—or indeed failed to transform—societies. And more recently he has turned his attention to the crisis facing liberal democracy, one accentuated by economic alienation and the threat of technological change. Deputy Editor Kanishk Tharoor spoke with Acemoglu about a stormy world of overlapping crises and about how the ship of liberal democracy might be steered back on course. You can find sources, transcripts, and more episodes of The Foreign Affairs Interview at https://www.foreignaffairs.com/podcasts/foreign-affairs-interview.

We look at the AI boom in detail, in the wake of comments by Sundar Pichai, the Google boss, in a BBC interview. He acknowledges the risks of a potential AI bubble. We hear the thoughts of the Nobel Prize-winning economist Daron Acemoglu as well as from a future of work strategist and a campaigner for tighter AI regulation.Also, what has Saudi Arabia's Crown Prince gained from a visit to the White House? And TotalEnergies faces war crime allegations over a Mozambique massacre.You can contact us on WhatsApp or send us a voicenote: +44 330 678 3033.

Sentamos en la mesa de Hora 25 de los Negocios al Premio Nobel de Economía 2024, Daron Acemoglu, para hablar del terremoto democrático y laboral que puede suponer la Inteligencia Artificial, del primer año de Donald Trump en la Casa Blanca o de la situación económica de España. 

Our speaker is Daron Acemoglu who won the Nobel Prize in Economics. Daron is a Professor at MIT and is also the co-author of Power and Progress: Our Thousand-Year Struggle over Technology and Prosperity. I want to learn about how AI will improve productivity as well as its effect on inequality. We will compare AI's impact with the industrial revolution to understand better how the choices that we make will alter work and our society. Get full access to What Happens Next in 6 Minutes with Larry Bernstein at www.whathappensnextin6minutes.com/subscribe

What the former Finance Minister of Chile Andres Velasco has called the Deliveroo effect is most evident in Poland. Despite unprecedented economic growth and prosperity, Velasco explains, Poles remain miserable. The problem, he suggests, is that we've become so used to the magical efficiencies of the digital revolution, that we expect instant miracles in both our political and economic lives. That's one of the core issues Velasco, now Dean of Public Policy at the London School of Economics, and a group of leading public policy experts address in an intriguing collection of essays entitled The London Consensus. What the authors - who include Philippe Aghion, the 2025 Nobel Prize winner in economics - explore is how to come up with economic principles for the 21st Century that make us both happier and more prosperous, while confronting an existential challenge like climate change that didn't even register in last century's Washington Consensus. But democracy, Velasco warns, can't work like a delivery app. We've layered regulations and participatory processes that slow everything down—making it nearly impossible to build housing in California or infrastructure anywhere in the West—while personalized technology trains us to expect results immediately. This fundamental mismatch between our expectations and reality is fueling authoritarian populism, eroding trust in experts like Velasco, and Aghion, and leaving entire regions behind in a Deliveroo stew of economic failure and cultural resentment. 1. The “Deliveroo Effect” Is Breaking Democracy We've become so accustomed to instant digital gratification that we expect the same speed from politics and economics. But democracy requires deliberation, participation, and time—creating a dangerous mismatch between expectations and reality that fuels populism and dissatisfaction. Even prosperous countries like Poland, the second-fastest growing economy since 1990, remain bitterly divided.2. The Washington Consensus Got Politics Catastrophically Wrong The 1989 economic framework naively assumed you could “sort out the economics” and democracy would naturally follow. It ignored local ownership of policies and believed growth alone would create liberal democracies. China's experience—getting rich without democratizing—proved this assumption completely wrong. The London Consensus puts politics at the center.3. Markets Need States, Not “Free Markets” Versus Government The old ideological battle between markets and socialism was never productive. Markets can't function without capable states to enforce rules, regulate finance, and provide infrastructure. The real debate isn't whether to have government intervention, but what kind—finding the delicate balance between competition and regulation that fosters innovation without allowing excessive monopoly power.4. “Left-Behind Regions” Are Driving Political Upheaval Trade and technology create geographically concentrated losses—the Rust Belt, northern England—that go beyond economics. These regions experience social breakdown, population flight, and feelings of abandonment that translate directly into votes for demagogues and populists. Compensating losers from globalization wasn't just economically smart; it was politically essential.5. We Need a “Good Jobs Agenda,” Not Just Growth Following economists like Dani Rodrik and Daron Acemoglu, the London Consensus argues that policy should be evaluated through the lens of job quality, not just GDP growth. Technology isn't destiny—it can be directed toward complementing human skills rather than destroying jobs. Every policy, from trade to AI regulation, should ask: will this create quality jobs with decent pay, benefits, and worker agency?Keen On America is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

