Podcasts about jerusalem demsas

Detractors frequently accuse Jonah Goldberg of being an irrelevant dinosaur. We at The Remnant are proud to say that only one of those accusations is correct. We are also proud to say that Jonah's dinosaurness does not prevent him from conversing with young, hip, and cool members of today's media landscape. “Prove it,” you say? Well, today we bring you a podcast with none other than Jerusalem Demsas, founder of The Argument and one of the coolest center-left pundits. Join Jonah and Jerusalem as they dive into positive and negative freedoms, the college landscape, the Democratic Socialists of America, Zohran Mamdani's policies, the Democratic Party, housing, playing the long game, the battle of ideas, our broken epistemic environment, weak political parties, oligarchy, money in politics, groupthink, and technology. Show Notes: —The Argument  —“‘Feel' as a Determinant of College Choice: Evidence from Campus Tour Weather” —Demsas: “Does the oligarchy exist?” —The Argument Podcast: What AOC and Bernie Sanders get wrong about the oligarchy The Remnant is a production of ⁠The Dispatch⁠, a digital media company covering politics, policy, and culture from a nonpartisan perspective. To access all of The Dispatch's offerings—including the Saturday Ruminant, audio versions of all our articles and newsletters, and Jonah's twice-weekly G-File—⁠click here⁠. Instructions on how to set up your members-only feed can be found here, and if you'd like to remove all ads from your podcast experience, consider becoming a premium Dispatch member ⁠by clicking here⁠.  Learn more about your ad choices. Visit megaphone.fm/adchoices

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

Progressives are highly critical of America, and for good reason. But we are losing more than problematic idols and ideals when we abandon patriotism. Jerusalem Demsas, founder and editor of “The Argument,” argues that if we're going to save America, we'll have to start loving it first.  She joins Offline to explain how liberals have ceded patriotism to the right, why No Kings is starting to fix this, and how to talk about our country's issues without undermining hope that America still holds promise.For a closed-captioned version of this episode, click here. For a transcript of this episode, please email transcripts@crooked.com and include the name of the podcast, episode title, and episode date.

This is a free preview of a paid episode. To hear more, visit andrewsullivan.substack.comJerusalem is a journalist and entrepreneur. She's a former staff writer at The Atlantic and a former policy writer and podcaster at Vox. Last year she founded The Argument, a liberal magazine on Substack, where she serves as CEO and editor-in-chief. We went at it on liberalism and how to reform the Democrats.For two clips of the episode — on Biden's biggest mistakes, and how DEI went off the rails — head to our YouTube page.Other topics: born in Ethiopia as an Eritrean Christian; why her father became an atheist then converted back to Christianity; growing up in suburban Maryland and becoming a citizen at age 14; the formative influence of Amartya Sen's The Argumentative Indian; being a Christian in a secular-left bubble; the stagnation in England before Thatcher; imposing liberalism on Iraq; torture under Bush; the long Great Recession; the American Rescue Plan and inflation; Biden ceding order on immigration; Greg Abbott exporting migrants to liberal cities; rural and retired voters most against immigration but least affected; cancel culture; the race card on immigration; the antisemite card on Israel; US aid to Israel; Hormuz and oil prices; Jerome Powell; DEI and the NYT lawsuit; diversity vs quotas; trans issues; the suicide canard; orgasm loss and FGM; opposition to bathroom bills reversed; Bostock; housing policy and abundance; ICE in Minneapolis; JD Vance; Kamala and Hillary; Jon Ossoff; and Keir's cautionary tale for moderate liberals.Browse the Dishcast archive for an episode you might enjoy. We have some real stars coming up: Ben Rhodes on Iran and speech-writing, Harvey Mansfield on modernity, HW Brands on the life of George Washington, John Gray on Trump's new world, Bob Wright on the evolutionary force of AI, Tiffany Jenkins on privacy in a liberal democracy, Daniel McCarthy on conservatism, Stephen Grosz on the struggles of love, and Robby George on all our disagreements. Please send any guest recs, dissents, and other comments to dish@andrewsullivan.com.

This is a free preview of a paid episode. To hear more, visit andrewsullivan.substack.comAdrian is a journalist and an old friend. We arrived in America on the same plane in 1984 and spent the first few days together in the same hotel room. After more than 20 years writing for The Economist, he became the global business columnist for Bloomberg Opinion. He's the author of several books, including The Aristocracy of Talent, and the co-author of many more with John Micklethwait, including The Right Nation. Adrian's new book is The Revolutionary Center: The Lost Genius of Liberalism. It's a terrific tonic for a philosophy as vital as it is in eclipse.For two clips of the episode — on how Enlightenment ideas got corrupted, and Big Tech's threat to liberalism — head to our YouTube page.Other topics: raised in rural Shropshire; his parents both teachers; his dissertation on the 11-plus (an exam that changed my life); when IQ tests were a liberal cause; Luther and the Reformation; the religious civil wars leading to the Enlightenment; Hobbes as a proto-liberal; the humanism of Erasmus; Montesquieu and the spirit of liberalism; John Stuart Mill and utilitarianism; Isaiah Berlin and pluralism; Graham Wallas and the Great Society; Lippmann; Leo Strauss; Thatcherism; consumerism vs. self-improvement; meritocracy threatened by the left; Foucault's folly; the EU and managerial liberalism; Brooks' bobos; affirmative action and DEI; why liberal democracy in Iraq didn't work; Oakeshott; Schmitt and friend-enemy; Trump's stark illiberalism and neo-royalism; King Charles; Putin ushering in a strongman era; Biden's open borders; the migration crisis and Brexit; the buffoonish Boris; the struggling Starmer; high culture and other upsides to elitism; Abundance; Deneen and post-liberalism; and Europe stepping up for Ukraine.Browse the Dishcast archive for an episode you might enjoy. We have some real stars coming up: Ben Rhodes on Iran and speech-writing, Harvey Mansfield on modernity, HW Brands on the life of George Washington, John Gray on Trump's new world, Bob Wright on the evolutionary force of AI, Tiffany Jenkins on privacy in a liberal democracy, Jerusalem Demsas on the state of the left, Daniel McCarthy on conservatism, Stephen Grosz on the struggles of love, and Robby George on pretty much everything. Please send any guest recs, dissents, and other comments to dish@andrewsullivan.com.

