Podcasts about Adam Smith

Krystal and Saagar discuss Trump threatens payback for Iran killing soldiers, Trump rages over Zohran Bibi arrest talks, China erases US AI lead, secret gov plot for biological war scenario. Annie Jacobsen: ​https://www.penguinrandomhouse.com/books/783250/biological-war-by-annie-jacobsen/​ Adam Smith: ​https://electadamsmith.com/ To become a Breaking Points Premium Member and watch/listen to the show AD FREE, uncut and 1 hour early visit: www.breakingpoints.com Merch Store: https://shop.breakingpoints.com/ See omnystudio.com/listener for privacy information.

Krystal has a heated debate with Rep Adam Smith on Israel, AIPAC, DSA and more. Annie Jacobsen: ​https://www.penguinrandomhouse.com/books/783250/biological-war-by-annie-jacobsen/​ Adam Smith: ​https://electadamsmith.com/ To become a Breaking Points Premium Member and watch/listen to the show AD FREE, uncut and 1 hour early visit: www.breakingpoints.com Merch Store: https://shop.breakingpoints.com/ See omnystudio.com/listener for privacy information.

Dive into why real capitalism lifts people while crony capitalism and big-government intervention drive up costs in housing, healthcare and energy. Alan breaks down the Founders' vision, Adam Smith's invisible hand and insights from Milton Friedman and Thomas Sowell to show how limited government unleashes prosperity. Plus, hear revealing soundbites from Senators Slotkin and Warner on voter ID and examine the explosive Detroit voter fraud documentary in the works. Don't miss this hard-hitting episode on freedom, fair elections, and fighting cronyism in America. Please take a moment to rate and review the show and then share the episode on social media. You can find me on Facebook, X, Instagram, GETTR, TRUTH Social, TikTok, YouTube and Rumble by searching for The Alan Sanders Show. And, consider becoming a sponsor of the show by visiting my Patreon page!

Matthew 9:35–10:5 | Adam Smith

Matthew 9:35–10:5 | Adam Smith

Tonight on The Last Word: Donald Trump's election attack threat looms over the midterms. Also, Trump absurdly claims the U.S. is “winning big in Iran.” Plus, relatives detail the violent past of the ICE agent in the Maine shooting. And The Washington Post reports the Reflecting Pool peeling is likely due to application flaws. Anderson Clayton, Bishop William J. Barber II, Nevada Secretary of State Cisco Aguilar, Rep. Adam Smith, Rep. Chris Pappas, and Jarrett Ley join Jonathan Capehart. To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In the third hour, Dick Fain, Hugh Millen and Jackson Felts bring back Bracket Friday as they discuss the best jerseys and uniforms in Pacific Northwest sports history, with one representative from most of the region’s most iconic looks, then they look ahead to the World Cup Final Sunday, then Congressman Adam Smith from the 9th District joins us.See omnystudio.com/listener for privacy information.

Congressman Adam Smith from the 9th District of Washington state joins Dick Fain to talk about the Protect College Sports Act and how it aims to change the landscape of the NCAA and NIL, the World Cup and Seattle’s success as a site, the hopeful return of the NBA with the Sonics soon, and the Seahawks new ownership in the Khosla family.See omnystudio.com/listener for privacy information.

In 1776 — that same year America declared its independence — Adam Smith published the equally revolutionary The Wealth of Nations, his founding explanation of national economic value. Two hundred and fifty years later, Tim O'Reilly argues in the free-market Economist that Elon Musk and his fellow tech barons are building a monarchical form of capitalism that the proto-democratic Smith would have hated. Musk, O'Reilly reports, believes that SpaceX will become “worth more than the rest of Earth”. The merchants are becoming princes, O'Reilly warns. And the rest of us are becoming peasants. Such is the road to serfdom in our AI age. So who should own the AI in our bewildering age of multi-trillion dollar start-ups like SpaceX, Anthropic and OpenAI? Or as That Was The Week publisher Keith Teare asks in his latest editorial, who should own the “intelligence” of our AI age? Keith uses a bottling plant as a metaphor to describe our dilemma. Since no single entity can own this intelligence — the sum total of our common experience — charging us for it would be like seizing the Earth's water supply and selling it back to us, Coca-Cola style, in plastic bottles. Except that the Hayekian Keith approves of the bottling process. Private companies, rather than governments, he argues, are most suited to doing this. For Keith, this dilemma is also an opportunity to redistribute the ownership of intelligence. He argues for a “Human Wealth Fund” into which every consequential AI company should put a slice of its equity. In the manner of Norway's sovereign wealth fund, this fund would be distributed to all citizens. Rather than Denmark, now we should become like Norway, a tiny homogenous nation with a cultural distaste for Muskian individual wealth. Not very realistic, I fear. On top of that, it's hard to imagine our tech princes collaborating on anything. Musk and Altman aren't on speaking terms while Altman and Amodei, who also loathe each other, are focused on their IPOs. Meanwhile, the Trump administration, which presumably would coordinate this fund, is pitching a $100,000-a-month fast feed of the president's posts. Keith's question, “who owns the intelligence”, is the right one. But the answer won't come from trickle-down funds set-up by our tech princes. Such supposed munificence is about as likely as America becoming Norway. Read the fine print of any “Human Wealth Fund” set up by Sam Altman and Elon Musk. As we should know all too well by now, when a “revolutionary” Silicon Valley gives stuff away, it turns out to be exorbitantly expensive. Free plastic bottles of intelligence, anyone? Five Takeaways •       Intelligence, Not AI. The week's framing shift: the word AI is too small, because AI is merely the tool for harvesting and delivering the thing itself — intelligence, the sum total of our common human experience. Keith argues the renaming is not semantic but political: the moment intelligence sits at the center of the discussion, everyone's opinion has to be shaped by what it actually is, and the idea that any single entity could own it starts to look as bizarre as owning the world's water supply. Andrew's rejoinder: they're still just words — though he concedes intelligence is the better one. •       Bottled Intelligence Is Good — The Question Is Who Benefits. Keith refuses the critic's role: bottling intelligence, like Google's bottling of the world's words into search, is a good thing, because only massively capitalized private companies can innovate at that scale — and between private entities and governments as owners of intelligence, he'll take the companies every time. What's wrong is the distribution of the benefits. Even insiders are complaining: Alex Karp is publicly angry at OpenAI and Anthropic's pricing, while China's Kimi K3 — released the day of recording and, Keith claims, better than Claude Fable — signals that very good models are about to get very cheap. •       Capitalism Adam Smith Would Hate. Tim O'Reilly argues in The Economist that Musk and his type are building a capitalism Smith would despise — founders as monarchs, a point Henry Farrell reinforces with a slide from Peter Thiel's startup class placing the king of a monarchy and the founder of a startup side by side. Keith's response is characteristically unsentimental: Smith would have hated everything since the Federal Reserve, and the founder-king structure — Larry and Sergey's voting shares, Zuckerberg's special rights, corporations bigger than countries with user bases bigger than China — is simply the stage of capitalism we're at. The question is whether there's a path from here to somewhere better. •       The Human Wealth Fund. Keith's path comes in two versions: government-down, a sovereign wealth fund holding AI equity for every citizen; or company-up, the AI companies voluntarily endowing a global fund — and it only takes one to move first, because everyone else would have to react. His proxy is Norway, where every citizen benefits from ownership — not payouts, ownership — in the oil fund; AI revenues, unlike Norwegian oil, could eventually drive most of a doubled global GDP. His critique of the Brynjolfsson economists' much-signed statement is that “must act now” is vacuous: he'd have added a point four naming the actual mechanism. •       The Bet. Andrew's counter-case: Musk and Altman loathe each other, the mob hates AI so thoroughly that no pro-AI politician can survive, the states from Newsom's California to Florida are embracing nothing, New York just enacted the first data center moratorium, and the founders — eyes on their IPOs — are in the pockets of the banks. Hence the wager: 5% of the Teare Wealth Fund says no Human Wealth Fund this year, and none in the twenties. Keith declined the bet, on principle: he's an advocate, and only through advocacy does public opinion change. As Andrew put it: keep fighting the good fight — maybe one of the crazy ideas will stick. About the Guest Keith Teare is the founder and editor of the That Was The Week tech newsletter, and Andrew's weekly co-host. A British-born Silicon Valley entrepreneur and investor, he was a co-founder of TechCrunch and runs the Palo Alto–based venture firm SignalRank. He and Andrew have been arguing about technology — productively — every week for years. References: •       That Was The Week — Keith's newsletter; this week's editorial argues that the word AI is too small, and that the central question of the age is who owns intelligence. •       Tim O'Reilly in The Economist — on Elon Musk building a form of capitalism that Adam Smith would hate, quoting Musk's claim that SpaceX will become worth more than the rest of the Earth. •       Henry Farrell — the big tech critic's companion piece, featuring the slide from Peter Thiel's startup class that plac...

