Podcasts about Industrial Revolution

This is Part 2! For Part 1, check the feed!This week we're joined by friend of the show, producer, writer and actor (and proud Welshman), Jonny Owen to discuss the golden age of Industrial Revolution era Wales!We'll chat copper in Swansea, the iron town of Merthyr Tydfil, and the sheer volume of steam coal dug out of the Cynon Valley, the Rhondda Valley and the Taff Valley.Jonny is heading to Hamburg in his one day Time Machine so what better time to ask you if this is a good idea? Or what you'd do with yours? Do email: hello@ohwhatatime.comPart 1 is released on Monday and Part 2 on Tuesday - but if you want more Oh What A Time and both parts at once, you should sign up for our Patreon! On there you'll now find:•The full archive of bonus episodes•Brand new bonus episodes each month•OWAT subscriber group chats•Loads of extra perks for supporters of the show•PLUS ad-free episodes earlier than everyone elseJoin us at

This week we're joined by friend of the show, producer, writer and actor (and proud Welshman), Jonny Owen to discuss the golden age of Industrial Revolution era Wales!We'll chat copper in Swansea, the iron town of Merthyr Tydfil, and the sheer volume of steam coal dug out of the Cynon Valley, the Rhondda Valley and the Taff Valley.Jonny is heading to Hamburg in his one day Time Machine so what better time to ask you if this is a good idea? Or what you'd do with yours? Do email: hello@ohwhatatime.comPart 1 is released on Monday and Part 2 on Tuesday - but if you want more Oh What A Time and both parts at once, you should sign up for our Patreon! On there you'll now find:•The full archive of bonus episodes•Brand new bonus episodes each month•OWAT subscriber group chats•Loads of extra perks for supporters of the show•PLUS ad-free episodes earlier than everyone elseJoin us at

This is episode 3 of a six-part series on food imperialism. Welcome to Edible Empire, a podcast by Planet Pulse Pacific about the hidden cost of our food.Food isn't just about taste—it's power. This podcast explores how empires and corporations built control through agriculture, reshaping cultures and feeding some at the expense of others.This episode traces the global impact of the palm oil industry from its indigenous roots to modern supply chains through three expert interviews.Dr Jonathan Robins opens by detailing the pre-colonial history of oil palm in West Africa and explaining how European empires weaponised the crop to fuel the Industrial Revolution, establishing structural patterns that mirror today's agribusiness models.Dr Helena Varkkey then unpacks the political economy of Malaysian production, exposing how entrenched relationships between political elites and conglomerates shield the industry from regulation.Farwiza Farhan brings a frontline conservation perspective from Indonesia, documenting how government-incentivised monoculture plantations drive deforestation, threaten endangered species, and violate Indigenous land rights. Together, the experts critique the effectiveness of voluntary sustainability certification schemes like the RSPO, debate whether palm oil can ever be truly sustainable at scale, and highlight the efforts of local communities fighting back against corporate food imperialism.Make sure you subscribe so you don't miss out on the next episode!Resources from this episode:https://www.mtu.edu/social-sciences/department/faculty/robins/https://commoditiesofempire.org.uk/about/jonathan-robins/https://theconversation.com/profiles/jonathan-e-robins-1227071https://seapeat.wixsite.com/homehttps://cgsea.org/https://umexpert.um.edu.my/helenav To view all the links to the websites and documents, visit the show notes on our website.Please support our work and enable us to deliver more content by buying us a coffee or becoming a member of Athletes for Nature.Follow us on Instagram and Facebook, subscribe to this podcast, and share this episode with your friends and family.

Episode: 1604 In which hydrogen balloons bind science to technology.  Today, hydrogen -- in 1783.

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

AI was supposed to make our lives better. Instead, it's made many of us scared and angry. Communities are protesting data centers across the country, and polling shows most Americans think AI is moving too fast.In today's episode, guest host Miles Bryan talks with independent journalist Jasmine Sun, who argues that public attitudes toward AI are undergoing a fundamental shift. Jasmine argues that AI is seen not simply as a technology to adopt, but as an elite political project to resist. The two discuss “AI populism,” the parallels with the Industrial Revolution, how public opinion about AI may affect US politics, and how the leaders of Silicon Valley are unprepared for the growing backlash against AI. Guest Host: Miles Bryan, Vox reporter and senior producer Guest:Jasmine Sun, journalist Find Jasmine's Substack here: https://jasmi.news/ We would love to hear from you. To tell us what you thought of this episode, email us at thegrayarea@vox.com or leave us a voicemail at 1-800-214-5749. Your comments and questions help us make a better show. And you can watch new episodes of The Gray Area on YouTube. Listen to The Gray Area ad-free by becoming a Vox Member: vox.com/members Learn more about your ad choices. Visit podcastchoices.com/adchoices

Live July 14, 2026 | Yaron Brook Show(Season 12, Episode 123)Knowles; Iran; ICE; Immigration; antitrust; Intel; Woke; HK; Bezos; Achievement | Yaron Brook ShowThe Right's War on Progress? Michael Knowles, Iran, ICE & Why Achievement Is Under AttackHas the political right become as hostile to capitalism and progress as the left?Michael Knowles' attack on the Industrial Revolution raises a much bigger question: why are so many conservatives abandoning the values that created modern civilization? Yaron Brook examines the growing alliance between populism, anti-capitalism, and anti-technology—and explains why defending reason, innovation, and individual achievement has never been more important.The conversation expands into the latest developments on Iran, immigration and ICE, antitrust attacks on Intel, DEI's retreat, Hong Kong's future, Jeff Bezos on wealth creation, medical breakthroughs, AI, philosophy, art, psychology, and much more—followed by an extensive audience Q&A covering Objectivism, morality, physics, education, music, constitutional rights, and today's cultural decline.If you value reason, freedom, and human achievement, this episode is for you.Watch now: https://youtube.com/live/zm1efvYAFvgMain Topics:00:00 Introduction: Spain vs. France, episode preview03:09 Michael Knowles attacks the Industrial Revolution07:36 Political violence, immigration & fact-checking Knowles12:17 How industrialization transformed civilization16:00 Why conservatives increasingly reject capitalism19:23 Technology, innovation & cultural change22:56 The Unabomber argument—and what's fundamentally wrong with it28:09 The Industrial Revolution: humanity's greatest achievement?29:06 Populism and the conservative abandonment of responsibility30:15 The collapse of pro-capitalist conservatism32:26 Freedom, capitalism & individual responsibility34:07 Left-wing vs. right-wing anarchism39:53 Iran, military strategy & American foreign policy46:27 ICE, immigration enforcement & political incentives54:08 Smithsonian history battles & antitrust politics59:44 Intel, industrial policy & government intervention1:04:03 DEI retreats while Hong Kong reinvents itself1:08:59 Jeff Bezos explains wealth creation1:13:12 Breakthroughs in cancer & Alzheimer's research1:16:52 The semiconductor boom1:18:38 Crime statistics, Super Chats & updates1:21:40 Ayn Rand Institute conference previewLive Audience Questions1:28:04 Does morality require survival—or merely intelligence? AI, emergence & ethics1:28:18 Did America's 1953 Iran intervention create today's Middle East crisis?1:40:43 What truly makes a genius like Newton or Ayn Rand?1:40:45 Why haven't we produced another Einstein?1:46:18 Why do philosophers question whether reality exists?1:46:24 Nuclear power's comeback: freedom or AI necessity?1:47:45 Can justice be achieved after irreversible wrongs?1:49:23 Why does humanity need art?1:54:55 Losing family and friends to irrational ideas—what should you do?1:56:05 Immigration enforcement, rights & moral responsibility1:57:58 Is empathy selfish?1:59:36 Celebrating political deaths—is nihilism becoming mainstream?2:00:34 Does the Constitution protect illegal immigrants?2:02:20 Are destructive philosophies rooted in low self-esteem?2:04:31 Is 1980s music objectively better than today's?2:06:41 Should ARI buy Yaron a private jet?2:07:25 Why do intellectuals ignore the human cost of bad ideas?2:09:56 Will Yaron watch Christopher Nolan's The Odyssey?2:12:10 What does "working class" actually mean?2:13:42 Why is Tamara de Lempicka so overlooked?#Objectivism #Capitalism #IndustrialRevolution #MichaelKnowles #Iran #Immigration #JeffBezos #Technology #AynRand #Inflation #OilPrices #Greedflation #Economics Subscribe for daily analysis on economics, politics, philosophy, technology, investing, and current events.The Yaron Brook Show is Sponsored by[The Ayn Rand Institute](https://www.aynrand.org/starthere)[Energy Talking Points, featuring AlexAI, by Alex Epstein](https://alexepstein.substack.com/)[Express VPN](https://www.expressvpn.com/yaron)[Hendershott Wealth Management](https://www.youtube.com/watch?v=X4lfC...) &(https://hendershottwealth.com/ybs/)[Michael Williams & The Defenders of Capitalism Project](https://www.DefendersOfCapitalism.com)[Support the Show]( / yaronbrookshow )[Sponsor the Show](askyaron@yaronbrookshow.com/)[One-time donation](https://bit.ly/2RZOyJJ)Join the [Yaron Brook Show YouTube channel]( / @yaronbrook )Like what you hear? Like, share, and subscribe to stay updated on new videos and help promote the [Yaron Brook Show](https://bit.ly/3ztPxTx)Continue the discussion by following Yaron on [Twitter](https://bit.ly/3iMGl6z) and [Facebook](https://bit.ly/3vvWDDC )Want to learn more about Ayn Rand and Objectivism? Visit the [Ayn Rand Institute](https://bit.ly/35qoEC3)Become a supporter of this podcast: https://www.spreaker.com/podcast/yaron-brook-show--3276901/support.Yaron is the executive chairman of the Ayn Rand Institute and a world class speaker. He is the coauthor of the national best-seller Free Market Revolution: How Ayn Rand's Ideas Can End Big Government, Equal is Unfair: America's Misguided Fight Against Income Inequality and In Pursuit of Wealth: The Moral Case for Finance. He speaks around the world on a variety of topics including the morality of capitalism, Ayn Rand and her philosophy, finance and economics, and the value of inequality.