À l'UQAM, rencontre rare entre le chercheur Yoshua Bengio et le Nobel d'économie Daron Acemoglu, autour d'un dialogue essentiel : l'IA va-t-elle trop vite pour nos sociétés ? Entre prudence scientifique et lucidité économique, ils évoquent la spéculation autour des géants de l'IA, les risques de dépendance technologique du Canada et la nécessité d'une gouvernance mondiale démocratique. Un échange dense, lucide et sans complaisance sur la course à l'intelligence artificielle et sur l'avenir de notre autonomie numérique.

There's a serious high-stakes policy fight at the heart of this.The Democrats didn't pick a fight over authoritarianism or tariffs or masked immigration agents in the streets. They picked one over health care. And the issue here is very real. Huge health insurance subsidies passed under President Joe Biden are set to expire at the end of this year, threatening to make health care premiums skyrocket and kick millions off their insurance.Neera Tanden was one of the architects of the Affordable Care Act and has worked in Democratic policymaking for decades. She is the president of the Center for American Progress and was a director of Biden's Domestic Policy Council. I asked her on the show to lay out the policy stakes of the shutdown and what a deal might look like.Mentioned:KFF Health Tracking PollThe Time Tax by Annie LowreyOne Big Beautiful Bill ActBook Recommendations:Why Nations Fail by Daron Acemoglu and James A. RobinsonThe Sirens' Call by Chris HayesEnd Times by Peter TurchinThoughts? Guest suggestions? Email us at ezrakleinshow@nytimes.com.You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast, and you can find Ezra on Twitter @ezraklein. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs.This episode of “The Ezra Klein Show” was produced by Rollin Hu. Fact-checking by Michelle Harris and Kate Sinclair. Our senior engineer is Jeff Geld, with additional mixing by Aman Sahota. Our executive producer is Claire Gordon. The show's production team also includes Marie Cascione, Annie Galvin, Kristin Lin, Jack McCordick, Marina King and Jan Kobal. Original music by Pat McCusker. Audience strategy by Kristina Samulewski and Shannon Busta. The director of New York Times Opinion Audio is Annie-Rose Strasser. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app.

داستان الهام بخش برای ملت‌هایی که دنبال توسعه می‌گردن، وقتی ایران داشت گذشته رو خرج می‌کرد ولی کره آینده رو می‌ساخت.متن: بهجت بندری، علی بندری با راهنمایی آرش رئیسی‌نژاد | ویدیو و صدا: حمیدرضا فرخ‌سرشتبرای دیدن ویدیوی این اپیزود اگر ایران هستید وی‌پی‌ان بزنید و روی لینک زیر کلیک کنیدیوتیوب بی‌پلاسکانال تلگرام بی‌پلاسمنابع و لینک‌هایی برای کنجکاوی بیشترSouth Korean Development Model by Milan LajčiakThe chaebol and the US military–industrial complex: Cold War geopolitical economy and South Korean industrialization by Jim GlassmanThe democratic transition by Fabrice Murtin and Romain WacziargPopulation Change and Development in KoreaINSTITUTIONS AS THE FUNDAMENTAL CAUSE OF LONG-RUN GROWTH by Daron Acemoglu, Simon Johnson, James RobinsonThe Park Chung Hee Era by by UNG-KOOK KIMKorea's Development Under Park Chung Hee By Hyung-A KimKorea's Rapid Export Expansion in the 1960s: How It Began,JUNGHO YOO*THE KOREAN MIRACLE (1962-1980) REVISITED: MYTHS AND REALITIES IN STRATEGY AND DEVELOPMENT Kwan S. KimLand Reform in Korea, 1950, Shin, Yong-HaThe Economic and Social Modernization of the Republic of Korea: 1945-1975,EDWARD S. MASONTenancy, Land Redistribution, and Economic Growth A Case of Korea, 1920-1960, Jea Hwan Hong, Duol Kimچرا ملت‌ها شکست می‌خورند، دارون عجم اوغلو، جیمز رابینسونراه باریک آزادی، دارون عجم اوغلو، جیمز رابینسونکره بعد از جنگ: اصلاحات ارضی (شروع ازسینگمان ری (Syngman Rhee) اوج در دوره پارک) Hosted on Acast. See acast.com/privacy for more information.