This is a free preview of a paid episode. To hear more, visit andrewsullivan.substack.comTom is a journalist and author. A former staff writer at GQ and Esquire, the film A Beautiful Day in the Neighborhood was based on his Esquire article on Fred Rogers. He's currently a senior writer at ESPN, and his new memoir is called In the Days of My Youth I Was Told What It Means to Be a Man. It was an intense conversation — about dads, sex, Catholicism, and growing older.For two clips of the episode — on being your dad's wingman as a kid, and the dark secrets that Catholic families often carry — head to our YouTube page.Other topics: his dad's serious injury at Normandy; emulating leading men in Hollywood; selling women's handbags; his extreme vanity and obsession with scents; “the first metrosexual”; women flocking to him; making Tom complicit in his countless affairs; how men benefitted from the early Sexual Revolution more than women; Vatican II; Tom's close relationship with his Catholic mom; Tom fearing his dad; the friends who worshipped him like a celebrity; hiding his Brooklyn accent; hiding extreme porn and dildos in his briefcase that Tom found; sadomasochism and bondage; dad's sleeping with both Zsa Zsa and Ava Gabor; a mystery mistress who spoke at his dad's funeral; Tom's grandmother who was a notorious adulteress in the press who pimped out Tom's dad and his aunt; and the challenge of writing my own memoir.Browse the Dishcast archive for an episode you might enjoy. We have some real stars coming up: Tiffany Jenkins on privacy in a liberal democracy, Adrian Wooldridge on “the lost genius of liberalism,” Jerusalem Demsas on the state of the left, Ben Rhodes on Iran and speech-writing, Harvey Mansfield on modernity, Daniel McCarthy on conservatism, HW Brands on the life of George Washington, John Gray on Trump's new world, Bob Wright on the evolutionary force of AI, Stephen Grosz on the struggles of love, and Robby George on pretty much everything. Please send any guest recs, dissents, and other comments to dish@andrewsullivan.com.

This is a free preview of a paid episode. To hear more, visit andrewsullivan.substack.comGreg is a lawyer, journalist, and author. He's the president of FIRE — the best free-speech group out there. His books include The Coddling of the American Mind (written with Jonathan Haidt), The Canceling of the American Mind (written with Rikki Schlott), and War On Words (written with Nadine Strossen). You can find him on Substack at The Eternally Radical Idea.For two clips of our convo — on whether Biden or Trump has been worse on free speech, and how to decrease wokeness on campus — head to our YouTube page.Other topics: his Russian dad's 100th birthday the day we taped; how he fled the Soviets as an orphan and came to America speaking 7 languages; his British mom coming over as a nanny; growing up among immigrants in Danbury as both a football player and nerd; studying 1st Amendment law at Stanford; the wane of gifted-and-talented programs (which Greg once taught); the declining support for free speech; family breakdown and protecting kids from bad speech; the perils of social media; race wars on X; censorship against porn and age-restriction laws; where Greg disagrees with Jon Haidt; free speech as a form of bullying; Nick Fuentes; how banning people from X increases groupthink; Jon Rauch; sex changes for kids; gay promiscuity; Covid censorship; AI worries; the killing of Charlie Kirk; the infamous Larry Bushart case; the Ozturk case; Rubio's anti-speech crusade against immigrants; Israel and BDS; antisemitism on campus; heckling vs shout-downs; viewpoint diversity; the FCC and Carr; jawboning and merger threats; the Ellisons; Trump threatening law firms; “hate” crimes; mass arrests in UK over speech; the Varsity Blues cheating scandal; and South Park.Browse the Dishcast archive for an episode you might enjoy. Coming up: Tom Junod on his dad and masculinity, Jerusalem Demsas on the state of the left, Tiffany Jenkins on privacy in a liberal democracy, Adrian Wooldridge on “the lost genius of liberalism,” HW Brands on the life of George Washington, Ben Rhodes on Iran, Harvey Mansfield on modernity, John Gray on Trump's new world, and Robby George on everything. Please send any guest recs, dissents, and other comments to dish@andrewsullivan.com.

This is a free preview of a paid episode. To hear more, visit andrewsullivan.substack.comJeff is a lawyer and a contributing opinion writer for the NYT, after a long run at The New Yorker and CNN. He has written many bestselling books, including True Crimes and Misdemeanors, The Oath, The Nine, and Too Close to Call. He appeared on the Dishcast in 2024 to talk lawfare, and in this episode we discuss his latest book, The Pardon: The Politics of Presidential Mercy.We recorded this episode a while back, and we're posting it this week after Trump promised mass pardons for White House staffers before he leaves office. For two clips of our convo — on Biden's corrupt pardons, and Trump's obscene pardons — head to our YouTube page.Other topics: how pardons can be a beautiful act of mercy; the varying powers among the states; Lincoln's amnesty for Confederate soldiers but not leaders; Andrew Johnson's pardon for Jefferson Davis; Johnson's impeachment; the thousand pardons of Rutherford B Hayes; Ford pardoning Nixon; Jimmy Carter pardoning resisters to the Vietnam War; the Willie Horton furlough and ad; HW's pardons for Iran-Contra; Clinton pardoning his own brother and Marc Rich; Dubya's refusal to pardon Scooter Libby against Cheney's wishes; Dubya advising Obama to have a set protocol; Trump pardoning crooks like Charles Kushner and Paul Manafort who could have testified against him; the blanket pardon of January 6ers; Kim Kardashian's role in Trump's pardons; the ICE killings in Minneapolis; and the need for presidents with some basic virtue.Browse the Dishcast archive for an episode you might enjoy. Coming up: Greg Lukianoff on free-speech fights, Jerusalem Demsas on the state of the left, Tiffany Jenkins on privacy in a liberal democracy, Adrian Wooldridge on “the lost genius of liberalism,” HW Brands on the life of George Washington, Ben Rhodes on foreign policy, and Tom Junod on his dad and masculinity. As always, please send any guest recs, dissents, and other comments to dish@andrewsullivan.com.

The Supreme Court tanks Donald Trump's tariff program in a 6-3 ruling supported by two of his hand-picked justices. Lovett talks to Jerusalem Demsas, economics writer and editor-in-chief of The Argument, about the epic presidential tantrum that followed and what Trump might do now. Then they discuss the findings from a new Argument poll about the backlash to trans rights, why Congress won't assert itself as a coequal branch, the way forward for housing policy, and why all the commentary about the anti-Trump resistance being "cringe" is missing the point.For a closed-captioned version of this episode, click here. For a transcript of this episode, please email transcripts@crooked.com and include the name of the podcast.