Attitudes around social media are changing. A Pew Research study found that about half of teens surveyed in 2024 felt that social media had a mostly negative impact on their age group. A group advocating a more considered relationship to tech held what they called a Summer of Ludd Festival, named after the Industrial Revolution era workers movement, the Luddites. The festival in New York City featured tech-free activities ranging from meditation to a workshop on how to flirt in real life. But in 2026, is it really possible to live without the tech we've become so addicted to? Guest host Manisha Krishnan, WIRED's senior culture editor, talks to Gowanus, the spokespuppet of a local Luddite movement, about the tenets of their group - and how they plan to grow it. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Violeta Bulc, former European Commissioner for Transport and coordinator of the book Leadership Challenged, featuring 24 authors from around the world. They explore the dangers of transhumanism, the misuse of artificial intelligence, and how Silicon Valley has lost its authority to lead on technology ethics. Drawing from her background as a computer engineer who worked in Silicon Valley, Bulc argues for creating global AI infrastructure with democratically agreed-upon standards—similar to how the early Internet was built. The conversation covers the manipulation of public consciousness, the importance of middle-class agency in social change, and why humanity needs to reclaim ownership of its collective knowledge before private enterprises consolidate total control. Bulc's book is available for free download at ecocivilization.earth.Timestamps00:00 Stewart introduces Violeta Bulc and her book Leadership Challenged, coordinated with 24 global authors discussing humanity's chance through better leadership approaches.05:00 Violeta explains her technology background and critiques artificial intelligence naming, arguing these are powerful data-processing tools without true intelligence, emphasizing unknown ethical standards embedded in AI systems.10:00 Discussion of transhumanism as investment buzzword serving elite agendas, comparing to previous Silicon Valley bubble while emphasizing humanity's unexplored relational, spiritual and energetic dimensions beyond industrial development.15:00 Stewart discusses mainstream culture's fragmentation since 2008, Silicon Valley's dystopian vision, and personal strategies for reducing dependency on AI tools through diversification and stepping back from reliance.20:00 Violeta explains historical civilization patterns and middle class destruction, expressing hope that emerging thoughts worldwide will eventually converge to shift current power dynamics and technological obsessions.25:00 Technology as tool versus misuse, emphasizing builders' responsibility and ethical frameworks needed, comparing AI regulation needs to automotive safety standards that weren't implemented early enough.30:00 Edward Bernays discussion revealing manipulation through public relations and psychological operations, leading to modern sock puppet armies used by nation states for narrative control online.35:00 Internet described as most democratic technological tool ever built, maintained by responsible groups preserving equality and inclusion principles through decentralized infrastructure and IP address accessibility.40:00 Proposal for global AI infrastructure with agreed rules treating applications as interfaces, questioning private enterprise ownership of humanity-generated data and advocating collective management with usage fees.45:00 Technology evolution patterns from mainframes to personal computing back to centralized cloud computing, emphasizing need to prevent domination while preserving entrepreneurship and collective decision rights.50:00 Quantum physics principles applied to human connection and responsibility, discussing EU ethical committees reviewing AI projects post-approval, emphasizing caring hearts over short-term quarterly corporate thinking.55:00 Violeta shares company transformation experiences moving away from competition models toward serving genuine market needs, concluding with book availability at ecocivilization.earth for free download.Key Insights1. Violeta Bulc argues that artificial intelligence is fundamentally misnamed because there is no actual intelligence within these systems. They are powerful computational tools capable of processing massive amounts of data and identifying patterns, but they lack genuine intelligence. What concerns her most is that this technology has owners with embedded interests and unknown ethical standards, yet society increasingly wants to build everything on these applications and even allow them to make decisions for us. She emphasizes that as someone with decades of experience in high-tech engineering, including work in Silicon Valley, she understands the architecture behind these systems and believes we must recognize them as tools rather than intelligent entities.2. During her time as European Commissioner, Bulc helped write the first European strategy on artificial intelligence, which included three critical elements she was proud of. First, there must always be a red button to switch off any application or technology when it causes harm. Second, there must be a responsible person behind every app who can be held accountable for its consequences. Third, there should be an ethical committee evaluating powerful applications to understand their potential consequences. Though these principles have been somewhat diluted over time, they represent an important framework for responsible technology development that prioritizes human oversight and accountability.3. Bulc observes that throughout human history, great civilizations have risen across all continents, not just in Europe or the Americas, and most brought themselves down through decadence, self-centeredness, and arrogance before being finished off by external forces. She believes Western civilization is currently at this point, having become accustomed to obtaining resources through force and authority while constantly readjusting moral standards to serve elite interests. The industrial revolution initially improved conditions for people because industry needed workers, which led to the emergence of a powerful middle class. However, the elite recognized that the middle class was the only segment of society truly interested in change, so they systematically worked to destroy it over the past twenty to thirty years.4. The Internet represents the most progressive democratic tool ever built in human society, according to Bulc. Its fundamental architecture, based on TCP IP protocol and packet switching, was designed to be non-hierarchical, allowing any computer with an IP address to be seen on the same level as powerful global corporations. The maintenance of Internet tables remains in the hands of people with high levels of awareness and responsibility who are faithful to its initial democratic mission. She had hoped this technology would bring the world together as the closest tool humanity has invented to support equality and inclusion, and despite the problems with applications built on top of it, the underlying infrastructure still maintains these democratic principles.5. Bulc proposes creating a global AI infrastructure with globally agreed rules and standards, similar to how the Internet functions. She argues that many AI tools currently claim ownership of humanity's knowledge, wisdom, and heritage without permission, manipulating data that rightfully belongs to all of humanity. Instead of allowing private enterprises to capture this data first and then charge people to access it, she envisions putting all of humanity's data into a commonly managed infrastructure with clear rules about who can use it, under what conditions, and with fees paid back to humanity. This approach would challenge the current fragmented network of privately owned data centers and restore collective ownership of human knowledge.6. The transhumanism movement represents an obsession rather than a thoughtful application of technology, in Bulc's view. She distinguishes between using transhumanism as a tool for exploring the universe under extreme conditions where humans cannot survive versus implementing it on Earth as a replacement for humanity. The fundamental problem is that the human characters building these machines and applications have questionable ethical models, and they will not allow the rest of humanity to coexist peacefully on the planet. She advocates for transhumanism to be used for space exploration while preserving Earth for humans who want to live as relational, spiritual, and social beings connected to the natural ecosystem.7. Bulc emphasizes that we must move beyond the competition model and think carefully about the consequences of our actions because humanity is too connected and interdependent to simply do things because we can. She applies three basic laws of quantum physics to everyday life: we are all connected and influence each other, the same ideas can emerge simultaneously around the world through entanglement, and the observer always makes a difference in any situation. The current rush to develop technology without pausing to assess consequences is a deliberate tool to prevent thinking, driven by fear of competition. However, her fourteen years of experience helping companies recover from financial trouble demonstrated that moving away from competition models and focusing on genuinely serving market needs creates sustainable, prominent players who work together with customers and local communities.