durée : 00:40:38 - L'Invité(e) des Matins - par : Guillaume Erner, Yoann Duval - Intelligence artificielle, pouvoir des géants de la tech, surveillance de masse, automatisation à outrance : les nouvelles technologies promettaient de libérer l'humanité, mais semblent aujourd'hui creuser les inégalités et menacer les démocraties. Comment reprendre le contrôle de notre destin ? - réalisation : Félicie Faugère - invités : Daron Acemoglu économiste au MIT et prix Nobel d'économie 2024

durée : 02:29:51 - Les Matins - par : Guillaume Erner, Yoann Duval - - réalisation : Félicie Faugère

durée : 00:03:53 - Sous les radars - par : Sébastien LAUGENIE - «L'accès à des informations fiables est la ressource fondamentale qui alimente l'économie du XXIe siècle», estiment une dizaine d'économistes dont les Nobel Joseph Stiglitz et Daron Acemoglu dans une déclaration collective publiée par le Forum sur l'information et la démocratie. Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

durée : 00:03:53 - Sous les radars - par : Sébastien LAUGENIE - «L'accès à des informations fiables est la ressource fondamentale qui alimente l'économie du XXIe siècle», estiment une dizaine d'économistes dont les Nobel Joseph Stiglitz et Daron Acemoglu dans une déclaration collective publiée par le Forum sur l'information et la démocratie. Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

Daron Acemoglu is an Institute Professor of Economics in the Department of Economics at the Massachusetts Institute of Technology. His books include (with James A. Robinson) Why Nations Fail, and (with Simon Johnson) Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity. In 2024, he was awarded the Nobel Prize in economics. In this week's conversation, Yascha Mounk and Daron Acemoglu discuss the impact of colonialism, the role of culture in civil society, and China's strengths and weaknesses. Email: leonora.barclay@persuasion.community Podcast production by Mickey Freeland and John Taylor Williams. Connect with us! Spotify | Apple | Google X: @Yascha_Mounk & @JoinPersuasion YouTube: Yascha Mounk, Persuasion LinkedIn: Persuasion Community Learn more about your ad choices. Visit megaphone.fm/adchoices

What comes first, a prosperous economy or stable democratic institutions? Nobel Prize-winning economist and MIT professor Daron Acemoglu joins Preet to discuss the economic stakes of shifting institutional norms in the U.S. He weighs in on President Trump's decision to fire key personnel at the Federal Reserve and Bureau of Labor Statistics, as well as the announcement that the government will take a roughly 10% equity stake in Intel.  Then, Preet answers a question about the latest developments in the Kilmar Abrego Garcia deportation case and discusses Governor Gavin Newsom's recent social media posts. In the bonus for Insiders, Acemoglu discusses what people often overlook when comparing the Industrial Revolution to the AI revolution.  Join the CAFE Insider community to stay informed without hysteria, fear-mongering, or rage-baiting. Head to cafe.com/insider to sign up. Thank you for supporting our work. Show notes and a transcript of the episode are available on our website.  You can now watch this episode! Head to CAFE's Youtube channel and subscribe. Have a question for Preet? Ask @PreetBharara on BlueSky, or Twitter with the hashtag #AskPreet. Email us at staytuned@cafe.com, or call 833-997-7338 to leave a voicemail. Stay Tuned with Preet is brought to you by CAFE and the Vox Media Podcast Network. Learn more about your ad choices. Visit podcastchoices.com/adchoices