Can we build an economy that delivers abundance without abandoning democratic accountability and economic equity? Recorded live at Democracy Journal's “Can't We All Just Get Along?” conference, this episode features a wide-ranging panel discussion on one of the most consequential debates shaping today's political economy: whether abundance and social democracy are in tension—or whether they're mutually reinforcing. Moderated by Ed Luce of the Financial Times, the panel brings together Baillee Brown (Inclusive Abundance), Jerusalem Demsas (The Argument), Mike Konczal (Economic Security Project), and Sandeep Vaheesan (Open Markets Institute) to wrestle with what it actually takes to deliver housing, clean energy, and public goods at scale—without ceding power to concentrated markets or hollowing out democratic governance. At a moment of deep political discontent and institutional distrust, this conversation helps clarify the real choices facing policymakers—and why getting this balance right is essential to rebuilding public faith in government. Ed Luce (moderator) is the U.S. national editor and a columnist at the Financial Times, where he writes on American politics, democracy, and global political economy. Baillee Brown (panelist) is a policy advocate and the founder of Inclusive Abundance, where she works with lawmakers to advance a pro-building, outcomes-focused approach to delivering housing, clean energy, and public goods. Jerusalem Demsas (panelist) is founder and Editor in Chief of The Argument a publication and podcast covering housing, economic policy, and the politics of affordability. Mike Konczal (panelist) is the Senior Director of Policy and Research at the Economic Security Project, where he focuses on inequality, housing, industrial policy, and the political economy of growth. Sandeep Vaheesan (panelist) is the legal director at the Open Markets Institute and a leading voice on antitrust, corporate power, and the role of public authority in building a more equitable economy. Website: http://pitchforkeconomics.com Facebook: Pitchfork Economics Podcast Bluesky: @pitchforkeconomics.bsky.social Instagram: @pitchforkeconomics Threads: pitchforkeconomics TikTok: @pitchfork_econ YouTube: @pitchforkeconomics LinkedIn: Pitchfork Economics Twitter: @PitchforkEcon, @NickHanauer Substack: ⁠The Pitch⁠

Harry talks with journalist Jerusalem Demsas about her case for a robust, combative liberalism capable of taking the fight to the current political power structure. Demsas has just launched a new publication—The Argument—dedicated to renewing and improving the kind of politics that helped fostered many of the country's best achievements. Harry asks Demsas about the shape of that revived liberalism, how she plans to persuade MAGA and other skeptics, and why she feels so optimistic in such a difficult time Learn more about your ad choices. Visit megaphone.fm/adchoices

Jerusalem Demsas is one of Cardiff's favorite econ and housing journalists, a previous New Bazaar guest, and now the founder and editor of The Argument, a new magazine dedicated to making “a positive, combative case for liberalism through sharp, well-argued opinion pieces, original reporting, and multimedia content that confronts the illiberal drift in our politics.” Jerusalem and Cardiff discuss: Her meaning of liberalismHow (if?) persuasion works The politics of immigration and why it matters for the economy Drawing lines vs reaching across themIf Twitter really is “acid on community” How to societally deal with short-form video addiction…… and how Jerusalem broke her own TikTok problemWhy asking if something is cringe is itself the most cringe Finally, Jerusalem makes Cardiff look and feel super old when his reference to Reality Bites flies right by her. They close by reflecting on a hopeful trend. Related links: The ArgumentOn the Housing Crisis (Jerusalem's book of essays)Ezra Klein's podcast interview with Ta-Nehisi Coates Hosted on Acast. See acast.com/privacy for more information.

As the U.S. slides into autocracy, Americans need to be reminded that liberalism can still solve the problems that Trump uses to fear monger. Jerusalem Demsas, founder and editor in chief of “The Argument,” joins Offline to explain what solutions for immigration and the economy would look like, her beef with the post-liberal left, and why she's staying on Twitter...and maybe you should too. Plus, what she's seeing on the ground at the National Conservatism Conference in Washington, DC—aka the place JD Vance gets his crazy blood and soil ideas.For a closed-captioned version of this episode, click here. For a transcript of this episode, please email transcripts@crooked.com and include the name of the podcast.

Third Way, a center-left think tank, released a list of words it thinks Democrats should stop using on Friday. The list included words like “intersectionality,” “body shaming,” “cisgender,” and “LGBTQIA+.” It sparked an online debate around the terms, which has caused many people to ask “what do Democrats and liberals actually believe?” Jerusalem Demsas is CEO and founder of a new media outlet called “The Argument,” and she joins the show to answer the question: What is a liberal?And in headlines, Russian Foreign Minister Sergey Lavrov defends the Russian war in Ukraine on NBC's “Meet the Press,” Kilmar Abrego Garcia – a Salvadoran immigrant who was deported despite a court order allowing him to stay in the country – returns home to Maryland only to be immediately threatened with deportation to Uganda, Democratic House Minority Leader Hakeem Jeffries criticizes President Trump over threats to deploy the National guard to Chicago, and the Department of Justice releases hundreds of pages of interviews with Ghislaine Maxwell, a collaborator of Jeffrey Epstein.Show Notes:Check out The Argument – www.theargumentmag.com/Call Congress – 202-224-3121Subscribe to the What A Day Newsletter – https://tinyurl.com/3kk4nyz8What A Day – YouTube – https://www.youtube.com/@whatadaypodcastFollow us on Instagram – https://www.instagram.com/crookedmedia/For a transcript of this episode, please visit crooked.com/whataday

While Trump keeps working hard on his own monetization and glorification—and delivers a Watergate practically every hour—the pro-democracy coalition must stay focused on winning next year's midterm elections. Trump is at the point of no return, Congress is becoming the only institution that can stop him, and holding onto that lever of power is his top priority. Meanwhile, not only did Trump look weak in Alaska, he also looked unpresidential. Plus, a new publication focused on the threats from the post-liberal right and left. Jerusalem Demsas and Garry Kasparov join Tim Miller. show notes Garry's Substack, "The Next Move" Jerusalem's "The Argument" Chess grandmaster Magnus Carlsen slamming the table after losing in June Trump comparing himself to Nixon *** THE BULWARK LIVE in Toronto, D.C. and NYC: Thebulwark.com/events *** Get 20% off your DeleteMe plan when you go to joindeleteme.com/BULWARK and use promo code BULWARK at checkout.