In this second sponsored episode with Findmypast — the follow-up to our Genealogy 101 introduction — Jonathan Thomas and Jen Baldwin, Research Specialist at Findmypast, go deeper into the records, techniques, and stories that turn a nervous beginner into a confident family historian. The episode covers the extraordinary richness of the 1921 census and 1939 register, why the newspaper archive is the place where ancestors stop being names and start being people, how the Industrial Revolution left its fingerprints across every family tree, the truth about name changes and spelling variations (it was almost never Ellis Island), how to avoid drowning in common surnames like Thomas or Smith, the unexpected discoveries that make genealogy so addictive — including Jen's own Cornish mail-order bride ancestor, an Irish great-great-grandfather with two arrests across two countries, and Audrey Thompson the World War II rat-catching champion — and how to connect all of it to the great currents of British history. The episode also covers parish records, military records, DNA testing, the role of offline archives, and the mindset that keeps family history a lifelong joy rather than a frustrating quest for perfection. If you haven't heard the first episode, Genealogy 101, go back and listen to that one first. Links Sponsor Findmypast — Start Your Free Trial (US) Findmypast — Start Your Free Trial (UK) British Newspaper Archive (via Findmypast) Findmypast Crime & Criminal Records The Family History Of — Findmypast Podcast Was Justice Served? Podcast — Jen Baldwin Also Referenced Genealogy 101 — Previous Episode with Jen Baldwin (listen first) Friends of Anglotopia Club Takeaways The 1921 census and 1939 register work best as a pair — together they bookend the interwar period, and used in combination they can anchor a family from living memory all the way back through the Victorian era. The 1921 is the most detailed census ever taken in England and Wales; the 1939 register is the only census-style record with full birth dates and tracks maiden-to-married name changes for women. The 1939 register is the foundational document of the NHS — a living document updated for decades after the war, making it uniquely valuable for women's research. ARP warden and Home Guard volunteer roles recorded in its margins bring the home front of World War II vividly to life. Less than 10% of historical records are currently available online. The other 90% live in county record offices, local archives, churches, and museums — and going to them in person, touching the original documents, is an irreplaceable experience that no screen can fully replicate. Names were almost never changed at Ellis Island. Spelling variations in records happen because clerks wrote what they heard, literacy levels were inconsistent, and people adapted their names to the culture they found themselves in. You are researching people, not names — and building a mountain of evidence is the only reliable way to confirm you have the right person. Common surnames are a solvable problem: search by address rather than name, find an unusual first name in the same family, use occupations as a generational signature, and use Findmypast's wildcard and name-variation search tools to cast a wider net before narrowing down. Military records are one of the most common gateways to unexpected discovery — attestation papers, service records, pension files, and medal rolls fill in the specifics of what an ancestor actually did, where they served, and what happened to them. The Pals battalions of World War I, where entire communities enlisted together and died together, are particularly powerful to research. Parish records predate civil registration by centuries — the earliest on Findmypast go back to the 1300s — and the parish chest records that accompany them reveal an ancestor's role in their community: tax records, poor relief, bastardy orders, apprenticeship documents, and more that have nothing to do with baptism but everything to do with understanding a life. DNA testing adds a powerful new dimension to family research — not as a replacement for documentary evidence, but as a corroborating tool that can confirm suspected connections, break through brick walls by connecting you to unknown cousins, and reveal migration patterns that the paper records haven't caught yet. The best cure for a genealogy brick wall is to switch branches entirely. Fresh eyes on a different part of the family tree — maternal lines, in-laws, siblings — often reveals the clue that unlocks the original problem. The answer to who your Thomas ancestor was is sometimes hiding in his wife's maiden name records. Replace the goal of a perfect family tree with the practice of curiosity. The satisfaction of genealogy is not in the quantity of ancestors collected but in the two-in-the-morning moment when everything suddenly clicks — and that moment always opens a hundred new questions. This is a lifelong hobby, and patience rewards it far more than brilliance does. Soundbites "She answers a mail order bride ad in Cornwall. She travels by herself across the Atlantic, gets on a train, goes to the highest incorporated town in Colorado at ten thousand feet, marries this man sight unseen. He dies in a mining accident. She waits a few months, answers another ad, gets on another train with her kids, moves to Nebraska. They shake hands, go to the courthouse, get married. That's not as unusual as you might think." — Jen on her Cornish ancestor Mary Daniels and the extraordinary ordinary lives migration records reveal. "Their name was not changed at Ellis Island. Did it happen occasionally? Yes. But not because someone at a table said, I think you look more like a Smith. Names change because clerks wrote what they heard. You are researching people, not names." — Jen on the biggest myth in genealogy. "I found a gentleman who fills the census in black ink — except for the marriage column, which is a giant red D that he has clearly spent a lot of time on. It's written over and over. You can see the imprint on the page. He felt very passionately about his divorce." — Jen on the 1921 census entry that is worth the price of a subscription on its own. "She volunteers for the Women's Land Army and the next thing you know she is the country's top rat catcher. She enters the competition, travels the country giving presentations, getting her picture taken by local media — the top rat catcher fighting to save Britain from the Nazis. Because we needed the crops." — Jen on Audrey Thompson, one of her favorite unexpected discoveries. "He gets arrested twice. Once in Ireland and once in Wales — the situation in Wales was a bar fight, quite literally, covered in the newspaper in a lot of detail. He ends up in Cork prison, then Wales, then Liverpool, then Pennsylvania. I know all of that because of newspapers, arrest records, and a passenger list. The records all connect to tell a much deeper story." — Jen on tracing an Irish ancestor through famine, crime, and migration. "There's only about a percent of historical materials actually available online. There are more records sitting in archives and churches and museums and little nuggets all over the country. Go to the county records office. Go to the cemetery. Touch grass. Actually get away from the screen — because it connects you with the past in a way you could never duplicate in an online experience." — Jen on why physical archives still matter. "The 1939 register is the foundational records for the National Health Service. It was a living document for decades after the war. When women got married from 1939 on, you have their maiden name recorded and then they would go in and scratch out the maiden name and write their married name over the top. For women's research, it's particularly important." — Jen on why the 1939 register is so valuable. "I went to the church in St. Dunstan's Parish in the East. They still have the same baptismal font sitting in the building. And the steps down to the Thames along Radcliffe Highway are still in the same place as they were in the 1600s when my ancestors would have been there. I believe I'm the first person in our direct line to go back to London since they left in 1635. That first trip over was extraordinarily special." — Jen on the moment family history becomes real. "In a Pals battalion, they recruited whole battalions from the same community. All those men were neighbors before the war. They went to school together, they married each other's sisters, they went to church together. You get this situation where you're looking at an entire village of people who enlist and then get caught up in a battle and hundreds of them die in the same night. This is more than just a pedigree chart. This is an opportunity to remember them." — Jen on World War I Pals battalions and why war memorials matter. "You make a discovery and you've been researching this for hours or sometimes months or years, and you finally find it. It's two o'clock in the morning and you are literally jumping up and down in front of your computer. And then you realize I found this thing — but now I have a hundred more questions. That's the bit that keeps people coming back." — Jen on the true nature of the genealogy addiction. Chapters 00:00 Introduction & Sponsor Message — Findmypast and the Genealogy 201 premise 02:07 Picking Up Where We Left Off — A recap of Genealogy 101 and the call to action 02:32 The 1921 Census and 1939 Register as a Pair — Bookending the interwar period 03:30 What Makes the 1921 Census So Special — Employer names, divorce, orphan data, and handwriting 03:37 The Man with the Giant Red D — A census entry that tells an entire emotional story 05:00 The 1939 Register — ARP wardens, full birth dates, maiden names, and the NHS connection 07:42 Why Was There No 1931 or 1941 Census? — A brief recap for new listeners 08:51 What the 1939 Register Tells Us That a Census Can't — Women's names, volunteer war roles, and evacuated children 10:06 Newspapers as the Place Where Ancestors Come Alive — The 19th-century explosion and what it captured 11:30 The Three Brothers and Their Mother — A World War I story told entirely through local newspapers 12:45 The Industrial Revolution and Migration — Following opportunity from countryside to city to colony 13:30 Mary Daniels from Cornwall — A mail order bride, Colorado, a mining accident, Nebraska, and the homestead that's still in the family 17:09 Why Ancestors Move Around — Following jobs, the 20-mile radius rule, and how records help you track them 19:38 What Findmypast Offers Beyond the Census — Partnerships, niche collections, and British-based expertise 21:00 Metropolitan Police Records, Parish Records, and the Federation of Family History Societies 21:53 The National Archives Partnership — Military records, crime records, and the Licenses to Pass Beyond the Seas 23:40 Jen's Own Ancestor in the 1635 Passenger List — Henry Collins, three children, four servants, and a socioeconomic revelation 24:55 Not Everything Is Online — Less than 10% of records are digitized; why you should still visit archives 26:58 Even the Emperor of Japan Uses Archives — A digression on Oxford, shipping records, and the world's most patient researcher 28:24 Name Changes and Spelling Variations — Why Ellis Island didn't do it and what actually happened 28:58 The Jacobs/Jacobich Problem — And Jen's own family dropping the E off Browne 31:43 You Are Researching People, Not Names — Common sense is queen 32:29 Findmypast's Name Variation Search and Wildcard Tool — How to cast a wider net 33:37 Dealing with Common Surnames — Occupations, addresses, unusual first names, and process of elimination 35:30 Suddenly Grateful for Abel — Jonathan's uncommon grandfather and why unusual names are genealogical gold 35:48 Unexpected Discoveries That Make It All Feel Alive — Military service, migration stories, and the records that connect 36:02 A Famine Survivor, Two Arrests, and a Bar Fight in Wales — Jen's Irish ancestor's journey 37:56 Audrey Thompson, Top Rat Catcher — How the Women's Land Army and newspaper archive combine 39:00 True Crime in the Records — A domestic servant, a storm, an affair caught in a parish church, and the real-life whodunit 40:50 British Remembrance vs. American Remembrance — Plaques in banks, poppies, and why the UK doesn't forget 42:17 Findmypast's War Memorial Collection — Photographed memorials with every name listed, accessible from Colorado 44:00 The Pals Battalions — Entire villages enlisting, fighting, and dying together in World War I 45:22 Visit War Memorials When You Travel — What they tell you about a village's past and the state of its memory 45:30 Perfectionism vs. Curiosity — Why the chase is better than the finish line 47:24 Up at Two in the Morning — The two o'clock discovery and the hundred questions it opens 48:02 When People Don't Find the Castle They Expected — Why your ordinary ancestors are more interesting than any aristocrat 49:38 Three Assignments for After This Episode — Parish records, switching branches, and citing your sources 49:59 Parish Records Explained — Baptism vs. birth, burial vs. death, parish chest records, and the records from the 1300s 53:31 Why Switching Branches Solves Problems — Fresh eyes, new records, and how the puzzle connects 55:27 Why Citing Your Sources Matters — Future you will forget, and Findmypast Workspaces can help 57:46 The Mindset That Turns a Beginner Into a Historian — Replace perfection with curiosity, embrace the lifelong hobby 59:09 Jen's Own Family Tree Back to 1635 — St. Dunstan's Parish, Radcliffe Highway, the baptismal font, and being the first to return 1:00:41 Jonathan's Takeaway — Time to call grandma and fill in the family tree 1:01:54 Wrap-Up and Sponsor Outro — Findmypast free trial links and an invitation to share discoveries Video Version

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

The English country house has been on the brink of ruination since at least the start of World War I—or perhaps the first chug of the Industrial Revolution—or was it the end of serfdom …? Propping up this dying, decadent institution has been a favored pastime of preservationists, architecture buffs, and earls for about as long as the institution has been around. In his new book, Noble Ambitions, historian Adrian Tinniswood peels back the wallpaper to show how these ancestral piles survived both World War II and the sunset of the British Empire—and in some ways, are more relevant than they ever were. This episode originally aired in 2021.Go beyond the episode:Adrian Tinniswood's Noble Ambitions: The Fall and Rise of the English Country House After World War IIFor the completionist, his previous book: The Long Weekend: Life in the English Country House, 1918-1939Revisit the famed 1974 Victoria & Albert exhibition “The Destruction of the Country House,” or go visit Agecroft Hall and Gardens in Richmond, Virginia, one of several country homes dismantled and reassembled on this side of the Atlantic. In England? Check out Sudbury Hall, which gets a shout out in the episodeThe first bestselling nonfiction book about the country house? Mark Girouard's Life in the English Country HouseRead Sam Knight's essay about the National Trust's recent report on colonialism and slavery: “Britain's Idyllic Country Houses Reveal a Darker History”If you haven't yet, you simply must watch Downtown AbbeyTune in every other week to catch interviews with the liveliest voices from literature, the arts, sciences, history, and public affairs; reports on cutting-edge works in progress; long-form narratives; and compelling excerpts from new books. Hosted by Stephanie Bastek.Subscribe: iTunes/Apple • Amazon • Google • Acast • PandoraHave suggestions for projects you'd like us to catch up on, or writers you want to hear from? Send us a note: podcast [at] theamericanscholar [dot] org. And rate us on iTunes! Hosted on Acast. See acast.com/privacy for more information.