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

Pramila Jayapal desperately begs for cash with a recipe you can Google free. The viral German soccer fan Freddy deactivated his X account. Rep. Adam Smith is issuing a warning to his party about letting in socialists. // LongForm: GUEST: Washington State Supreme Court Candidate Dave Larson says the way Washington picks its judges is a threat to your rights. // Quick Hit: People on both sides of the aisle want Mitch McConnell to provide an update on his health.

“We have a Justice Department which is now 100% the political pawn of the president,” warns Brookings senior fellow Jonathan Rauch. “He points, and they shoot.” Point and shoot. Like an old Kodak camera. Not exactly assuring words, you might think, from a man who begins our conversation looking back at the first six months of 2026 by announcing that he's significantly less alarmed than he was a year ago. Yes, Rauch acknowledges, Trump's approval ratings have sunk, the courts have pushed back, Elon Musk's DOGE rampage has petered out. And yet the pointing and the shooting goes on. Rauch, who only months ago diagnosed eighteen “distinct and unmistakable signs” of an American fascism in a much touted Atlantic piece, now admits he may never crack the Trumpian code. Every time you nail it to the wall, he says, it morphs, creeps or sails away. Like an Iranian gunboat in Hormuz. Slippery stuff for the liberal Brookings analyst. Fascism one month, McKinley-style imperialism the next, then Gilded Age plutocracy — although without those ontologically undeniable Carnegie libraries. Meanwhile, America's 250th birthday party fizzled into what Rauch calls a “damp squib,” its reflecting pool turning an opaque green rather than a clarifying blue. A muddy madness in DC. Still, amidst all the opacity, Rauch remains a defiantly optimistic liberal. In contrast with yesterday's guest, the reality hallucinating Turi Munthe, Rauch believes not only that there is an ontological reality, but that it's good. Frank Fukuyama was right, Rauch insists. Liberalism is not only the only political system that creates wealth, produces knowledge and settles disputes, but also establishes an undeniable reality. Liberals just need to relearn how to clearly tell its story. Perhaps. Though storytelling is certainly simpler when nobody is waving a gun at you. Five Takeaways •       Less Alarmed, Still Scared. Rauch opens with the good news: he is significantly less alarmed than he was a year ago, when the administration was running rampage, putting agencies out of business and demanding Greenland. Approval ratings have dropped, so Trump has less political space; the courts have pushed back, so he has less judicial space; Stephen Miller has vanished from view. And then comes the caveat that gives the episode its title: the Justice Department is now 100% the political pawn of the president — he points, and they shoot — and Trump has shown that as his ratings fall, he becomes more willing, not less, to use those tools. •       I May Never Crack the Code. Only months ago, Rauch diagnosed eighteen distinct and unmistakable signs of a modern American reinvention of fascism in The Atlantic. He doesn't regret the essay — but he has gone back to being confused. The Trump phenomenon is slippery: every time you nail it to the wall, it morphs, creeps or slides away. Fascism one month, McKinley-style imperialism in Venezuela the next, an Iran war with no rationale at all. Trump is such an improviser, and so disorganized, that Rauch concedes there is an element of randomness he may never decode — though he accepts Andrew's suggestion that attention is now the coin of the political realm. •       Not the Gilded Age — No Carnegie Libraries. The new inequality, Rauch argues, is different in kind: a class of people almost superhuman in the wealth they control, and strangely narcissistic and nihilistic toward the broader society. The Gilded Age tycoons did some bad things, but they also built — Carnegie's libraries, Mellon's National Gallery, Rockefeller's University of Chicago, Stanford's university. This group builds rockets and sounds, in the case of Marc Andreessen, like a parody of an Ayn Rand novel — or, as Andrew corrects him, not a parody at all: they simply repeat what they've read. Even so, Rauch is not sorry to see politics reacting to a world where Musk can casually drop $300 million into a presidential race. •       The Gloves-Off Court and the Accelerating Presidency. The Supreme Court term brought the clearest statement yet of the conservative agenda: Humphrey's Executor overturned after eighty years, making it far easier for presidents to fire agency heads at will; what remained of the Voting Rights Act effectively gutted; birthright citizenship surviving by a shockingly narrow margin. The imperial presidency is not new, Rauch notes — what's new is the speed. A president can now simply refuse to run a congressionally mandated agency, and the Senate, forty quietly nixed nominations notwithstanding, remains lacking in spine. The Todd Blanche nomination, he says, is the next test of whether any line exists at all. •       Fukuyama Was Right — and Liberals Should Say So. Rauch sees a moral vacuum and, for the first time, a craving to fill it: the pope's AI encyclical, multi-faith clergy bearing witness in Minnesota, the Episcopalians and Latter-day Saints finding their voices. His prescription for the second half of 2026 is a liberal one, in the nineteenth-century sense — science, markets, constitutions, rule of law. Fukuyama, widely misunderstood, was right: there is only one system that produces knowledge, peace, freedom, and wealth on a global scale, and it's ours. It needs fixing — he cheers the bipartisan housing bill Trump refused to sign — but liberals must relearn how to tell that story, and how to brag. About the Guest Jonathan Rauch is a senior fellow in Governance Studies at the Brookings Institution and a contributing writer at The Atlantic. He is the author of nine books, including The Constitution of Knowledge: A Defense of Truth (2021), Cross Purposes: Christianity's Broken Bargain with Democracy (Yale, 2025), and Kindly Inquisitors: The New Attacks on Free Thought. A recipient of the National Magazine Award, he serves on the boards of Heterodox Academy and Civic Life, and is a longtime friend of the show. References: •       Rauch's Atlantic essay identifying eighteen “distinct and unmistakable signs” of a modern American reinvention of fascism — the piece he stands by, even as he admits the phenomenon keeps morphing. •       His recent essays for The UnPopulist on why liberal societies need grand stories about themselves, and why liberals must relearn how to brag about liberalism. •       Jonathan Rauch and Peter Wehner in The New York Times — the earlier argument, which Rauch says still holds, that the Republican Party is more dangerous to the constitution and the rule of law than the Democratic Party. •       Tim O'Reilly in The Economist — on Elon Musk building a form of capitalism that Adam Smith would hate. •       Francis Fukuyama — whose widely misunderstood The End of History thesis Rauch defends: there is only one system that creates wealth, produces knowledge, and settles political disputes on a global scal...