In Berkeley Talks episode 225, The Atlantic journalists Yoni Appelbaum and Jerusalem Demsas discuss the decline of housing mobility in the United States and its impact on economic opportunity in the country. Appelbaum, author of the 2025 book Stuck: How the Privileged and the Propertied Broke the Engine of American Opportunity, began by tracing the history of housing mobility in the U.S. and its rapid decline in recent decades. He noted that in the 19th century, one out of three Americans moved to a new residence every year, and as late as 1970, one in five did. Today, only one in 13 people in the U.S. pack up their things and find a new place to live on an annual basis. “These constant moves in America, made possible by the constant construction of new housing, created a new kind of social order,” said Appelbaum, and most people “ended up better off for it.” The sharp decline in residential relocation, he said, caused largely by rising housing costs and restrictive zoning, is a major driver of the decline of social mobility, “the largest and least remarked change in America of the last 50 years.” Building on Appelbaum's argument, Demsas said that exclusionary housing policies have shifted mobility from a widespread opportunity to a privilege for the affluent and well-educated. “Most Americans no longer stand to gain by moving toward the places in this country that offer them the greatest opportunities — the greatest professional opportunities, the best education for their children,” said Demsas, author of the 2024 book On the Housing Crisis: Land, Development, Democracy. Instead, they move toward affordability, she said, which deepens inequality and limits their potential for economic advancement. The conversation, held in March 2025, was moderated by Paul Pierson, a UC Berkeley professor of political science and director of the Berkeley Economy and Society Initiative (BESI). The event was co-sponsored by BESI and the Berkeley Center for American Democracy.Watch a video of the conversation and read more about the speakers.Listen to the episode and read the transcript on UC Berkeley News (news.berkeley.edu/podcasts).Music by Blue Dot Sessions.Photo by Daniel Abadia/Unsplash+ Hosted on Acast. See acast.com/privacy for more information.

In Abundance, journalists Ezra Klein and Derek Thompson explain how one generation's solutions have become the next generation's problems and offer a call to rethink big, entrenched problems that seem mired in systemic scarcity, from climate change and housing to education and healthcare. In conversation with Jerusalem Demsas, a staff writer at The Atlantic, host of their policy podcast “Good on Paper,” and the author of On the Housing Crisis.  This program was held on March 20, 2025 in partnership with The Atlantic. 

As the second Trump administration dismantles federal DEI programs and removes trans Americans from the military, the crusade on “wokeness” seems to be a core focus of the president's second term. In this encore episode, host Jerusalem Demsas speaks with the New York Times columnist Michelle Goldberg about the end of wokeness and why we might miss it when it's gone.  Get more from your favorite Atlantic voices when you subscribe. You'll enjoy unlimited access to Pulitzer-winning journalism, from clear-eyed analysis and insight on breaking news to fascinating explorations of our world. Subscribe today at TheAtlantic.com/podsub. Learn more about your ad choices. Visit megaphone.fm/adchoices

There's a housing crisis in America: high interest rates, not enough homes, and regulations that seem to favor building massive “McMansions” instead of more diverse housing stock. How did we get here, and can we find our way out? Post columnist Heather Long talks to the Atlantic's Jerusalem Demsas, who's written a book on the housing crisis, and Bryan DeHenau, a Michigan roofer who sees the struggles in the building industry on the ground every day.Additional Reading:Heather Long and Amanda Shendruk: “The new American Dream should be a townhouse”Heather Long talks with Bryan DeHenau about his ideas for how to build more homes in America: “A Michigan roofer's smart plan to end the housing crisis”Jerusalem Demsas: “An American-Style Housing Crisis in New Zealand”

Are young men becoming radicalized? Could they be further to the right than even their fathers and grandfathers? These questions have yet to be answered definitively, but in some countries, electoral results and polls suggest that a meaningful group of young men may be finding a home in radical spaces. In this encore episode, host Jerusalem Demsas speaks to Dr. Alice Evans, a researcher at King's College London, who has been traveling the world, trying to uncover the reason some societies are more equal than others. Her insights help explain why some young men may be turning against the tide of egalitarianism. Share understanding this holiday season. For less than $2 a week, give a yearlong Atlantic subscription to someone special. They'll get unlimited access to Atlantic journalism, including magazine issues, narrated articles, puzzles, and more. Give today at TheAtlantic.com/podgift. Learn more about your ad choices. Visit megaphone.fm/adchoices

With just over a week to go until Election Day and polls showing the tightest race imaginable. All eyes are on Pennsylvania. Neither candidate, it seems, could win the presidency without taking the Keystone State. Join moderator Jeffrey Goldberg, Anne Applebaum of The Atlantic, Dan Balz of The Washington Post, Dana Bash of CNN and Jerusalem Demsas of the Atlantic to discuss this and more.

The zoning debate between NIMBYs and YIMBYs is fueling a housing crisis felt nationwide. Jerusalem Demsas, staff writer at The Atlantic, joins host Krys Boyd to discuss why she feels decisions about land need to be accountable to the public, why zoning boards and preservationists are hurting home affordability, and why the buck should stop at elected officials. Her book is “On the Housing Crisis: Land, Development, Democracy.”

WNYC's election series “America, Are We Ready?” looks at the presidential candidates' different approaches to housing costs. Kimberly Adams, senior Washington correspondent for Marketplace and the co-host of the Marketplace podcast, “Make Me Smart", and Jerusalem Demsas, Atlantic staff writer focusing on housing policy, discuss the policies proposed by Donald Trump and Kamala Harris, and listeners tell us where they stand.

As Trump exploits hurricane victims for political gain, Jen breaks down how his lies have wreaked havoc on the communities and institutions that make America strong. Secretary of Homeland Security Alejandro Mayorkas joins Jen to discuss Trump's dangerous rhetoric falsehoods, which force the rest of country to pick up the pieces rather than join in a common cause. Next, Congressman Dan Goldman joins to address why Republican leaders like Speaker Mike Johnson still choose to reject reality and refuse to acknowledge that Trump lost the 2020 election. Jen also addresses Vice President Harris's recent media blitz, geared toward new media platforms over legacy outlets. Sarah Matthews, Jerusalem Demsas and Jonathan Martin discuss Harris's strategy, as well her appearance on 60 Minutes. Check out our social pages below:https://twitter.com/InsideWithPsakihttps://www.instagram.com/InsideWithPsaki/https://www.tiktok.com/@insidewithpsakihttps://www.msnbc.com/jen-psaki