The conversation around AI has officially moved beyond the technology sector — and the implications for wealth management are worth paying attention to. In this episode of The FutureProof Advisor, I explore three developments that together paint a picture of how quickly the world is adapting to AI: a sweeping ethical framework from one of the world's most influential institutions drawing direct parallels to the Industrial Revolution, the rapid evolution from generative AI toward autonomous computer and robotic control, and one of the most widely used tools in the world quietly rebuilding itself around AI to stay relevant. Each of these signals the same thing — this is no longer a future consideration. It's a present one.The shift toward autonomous AI control is perhaps the most significant near-term development for firms to understand. We're moving from AI that generates content to AI that executes tasks — navigating systems, controlling interfaces, and completing workflows with minimal human input. For wealth management firms, that changes how we think about everything from operational efficiency to the boundaries of human oversight. The firms that thrive won't be the ones that react to this shift — they'll be the ones that started exploring it before they had to.The thread running through all three of these stories is the same challenge every firm is navigating right now: how to manage today's business while staying genuinely curious about what comes next. That means building intentional habits around exploring new tools, having honest conversations with clients about how AI is being used, and creating space inside your firm for the kind of forward-looking experimentation that doesn't show up on a quarterly scorecard but quietly determines where you'll be in five years.

Joel Mokyr co-won the 2025 economics Nobel for exploring the question that traces back to the beginning of economics: how did sustained economic growth suddenly become normal? For nearly all of human history, cleverness didn't compound. What changed, according to Mokyr, was twofold: first, you need to know why something works, so that one advance can seed the next; second, you need a culture willing to tolerate the disruption. His new book contrasts Europe with China, showing how Europeans learned to cooperate with people they weren't related to, in guilds, monasteries, cities, and universities, while China organized itself around the extended clan. One path led to internal stability and peace; the other, more restless and outward-looking, was the one that decided the world could always be made better. Tyler and Joel discuss European corporations vs. Chinese clans, why the Catholic Church became obsessed with cousin-marriage, how persistent cultural trends really are, why Chinese cities became so populous relative to Europe, why it took so long for European living standards to surpass China's, why sinified invaders kept getting swallowed by the dynasties they conquered, how geography kept Europe fragmented and China unified, where India fits into the story, why the Romans never made spectacles, why British soldiers stood two inches taller than the French, what powered the sudden rise of 19th-century German science, how disruptive winning a Nobel is, and much more. Read a full transcript enhanced with helpful links, or watch the full video on the new dedicated Conversations with Tyler channel. Recorded February 20th, 2026. This episode was made possible through the support of the John Templeton Foundation. Other ways to connect Follow us on X and Instagram Follow Tyler on X Sign up for our newsletter Join our Discord Email us: cowenconvos@mercatus.gmu.edu Learn more about Conversations with Tyler and other Mercatus Center podcasts here. Timestamps: 00:00:00 - Intro 00:00:54 - Europe vs. China's Paths to Prosperity 00:10:22 - China's Growth 00:13:24 - Europe's Growth 00:18:56 - The Fall of Song China 00:21:56 - India 00:25:08 - Industrial Revolution 00:39:52 - 19th-Century German Science 00:43:37 - Being a Nobel Laureate 00:45:29 - Outro Photo Credit: Shane Collins

In a world of constant, rapid digital transformation, one industry has struggled to evolve: construction. With all the gains made in efficiency and productivity, what's holding the construction industry back?  Brian Potter is a senior fellow at the Institute for Progress and author of the book, The Origins of Efficiency which charts the history of production efficiency, examining the great leaps forward with inventions like penicillin, the light bulb, and automobiles.  Brian joins Greg to share his experience in the construction industry that prompted his research into productivity and why construction productivity appears flat compared with manufacturing and agriculture. They also discuss distinctions between labor productivity and overall efficiency, the central role of scale and fixed costs, why tacit knowledge makes process transfer hard across plants and countries, and political obstacles such as guilds and unions resisting automation, including AI. *unSILOed Podcast is produced by University FM.* Episode Quotes: A factory is like a big sociotechnical machine 16:55: A factory is like a big sociotechnical machine where some of the capability lives in the machine, and a lot of it lives in, like, the processes that have been implemented and the heads of the people that are working on the line and know exactly what they have to do to make this work effectively. And it all kind of works together as one uniform thing. A lot of that is, like, not necessarily written down any place. It's either in this knowledge of these guys' heads, it's not written down, or it's, like, an emergent property of how all these things kind of work together, and it's very hard to decompose that and transfer it to a new place. It's often quite difficult to do that.  Has scale been the primary driver of efficiency since the Industrial Revolution? 08:42: Scale is a really big part of it for a lot of reasons. One is that just scaling effects are very powerful on their own. And then two is that scaling actually ends up being like a gating mechanism for a lot of other efficiency improvements in the sense that a lot of other efficiency improvements that you might implement need some sort of level, need some sort of scale, to be able to deploy them because they operate like large fixed costs. What is the genesis of “The Origins of Efficiency” 05:59: To understand why construction is so hard to make more efficient, I need to understand what other industries are doing to get more efficient. What strategies are they employing? And then I could understand why those strategies don't work in construction or whatever. Show Links: Recommended Resources: Adam Smith and The Pin Factory Katerra Henry Ford Do Management Interventions Last? Evidence from India by Bloom, Roberts, McKenzie, and Mahajan Nicholas Bloom - The Science of Management John Roberts - The In's and Out's of Organizational Economics Scale and Scope: The Dynamics of Industrial Capitalism The Visible Hand: The Managerial Revolution in American Business Frederick Winslow Taylor Joel Mokyr Guest Profile: Fellow Profile at Institute for Progress Professional Profile on LinkedIn Guest Work: The Origins of Efficiency  Construction Physics Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

How the Industrial Revolution and foreign investment made some nations rich while others stayed poor, closing with Mises's defense of capitalism—not because capitalists are good people, but because the market economy benefits mankind and safeguards freedom.

The liberal ideal of the individual and rational, welfare-serving law, and a defense of the Industrial Revolution against the myth that early capitalism degraded the common man.

This week we're heading back to Victorian London, where the richest city on Earth was quietly drowning in its own waste.As London's population exploded during the Industrial Revolution, millions of gallons of sewage, animal carcasses and factory waste poured straight into the River Thames. The result was The Great Stink of 1858 a summer so unbearably foul that Parliament was forced to flee its own chambers, and an engineering project was launched that would transform London forever.We also look at the pioneering doctor who proved cholera wasn't spread by "bad air", the Irish labourers who built the hidden city beneath London, and the extraordinary sewer system that still serves the capital today.Plus, we finish with the astonishing true story of a Japanese husband who paid rent on his wife's murder scene for 26 years in the hope that advances in DNA technology would finally identify her killer.⚠️ Content warning: This episode contains extensive discussion of sewage, bodily waste, disease and decomposition.

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

Had an eye opening conversation with Alex Mehr (Famous.ai) and Jeff Gunsberg—about AI Here are 5 takeaways from our talk: 1. AI isn't software. It's a reasoning utility. For the first time, businesses can add "thinking" without adding people. That changes everything—from org charts to margins. 2. Demand-limited vs. supply-limited businesses will diverge. If you're demand-limited, AI boosts profit margins. If you're supply-limited, AI unlocks explosive growth. But only if you apply it. 3. The real skill isn't prompting—it's taste. AI can produce. Humans must judge. Common sense, intuition, and knowing what good looks like are now premium skills. 4. Your future job is managing AI, not competing with it. The winners won't "do the work." They'll guide, critique, and train the systems that do. 5. Execution just got democratized. If you're an idea machine who struggled to execute—AI just removed your biggest obstacle. Ideas + action now travel at the same speed. This is an Industrial-Revolution-level shift. You don't need to panic—but you do need to adapt, learn, and think two moves ahead. Connect with Jon Dwoskin: Twitter: @jdwoskin Facebook: https://www.facebook.com/jonathan.dwoskin Instagram: https://www.instagram.com/thejondwoskinexperience Website: https://jondwoskin.comLinkedIn: https://www.linkedin.com/in/jondwoskin Email: jon@jondwoskin.com Get Jon's Book: The Think Big Movement: Grow your business big. Very Big! Connect with Jeff Gunsberg: Website: https://title-connect.com     *E - explicit language may be used in this podcast.