KGMI's Adam Smith and Dianna Hawryluk chat about the Northwest Raspberry Festival in Lynden, PRIDE Ferndale, Sunnyland Stomp in Bellingham, and Punk Fest in Fairhaven.

This week's Chairman's Report is a reprint of an article written by Professor John Cochrane looking back at Adam Smith's work, "Wealth of Nations".

Adam Smith wird bis heute immer wieder zitiert, dabei wird er aber missverstanden. Von ihm stammt die Idee der «unsichtbaren Hand des Marktes», ein Kernargument für eine freie Marktwirtschaft, möglichst ohne Staat.

Every ambitious man wants to win, but very few learn how to lose well. In this episode, Mayhew explores why setbacks in business can feel so personal, why rejection often attacks our sense of self-worth, and how the stories we tell ourselves create far more suffering than the event itself. If you've ever replayed a sales call, obsessed over a lost client, or questioned your own ability after hearing "no", this episode is for you. Make the change and book a call with Adam Smith: https://calendly.com/adamsmith-agameconsultancy/meeting-with-adam-smith-a-gameEmail Us: hello@agameconsultancy.comAdam SmithAdam Smith is an entrepreneur, transformational coach, NLP Practitioner and Timeline Therapist with extensive experience building businesses, leading teams and developing high-performing client relationships. Combining strong commercial acumen with deep expertise in human behaviour, he helps founders overcome the unconscious patterns that limit growth, leadership and fulfilment. Known for his ability to build trust quickly, create meaningful connections and challenge clients to think differently, Adam has helped business owners improve performance, increase confidence and achieve sustainable success without compromising who they are.Connect with Adam Smith: https://www.linkedin.com/in/adam-smith-high-performance-coach/Adam MayhewAdam Mayhew is an entrepreneur and transformational coach who helps founders build successful businesses without sacrificing their health, relationships or peace of mind. Drawing on his own journey through burnout, alcohol dependency and feeling disconnected despite outward success, he helps ambitious business owners develop greater clarity, resilience and self-awareness. His coaching combines mindset, performance and wellbeing to help founders make better decisions, improve their energy and create a life that feels aligned with the success they have worked hard to achieve. Adam is a qualified Nutritional Therapist, Mindfulness Teacher, and CNHC-registered Health Practitioner.Connect with Adam Mayhew: https://www.linkedin.com/in/adam-mayhew-nutrition-coaching/To find out more about Smith & Mayhew: https://agameconsultancy.com/about/

AI is commoditizing specialization — and the move isn't to specialize harder. It's to elevate. AI is running the 250-year division of labor in reverse, collapsing roles that used to be separate into one.Since Adam Smith's pin factory in 1776, progress meant slicing work into ever-narrower specialties — Babbage extended it to cognitive work, Coase explained why firms hoard coordination to make it pay. Junior and Dr. Tim Clark argue AI has flipped the whole arc. When the cost of coordination falls toward zero and deep expertise gets commoditized by "computational cognition," labor stops dividing and starts converging. At LeaderFactor, three roles that once had nothing to do with each other are merging into one, and the org chart no longer looks conventional.So what do you actually do about it? The episode gives leaders and L&D a practical filter. Every task sorts into what AI can do autonomously, what needs a human in the loop, and what has to stay uniquely human. That maps onto two algorithms: the AI algorithm — process information, identify patterns, generate outputs — and the human algorithm — assign value, exercise judgment, bear responsibility. The instruction is direct: cede the AI algorithm's ground, elevate into the human one, and stop binding your identity to a role that's now perishable. The scarce trait is no longer domain expertise. It's high agency.Chapters00:00 — Is AI reversing the division of labor?01:36 — Adam Smith, the pin factory, and 250 years of specialization04:23 — Babbage brings the division of labor to cognitive work06:22 — Coase: why firms exist and what falling coordination costs change07:46 — When agents talk to agents, coordination cost goes to zero08:18 — The Grand Convergence: how LeaderFactor's org chart changed12:36 — Marginalization forces a choice: elevate or be displaced13:49 — Why "upskilling" is dead — it's access vs. motivation now15:26 — High agency beats domain expertise16:14 — The AI algorithm: process, pattern, generate18:49 — Don't trust the insulation: step changes are coming19:35 — The human algorithm: assign value, judge, bear responsibility20:11 — The practical move: objective → responsibilities → roles23:00 — Filtering roles: autonomous, augmented, uniquely human24:49 — The psychology of a role that keeps changing26:37 — Bind yourself to value creation, not a title28:19 — Recap and final thoughts29:29 — Read Leading Through AI + free skill previewsReady for more? Take a look at our resources below. 

Matthew 9:27–34 | Adam Smith

Matthew 9:27–34 | Adam Smith

Live July 11, 2026 | Yaron Brook Show(Season 12, Episode 121)Commenting on Dr. Mike Israetel -- Capitalism, Pharma, Drugs & FDA | Yaron Brook ShowIs profit immoral—or is it the greatest force for human progress ever discovered?From World Cup fever and elite athletic performance to pharmaceutical innovation, patents, healthcare freedom, altruism, religion, and the philosophy behind capitalism, Yaron Brook takes on some of today's biggest intellectual controversies.Why do so many people resent profit? Why do we praise scientists while ignoring the entrepreneurs who make innovation possible? Should individuals—not governments—decide what medical risks they're willing to take? And why do ideas like altruism and collectivism continue to dominate modern culture?The discussion concludes with an outstanding live Q&A covering guilt and altruism, truth and rationality, aging and end-of-life ethics, Adam Smith on monopolies, radical Islam and the political spectrum, sunscreen myths, religion and freedom, and Richard Hanania's Kakistocracy.Whether you're interested in economics, philosophy, politics, healthcare, entrepreneurship, or Objectivism, this episode offers a perspective you won't hear anywhere else.Main Topics:00:00 Introduction & World Cup excitement02:13 What soccer teaches about virtue and excellence09:32 Why elite athletes deserve our admiration11:06 Enjoying sports without making them your life13:02 Bodybuilding, fitness, and Michael Israetel15:21 Michael Israetel's intellectual journey19:12 Why profit benefits everyone22:45 The myth of "excess profits" and collectivism30:21 Ambition, culture, and economic productivity32:25 Money vs. intrinsic motivation35:10 Property rights, freedom, and capitalism37:44 Pharmaceutical profits drive innovation40:28 Did Jonas Salk reject patents?43:21 Why patents matter47:10 Altruism versus self-interest in science49:02 Why businessmen are innovators too50:30 Passion and financial incentives54:00 The myth of sacrificing for society57:03 How profit improves everyone's lives1:00:37 Incentives, trade, and globalization1:03:17 Why FDA regulation slows innovation1:05:25 Should patients decide their own medical risks?1:10:09 Healthcare: individualism vs. collectivism1:13:14 Patient-doctor decision making1:14:13 Fighting today's anti-science movement1:15:58 London Documentary fundraising update1:17:17 The truth about ancient life expectancy1:19:27 Upcoming Phoenix courses1:21:24 Ayn Rand Institute conference in Austin1:23:06 Alex Epstein, AI, and the future of energyLive Audience Questions1:25:34 Is altruism really driven by guilt—and why do facts fail to persuade true believers?1:28:45 How can people go through life without treating truth as an objective standard?1:30:06 What is the rational approach to aging, dependence, and end-of-life care?1:32:02 Why classify radical Islamists with the political left rather than the right?1:33:44 Did Adam Smith actually support government regulation to prevent monopolies?1:35:02 A practical question: Should you wear sunscreen every day—even when it's cloudy?1:37:26 Which poses the greater threat to individual freedom today—Catholicism or Islam?1:43:54 Richard Hanania's Kakistocracy: Is it worth reading despite its philosophical inconsistencies?Subscribe for daily analysis on economics, politics, philosophy, technology, investing, and current events.#pharmaceutical #Capitalism #Objectivism #Trump #Economics #Profit #FreeMarkets #Innovation #Entrepreneurship #Healthcare #worldcup The Yaron Brook Show is Sponsored by[The Ayn Rand Institute](https://www.aynrand.org/starthere)[Energy Talking Points, featuring AlexAI, by Alex Epstein](https://alexepstein.substack.com/)[Express VPN](https://www.expressvpn.com/yaron)[Hendershott Wealth Management](https://www.youtube.com/watch?v=X4lfC...) &(https://hendershottwealth.com/ybs/)[Michael Williams & The Defenders of Capitalism Project](https://www.DefendersOfCapitalism.com)[Support the Show]( / yaronbrookshow )[Sponsor the Show](askyaron@yaronbrookshow.com/)[One-time donation](https://bit.ly/2RZOyJJ)Join the [Yaron Brook Show YouTube channel]( / @yaronbrook )Like what you hear? Like, share, and subscribe to stay updated on new videos and help promote the [Yaron Brook Show](https://bit.ly/3ztPxTx)Continue the discussion by following Yaron on [Twitter](https://bit.ly/3iMGl6z) and [Facebook](https://bit.ly/3vvWDDC )Want to learn more about Ayn Rand and Objectivism? Visit the [Ayn Rand Institute](https://bit.ly/35qoEC3)Become a supporter of this podcast: https://www.spreaker.com/podcast/yaron-brook-show--3276901/support.Yaron is the executive chairman of the Ayn Rand Institute and a world class speaker. He is the coauthor of the national best-seller Free Market Revolution: How Ayn Rand's Ideas Can End Big Government, Equal is Unfair: America's Misguided Fight Against Income Inequality and In Pursuit of Wealth: The Moral Case for Finance. He speaks around the world on a variety of topics including the morality of capitalism, Ayn Rand and her philosophy, finance and economics, and the value of inequality.