Dreaming of a Trump victory, Republicans have a wish list of health policy changes — including loosening Affordable Care Act regulations to make cheaper coverage available and ending Medicare drug price negotiations. Meanwhile, after the first publicly reported death stemming from a state abortion ban, Vice President Kamala Harris is emphasizing the consequences of Trump's work to overturn Roe v. Wade. Tami Luhby of CNN, Shefali Luthra of The 19th, and Joanne Kenen of Politico and Johns Hopkins University join KFF Health News senior editor Emmarie Huetteman to discuss these stories and more. Plus, for “extra credit” the panelists suggest health policy stories they read this week that they think you should read, too: Emmarie Huetteman: The Washington Post's “What Warning Labels Could Look Like on Your Favorite Foods,” by Lauren Weber and Rachel Roubein. Shefali Luthra: KFF Health News' “At Catholic Hospitals, a Mission of Charity Runs Up Against High Care Costs for Patients,” by Rachana Pradhan. Tami Luhby: Politico Magazine's “Doctors Are Leaving Conservative States To Learn To Perform Abortions. We Followed One,” by Alice Miranda Ollstein. Joanne Kenen: The New York Times' “This Chatbot Pulls People Away From Conspiracy Theories,” by Teddy Rosenbluth, and The Atlantic's “When Fact-Checks Backfire,” by Jerusalem Demsas. Hosted on Acast. See acast.com/privacy for more information.

America is in the middle of a massive housing crisis, with a shortage of 4 to 7 million homes compared to the demand. The solution seems simple—just build more homes—but getting there is a lot trickier than it sounds. This week, Adam sits down with journalist Jerusalem Demsas, author of On the Housing Crisis: Land, Development, Democracy, to break down the real reasons behind the housing shortage and explore what we can actually do to get more people into homes. Find Jerusalem's book at factuallypod.com/booksSUPPORT THE SHOW ON PATREON: https://www.patreon.com/adamconoverSEE ADAM ON TOUR: https://www.adamconover.net/tourdates/SUBSCRIBE to and RATE Factually! on:» Apple Podcasts: https://podcasts.apple.com/us/podcast/factually-with-adam-conover/id1463460577» Spotify: https://open.spotify.com/show/0fK8WJw4ffMc2NWydBlDyJAbout Headgum: Headgum is an LA & NY-based podcast network creating premium podcasts with the funniest, most engaging voices in comedy to achieve one goal: Making our audience and ourselves laugh. Listen to our shows at https://www.headgum.com.» SUBSCRIBE to Headgum: https://www.youtube.com/c/HeadGum?sub_confirmation=1» FOLLOW us on Twitter: http://twitter.com/headgum» FOLLOW us on Instagram: https://instagram.com/headgum/» FOLLOW us on TikTok: https://www.tiktok.com/@headgum» Advertise on Factually! via Gumball.fmSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most Americans are now acutely aware that we have a housing crisis, but Atlantic writer Jerusalem Demsas says that we have much less clarity about what's causing it. “All too often,” she writes, “explanations center around identifying a villain: greedy developers, or private equity companies, or racist neighbors, or gentrifiers, or corrupt politicians.” All which may be true, she says, but they fail to identify the root cause, that housing decisions are made at the hyper local level, in a tangle of zoning boards, historical preservation committees and sparsely attended meetings, “where no one is watching and no one is accountable.” We talk to Demsas about her new collection of essays, “On the Housing Crisis: Land, Development, Democracy” and why she thinks local governments are to blame for the housing shortage. Guests: Jerusalem Demsas, staff writer, Atlantic Magazine; author, On the Housing Crisis: Land, Development, Democracy

Kamala Harris exceeded expectations Tuesday in what, as of now, stands to be the only debate between her and Donald Trump. But will that success help her earn more votes? Join moderator Jeffrey Goldberg, Ashley Parker of The Washington Post, Eugene Daniels of Politico, Jerusalem Demsas of The Atlantic and Asma Khalid of NPR to discuss this and more.

At the debate last night, Kamala Harris opened her remarks by talking about the need for America to fix its housing crisis. And crisis it is, at least according to Jerusalem Demsas, a staff writer at The Atlantic who has written extensively on the increasing scarcity and rising cost of American housing. In her new collection of essays, On the Housing Crisis, Demsas suggests that the best way to confront this crisis is to aggressively construct new housing. Build Baby Build, in other words. And, for Demsas at least, the sooner the better.Jerusalem Demsas is a staff writer at The Atlantic where she is an established voice on the housing crisis and local democracy. Her writing spans issues from infrastructure, labor economics, and federalism to race, gender, mobility and the politics of exclusion. She was recognized for her work in 2023 by the American Society of Magazine Editors (ASME) with the ASME Next Award for journalists under 30. Demsas is also a Visiting Fellow with the Center for Economy and Society at the SNF Agora Institute at Johns Hopkins University. Prior to writing at the Atlantic, Demsas was a policy journalist at Vox where she also cohosted the popular policy podcast The Weeds.Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

This is a free preview of a paid episode. To hear more, visit www.politix.fmThis week, Brian takes a much needed vacation and Matt is joined by The Atlantic's Jerusalem Demsas to talk about housing and the 2024 campaign. * Barack Obama's striking and unexpected embrace of the YIMBY diagnosis of the housing issue at the Democratic National Convention. * Kamala Harris' more equivocal embrace of the same formula along with some other …

The prevailing narrative of remote work has often been boiled down to: Workers love it, and bosses hate it. But according to Natalia Emanuel, a labor economist at the Federal Reserve Bank of New York, it may not be that simple. Emanuel co-authored a study, looking at software engineers at an unnamed Fortune 500 company where half of the workers were functionally remote. What she found was that there were varying tradeoffs for each scenario—working remotely or working in the office—depending on an employee's age, experience, gender, and more. So was the Great Remote-Work Experiment a success? That's what the first episode of The Atlantic's Good on Paper podcast—hosted by Jerusalem Demsas—dives into. Get more from your favorite Atlantic voices when you subscribe. You'll enjoy unlimited access to Pulitzer-winning journalism, from clear-eyed analysis and insight on breaking news to fascinating explorations of our world. Subscribe today at TheAtlantic.com/podsub. Learn more about your ad choices. Visit megaphone.fm/adchoices

There's a lot going on this week. Sarah and Beth discuss several of the big stories. Then, we break down one of the most complex, important issues in America: housing. Jerusalem Demsas of The Atlantic joins us for a great conversation. TOPICS DISCUSSEDLouisiana's 10 Commandments LawThe Supreme Court on Guns and Domestic ViolenceHousing in America with Jerusalem DemsasOutside of Politics: Travis Kelce on Stage with Taylor Swift at the Eras TourVisit our website for complete show notes and episode resources. Hosted on Acast. See acast.com/privacy for more information.