Live July 4, 2026 | Yaron Brook Show(Season 12, Episode 114)4th of July -- What's to Celebrate? | Yaron Brook ShowThe Radical Revolution That Changed the World—And Why America's Founding Ideals Are Worth Fighting ForAmerica didn't become exceptional because of its geography, military, or natural resources. It became exceptional because it embraced one revolutionary moral principle: the individual has the right to live for his own sake.On this special Independence Day episode, Yaron Brook explores what Americans should actually celebrate on the Fourth of July—not blind patriotism, but the radical ideas that transformed history. From the Declaration of Independence to individual rights, capitalism, entrepreneurship, and Ayn Rand's moral defense of freedom, this episode asks whether America still deserves to be called the land of liberty—and what must be done to preserve it.As America approaches its 250th anniversary, are we honoring the principles that made this country great... or abandoning them?Join the conversation live and challenge the ideas.Watch now: https://youtube.com/live/Vakv4lZ54UcTimestamps00:00 Introduction00:27 Why celebrate the Fourth of July?03:03 Is America still worth celebrating?05:33 The revolutionary idea of individual rights08:01 Why the Declaration of Independence changed history10:49 America as mankind's greatest experiment12:39 Opportunity, achievement, and American success15:06 What "all men are created equal" really means17:23 Equality before the law vs. equality of outcome20:25 Life, liberty, property & the pursuit of happiness23:01 Freedom, reason, and choosing your own life28:28 Immigration and the promise of America30:03 Why reason made America prosperous32:58 The Industrial Revolution and America's innovators44:24 Celebrating entrepreneurs—from Edison to Musk47:20 America's creed and its global impact49:50 Ayn Rand and America's founding ideals53:02 Why freedom requires a moral foundation55:33 Recommitting to the philosophy of liberty59:33 What America should celebrate this Independence DayLive Audience Questions1:07:30 Why do postmodernists reject objective truth?1:10:32 America at 250—what should we celebrate?1:11:38 Which great thinkers came from the Netherlands?1:14:57 Jefferson's original Declaration draft—better than the final?1:16:07 Has Kant finally lost his influence?1:16:38 Is conservative patriotism preferable to socialist anti-Americanism?1:19:13 Why did Yaron say Objectivism may win in 150 years?1:19:58 Thomas Paine: "Start the world over again."1:20:30 America's freethought movement and Robert Ingersoll1:21:16 Conservatives regulate; progressives redistribute?1:21:34 Why are young people drawn to Catholic aesthetics?1:22:54 Favorite Yaron quote: "That's my pie!"1:25:32 How did the Colonists defeat the British Empire?1:30:39 Did the Founders underestimate political parties?If you enjoy these discussions, become a supporter, subscribe, and share this episode with someone who believes freedom is worth defending.#america250 #FourthOfJuly #DeclarationOfIndependence #foundingfathers #AmericanHistory  #Trump #Capitalism #IndividualRights #Freedom #Liberty #AynRand #ObjectivismThe Yaron Brook Show is Sponsored by[The Ayn Rand Institute](https://www.aynrand.org/starthere)[Energy Talking Points, featuring AlexAI, by Alex Epstein](https://alexepstein.substack.com/)[Express VPN](https://www.expressvpn.com/yaron)[Hendershott Wealth Management](https://www.youtube.com/watch?v=X4lfC...) &(https://hendershottwealth.com/ybs/)[Michael Williams & The Defenders of Capitalism Project](https://www.DefendersOfCapitalism.com)[Support the Show]( / yaronbrookshow )[Sponsor the Show](askyaron@yaronbrookshow.com/)[One-time donation](https://bit.ly/2RZOyJJ)Join the [Yaron Brook Show YouTube channel]( / @yaronbrook )Like what you hear? Like, share, and subscribe to stay updated on new videos and help promote the [Yaron Brook Show](https://bit.ly/3ztPxTx)Continue the discussion by following Yaron on [Twitter](https://bit.ly/3iMGl6z) and [Facebook](https://bit.ly/3vvWDDC )Want to learn more about Ayn Rand and Objectivism? Visit the [Ayn Rand Institute](https://bit.ly/35qoEC3)Become a supporter of this podcast: https://www.spreaker.com/podcast/yaron-brook-show--3276901/support.Yaron is the executive chairman of the Ayn Rand Institute and a world class speaker. He is the coauthor of the national best-seller Free Market Revolution: How Ayn Rand's Ideas Can End Big Government, Equal is Unfair: America's Misguided Fight Against Income Inequality and In Pursuit of Wealth: The Moral Case for Finance. He speaks around the world on a variety of topics including the morality of capitalism, Ayn Rand and her philosophy, finance and economics, and the value of inequality.

It's July. America just turned 250. And Candace is pulling a powerful thread through this month's focus in Pet Boss Nation: Work Smarter, Earn More, Adapt Faster. In 250 years, this country has survived wars, depressions, booms, the Industrial Revolution, the rise of the automobile, the telephone, the internet. Entire industries disappeared. Entire industries that didn't exist a generation ago became the backbone of the economy. The pattern? The businesses that lasted weren't the ones who had it all figured out. They were the ones who kept adjusting. Who paid attention. Who weren't afraid to pivot. Candace discusses:

The sense of smell is often linked to the dark, the antisocial, the primitive—the very opposite of modernity and progress. Today we live in an almost odorless world, where everything is reduced to images. Yet smell plays a vital role in how we relate to others and our surroundings, forming our experiences and our memories. Tracing a history of smell from the first ancient cities, through medieval plagues and the Industrial Revolution to the present day, Smell: The Tale of a Fading Sense (Reaktion, 2026) is a tribute to the sense of smell in all its beauty and disgust. Along the way, Bjørn Berge introduces us to twenty iconic scents—from blood and soil to the ocean—and invites readers to reflect on and reawaken their senses. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

The sense of smell is often linked to the dark, the antisocial, the primitive—the very opposite of modernity and progress. Today we live in an almost odorless world, where everything is reduced to images. Yet smell plays a vital role in how we relate to others and our surroundings, forming our experiences and our memories. Tracing a history of smell from the first ancient cities, through medieval plagues and the Industrial Revolution to the present day, Smell: The Tale of a Fading Sense (Reaktion, 2026) is a tribute to the sense of smell in all its beauty and disgust. Along the way, Bjørn Berge introduces us to twenty iconic scents—from blood and soil to the ocean—and invites readers to reflect on and reawaken their senses. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/european-studies

The sense of smell is often linked to the dark, the antisocial, the primitive—the very opposite of modernity and progress. Today we live in an almost odorless world, where everything is reduced to images. Yet smell plays a vital role in how we relate to others and our surroundings, forming our experiences and our memories. Tracing a history of smell from the first ancient cities, through medieval plagues and the Industrial Revolution to the present day, Smell: The Tale of a Fading Sense (Reaktion, 2026) is a tribute to the sense of smell in all its beauty and disgust. Along the way, Bjørn Berge introduces us to twenty iconic scents—from blood and soil to the ocean—and invites readers to reflect on and reawaken their senses. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/science-technology-and-society

The sense of smell is often linked to the dark, the antisocial, the primitive—the very opposite of modernity and progress. Today we live in an almost odorless world, where everything is reduced to images. Yet smell plays a vital role in how we relate to others and our surroundings, forming our experiences and our memories. Tracing a history of smell from the first ancient cities, through medieval plagues and the Industrial Revolution to the present day, Smell: The Tale of a Fading Sense (Reaktion, 2026) is a tribute to the sense of smell in all its beauty and disgust. Along the way, Bjørn Berge introduces us to twenty iconic scents—from blood and soil to the ocean—and invites readers to reflect on and reawaken their senses. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices

The sense of smell is often linked to the dark, the antisocial, the primitive—the very opposite of modernity and progress. Today we live in an almost odorless world, where everything is reduced to images. Yet smell plays a vital role in how we relate to others and our surroundings, forming our experiences and our memories. Tracing a history of smell from the first ancient cities, through medieval plagues and the Industrial Revolution to the present day, Smell: The Tale of a Fading Sense (Reaktion, 2026) is a tribute to the sense of smell in all its beauty and disgust. Along the way, Bjørn Berge introduces us to twenty iconic scents—from blood and soil to the ocean—and invites readers to reflect on and reawaken their senses. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts.  Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/book-of-the-day

In "Ending the 60% Waste: The Radical Shift Trucking Needs Right Now," Joe Lynch and Erik Malin, Founder and CEO of Tetro, discuss how treating trucking as a function of time, rather than miles, is the only way to eliminate the massive inefficiencies plaguing drivers and future autonomous fleets. Time is the real commodity.  About Erik Malin Erik Malin is the founder and CEO of Tetro, a technology company rebuilding trucking for the autonomous era. He has spent his entire career in freight, where his conviction that the industry measures the wrong thing, miles instead of time, became the thesis behind Tetro. Previously, Erik was on the founding team at Baton, a freight-tech startup acquired by Ryder, and led operations at FreightTech unicorn Loadsmart. About Tetro Tetro is a technology company that operates its own trucking fleet to recapture lost supply chain time and build essential data assets. Because 60% of time is currently wasted in trucking, the most common job in America has suffered perhaps the greatest wage suppression in history, and autonomous technology will never reach its full potential since a driverless truck still loses that same 60%. By running its own fleet, Tetro is actively building the data asset necessary to eliminate this massive inefficiency and unlock the true potential of modern freight. Key Takeaways: Ending the 60% Waste: The Radical Shift Trucking Needs Right Now In "Ending the 60% Waste: The Radical Shift Trucking Needs Right Now," Joe Lynch and Erik Malin, Founder and CEO of Tetro, discuss how treating trucking as a function of time, rather than miles, is the only way to eliminate the massive inefficiencies plaguing drivers and future autonomous fleets. Time is the real commodity. The 60% Waste Phenomenon: The trucking industry suffers from massive systemic inefficiency, where 60% of potentially productive capacity is completely lost to time leakage across the entire system. A Utilization Issue, Not a Driver Shortage: Contrary to popular belief, the core issue in American trucking is utilization rather than a shortage of drivers. Public company financials show that drivers are often only productive for 4.5 hours out of their 11 federally regulated daily driving hours. The Flawed Legacy Framework of Miles: The industry still relies on a metric inherited from the Industrial Revolution—paying and planning by the mile rather than by time. This creates a severe misalignment between demand and capacity because the industry remains functionally blind to duration. The Autonomous Vehicle Myth: There is a flawed industry assumption that autonomous trucks will seamlessly solve supply chain issues. However, because a driverless truck will still lose that exact same 60% of dead time at facilities, autonomy cannot reach its full potential without solving the underlying time-tracking problem. Operating a Fleet as a Mobile Research Lab: Tetro operates its own trucking fleet not to simply be a carrier, but to generate the highly specific, proprietary data asset required to address this waste. You cannot infer or partner your way into this information; it requires proprietary hardware and execution tracking to create. Shifting From Miles to Time via AI: Tetro developed an AI forecasting tool that converts traditional commoditized lane rates per mile into a rate per hour before committing to freight. This reveals significant market mispricing, exposing "bad actors" (facilities that notoriously waste time) and highlighting efficient shippers trading at a hidden premium. Unlocking Trapped Facility Upside: Internal data shows that 30% to 70% of a driver's on-site time at a facility is completely dead time where nothing is happening. By quantifying this data, Tetro can partner with shippers to release that trapped time, creating faster inventory turns and reducing the need for costly secondary assets like drop trailers. Learn More About Ending the 60% Waste: The Radical Shift Trucking Needs Right Now Erik Malin | Linkedin Tetro | Linkedin Tetro The Logistics of Logistics Podcast If you enjoy the podcast, please leave a positive review, subscribe, and share it with your friends and colleagues. The Logistics of Logistics Podcast: Google, Apple, Castbox, Spotify, Stitcher, PlayerFM, Tunein, Podbean, Owltail, Libsyn, Overcast Check out The Logistics of Logistics on Youtube