While Adam Smith is celebrated in some circles as the “Father of Free-Market Economics” (Austrians would disagree), his writings on the “disadvantages” of the worker are misleading.Original article: https://mises.org/mises-wire/how-adam-smith-helped-create-modern-unionism

While Adam Smith is celebrated in some circles as the “Father of Free-Market Economics” (Austrians would disagree), his writings on the “disadvantages” of the worker are misleading.Original article: https://mises.org/mises-wire/how-adam-smith-helped-create-modern-unionism

In this episode, we explore one of the most important leadership concepts emerging in the age of artificial intelligence: universal vulnerability.For years, psychological safety has been understood as a deeply personal experience—shaped by individual histories, workplace relationships, and whether vulnerability is consistently rewarded or punished. But AI introduces something entirely new. For the first time, every professional, regardless of industry or role, is confronting the same fundamental uncertainty.This conversation unpacks why AI isn't simply another wave of technological disruption. Unlike previous innovations that transformed specific industries, AI reaches across every function, every organization, and every career path simultaneously. More importantly, it challenges something far deeper than our workflows—it challenges our identities.Drawing on ideas from Adam Smith's division of labor, Joseph Schumpeter's creative destruction, Clayton Christensen's disruptive innovation, and modern research on psychological safety, this episode explains why today's AI revolution is fundamentally human before it's technological.You'll discover:What "universal vulnerability" means and why it matters.Why AI creates an identity crisis—not just a skills gap.How psychological safety becomes a competitive advantage during rapid technological change.Why leaders must create environments where uncertainty can be discussed openly.Practical insights for helping teams navigate fear, change, and continuous reinvention.As AI reshapes the future of work, the organizations that thrive won't simply adopt new technology faster—they'll build cultures where people feel safe enough to evolve alongside it. This episode offers a framework for understanding that challenge and leading through it.

KGMI's Adam Smith and Dianna Hawryluk chat about Northwest Tune-Up, Pride IN Bellingham, the Firecracker Car Show in Ferndale, and live music in Ferndale and Bellingham.

Today I'm speaking with Mordecai Kurz, Joan Kenney Professor of Economics Emeritus at Stanford University. We are discussing his latest book, Private Power and Democracy's Decline: How to Make Capitalism Support Democracy (MIT Press, 2026). After its high-water mark several decades ago, democracy's status continues to slide globally. Capitalism and democracy, which once seemed to complement each other, now appear at odds. Free-market policies and monopolistic technologies have enriched many while driving inequalities that harm workers. Many have opined on how to fix the political and economic problems of our day, from an embrace of radical libertarian policy to socialist ownership of the means of production. Mordecai Kurz's extensive study of capitalism and democracy charts a path for balancing economic and political freedom. Since the days of Adam Smith, technology has changed rapidly, necessitating new formulations that take into account the private power centers that exercise control much like monarchies did in the Age of Enlightenment. Despite the imbalance, capitalism still remains a driver of technological progress and innovation. How can we make both capitalism and democracy work for the good of everyone? I'm happy today to get the chance to speak with such an illustrious scholar and to learn a bit more about how to understand this defining puzzle of our age. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Today I'm speaking with Mordecai Kurz, Joan Kenney Professor of Economics Emeritus at Stanford University. We are discussing his latest book, Private Power and Democracy's Decline: How to Make Capitalism Support Democracy (MIT Press, 2026). After its high-water mark several decades ago, democracy's status continues to slide globally. Capitalism and democracy, which once seemed to complement each other, now appear at odds. Free-market policies and monopolistic technologies have enriched many while driving inequalities that harm workers. Many have opined on how to fix the political and economic problems of our day, from an embrace of radical libertarian policy to socialist ownership of the means of production. Mordecai Kurz's extensive study of capitalism and democracy charts a path for balancing economic and political freedom. Since the days of Adam Smith, technology has changed rapidly, necessitating new formulations that take into account the private power centers that exercise control much like monarchies did in the Age of Enlightenment. Despite the imbalance, capitalism still remains a driver of technological progress and innovation. How can we make both capitalism and democracy work for the good of everyone? I'm happy today to get the chance to speak with such an illustrious scholar and to learn a bit more about how to understand this defining puzzle of our age. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/intellectual-history

Today I'm speaking with Mordecai Kurz, Joan Kenney Professor of Economics Emeritus at Stanford University. We are discussing his latest book, Private Power and Democracy's Decline: How to Make Capitalism Support Democracy (MIT Press, 2026). After its high-water mark several decades ago, democracy's status continues to slide globally. Capitalism and democracy, which once seemed to complement each other, now appear at odds. Free-market policies and monopolistic technologies have enriched many while driving inequalities that harm workers. Many have opined on how to fix the political and economic problems of our day, from an embrace of radical libertarian policy to socialist ownership of the means of production. Mordecai Kurz's extensive study of capitalism and democracy charts a path for balancing economic and political freedom. Since the days of Adam Smith, technology has changed rapidly, necessitating new formulations that take into account the private power centers that exercise control much like monarchies did in the Age of Enlightenment. Despite the imbalance, capitalism still remains a driver of technological progress and innovation. How can we make both capitalism and democracy work for the good of everyone? I'm happy today to get the chance to speak with such an illustrious scholar and to learn a bit more about how to understand this defining puzzle of our age. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/politics-and-polemics