It's Money Talks…live! This episode was taped at The Hewlett Foundation's annual New Common Sense Conference, where Felix Salmon chatted with Jerusalem Demsas, who covers housing and infrastructure for The Atlantic. They discuss the NIMBY mentality, regulatory tangles holding back real estate development, and what we have to give up to gain more affordable housing.If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get an ad-free experience across the network and an additional segment of our regular show every week. You'll also be supporting the work we do here on Slate Money. Sign up now at slate.com/moneyplus to help support our work.Podcast production by Jared Downing and Cheyna Roth. Hosted on Acast. See acast.com/privacy for more information.

It's Money Talks…live! This episode was taped at The Hewlett Foundation's annual New Common Sense Conference, where Felix Salmon chatted with Jerusalem Demsas, who covers housing and infrastructure for The Atlantic. They discuss the NIMBY mentality, regulatory tangles holding back real estate development, and what we have to give up to gain more affordable housing. If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get an ad-free experience across the network and an additional segment of our regular show every week. You'll also be supporting the work we do here on Slate Money. Sign up now at slate.com/moneyplus to help support our work. Podcast production by Jared Downing and Cheyna Roth. Learn more about your ad choices. Visit megaphone.fm/adchoices

It's Money Talks…live! This episode was taped at The Hewlett Foundation's annual New Common Sense Conference, where Felix Salmon chatted with Jerusalem Demsas, who covers housing and infrastructure for The Atlantic. They discuss the NIMBY mentality, regulatory tangles holding back real estate development, and what we have to give up to gain more affordable housing. If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get an ad-free experience across the network and an additional segment of our regular show every week. You'll also be supporting the work we do here on Slate Money. Sign up now at slate.com/moneyplus to help support our work. Podcast production by Jared Downing and Cheyna Roth. Learn more about your ad choices. Visit megaphone.fm/adchoices

It's Money Talks…live! This episode was taped at The Hewlett Foundation's annual New Common Sense Conference, where Felix Salmon chatted with Jerusalem Demsas, who covers housing and infrastructure for The Atlantic. They discuss the NIMBY mentality, regulatory tangles holding back real estate development, and what we have to give up to gain more affordable housing. If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get an ad-free experience across the network and an additional segment of our regular show every week. You'll also be supporting the work we do here on Slate Money. Sign up now at slate.com/moneyplus to help support our work. Podcast production by Jared Downing and Cheyna Roth. Learn more about your ad choices. Visit megaphone.fm/adchoices

It's Money Talks…live! This episode was taped at The Hewlett Foundation's annual New Common Sense Conference, where Felix Salmon chatted with Jerusalem Demsas, who covers housing and infrastructure for The Atlantic. They discuss the NIMBY mentality, regulatory tangles holding back real estate development, and what we have to give up to gain more affordable housing. If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get an ad-free experience across the network and an additional segment of our regular show every week. You'll also be supporting the work we do here on Slate Money. Sign up now at slate.com/moneyplus to help support our work. Podcast production by Jared Downing and Cheyna Roth. Learn more about your ad choices. Visit megaphone.fm/adchoices

What do most people not understand about the news media? I would say two things. First: The most important bias in news media is not left or right. It's a bias toward negativity and catastrophe. Second: That while it would be convenient to blame the news media exclusively for this bad-news bias, the truth is that the audience is just about equally to blame. The news has never had better tools for understanding exactly what gets people to click on stories. That means what people see in the news is more responsive than ever to aggregate audience behavior. If you hate the news, what you are hating is in part a collective reflection in the mirror. If you put these two facts together, you get something like this: The most important bias in the news media is the bias that news makers and news audiences share toward negativity and catastrophe. Jerusalem Demsas, a staff writer at The Atlantic and the host of the podcast Good on Paper, joins to discuss a prominent fake fact in the news — and the psychological and media forces that promote fake facts and catastrophic negativity in the press. If you have questions, observations, or ideas for future episodes, email us at PlainEnglish@Spotify.com. Host: Derek Thompson Guest: Jerusalem Demsas Producer: Devon Baroldi Links: "The Maternal-Mortality Crisis That Didn't Happen" by Jerusalem Demsas https://www.theatlantic.com/ideas/archive/2024/05/no-more-women-arent-dying-in-childbirth/678486/ The 2001 paper "Bad Is Stronger Than Good" https://assets.csom.umn.edu/assets/71516.pdf Derek on the complex science of masks and mask mandates https://www.theatlantic.com/newsletters/archive/2023/03/covid-lab-leak-mask-mandates-science-media-information/673263/ Learn more about your ad choices. Visit podcastchoices.com/adchoices

Later this summer, the Supreme Court will rule on City of Grants Pass v. Johnson, one of the most important cases on homelessness to come up in a long time. The court will rule on whether someone can be fined, jailed, or ticketed for sleeping or camping in a public space when they're homeless and have nowhere else to go. We talk to Atlantic writer and Good on Paper host Jerusalem Demsas about the case and what it may or may not solve. Homelessness has exploded since the 1980s, mostly in cities where housing costs have gone up. Criminalizing—or not criminalizing—people sleeping in public does not change the fact that many people have no other option, and that people who do have places to sleep can't help but notice their cities have a huge homelessness problem. Get more from your favorite Atlantic voices when you subscribe. You'll enjoy unlimited access to Pulitzer-winning journalism, from clear-eyed analysis and insight on breaking news to fascinating explorations of our world. Subscribe today at TheAtlantic.com/podsub. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this week's conversation between Dr. James Emery White and co-host Alexis Drye, they discuss the widespread campus protests that have been dominating the headlines of late. More than 2800 students have been arrested across 50 campuses across the U.S. Many Americans are unsure about what to think about all of these protests, and have mixed feelings about the response from college administrators to the protests. So how should we think Christianly about what's happening across the country? Episode Links The best place to begin is by trying to have a deeper understanding of the conflict itself. While today's conversation does explore that, we'd suggest you go back and listen to CCP80: On the War in Israel. Dr. White and Alexis also mentioned the number of news stories of late tied to these protests happening across the U.S. Here are the ones specifically tied to today's conversation - we hope you'll take the time to read them: Jerusalem Demsas, “The Problem With America's Protest Feedback Loop,” The Atlantic, May 10, 2024. Amaris Encinas, “Rabbi decries act of ‘senseless hatred' after dozens of headstones damaged at Jewish cemetery in NY,” USA Today, May 7, 2024. Livia Albck-Ripka, “Hillary Clinton Accuses Protesters of Ignorance of Mideast History,” The New York Times, May 9, 2024. “A Few Graduations Are Disrupted by Protest, but Many Are Held as Planned,” The New York Times, updated May 13, 2024. Elizabeth E. Evans, “Amid surge of campus protests, chaplains find reason for hope in their students,” Religion News Service, May 10, 2024. Kirsten Grieshaber, “At time of rising antisemitism, Holocaust survivors take on denial and hate in new digital campaign, “ Associated Press, May 2, 2024. Barbara Sprunt, “House passes bill aimed to combat antisemitism amid college unrest,” NPR, May 2, 2024. David French, “Colleges Have Gone off the Deep End. There Is a Way Out.” The New York Times, April 28, 2024. For those of you who are new to Church & Culture, we'd love to invite you to subscribe (for free of course) to the twice-weekly Church & Culture blog and check out the Daily Headline News - a collection of headlines from around the globe each weekday. We'd also love to hear from you if there is a topic that you'd like to see discussed on the Church & Culture Podcast in an upcoming episode. You can find the form to submit your questions at the bottom of the podcast page HERE.