If last week's episode left you wondering what you can actually do about data center development in your community, this week's episode is the flip side. Carolyn Woodard shares a new national workforce initiative just launched last week, then digs into the tools and resources available for nonprofits and community members who want to shape how AI infrastructure gets built - before it arrives, not after.She also closes with a look at what's happening in Europe and Africa, tying it back to an earlier episode on how where you are in the world determines the environmental footprint of your AI use, and why that makes local action matter globally.This episode covers:RAISE US, a new national nonprofit launched June 25 with bipartisan leadership from former Commerce Secretary Gina Raimondo and former Indiana Governor Eric Holcomb, has secured more than $500 million toward a $1 billion goal. Anchor partners include Amazon, Anthropic, Microsoft, and the OpenAI Foundation. If you work in workforce development in Arkansas, Connecticut, Maryland, or Utah, pay attention now, pilots are beginning.The Virginia legislature just wrapped its biannual budget, and the data center tax break fight made it in. Legislators kept the existing break but imposed a new tax on large data center companies to offset it with more to come next session. A local example of the policy battle playing out in real time, and ways that citizen involvement makes a difference in statewide policy.The Erin Brockovich Data Center Reporting Project lets citizens report data center problems - energy, noise, water - but also has a "Communities Making a Difference" map that documents real wins. Community organizing is working.41 mayors from six continents — representing more than 90 million people — signed the Global Urban Data Centres Pact at London Climate Action Week on June 23. About half the signatories are U.S. cities including Seattle, Chicago, Miami, Phoenix, and Palo Alto. They're setting common standards on clean energy, water use, and site selection, creating a benchmark for requirements for other cities and communities to use.Sierra Club's 2026 Data Center Policy Guidance and Good Jobs First's moratorium bill tracker (300+ bills across 30+ states) give you concrete tools to find what's happening in your state and how to plug in.Europe's record-breaking heat wave is straining data center cooling systems and sparking a movement toward European-owned AI infrastructure. The African Development Bank and UNDP have launched a $10 billion initiative so Africa becomes a producer of AI, not a consumer of tools built elsewhere. As Carolyn puts it: the Industrial Revolution took 30–40 years for communities to push back. We don't have to wait that long this time.Resources Mentioned:RAISE US – https://www.raiseus.aiErin Brockovich Data Center Reporting Project – https://brockovichdatacenter.comErin Brokovich Data Center Reporting Project - Victory Map https://brockovichdatacenter.com/community-and-legislation.html#mapCommunities Making a Difference – Brockovich Data Center Project – https://brockovichdatacenter.com/community-impact.htmlGlobal Urban Data Centres Pact – C40 Cities – https://www.c40.org/news/mayors-from-around-the-world-unite-in-call-for-sustainable-urban-data-centres/Sierra Club 2026 Data Center Policy Guidance – Sierra Club – https://www.sierraclub.org/issues/climate/data-centersData Center Moratorium Bill Tracker – Good Jobs First – https://goodjobsfirst.org/data-center-moratorium-bills-are-spreading-in-2026/Africa AI 10 Billion Initiative – African Development Bank – https://www.afdb.org/en/news-and-events/press-releases/african-development-bank-undp-and-partners-launch-ai-10-billion-initiative-during-2026-nairobi-ai-forum-91104Why Community Benefit Agreements Are Necessary for Data Centers – Brookings Institution – https://www.brookings.edu/articles/why-community-benefit-agreements-are-necessary-for-data-centers/ _______________________________Start a conversation :)Register to attend a webinar in real time, and find all past transcripts at https://communityit.com/webinars/email Carolyn at cwoodard@communityit.comon LinkedIn on reddit/r/nonprofitITmanagementon the Community IT websiteThanks for listening. 

Host Dr. Tom Lodise interviews Dr. Marc Scheetz about his Pharmacotherapy editorial, “AI in ID Pharmacy: We Should Not Be Afraid to Put the CAR (Not the Cart) Before the Horse,” discussing how AI is already mainstream and increasingly used in healthcare. Scheetz uses the Industrial Revolution and the John Henry story to argue clinicians should not compete with machines but learn to make them work with us, as AI can outperform humans in many tasks. His optimistic view is that AI will reduce computer-bound work and preserve human connection in patient care. Read the full editorial at: https://accpjournals.onlinelibrary.wiley.com/doi/10.1002/phar.70103

In this series, Jeff & Andy dive into a mix of useless facts, myths, forgotten stories, and strange truths.In this episode, Jeff shares facts about giraffes in spirit of the missing giraffe in Texas, and Andy tells how our sleep habits changed after the Industrial Revolution.This series is brought to you by the amazing Cedar Run Decoys.

It's Pollinator Week, and the bugs need us more than ever. Not just bees: butterflies, moths, wasps, flies, beetles, midges, hummingbirds … Around 90 percent of the world's flowering plants and 75 percent of our major food crops rely on pollinators, and they're dying. Nowhere is insect decline more intimately entwined with our own than with honeybees, 2.7 million colonies of which are hauled around the country to pollinate American crops—most often, California almond trees. Since 2012, Jennie Durant has been studying the social and environmental drivers of bee decline, and her new book, Bitter Honey, combines her research with dozens of interviews with beekeepers, conservationists, scientists, and farmers.There's no single answer to what's killing the bees—pesticides, monoculture crops, overwork, parasites, viruses, competition for decreasing forage, the list goes on, and climate change exacerbates all of it—but that also means there are many ways we can still save them.Jennie Durant is a writer, researcher, and author whose work explores why bees and other pollinators are declining, and what it will take to build a more just and sustainable food system.Go beyond the episode:Jennie Durant's Bitter Honey: Big Ag's Threat to Bees and the Fight to Save ThemIt's not too late to celebrate Pollinator Week!Learn to identify some of the 4,000-odd bee species in North AmericaSave the Beltsville Bee Lab!Tune in every other week to catch interviews with the liveliest voices from literature, the arts, sciences, history, and public affairs; reports on cutting-edge works in progress; long-form narratives; and compelling excerpts from new books. Hosted by Stephanie Bastek.Subscribe: iTunes/Apple • Amazon • Google • Acast • PandoraHave suggestions for projects you'd like us to catch up on, or writers you want to hear from? Send us a note: podcast [at] theamericanscholar [dot] org. And rate us on iTunes! Hosted on Acast. See acast.com/privacy for more information.

This week is the start of a few shows on the bad mic.  And it is all my fault.But "A" mic is still better than no show at all.Let's talk salt in late 19th century America.  It used to be all about food but isn't any longer.  Listen along as we look out how the Industrial Revolution - especially around food - runs on salt.  And how the living away from the farm and shopping in the stores needs salt to happen at all.Music Credit: Fingerlympics by Doctor TurtleShow Notes: https://thehistoryofamericanfood.blogspot.com/Email: TheHistoryofAmericanFood at gmail dot comThreads: @THoAFoodInstagram: @THoAFood& some other socials... @THoAFood

Modern historians rarely have told the truth about the history of capitalism, and especially in the early days of the Industrial Revolution. It is time to set the record straight.Original article: https://mises.org/mises-wire/lies-damn-lies-and-history-capitalism

Modern historians rarely have told the truth about the history of capitalism, and especially in the early days of the Industrial Revolution. It is time to set the record straight.Original article: https://mises.org/mises-wire/lies-damn-lies-and-history-capitalism

What if the next Cold War isn't fought with tanks, missiles, or aircraft carriers—but with quantum computers, artificial intelligence, digital currencies, and resources mined from the Moon?In this explosive episode of On the Record, Christian Briggs examines the growing technological rivalry between the United States and China and explores a disturbing question: What happens if Beijing wins the race for the technologies that will define the twenty-first century? The discussion begins with a stark reality. Throughout history, the nations that mastered the defining technologies of their era ultimately dominated the global order. Britain rode the Industrial Revolution. America led the age of aviation, computing, and nuclear power. Today, a new battle is emerging around artificial intelligence, advanced semiconductors, quantum computing, critical minerals, and digital infrastructure. The episode explores how quantum computing could eventually shatter modern encryption, allowing future governments to unlock vast archives of communications once thought permanently secure. Intelligence agencies already operate under a chilling principle known as "harvest now, decrypt later," collecting encrypted data today in anticipation of tomorrow's quantum breakthroughs. From there, the conversation moves into the resource war unfolding beneath the headlines. Gold, silver, rare earth elements, lithium, gallium, and other strategic materials are becoming the ammunition of the digital age. China has spent decades building dominant positions across critical supply chains while many Western nations outsourced production in pursuit of efficiency. The most surprising section focuses on China's lunar ambitions. The program examines Helium-3, a rare isotope believed to exist in abundance on the Moon and potentially valuable for advanced scientific and technological applications. Whether or not Helium-3 ultimately fulfills its promise, China's investments in lunar infrastructure, quantum research, AI, and strategic resources reveal a nation thinking decades ahead while much of the West remains focused on short-term political cycles. Finally, Briggs paints a picture of the world in 2035. If China's strategy succeeds, global trade, communications, artificial intelligence, manufacturing, and financial systems could become increasingly dependent on Chinese-built platforms and standards. The question is not whether this future is inevitable. The question is whether the United States is moving with enough urgency to ensure it never happens. This episode argues that the battle for global power has already begun—and most people don't even realize they're living through it. 