Today I'm speaking with Mordecai Kurz, Joan Kenney Professor of Economics Emeritus at Stanford University. We are discussing his latest book, Private Power and Democracy's Decline: How to Make Capitalism Support Democracy (MIT Press, 2026). After its high-water mark several decades ago, democracy's status continues to slide globally. Capitalism and democracy, which once seemed to complement each other, now appear at odds. Free-market policies and monopolistic technologies have enriched many while driving inequalities that harm workers. Many have opined on how to fix the political and economic problems of our day, from an embrace of radical libertarian policy to socialist ownership of the means of production. Mordecai Kurz's extensive study of capitalism and democracy charts a path for balancing economic and political freedom. Since the days of Adam Smith, technology has changed rapidly, necessitating new formulations that take into account the private power centers that exercise control much like monarchies did in the Age of Enlightenment. Despite the imbalance, capitalism still remains a driver of technological progress and innovation. How can we make both capitalism and democracy work for the good of everyone? I'm happy today to get the chance to speak with such an illustrious scholar and to learn a bit more about how to understand this defining puzzle of our age. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/law

Today I'm speaking with Mordecai Kurz, Joan Kenney Professor of Economics Emeritus at Stanford University. We are discussing his latest book, Private Power and Democracy's Decline: How to Make Capitalism Support Democracy (MIT Press, 2026). After its high-water mark several decades ago, democracy's status continues to slide globally. Capitalism and democracy, which once seemed to complement each other, now appear at odds. Free-market policies and monopolistic technologies have enriched many while driving inequalities that harm workers. Many have opined on how to fix the political and economic problems of our day, from an embrace of radical libertarian policy to socialist ownership of the means of production. Mordecai Kurz's extensive study of capitalism and democracy charts a path for balancing economic and political freedom. Since the days of Adam Smith, technology has changed rapidly, necessitating new formulations that take into account the private power centers that exercise control much like monarchies did in the Age of Enlightenment. Despite the imbalance, capitalism still remains a driver of technological progress and innovation. How can we make both capitalism and democracy work for the good of everyone? I'm happy today to get the chance to speak with such an illustrious scholar and to learn a bit more about how to understand this defining puzzle of our age. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices

Today I'm speaking with Mordecai Kurz, Joan Kenney Professor of Economics Emeritus at Stanford University. We are discussing his latest book, Private Power and Democracy's Decline: How to Make Capitalism Support Democracy (MIT Press, 2026). After its high-water mark several decades ago, democracy's status continues to slide globally. Capitalism and democracy, which once seemed to complement each other, now appear at odds. Free-market policies and monopolistic technologies have enriched many while driving inequalities that harm workers. Many have opined on how to fix the political and economic problems of our day, from an embrace of radical libertarian policy to socialist ownership of the means of production. Mordecai Kurz's extensive study of capitalism and democracy charts a path for balancing economic and political freedom. Since the days of Adam Smith, technology has changed rapidly, necessitating new formulations that take into account the private power centers that exercise control much like monarchies did in the Age of Enlightenment. Despite the imbalance, capitalism still remains a driver of technological progress and innovation. How can we make both capitalism and democracy work for the good of everyone? I'm happy today to get the chance to speak with such an illustrious scholar and to learn a bit more about how to understand this defining puzzle of our age. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices

Tonight on The Last Word: Donald Trump's war in Iran flares up amid the NATO Summit in Turkey. Also, Democrats pull their endorsements of Graham Platner after a sexual assault claim. And Trump brags about dropping crypto investigations. Rep. Adam Smith, Jessica Mackler, Rep. Chellie Pingree, and Eric Lipton join Lawrence O'Donnell. To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Patrick Ryan is the author of "The Moral Market: Adam Smith and the Promise of Virtuous Capitalism." Patrick argues that capitalism was never intended to be separated from morality. Modern business leaders often treat ethics as a compliance issue or a reputational concern. Adam Smith saw it differently. He believed markets only function when they are built on justice, reciprocity, and trust. In this conversation, Patrick explains how shareholder primacy reshaped leadership thinking, why leaders should measure success beyond short-term financial performance, and how Smith's concept of the impartial spectator can help leaders make better decisions when competing interests collide. For leaders trying to balance performance, responsibility, and long-term value creation, this conversation offers a practical framework for making decisions that are both effective and principled. Find episode 519 on The Leadership Podcast, on YouTube, channel @theleadershippodcast, or wherever you get your podcasts! Watch this Episode on YouTube | Patrick Ryan on The Leadership Idea We Missed for 250 Years https://bit.ly/TLP-519 Key Moments [04:20] What have leaders fundamentally misunderstood about capitalism as a moral system? [08:44] How does the duty of care in law connect to Smith's impartial spectator concept? [12:40] What practices should leaders adopt to institutionalize the impartial spectator? [15:53] Performative leadership versus legitimate storytelling. [21:14] Where does the rules and system play versus personal responsibility in leadership? [25:25] Where have you personally struggled between what's profitable and what's just? [28:53] For busy leaders without time to reflect—what's your closing advice? Memorable Quotes "Morality is not a friction. It's actually the lubricant that makes markets work." "Prosperity without justice is mere plunder." "He believed that economic exchange was a way of creating trust, which is quite interesting really, using capital, an early form of capitalism, using competition, using economic exchange as a way of creating a commonality as opposed to splitting people apart." "Morality and respect, and that level of reciprocity is the oil that generates everything. It's not extractive, it's value additive." "If a leader that performs integrity creates a culture that performs integrity, rather than a leader that is and has integrity. The tone from the top. It's vitally important." "Legitimate storytelling is the honest description of something that's actually happening. It's not a dishonest description of something you would like to happen." "The majority of us want to live a happy, healthy life where we have created value for our companies, we've created happiness in our families, we've done good works for our communities, and we're able to point to that and be proud that we've made that difference." "You should neither be rewarded nor penalized for moral luck. You should be rewarded or penalized for the actions that you took yourself and the effect that you had in creating value." Explore the full archive at www.theleadershippodcast.com or wherever you get your podcasts! These are the books mentioned in this episode Resources Mentioned The Leadership Podcast | theleadershippodcast.com Sponsored by | www.darley.com Rafti Advisors. LLC | www.raftiadvisors.com Self-Reliant Leadership. LLC | selfreliantleadership.com Patrick Ryan LinkedIn | www.linkedin.com/in/patrickryan

 In this powerful episode, Francesca opens up about the fears that can appear even after achieving the things you once dreamed of. From building a successful business and creating a life she's proud of, to questioning whether she's making the right decisions, this conversation explores why external success doesn't always quiet the internal voice of doubt. With Smith, they unpack self-worth, identity, childhood experiences, and the hidden beliefs that can keep high-performing people stuck. Make the change and book a call with Adam Smith: https://calendly.com/adamsmith-agameconsultancy/meeting-with-adam-smith-a-gameEmail Us: hello@agameconsultancy.comAdam SmithAdam Smith is an entrepreneur, transformational coach, NLP Practitioner and Timeline Therapist with extensive experience building businesses, leading teams and developing high-performing client relationships. Combining strong commercial acumen with deep expertise in human behaviour, he helps founders overcome the unconscious patterns that limit growth, leadership and fulfilment. Known for his ability to build trust quickly, create meaningful connections and challenge clients to think differently, Adam has helped business owners improve performance, increase confidence and achieve sustainable success without compromising who they are.Connect with Adam Smith: https://www.linkedin.com/in/adam-smith-high-performance-coach/Adam MayhewAdam Mayhew is an entrepreneur and transformational coach who helps founders build successful businesses without sacrificing their health, relationships or peace of mind. Drawing on his own journey through burnout, alcohol dependency and feeling disconnected despite outward success, he helps ambitious business owners develop greater clarity, resilience and self-awareness. His coaching combines mindset, performance and wellbeing to help founders make better decisions, improve their energy and create a life that feels aligned with the success they have worked hard to achieve. Adam is a qualified Nutritional Therapist, Mindfulness Teacher, and CNHC-registered Health Practitioner.Connect with Adam Mayhew: https://www.linkedin.com/in/adam-mayhew-nutrition-coaching/To find out more about Smith & Mayhew: https://agameconsultancy.com/about/