C dans l'air du 4 septembre 2025 - Faut-il plus taxer les riches ?En cette rentrée, la question revient en force dans le débat politique. Et cette fois, c'est le Parti socialiste qui remet le sujet sur la table en proposant d'inclure la taxe Zucman au budget 2026, sur lequel le gouvernement promet de faire des compromis, alors qu'il se trouve sur la sellette à l'approche d'un vote de confiance prévu le 8 septembre.Mais de quoi parle-t-on ? Qu'est-ce que la taxe Zucman, au cœur du contre-budget du PS ? Adoptée à l'Assemblée nationale en février dernier (par 116 voix contre 39), puis rejetée au Sénat en juin, cette mesure, inspirée des travaux de l'économiste Gabriel Zucman (qui lui a donné son nom), propose un impôt plancher sur la fortune (IPF) équivalent à 2 % du patrimoine net des ultra-riches dépassant 100 millions d'euros — soit 0,01 % des contribuables, environ 1 800 foyers français. Avec à la clé : entre 15 et 25 milliards de recettes supplémentaires.Soutenue par la gauche, contestée par la droite et l'extrême droite, la mise en œuvre de cette taxe soulève de nombreux débats depuis des semaines.Cet été, sept prix Nobel d'économie ont apporté leur soutien à la mesure : Daron Acemoglu (2024), George Akerlof (2001), Abhijit Banerjee (2019), Esther Duflo (2019), Simon Johnson (2024), Paul Krugman (2008) et Joseph Stiglitz (2001), réunis dans une tribune du Monde parue en juillet. D'autres, en revanche, comme le président du Medef, Patrick Martin, évoquent la menace de l'exil fiscal.Parallèlement, alors qu'une vague historique de transmissions se profile, les propositions de réforme de l'impôt sur les successions se multiplient. Dans un entretien accordé aux Échos, la présidente de l'Assemblée nationale, Yaël Braun-Pivet (Renaissance), évoque la nécessité de ne pas « exclure d'emblée toute hausse d'impôts » et de « se pencher sur la taxation des "super-héritages" ». L'élue des Yvelines explique : « 0,1 % des héritiers reçoivent des montants supérieurs à 13 millions d'euros et ne paient en moyenne que 10 % de droits de succession. »Aujourd'hui, près de la moitié des ménages français ne touche pas d'héritage. Parmi ceux qui en bénéficient, 87 % ne paient aucun droit de succession (chaque parent peut donner jusqu'à 100 000 euros par enfant sans qu'il y ait de droits de donation à payer). Au-dessus, le taux moyen d'imposition effectif sur les successions en France est d'environ 5 %.Autre sujet dans le débat : le train de vie des élus. En réponse à de nombreux messages de Français, le Premier ministre a promis de passer au crible et de supprimer d'éventuels avantages indus dont bénéficieraient les responsables politiques. Il a confié une mission à l'ex-député socialiste René Dosière pour les identifier.LES EXPERTS :- EMMANUEL DUTEIL - Directeur de la rédaction - L'Usine Nouvelle - THOMAS PORCHER - Économiste, professeur à la Paris School of Business, auteur de Le vacataire - RAPHAËLLE BACQUÉ - Grand reporter - Le Monde , auteure de Successions - CAROLINE MICHEL-AGUIRRE - Grand reporter au service France – Nouvel Obs

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

Will artificial intelligence help you do your job, or will it just straight-up do your job and leave you unemployable? Or will the future bring something else entirely — either between those two extremes or a world that we simply cannot imagine yet? And are we already starting to see signs of that future emerging? On this episode of The New Bazaar, Cardiff is joined by economist Nathan Goldschlag, Research Director at the Economic Innovation Group. Until recently, Nathan was Principal Economist at the U.S. Census Bureau's Center for Economic Studies, where among other things he led research on the impact of technology, including AI, on the economy. Any worthwhile list of the world's best economists on the subject of AI and work would have to include him. Cardiff and Nathan go through Nathan's own research* and also filter out the megaton of nonsense on the topic and discuss some of the work done by others — research, essays, meanderings — that they think is actually worth sharing with listeners. They discuss, among other things: How many businesses are now using AI to produce goods and servicesHow have things changed since the launch and popularization of large language modelsEconomic growth consequences of AIWhether “learn to code” is still good advice The skills that still matter To steer or not to steer the AI future* Nathan's research on AI was done in collaboration with a large team of researchers at the Center for Economic Studies at the U.S. Census Bureau including Emin Dinlersoz, Lucia Foster, David Beede, John Haltiwanger, Zach Kroff, Nikolas Zolas, Gary Anderson, and Eric Childress, along with program area partners including Kathryn Bonney, Cory Breaux, Cathy Buffington, and Keith Savage, as well as academic partners including Daron Acemoglu, Erik Brynjolfsson, Kristina McElheran, and Pascual Restrepo. Related links:The impact of AI on the workforce: Tasks versus jobs?Tracking Firm Use of AI in Real Time: A Snapshot from the Business Trends and Outlook Survey.The Rapid Adoption of Generative AI | NBERAnswering the Call of AutomationAI-2027.comTyler Cowen - the #1 bottleneck to AI progress is humansDriverless trucks are coming and unions aren't happy about itGenerative AI at Work Hosted on Acast. See acast.com/privacy for more information.