Stewart Alsop hosts a conversation with Oliver Polzin, a founding team member of Meow Wolf and naturalist, exploring the intersection of creativity, conservation, and architecture. Oliver discusses his current postgraduate work at SCI-Arc in Los Angeles studying synthetic landscapes through an architectural lens, his deep fascination with Pleistocene megafauna and the La Brea Tar Pits, and his vision for creating a "biophilic culture" that reframes humanity's relationship with other species and ecosystems. The discussion ranges from Oliver's early work building mud caves at Meow Wolf to his current explorations of AI-assisted design tools, 3D printing with recycled materials, holistic grazing management systems for the Great Plains, and the ancient Amazonian practice of creating terra preta soil—all part of his broader investigation into how we can design interventions for climate and conservation issues while maintaining what makes us fundamentally human.Timestamps00:00 Stewart introduces Oliver Polzin from Meow Wolf's founding team and discusses how his yoga teaching there inspired the podcast's exploration of creativity and stress relationships.05:00 Oliver describes his architecture graduate program studying climate and conservation through synthetic landscapes, contrasting dark green naturalist ecology with bright green capitalist environmentalism.10:00 Discussion of conservation ethics and AI's potential for monitoring environmental systems, with Oliver explaining his journey from painting to experimental mud construction at early Meow Wolf.15:00 Stewart shares his robotics learning journey with ESP32s in Buenos Aires while Oliver questions humanoid robot design, suggesting functional form factors matter more than human resemblance.20:00 Oliver explores cardboard as material obsession and explains treasure hunt mechanics in Meow Wolf exhibits, creating dopamine-driven discovery experiences through layered storytelling.25:00 Stewart describes creating treasure hunts for Spanish learners in Buenos Aires parks while Oliver validates experiential art's growing importance in an increasingly digital culture.30:00 Conversation shifts to three-d printing flexible filaments for architectural models and Oliver's megafauna book project about La Brea Tar Pits Pleistocene fossils.35:00 Oliver connects Earth consciousness to Pale Blue Dot perspective, arguing humans face developmental threshold understanding planetary responsibility after 300,000 years as anatomically modern species.40:00 Deep dive into end-Pleistocene extinction events and megafauna loss, discussing two-ton capybaras and how predator relationships shaped human psychology and anxiety responses.45:00 Oliver presents speculative Great Plains biopreserve concept with de-extinct megafauna, contrasting holistic rotational grazing with destructive monoculture agriculture systems.50:00 Discussion concludes with Amazonian dark earth technology and indigenous landscape management, emphasizing need for biophilic culture embracing deep time ecological perspective.Key Insights1. Oliver Polzin is part of the founding team of Meow Wolf and is currently studying at SCI-Arc in Downtown LA in a postgraduate program called Synthetic Landscapes, which examines global scale climate and conservation issues through an architectural lens. Architecture exists between art and science, and he believes architectural thinking offers a valuable framework for designing interventions for climate and conservation challenges. This program represents a significant evolution from his earlier work at Meow Wolf, where he created immersive experiential art installations using materials like adobe and cardboard.2. There is an important distinction in ecological thought between what Paul Kingsnorth calls dark green and light green approaches to environmentalism. The dark green strain represents the older naturalist movement from the early twentieth century, focusing on biological systems, ecosystems, and endangered species. Light green emerged in the 1970s after the Earth Day movement and centers on clean energy, solar panels, and wind power as a way to maintain our current lifestyle. Oliver argues that the bright green approach represents a capitalist overlay that has captured the conservation movement, whereas true conservation requires focusing on actual biological systems rather than just technological solutions.3. The experiential art form that Meow Wolf pioneered still has enormous untapped potential, particularly as society becomes increasingly digital. Oliver believes there will be a huge wave of experiential desire in this decade as people crave human connection and real-world excitement. The treasure hunt and scavenger hunt format represents a compelling form of real-life RPG that creates meaningful human interactions. This type of experience design, which Meow Wolf developed through installations like the House of Eternal Return, plays with human dopamine systems by compelling people to open doors, explore spaces, and follow narrative threads through physical environments.4. The architectural model or dollhouse concept represents a crucial rhetorical tool that Oliver is learning to apply to climate and conservation work. Architects have long created physical models to show stakeholders what a building will be like, and this practice of showing a story in compelling ways for different types of brains is essential for getting traction on projects. While architectural models used to be made from foam core, paper, and balsa wood, they are now largely created through 3D printing, which allows for incredibly complex forms and interlocking structures that would have been impossible to construct manually.5. Oliver is obsessed with megafauna and the end Pleistocene extinction event that occurred roughly twelve thousand years ago. For three hundred thousand years, anatomically modern humans existed alongside massive beasts like short faced bears and American lions, and we were the smaller creatures in the ecosystem. The extinction of over one hundred genera of animals over ninety nine pounds, combined with sea level rise of nearly four hundred feet, fundamentally changed human existence and led to the development of agriculture and civilization. Much of our current psychological development, including anxiety responses, is still based on this time period when we lived among these massive animals.6. The current food system in the Great Plains is fundamentally broken compared to the historical managed food system maintained by Plains tribes, who sustained thirty to sixty million bison through 1800. Oliver explored a speculative project about turning the Great Plains into a massive biopreserve of de-extinct megafauna, contrasting the natural system of rotational grazing where predators keep herds moving with the current monoculture crop agriculture that requires external inputs like fertilizer, pesticides, and herbicides. The natural system builds soil and increases fecundity, while industrial agriculture degrades soil, creates toxic runoff, and produces genetically modified crops that feed animals in toxic concentrated feeding operations.7. The fundamental challenge facing humanity now is creating what Oliver calls a biophilic or ecophilic culture that is loving of other species and our home planet. This requires both psychological shifts and changes in how we design systems at all scales. The Amazon provides a powerful example of this, as recent LiDAR mapping has revealed that what appeared to be pristine wilderness was actually a vast tended garden created by indigenous civilizations who developed technologies like Amazonian dark earth through burning middens with various additives. These cultures understood how to be embedded in a web with other species while playing an important orchestrating role, offering a model for how humans might relate to other forms of life in our current era.

Mercury Retrograde, London Walks & Astrology Through HistoryIn this special episode of The Awake Space, Laurie records her final podcast from London while walking through Chelsea and visiting the iconic Victoria and Albert Museum. Rather than a traditional studio episode, this is a reflective conversation about Mercury retrograde, navigating life's unexpected twists, and understanding astrology through the lens of history, culture, and philosophy. Along the way, Laurie shares personal updates, observations from her trip, and a fascinating exploration of how astrology evolved alongside science, art, religion, and society. Why Mercury retrograde is often misunderstoodNavigating disruptions, delays, and changing plansPersonal reflections from Laurie's time in LondonFamily concerns and embracing life's unfolding cyclesUpdates on upcoming Awake Space events and programsA walking tour through Chelsea and the Victoria & Albert MuseumThe connection between astrology, astronomy, and navigationHow astrology evolved from ancient civilizations through the RenaissanceThe role of philosophy, science, religion, and culture in shaping astrological practiceMercury retrograde often gets blamed for everything that goes wrong, but Laurie argues that its real function is redirection, revision, and review. The frustrations and delays are invitations to adjust course rather than reasons to panic. Sometimes Plan B turns out to be better than Plan A. Whether dealing with travel disruptions, family concerns, or changing circumstances, Laurie reflects on viewing life through the lens of cycles rather than labeling experiences as good or bad. The challenge is not avoiding obstacles but learning how to meet them. Astrology is not frozen in ancient times. Laurie explores how modern astrology developed through centuries of observation, mathematics, philosophy, and cultural exchange, particularly through the contributions of scholars in the Islamic world, Renaissance Europe, and later scientific thinkers. The way astrology is practiced today reflects the eras that came before us. Just as art, science, religion, and politics evolved, astrology evolved alongside them. Understanding that history provides important context for interpreting astrology in the modern world. From the Industrial Revolution to artificial intelligence, societies face recurring challenges around technological change. Laurie draws parallels between historical transitions and today's rapidly changing world, emphasizing the importance of adaptability and discernment. 00:00 Welcome from London and St. Luke's Church02:00 Personal reflections on travel, family, and change05:00 Awake Space updates and upcoming events07:30 Why Mercury retrograde isn't the real problem10:20 Walking through Chelsea toward the V&A Museum14:25 How to navigate Mercury retrograde successfully22:50 Ancient astrology, Sumeria, and historical roots27:00 Entering the Victoria & Albert Museum30:00 Medieval manuscripts, scribes, and astrology's history40:00 Navigation instruments, astronomy, and astrology44:00 Evolution versus tradition in astrological practice48:00 Renaissance art and changing philosophies53:00 Industrialization, technology, and cultural shifts58:00 The Industrial Revolution and lessons for today01:03:00 Timekeeping, clocks, and changing worldviews01:07:00 Mercury, mythology, and astrological symbolism01:09:00 Why discernment matters in astrology01:11:00 Final reflections and closing thoughtsThe 2027 Year Ahead Presentation (rescheduled to July 11)The Awake Space CommunityVictoria & Albert MuseumSt. Luke's Church, ChelseaWilliam LillyGalileo GalileiCopernicusCarl JungRoberto AssagioliBenjamin DykesIn This EpisodeKey TakeawaysMercury Retrograde Is Not the VillainLife Happens in CyclesAstrology Is a Living TraditionHistory Shapes InterpretationAdaptation Is a Human SkillTimestampsMentioned in This Episode

This week on Labor History Today (originally broadcast 1/11/2026), Simon Sapper talks with historian Martin Wright, co-author of Made by Labour: A Material and Visual History of British Labor, 1780–1924. The book traces the rise of the world's first modern labor movement through banners, boxes, coins, tools, and images created by working people during the Industrial Revolution and beyond—right up to the moment labor stood on the brink of political power in the 1920s. Questions, comments, or suggestions are welcome, and to find out how you can be a part of Labor History Today, email us at LaborHistoryToday@gmail.com Labor History Today is produced by the Labor Heritage Foundation and the Kalmanovitz Initiative for Labor and the Working Poor. #LaborRadioPod #History #WorkingClass #ClassStruggle @GeorgetownKILWP #LaborHistory @UMDMLA @ILLaborHistory @AFLCIO @StrikeHistory #LaborHistory @wrkclasshistory  

Every brand team is being handed AI tools and told to do more with less. What they're not being told is that without taste and a real idea, the output is just sophisticated noise at scale.Matt Scribner is a growth designer at Atlassian — a company whose brand touches tens of thousands of visitors a week — and he's been inside the AI-enabled design workflow longer than most. His take isn't a sales pitch for the tools or a doomsday headline about job loss. It's something more useful: an honest account of where AI actually helps, where it quietly fails, and why the designers who thrive won't be the ones who prompt the best.We also cover:AI can get you to 60% — but if you couldn't get past 60% without it, you're not getting past it with it eitherWe're heading toward a "singularity of design" where everything starts looking the same, and the only antidote is taste you built before the tools existedThe Arts and Crafts movement followed the Industrial Revolution for a reason — and the same correction is coming for brand

Welcome to the first episode of our new series all about workers' rights. My guest this week is Christina Hajagos-Clausen who is the IndustriALL Global Union's director for the Textile, Garment, Shoe and Leather Sector. Our interview was recorded during the organisation's 4th Global Congress held in Sydney at the end of last year, at "a critical moment. Workers everywhere are being hit by converging crises, growing inequality, the climate emergency, digital disruption and the increasing concentration of corporate power." So how can workers ensure get to help shape a future that is fair, democratic and just?This is an expansive conversation that covers everything from: Why are trade unions necessary to the New Industrial Revolution, automation and AI. We explore what unions doing in the global textile & garment sector to shape a just transition. We look at specific garment producing countries and stories - including whether or not to boycott Made in Myanmar - plus the whole idea of the Labor movement as a check on fascism everywhere.If you find the interview valuable, please help us share it.Find links and further reading at thewardrobecrisis.comSupport the show on Substack - wardrobecrisis.substack.comTell us what you think. Find Clare on Instagram @mrspress Hosted on Acast. See acast.com/privacy for more information.