Show Notes: Jenny Davidson describes her continued passion for literature, reading, and writing, her interest in primatology, adventures in Tanzania, and her new hobbies.  Form and Technique in Powerlifting  She details her fitness routine, including powerlifting, running, swimming, triathlon, and yoga. Jenny shares her experience with a longstanding injury affecting her ability to do high-volume aerobic training. Jenny explains the basics of powerlifting, distinguishing it from Olympic lifting. She describes her favorite lift, the deadlift, and the other two main lifts: squat and bench press. Jenny shares her powerlifting achievements, including her recent competitions and her current lifting numbers. She emphasizes the importance of proper form and technique in powerlifting. Health and Community through Fitness Jenny discusses the impact of her exercise routine on her mood and energy levels. She describes how exercise helps her maintain a schedule and provides a sense of community through fitness activities. Jenny shares her experience with yoga and how it has become a significant part of her life. She mentions her weekly private Pilates sessions for therapeutic purposes. A Career in Literature Jenny talks about her career in literature, starting with her graduate studies at Yale. She specializes in 18th-century British literature and has been teaching at Columbia for 25 years. She mentions several books, including Clarissa and Moby Dick. Jenny enjoys teaching a mix of introductory and specialized classes, including comparative European novels and single-author classes. She shares her enthusiasm for teaching complex literary texts and the unique challenges they present.  Recommended 18th Century Literature Jenny recommends Jonathan Swift's Gulliver's Travels and his other prose satires as essential reading. She highlights Frances Burney's novel Evelina and Edward Gibbon's History of the Decline and Fall of the Roman Empire. Jenny also recommends Adam Smith's Wealth of Nations and Edmund Burke's Reflections on the Revolution in France. She mentions Laurence Sterne's Tristram Shandy and A Sentimental Journey as accessible entry points to 18th-century literature. The Rise of Literacy Jenny discusses the rise of literacy and popular print in the late 17th and early 18th centuries. She explains how books like Pamela by Richardson were shared among multiple readers, increasing their reach. Jenny notes the evidence of reading among both elite and working-class individuals. She highlights the importance of marginalia and letters in understanding the reading habits of the time. Reading Habits Jenny shares her unusual relationship with reading, being a very fast reader from a very early age. She describes her reading habits, including rereading books she teaches and reading modern novels for pleasure. Jenny mentions her book, Reading Style: A Life in Sentences, which explores her life as a reader. She reflects on the challenges of finding enough reading material during demanding times of the semester. Primatology in Tanzania Jenny discusses her recent trips to Tanzania and her interest in primatology and being influenced by Jane Goodall from a young age. She shares her experience learning Swahili and conducting oral history interviews with Tanzanian field assistants. Jenny plans to create an oral history digital repository at Columbia and potentially publish essays or a book on her findings. She describes her collaboration with a Tanzanian guide, Rama, and their efforts to revive a tree-planting education program. AI in Humanities Education Jenny discusses the potential and limitations of AI in humanities education. She expresses concerns about students using AI for assignments, viewing it as a form of cheating. Jenny shares her positive experience with using AI for transcription of oral history interviews. She emphasizes the importance of creating a quiet mind and immersive reading environments for deep learning. Harvard Reflections Jenny talks about classes with Barbara Johnson, Elaine Scarry, and Writing Narrative History with Simon Schama. She also mentions the class Judith Clara taught on political obligation, and a class called The Development of the Modern State taught by Stanley Hoffman, Peter Hall, and Tom Ertman. Timestamps: 02:21: Powerlifting and Technique  07:45: Impact of Exercise on Personal Life  10:17: Career in Literature and Teaching  13:00: Recommended 18th-Century Literature  13:15: Reading Public in the 18th Century  26:45: Jenny's Reading Habits  29:09: Tanzania Research and Oral History  35:39: AI and Humanities Education  Links: Facebook: https://www.facebook.com/groups/214744151893608/user/112738 LinkedIn: https://www.linkedin.com/in/jenny-davidson-a89a7b1b5   *AI generated transcript and show notes.  

Matthew 9:18–26 | Adam Smith

Matthew 9:18–26 | Adam Smith

685. Today author Adam J. Smith joins us to talk about his writing. Based in Covington, Adam uses his adopted home state of Louisiana as the backdrop for his fiction. Through his books, readers are introduced to Callier, Louisiana, a seemingly quiet town where deep secrets and unexpected dangers hide just beneath the surface. From the chilling pursuit of a small town killer in The Callier Cutter to the battle against institutional corruption in Your Honor, Smith captures the tension of local mystery. He also writes youth fiction using Callier as a starting point for a fantasy portal to Adventure Land. Smith weaves the unique spirit and pacing of Louisiana life into unforgettable tales of suspense, community, and adventure. Now available: Liberty in Louisiana: A Comedy. The oldest play about Louisiana, author James Workman wrote it as a celebration of the Louisiana Purchase. Now it is back in print for the first time in 222 years. Order your copy today! This week in the Louisiana Anthology. C'lestine Eustis and James Herndon. Cooking in Old Creole Days. Gumbo Filé. (First English recipe). "Put into a casserole (saucepan) a spoonful of pure lard and one of flour, stir it well until it is of a light brown. Chop an onion into small pieces and throw them in. Cut up a fat capon or chicken into small pieces and put these in the casserole with the flour and lard. Stir it all the while until the chicken is nearly done. When the whole is well browned, add a slice of ham, cut up small. Throw in two or three pods of red pepper, and salt to your taste. Now add a quart of boiling water, and leave it on the fire for two hours and a half. A quarter of an hour before dinner is served add three dozen oysters with their liquor. Just before taking the soup off the fire, put in a tablespoonful of filet, stirring it all the while. Let it boil one minute and then serve. Do not put in too much filet; the spoon should not be full. Indeed, half a tablespoonful is enough." Louise Livingston Hunt, New Orleans. This week in Louisiana history. July 3, 1870. The riverboat Robert E. Lee defeated the Natchez in a race on the Mississippi.  This week in New Orleans history. July 3, 1964: Following the passage of the Civil Rights Act, major New Orleans hotels and restaurants began the official process of desegregation. This week in Louisiana. GalaxyCon New Orleans  Opening Weekend July 10'12, 2026 New Orleans Ernest N. Morial Convention Center, 900 Convention Center Blvd New Orleans, LA 70130 Website: galaxycon.com GalaxyCon opens its three‑day pop‑culture festival on July 10, bringing celebrity guests, cosplay, comics, gaming, and fan meet‑ups to the New Orleans Ernest N. Morial Convention Center. The weekend features panels, autograph sessions, photo ops, and a massive exhibitor hall: Friday, July 10: 2 p.m. - 1 a.m. Saturday, July 11: 10 a.m. - 1 a.m. Sunday, July 12: 10 a.m. - 8 p.m. GalaxyCon is billed as a '3‑Day Festival of Fandom,' with appearances from actors, voice actors, creators, cosplayers, and fan groups across sci‑fi, fantasy, anime, comics, and gaming. Tickets range from $50.00 to $350. Postcards from Louisiana. The Rock Block Band at Felix's Restaurant and Oyster Bar. Listen on Apple Podcasts. Listen on audible. Listen on Spotify. Listen on TuneIn. Listen on iHeartRadio. The Louisiana Anthology Home Page. Like us on Facebook. 