There is so much we need to build right now. The housing crunch has spread across the country; by one estimate, we're a few million units short. And we also need a huge build-out of renewable energy infrastructure — at a scale some experts compare to the construction of the Interstate highway system.And yet, we're not seeing anything close to the level of building that we need — even in the blue states and cities where housing tends to be more expensive and where politicians and voters purport to care about climate change and affordable housing.Jerusalem Demsas is a staff writer at The Atlantic who obsesses over these questions as much as I do. In this conversation, she takes me through some of her reporting on local disputes that block or hinder projects, and what they say about the issues plaguing development in the country at large. We discuss how well-intentioned policies evolved into a Kafka-esque system of legal and bureaucratic hoops and delays; how clashes over development reveal a generational split in the environmental movement; and what it would take to cut decades of red tape.Mentioned:“Colorado's Ingenious Idea for Solving the Housing Crisis” by Jerusalem Demsas“The Culture War Tearing American Environmentalism Apart” by Jerusalem Demsas“Why America Doesn't Build” by Jerusalem DemsasBook Recommendations:Don't Blame Us by Lily GeismerThe Bulldozer in the Countryside by Adam RomeA Swim in a Pond in the Rain by George SaundersThoughts? Guest suggestions? Email us at ezrakleinshow@nytimes.com.You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs.This episode of “The Ezra Klein Show” was produced by Kristin Lin. Fact-checking by Michelle Harris with Kate Sinclair. Our senior engineer is Jeff Geld. Our senior editor is Claire Gordon. The show's production team also includes Annie Galvin, Rollin Hu and Aman Sahota. Original music by Isaac Jones. Audience strategy by Kristina Samulewski and Shannon Busta. The executive producer of New York Times Opinion Audio is Annie-Rose Strasser. Special thanks to Sonia Herrero.

It's the third anniversary of Jan. 6 and just over 300 days until the election. The leading Republican is the man who many believe inspired the insurrection and President Biden is sagging in popularity. Join moderator Jeffrey Goldberg, Peter Baker of The New York Times, Laura Barrón-López of PBS NewsHour, Josh Dawsey of The Washington Post and Jerusalem Demsas of The Atlantic to discuss more.

In this special episode, Tyler sat down with Jerusalem Demsas, staff writer at The Atlantic, to discuss three books: The Dispossessed by Ursula K. Le Guin, Gulliver's Travels by Jonathan Swift, and Of Boys and Men by Richard V. Reeves. Spanning centuries and genres and yet provoking similar questions, these books prompted Tyler and Jerusalem to wrestle with enduring questions about human nature, gender dynamics, the purpose of travel, and moral progress, including debating whether Le Guin prefers the anarchist utopia she depicts, dissecting Swift's stance on science and slavery, questioning if travel makes us happier or helps us understand ourselves, comparing Gulliver and Shevek's alienation and restlessness, considering Swift's views on the difficulty of moral progress, reflecting on how feminism links to moral progress and gender equality, contemplating whether imaginative fiction or policy analysis is more likely to spur social change, and more. Read a full transcript enhanced with helpful links, or watch the full video. Recorded May 22nd, 2023. Other ways to connect Follow us on X and Instagram Follow Tyler on X Follow Jerusalem on X Join our Discord Email us: cowenconvos@mercatus.gmu.edu Learn more about Conversations with Tyler and other Mercatus Center podcasts here.

The New York Times journalist talks about the difficulties of early parenthood, the lure of communal living, and why he loves Burning Man.Want more from Ezra on the topics in today's episode? We recommend the following: This episode of The Ezra Klein Show with scholar Kristen Ghodsee on communes and intentional communities (https://www.nytimes.com/2023/06/09/opinion/ezra-klein-kristen-ghodsee.html), a conversation The Atlantic's Jerusalem Demsas about homelessness and the origins of our current housing crisis (https://www.nytimes.com/2023/07/18/opinion/ezra-klein-podcast-jerusalem-demsas.html), an interview with writer Sheila Liming on loneliness in America (https://www.nytimes.com/2023/04/18/opinion/ezra-klein-podcast-sheila-liming.html), and two interviews he's done with child psychologist Alison Gopnik (https://www.vox.com/podcasts/2019/6/13/18677595/alison-gopnik-changed-how-i-think-about-love, https://www.nytimes.com/2021/04/16/podcasts/ezra-klein-podcast-alison-gopnik-transcript.html). Finally, Annie Lowrey's piece about her experiences with pregnancy, childbirth and early parenting: “What Counts As the Life of the Mother?” (https://www.theatlantic.com/ideas/archive/2022/08/pregnancy-birth-complication-abortion-life-of-mother/671006/).Did you know we have a weekly email newsletter for the Death, Sex & Money community? Every Wednesday we send out a note from Anna, fascinating listener letters from our inbox, and updates from the show. Sign up at deathsexmoney.org/newsletter, and follow the show on Twitter, Facebook, and Instagram.Got a story to share? Email us at deathsexmoney@wnyc.org. Hosted on Acast. See acast.com/privacy for more information.