Is artificial intelligence going to put us all out of work? There have been a range of predictions about the economic impact of AI, ranging from the modest to the fantastical. Nobel Prize-winning MIT professor Daron Acemoglu joins David Rothkopf to get to bottom of what we can really expect out of the AI revolution.  This material is distributed by TRG Advisory Services, LLC on behalf of the Embassy of the United Arab Emirates in the U.S.. Additional information is available at the Department of Justice, Washington, DC. Learn more about your ad choices. Visit megaphone.fm/adchoices

It's true that robots (and other smart technologies) will kill many jobs. It may also be true that newer collaborative robots (“cobots”) will totally reinvigorate how work gets done. That, at least, is what the economists are telling us. Should we believe them? SOURCES:David Autor, professor of economics at the Massachusetts Institute of Technology.James Rosenman, C.E.O. of Andrus on Hudson senior care community.Karen Eggleston, economist at Stanford University.Yong Suk Lee, professor of technology, economy, and global affairs at the University of Notre Dame. RESOURCES:"Robots and Labor in Nursing Homes," by Yong Suk Lee, Toshiaki Iizuka, and Karen Eggleston (NBER Working Paper, 2024)."Global Robotics Race: Korea, Singapore and Germany in the Lead," by International Federation of Robotics (2024)."Unmet Need for Equipment to Help With Bathing and Toileting Among Older US Adults," by Kenneth Lam, Ying Shi, John Boscardin, and Kenneth E. Covinsky (JAMA Internal Medicine, 2021)."Robots and Labor in the Service Sector: Evidence from Nursing Homes," by Karen Eggleston, Yong Suk Lee, and Toshiaki Iizuka (NBER Working Papers, 2021).The Work of the Future: Building Better Jobs in an Age of Intelligent Machines, by David Autor, David Mindell, Elisabeth Reynolds, and the MIT Task Force on the Work of the Future (2020)."Robots and Jobs: Evidence from US Labor Markets," by Daron Acemoglu and Pascual Restrepo (University of Chicago Press, 2020)."The Slowdown in Productivity Growth and Policies That Can Restore It," by Emily Moss, Ryan Nunn, and Jay Shambaugh (The Hamilton Project, 2020)."The China Shock: Learning from Labor Market Adjustment to Large Changes in Trade," by David H. Autor, David Dorn, and Gordon H. Hanson (NBER Working Papers, 2016)."Deregulation at Heart of Japan's New Robotics Revolution," by Sophie Knight and Kaori Kaneko (Reuters, 2014). EXTRAS:"What Do People Do All Day?" by Freakonomics Radio (2024)."Did China Eat America's Jobs?" by Freakonomics Radio (2017).

We have always been a nation of drinkers — but now there are more daily users of cannabis than alcohol. Considering alcohol's harms, maybe that's a good thing. But some people worry that the legalization of cannabis has outpaced the research. (Part one of a four-part series.) SOURCES:Jon Caulkins, professor of operations research and public policy at Carnegie Mellon University.Yasmin Hurd, director of the Addiction Institute at Mount Sinai.Michael Siegel, professor of public health and community medicine at Tufts University.Tom Standage, deputy editor of The Economist.Ryan Stoa, associate professor of law at Louisiana State University. RESOURCES:"Cannabis Tops Alcohol as Americans' Daily Drug of Choice," by Christina Caron (The New York Times, 2024)."Deaths from Excessive Alcohol Use — United States, 2016–2021," by Marissa B. Esser, Adam Sherk, Yong Liu, and Timothy S. Naimi (Morbidity and Mortality Weekly Report, 2024)."Nixon Started the War on Drugs. Privately, He Said Pot Was ‘Not Particularly Dangerous,'" by Ernesto Londoño (The New York Times, 2024)."A Brief Global History of the War on Cannabis," by Ryan Stoa (The MIT Press Reader, 2020).Craft Weed: Family Farming and the Future of the Marijuana Industry, by Ryan Stoa (2018)."How the Sugar Industry Shifted Blame to Fat," by Anahad O'Connor (The New York Times, 2016)."The Perils of Ignoring History: Big Tobacco Played Dirty and Millions Died. How Similar Is Big Food?" by Kelly D. Brownell and Kenneth E. Warner (The Milbank Quarterly, 2009).A History Of The World In Six Glasses, by Tom Standage (2005)."Cancer and Coronary Artery Disease Among Seventh-Day Adventists," by E. L. Wynder, F. R. Lemon, and I. J. Bross (Cancer, 1959). EXTRAS:"Why Is the Opioid Epidemic Still Raging?" series by Freakonomics Radio (2024)."Daron Acemoglu on Economics, Politics, and Power," by People I (Mostly) Admire (2024)."Let's Be Blunt: Marijuana Is a Boon for Older Workers," by Freakonomics Radio (2021)."What's More Dangerous: Marijuana or Alcohol?" by Freakonomics Radio (2014).