In 1776, a work was published that challenged an empire, questioned old systems of power, and helped reshape the modern world. But this wasn't the Declaration of Independence. It was a dense, ambitious book about trade, labor, money, and prosperity that changed how people understood nations and wealth itself. It attacked mercantilism, defended markets, and introduced ideas that are still debated 250 years later. Learn more about Adam Smith and The Wealth of Nations on this episode of Everything Everywhere Daily. Shop the store at Shop.Everything-Everywhere.com Sponsors Hexclad Get 10% off your order at hexclad.com/DAILY Mint Mobile Save 50% on Unlimited premium wireless plans starting at $15/month at MintMobile.com/EED Quince Go to quince.com/daily for 365-day returns, plus free shipping on your order! DripDrop Go to dripdrop.com and use promo code EVERYTHING for 20% off your first order! Subscribe to the podcast!  https://everything-everywhere.com/everything-everywhere-daily-podcast/ -------------------------------- Executive Producer: Charles Daniel Associate Producers: Austin Oetken & Cameron Kieffer   Become a supporter on Patreon: https://www.patreon.com/everythingeverywhere Discord Server: https://discord.gg/Ds7Rx7jvPJ Instagram: https://www.instagram.com/everythingeverywhere/ Facebook Group: https://www.facebook.com/groups/everythingeverywheredaily Twitter: https://twitter.com/everywheretrip Website: https://everything-everywhere.com/  Disce aliquid novi cotidie Learn more about your ad choices. Visit megaphone.fm/adchoices

The Declaration of Independence, which founded the United States as an independent nation, was written in 1776—the same year that Adam Smith published The Wealth of Nations, the most prominent early treatise on international capitalism. Whether that represents a coincidence remains an ongoing debate. What's clear, however, is that over the past two and a half centuries, the United States emerged as a slave economy, a free labor economy, a debtor nation, a creditor nation, and ultimately a powerful empire. Adam and Cameron discuss. Learn more about your ad choices. Visit megaphone.fm/adchoices

Pramila Jayapal desperately begs for cash with a recipe you can Google free. The viral German soccer fan Freddy deactivated his X account. Rep. Adam Smith is issuing a warning to his party about letting in socialists. // LongForm: GUEST: Let's Go Washington's Brian Heywood on SCOTUS upholding transgender athlete bans as well as their decision to hear a parental rights case out of Washington. // Quick Hit: A House Republican explains why he has been absent for months. Constitutional law professor Jonathan Turley pushes back on criticism of Justice Amy Coney Barrett.

In this episode, Dr. Killeen reflects on a thought from Adam Smith that defines happiness with surprising simplicity: being healthy, out of debt, and having a clear conscience. While all three matter, Dr. Killeen takes a closer look at the financial burden many dentists quietly carry throughout their careers. From student loans to practice acquisitions and equipment purchases, debt often becomes a normal part of dentistry. But when uncertainty creeps in, those obligations can create stress that affects more than just your finances. They can influence your decisions, your leadership, and your overall well-being. He explores why reducing debt is about more than improving your balance sheet. It's about creating freedom, flexibility, and peace of mind. Sometimes the most valuable investment isn't the next piece of equipment or expansion project. It's reducing the weight you're carrying so you can lead and live with greater confidence.

If you follow politics at all, your head must be on a swivel (and hurting) with all of the upheaval going in America and abroad. In many cases, promises were made but not kept which may be making you rethink some of your recent choices at the ballot box and even squirm a bit.Comedian JP Sears dissects these recent events and, like the deft, wise humorist he is, reminds us that giving our power as citizens way too easily on promises not delivered makes us the punchline of the joke, a lesson all of us can learn from as we create a better destiny for ourselves and the world this week on Spirit Gym.Learn more about JP and his work on his website. Check out his new adventure, the Better Man Project, on YouTube. Follow him on social media via Facebook, Twitter/X, Instagram, YouTube, TikTok and Rumble.Timestamps2:51 JP leaves the standup comedy world.9:32 “As a country, how did we get to this place where THAT can happen?”17:28 Feeling gratitude for living through the COVID era.23:22 The devil in the world right now gives us plenty of opportunities to look inside ourselves and ask, how am I doing this…29:33 Our blind spots are easy sources for laughter.36:21 Comparing our shadow work to bowel movements.44:07 JP and Paul do some “yin-yagging.”53:48 “If Trump is a joke, what is the punchline?”1:06:04 The dual lack of common sense and wisdom is becoming fatal on a societal scale.1:16:52 What really matters the most to you?1:24:44 The defeat of U.S. Rep. Thomas Massie (R-KY) in the 2026 House primary.1:46:42 The Bibi Files.1:52:38 How do we create our destiny?ResourcesMilo of CrotonThe myth of Midas' Golden TouchThe Modern Wisdom podcastIsaiah 45:7Nonviolent Communication: A Language of Life by Marshall RosenbergThe Lord of the Rings: The Motion Picture TrilogyVoices of the First Day by Robert LawlorThe American Israel Public Affairs Committee (AIPAC)The Epstein Files scandalThe Shawn Ryan Show on YouTubePsycho-Cybernetics: Updated and Expanded by Maxwell MaltzPaul's solocast on Shadow WorkPaul's podcast conversations with Sean O'Laoire, Anne Helfer, James Hollis and Mark England and Kimberly KestingThe work of Adam Smith, Laozi (Lao Tzu), Louis Hamilton and Earl NightingaleTao Te Ching: The Book of Meaning and Life by Richard WilhelmBoys Adrift by Leonard SaxPaul's appearance on London RealThe Alex Jones Show on SpreakerFind more resources for this episode on our website.Music Credit: Meet Your Heroes (444Hz), Composed, mixed, mastered and produced by Michael RB Schwartz of Brave Bear MusicThanks to our awesome sponsors:PaleovalleyBIOptimizers US and BIOptimizers UK PAUL15Organifi CHEK20Wild PasturesPique LifeSpirit GymCHEK InstituteWe may earn commissions from qualifying purchases using affiliate links.