The New York Times journalist Ezra Klein thinks a lot about the impacts of policy and systems on our personal lives. On his podcast, The Ezra Klein Show, he recently mentioned how American society insufficiently supports families of young kids, and wondered why living in community is so hard, and the isolation that it can breed as a result. Ezra's thinking about all of these issues in his own life as well: he's married to fellow journalist Annie Lowrey, and they have two young kids. The family moved to California before the pandemic, and after a health crisis they struggled to find the support they needed for their family. They eventually decide to move back to the East Coast, and as they settle into their lives in New York, Ezra's thinking a lot about the tradeoffs of two-parent households. “I don't believe people are meant to do this. You know, two parents plus kids, it's too few people,” he said. Ezra and Anna talk about the beloved communal spaces of his 20s and 30s, the tension between autonomy and community, and why he believes our emphasis on two-parent families is “a cultural mistake.”   Want more from Ezra on the topics in today's episode? We recommend the following: This episode of The Ezra Klein Show with scholar Kristen Ghodsee on communes and intentional communities, a conversation with The Atlantic's Jerusalem Demsas about homelessness and the origins of our current housing crisis, an interview with writer Sheila Liming on loneliness in America, and two interviews he's done with child psychologist Alison Gopnik. Finally, you can read Annie Lowrey's piece about her experiences with pregnancy, childbirth and early parenting here.

The New York Times journalist talks about the difficulties of early parenthood, the lure of communal living, and why he loves Burning Man.Want more from Ezra on the topics in today's episode? We recommend the following: This episode of The Ezra Klein Show with scholar Kristen Ghodsee on communes and intentional communities (https://www.nytimes.com/2023/06/09/opinion/ezra-klein-kristen-ghodsee.html), a conversation The Atlantic's Jerusalem Demsas about homelessness and the origins of our current housing crisis (https://www.nytimes.com/2023/07/18/opinion/ezra-klein-podcast-jerusalem-demsas.html), an interview with writer Sheila Liming on loneliness in America (https://www.nytimes.com/2023/04/18/opinion/ezra-klein-podcast-sheila-liming.html), and two interviews he's done with child psychologist Alison Gopnik (https://www.vox.com/podcasts/2019/6/13/18677595/alison-gopnik-changed-how-i-think-about-love, https://www.nytimes.com/2021/04/16/podcasts/ezra-klein-podcast-alison-gopnik-transcript.html). Finally, Annie Lowrey's piece about her experiences with pregnancy, childbirth and early parenting: “What Counts As the Life of the Mother?” (https://www.theatlantic.com/ideas/archive/2022/08/pregnancy-birth-complication-abortion-life-of-mother/671006/).Did you know we have a weekly email newsletter for the Death, Sex & Money community? Every Wednesday we send out a note from Anna, fascinating listener letters from our inbox, and updates from the show. Sign up at deathsexmoney.org/newsletter, and follow the show on Twitter, Facebook, and Instagram.Got a story to share? Email us at deathsexmoney@wnyc.org. Hosted on Acast. See acast.com/privacy for more information.

Ever wonder how homelessness has gotten so bad in America? And why there appears to be no successful efforts to solve it? The Atlantic's Jerusalem Demsas joins Tim and JVL to answer every question they have about this massive problem. It's a truly in-depth discussion you won't want to miss! Watch the Tim and JVL interview Jerusalem on our official YouTube channel here: https://youtu.be/qCsd1O4-hdI Learn more about your ad choices. Visit podcastchoices.com/adchoices

California has around half of the nation's unsheltered homeless population. The state's homelessness crisis has become a talking point for Republicans and a warning sign for Democrats in blue cities and states across the country.Last month, the Benioff Homelessness and Housing Initiative at the University of California, San Francisco, released a landmark report about homelessness in the state, drawing from nearly 3,200 questionnaires and 365 in-depth interviews. It is the single deepest study on homelessness in America in decades. And the report is packed with findings that shed new light not only on California's homelessness problem but also on housing affordability nationwide.Jerusalem Demsas is a staff writer at The Atlantic who has written extensively about the interlocking problems of housing affordability and homelessness in America. So I asked her on the show to walk me through the core findings of the study, what we know about the causes of homelessness, and what solutions exist to address it. We discuss the surprising process by which people end up homeless in the first place, the “scarring” effect that homelessness can have on their future prospects, the importance of thinking of homelessness as a “flow,” not a “stock,” the benefits and limitations of “housing first” approaches to end homelessness, why Republican proposals for being tougher on the homeless can make the problem worse, why neither generous social safety nets nor private equity firms are to blame for homelessness, and more.Book Recommendations:Homelessness Is a Housing Problem by Gregg Colburn and Clayton Page AldernChildren of Time by Adrian TchaikovskyStrangers to Ourselves by Rachel AvivListen to this podcast in New York Times Audio, our new iOS app for news subscribers. Download now at nytimes.com/audioappThoughts? Guest suggestions? Email us at ezrakleinshow@nytimes.com.You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast, and you can find Ezra on Twitter @ezraklein. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs.This episode of “The Ezra Klein Show” was produced by Rollin Hu. Fact-checking by Michelle Harris, with Mary Marge Locker. The senior engineer is Jeff Geld. The senior editor is Rogé Karma. The show's production team also includes Emefa Agawu, Annie Galvin and Kristin Lin. Original music by Isaac Jones. Audience strategy by Kristina Samulewski and Shannon Busta. The executive producer of New York Times Opinion Audio is Annie-Rose Strasser. Special thanks to Sonia Herrero.

Sean Illing talks with writer and reporter Jerusalem Demsas about the causes of homelessness in America. They discuss our ideas of home ownership, and how our country's cultural expectations and policies are working against us.  Host: Sean Illing (@seanilling), host, The Gray Area Guest: Jerusalem Demsas (@JerusalemDemsas) staff writer, The Atlantic References:  “The Homeownership Society Was a Mistake” by Jerusalem Demsas (The Atlantic; Dec. 20, 2022) “The Obvious Answer to Homelessness and Why Everyone's Ignoring It” by Jerusalem Demsas (The Atlantic; Dec. 12, 2022) “The Billionaire's Dilemma” by Jerusalem Demsas (The Atlantic; Aug. 4, 2022) “Stuck! The Law and Economics of Residential Stagnation” by David Schleicher (Yale Law Review; Oct. 2017) “Black Americans And The Racist Architecture of Homeownership” by Alisa Chang, Christopher Intagliata, and Jonaki Mehta (NPR; May 8, 2021)   Enjoyed this episode? Rate The Gray Area ⭐⭐⭐⭐⭐ and leave a review on Apple Podcasts. Subscribe for free. Be the first to hear the next episode of The Gray Area. Subscribe in your favorite podcast app. Support The Gray Area by making a financial contribution to Vox! bit.ly/givepodcasts This episode was made by:  Producer: Erikk Geannikis Editor: Amy Drozdowska Engineer: Patrick Boyd Editorial Director, Vox Talk: A.M. Hall Learn more about your ad choices. Visit podcastchoices.com/adchoices