Eight decades after independence, India still isn't free. Rahul Ahluwalia joins Amit Varma in episode 447 of The Seen and the Unseen to explain why economic growth has such a positive humanitarian impact -- and freedom lies at its heart. (FOR FULL LINKED SHOW NOTES, GO TO SEENUNSEEN.IN.) Also check out: 1. Rahul Ahluwalia on Twitter, LinkedIn and FED. 2. Foundation for Economic Development. 3. Growth is Good -- Rahul Ahluwalia's podcast at FED. 4. Deepak VS and the Man Behind His Face — Episode 373 of The Seen and the Unseen. 5. Swaminathan Aiyar's columns at ToI and his own website. 6. Gurcharan Das's columns at ToI and his own website. 7. India Unbound — Gurcharan Das. 8. The Life and Times of Gurcharan Das — Episode 425 of The Seen and the Unseen. 9. The Importance of the 1991 Reforms — Episode 237 of The Seen and the Unseen (w Shruti Rajagopalan and Ajay Shah). 10. The Life and Times of Montek Singh Ahluwalia — Episode 285 of The Seen and the Unseen. 11. The Forgotten Greatness of PV Narasimha Rao — Episode 283 of The Seen and the Unseen (w Vinay Sitapati). 12. Naushad Forbes Wants to Fix India — Episode 282 of The Seen and the Unseen. 13. The Life and Times of KP Krishnan — Episode 355 of The Seen and the Unseen. 14. Lant Pritchett Is on Team Prosperity — Episode 379 of The Seen and the Unseen. 15. The Life and Times of the Indian Economy -- Episode 387 of The Seen and the Unseen (w Rajeswari Sengupta). 16. Why Freedom Matters — Episode 10 of Everything is Everything. 17. The Reformers — Episode 28 of Everything is Everything. 18. India's Massive Pensions Crisis — Episode 347 of The Seen and the Unseen (w Ajay Shah & Renuka Sane). 19. The Tragedy of Our Farm Bills — Episode 211 of The Seen and the Unseen (w Ajay Shah). 20. The 1991 Project. 21. Stay Away From Luxury Beliefs -- Episode 46 of Everything is Everything. 22. Gig Work is AWESOME! -- Episode 124 of Everything is Everything. 23. Reading Lolita in Tehran -- Azar Nafisi. 24. India's Problem is Poverty, Not Inequality -- Amit Varma. 25. A Venture Capitalist Looks at the World — Episode 213 of The Seen and the Unseen (w Sajith Pai). 26. Public Choice Theory Explains SO MUCH -- Episode 33 of Everything is Everything. 27. Population Is Not a Problem, but Our Greatest Strength -- Amit Varma. 28. The short history of global living conditions and why it matters that we know it -- Max Roser. 29. The Florentines -- Paul Strathern. 30. National output without government? State capacity and welfare measurement -- Vincent Geloso and Chandler Reilly. 31. Atlas Shrugged -- Ayn Rand. 32. The Wealth of Nations -- Adam Smith. 33. Understanding the State -- Episode 25 of Everything is Everything. 34. Every Act of Government Is an Act of Violence -- Amit Varma. 35. Economic growth is enough and only economic growth is enough — Lant Pritchett with Addison Lewis. 36. Where Has All the Education Gone? — Lant Pritchett. 37. Looking like a state: The seduction of isomorphic mimicry -- Matt Andrews, Lant Pritchett and Michael Woolcock. 38. Sixteen Stormy Days — Tripurdaman Singh. 39. The First Assault on Our Constitution — Episode 194 of The Seen and the Unseen (w Tripurdaman Singh). 40. Nehru: The Debates that Defined India — Tripurdaman Singh and Adeel Hussain. 41. Nehru's Debates — Episode 262 of The Seen and the Unseen (w Tripurdaman Singh and Adeel Hussain). 42. The Right to Property — Episode 26 of The Seen and the Unseen (w Shruti Rajagopalan). 43. Caged Tiger: How Too Much Government Is Holding Indians Back — Subhashish Bhadra. 44. Subhashish Bhadra on Our Dysfunctional State — Episode 333 of The Seen and the Unseen. 45. Colours of the Cage: A Prison Memoir — Arun Ferreira. 46. Shikha Dalmia Is the Unpopulist -- Episode 403 of The Seen and the Unseen. 47. DeMon, Morality and the Predatory Indian State — Episode 85 of The Seen and the Unseen (w Shruti Rajagopalan). 48. Narendra Modi Takes a Great Leap Backwards — Amit Varma. 49. Horseshoe Theory and the Median Voter Theorem. 50. Government's End: Why Washington Stopped Working — Jonathan Rauch. 51. Denial: My 25 Years Without a Soul -- Jonathan Rauch. 52. The Logic of Collective Action -- Mancur Olson. 53. Anarchy, State and Utopia — Robert Nozick. 54. A Theory of Justice — John Rawls. 55. India After Gandhi — Ramachandra Guha. 56. Arguments for Liberty -- Edited by Aaron Ross Powell and Grant Babcock. 57. Johan Norberg on Amazon and YouTube. 58. State Building -- Francis Fukuyama. 59. India Needs Decentralization -- Episode 47 of Everything is Everything. 60. Fixing Indian Education — Episode 185 of The Seen and the Unseen (w Karthik Muralidharan). 61. Education in India — Episode 77 of The Seen and the Unseen (w Amit Chandra). 62. The Profit Motive in Education — Episode 9 of The Seen and the Unseen (w Parth Shah). 63. Our Unlucky Children (2008) -- Amit Varma. 64. Fund Schooling, Not Schools (2007) -- Amit Varma. 65. Profit = Philanthropy -- Amit Varma. 66. Praise for intelligence can undermine children's motivation and performance — Claudia Mueller and Carol Dweck. 67. Controlling Your Dopamine For Motivation, Focus & Satisfaction -- Huberman Lab. 68. Master Your Sleep & Be More Alert When Awake -- Huberman Lab. 69. Why We Sleep — Matthew Walker. 70. Matthew Walker on the Huberman Lab podcast. 71. The Frido 3D eye mask Amit uses. 72. John Collison's tweet on the world being a museum of passion projects. 73. The Beatles, Elvis Presley, Dean Martin, Metallica, Black Sabbath and Iron Maiden on Spotify. 74. Love Me Tender and Can't Help Falling in Love -- Elvis Presley. 75. Aaj -- Bloodywood. 76. Bekhauf -- Bloodywood (featuring BABYMETAL) 77. Coke Studio Bharat Season 2 and Season 3. 78. Dr Dre, Snoop Dogg and Eminem on Spotify. 79. The Next Episode -- Dr Dre featuring Snoop Dogg, Kurupt, Nate Dogg. 80. Ravi Shankar at Monterey Pop. 81. Ain't No Man Alive Can Handle Me -- Dumpster Grooves. 82. Alistair MacLean and PG Wodehouse on Amazon. 83. The Ultimate Hitchhiker's Guide to the Galaxy -- Douglas Adams. 84. Animal Farm -- George Orwell. 85. Building State Capability: Evidence, Analysis, Action -- Matt Andrews, Lant Pritchett and Michael Woolcock. 86. The Rebirth of Education: Schooling Ain't Learning -- Lant Pritchett. 87. Lant Pritchett On Growth, Development & Income Inequality -- FED Dialogues. 88. In Service of the Republic — Vijay Kelkar & Ajay Shah. 89. Random Critical Analysis. 90. Chupke Chupke -- Hrishikesh Mukherjee. Amit Varma runs a course called Life Lessons, which aims to be a launchpad towards learning essential life skills all of you need. For more details, and to sign up, click here. And have you read Amit's newsletter? It's madly active right now! Subscribe right away to The India Uncut Newsletter! It's free! Also check out Amit's online course, The Art of Clear Writing. Episode art: 'Marketplace of Ideas' by Simahina.

Tonight on The Last Word: A federal judge orders the Trump administration to explain the Kennedy Center tarp. Also, the House Oversight Committee subpoenas Jeffrey Epstein associate Leon Black. Plus, Republican support grows for prosecuting women who get abortions. And the Supreme Court allows Trump to end Haitian TPS protections. Rep. Adam Smith, Rep. Joyce Beatty, Rep. James Walkinshaw, Michele Goodwin, and Dr. Amy Acton join Jonathan Capehart. To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Moral Foundations of the American Revolution. Guest: David C. Rose. David C. Rose explains that the American Revolution was driven by men who considered themselves "independents" rather than rebels. Drawing on Adam Smith's Theory of Moral Sentiments, he argues that humans crave approval and follow cultural norms. Over time, these norms shifted toward "moral don'ts" or guardrails, fostering a freethinking mindset. 151876