Have you ever sat in a meeting about AI, nodded along, then thought, "I've got no idea what they're talking about, and I'm meant to be leading this"? If so, you're in good company.In this episode, I chat with Em and AI expert James Killick to answer the question every leader is quietly asking. In 2026, does AI get you promoted, or replaced?Here's the truth. AI isn't coming for leaders. It's coming for the leaders who live in the detail and do the work of the people below them. Your job hasn't changed since the Industrial Revolution. Take your people, your tools, and your budget, and turn them into something valuable.In this episode:Why AI replaces the technical work, not the leadership work, and who that puts at riskThe "automate last" rule, and why 9 in 10 AI projects failHow to treat AI like an over-enthusiastic intern, so your judgement matters more, not lessThe one mindset shift: lead AI inward, lead people outwardWhat to hand to AI first, and the 20% only you can doThe window is open right now. Move first and you get ahead. Sit still and you get left behind.Leadership Beyond the Theory June cohort is open. Doors close Friday 26 June. Join now: https://go.leadershipbeyondthetheory.com/————————Connect with James:Instagram: https://www.instagram.com/ai_orchestrator/LinkedIn: https://www.linkedin.com/in/james-killick/YouTube + Podcast: https://www.youtube.com/@james-killickJoin his FREE Skool community: https://www.skool.com/make-money-with-aiGet training or his DFY AI services: njin.co————————You can connect with me at:Website: https://www.yourceomentor.comFacebook: https://www.facebook.com/yourceomentorInstagram: https://www.instagram.com/yourceomentorLinkedin: https://www.linkedin.com/in/martin-moore-075b001/Youtube: https://www.youtube.com/@YourCEOMentor————————Our mission here at Your CEO Mentor is to improve the quality of leaders, globally. Your boss wants more with less. Your team wants less, full stop. You're stuck in the middle.Leadership Beyond the Theory is 9 weeks to promotion-ready leadership. 2,800+ leaders from 150+ organisations. 99% would recommend. Doors are now open for the June 2026 cohort, they close Fri 26 June!Join the cohort here: https://go.leadershipbeyondthetheory.com/ Hosted on Acast. See acast.com/privacy for more information.

SpaceX has become one of the most anticipated investment stories in modern market history. Between Elon Musk's popularity, the company's technological achievements, and years of speculation about a public offering, investor excitement is reaching fever pitch. But what happens after the hype? Lance Roberts & Jon Penn examine the lessons to be learned from previous high-profile IPOs, and why some of the biggest investing mistakes occur after the initial excitement fades. We discuss valuation, investor psychology, momentum chasing, and the risks that emerge when enthusiasm becomes disconnected from fundamentals. We also look at the growing speculative interest surrounding leveraged products tied to the SpaceX theme, and why investors should be cautious when Wall Street starts packaging excitement into increasingly aggressive investment vehicles. Here's a topical rundown of today's show: 0:00 - INTRO 0:56 - America's 250th Anniversary Time Capsule & Space-X IPO 3:48 - The Bullish Setup Returns 8:18 - Back from Vacay... 9:32 - IPO's & Space-X 12:04 - What Happens Next - the Advantage in Waiting 14:19 - The FOMO Factor 17:53 - What Could Possibly Go Wrong? 19:02 - Has AI Lost Steam? (The New U.S.Industrial Revolution) 21:37 - What's Next After Iran War? (Economic Pressure Index) 24:08 - Two Things Driving Markets: Profitability & Optimistic Earnings Estimates 25:17 - Italian Gasoline Prices 28:38 - Interest Rates, Bonds, & Kevin Warsh at the Fed 33:59 - A Tip about TIPS 35:44 - Why You Should Own Some Bonds 37:59 - The Three Components of Investing: Safety, Liquidity, & Returns 41:01 - Annuities as Bond "Alternatives?" Hosted by RIA Advisors Chief Investment Strategist, Lance Roberts, CIO,w Senior Investment Advisor, Jonathan Penn, CFP Produced by Brent Clanton, Executive Producer ------- Do you enjoy our content? Rate us on Google: https://bit.ly/4b9JtEo ------- Watch Today's Full Video on our YouTube Channel: https://youtube.com/live/Xr1Ut115-xA ------- Watch today's "Before the Bell" feature, "Bullish Setup Returns," here: https://youtu.be/ox4_xMsXqt4 ------- Watch our previous show, "Bull Market Pullback - Is the Correction Over?" https://youtube.com/live/csXApjrvlNY?feature=share ------- Articles mentioned in this report: "May Inflation Print: Why the 4.2% Headline Is an Oil Story," https://realinvestmentadvice.com/resources/blog/may-inflation-print-why-the-4-2-headline-is-an-oil-story/ --- Get more info & commentary: https://realinvestmentadvice.com/insights/real-investment-daily/ ------- * REGISTER for our next Candid Coffee, "Beyond Protection: What Life Insurance Can Really Do," Saturday, June 20, 2026: https://streamyard.com/watch/WauFUig8HFtb --- Visit our Site: https://www.realinvestmentadvice.com Contact Us: 1-855-RIA-PLAN --- Subscribe to SimpleVisor : https://www.simplevisor.com/register-new --- Connect with us on social: https://twitter.com/RealInvAdvice https://twitter.com/LanceRoberts https://www.facebook.com/RealInvestmentAdvice/ https://www.linkedin.com/in/realinvestmentadvice/ #StockMarket #MarketUpdate #Investing #ArtificialIntelligence #SectorRotation #SpaceX #ElonMusk #IPO #Bonds #Annuities #KevinWarsh

SpaceX has become one of the most anticipated investment stories in modern market history. Between Elon Musk's popularity, the company's technological achievements, and years of speculation about a public offering, investor excitement is reaching fever pitch. But what happens after the hype? Lance Roberts & Jon Penn examine the lessons to be learned from previous high-profile IPOs, and why some of the biggest investing mistakes occur after the initial excitement fades. We discuss valuation, investor psychology, momentum chasing, and the risks that emerge when enthusiasm becomes disconnected from fundamentals. We also look at the growing speculative interest surrounding leveraged products tied to the SpaceX theme, and why investors should be cautious when Wall Street starts packaging excitement into increasingly aggressive investment vehicles. Here's a topical rundown of today's show: 0:00 - INTRO 0:56 - America's 250th Anniversary Time Capsule & Space-X IPO 3:48 - The Bullish Setup Returns 8:18 - Back from Vacay... 9:32 - IPO's & Space-X 12:04 - What Happens Next - the Advantage in Waiting 14:19 - The FOMO Factor 17:53 - What Could Possibly Go Wrong? 19:02 - Has AI Lost Steam? (The New U.S.Industrial Revolution) 21:37 - What's Next After Iran War? (Economic Pressure Index) 24:08 - Two Things Driving Markets: Profitability & Optimistic Earnings Estimates 25:17 - Italian Gasoline Prices 28:38 - Interest Rates, Bonds, & Kevin Warsh at the Fed 33:59 - A Tip about TIPS 35:44 - Why You Should Own Some Bonds 37:59 - The Three Components of Investing: Safety, Liquidity, & Returns 41:01 - Annuities as Bond "Alternatives?" Hosted by RIA Advisors Chief Investment Strategist, Lance Roberts, CIO,w Senior Investment Advisor, Jonathan Penn, CFP Produced by Brent Clanton, Executive Producer ------- Do you enjoy our content? Rate us on Google: https://bit.ly/4b9JtEo ------- Watch Today's Full Video on our YouTube Channel: https://youtube.com/live/Xr1Ut115-xA ------- Watch today's "Before the Bell" feature, "Bullish Setup Returns," here: https://youtu.be/ox4_xMsXqt4 ------- Watch our previous show, "Bull Market Pullback - Is the Correction Over?" https://youtube.com/live/csXApjrvlNY?feature=share ------- Articles mentioned in this report: "May Inflation Print: Why the 4.2% Headline Is an Oil Story," https://realinvestmentadvice.com/resources/blog/may-inflation-print-why-the-4-2-headline-is-an-oil-story/ --- Get more info & commentary: https://realinvestmentadvice.com/insights/real-investment-daily/ ------- * REGISTER for our next Candid Coffee, "Beyond Protection: What Life Insurance Can Really Do," Saturday, June 20, 2026: https://streamyard.com/watch/WauFUig8HFtb --- Visit our Site: https://www.realinvestmentadvice.com Contact Us: 1-855-RIA-PLAN --- Subscribe to SimpleVisor : https://www.simplevisor.com/register-new --- Connect with us on social: https://twitter.com/RealInvAdvice https://twitter.com/LanceRoberts https://www.facebook.com/RealInvestmentAdvice/ https://www.linkedin.com/in/realinvestmentadvice/ #StockMarket #MarketUpdate #Investing #ArtificialIntelligence #SectorRotation #SpaceX #ElonMusk #IPO #Bonds #Annuities #KevinWarsh

Given by Sozan Michael McCord at City Center on June 13th, 2026 Sozan Michael McCord teaches that when we look at the cause of suffering, we see that it is grasping and clinging or aversion and not accepting what is. We have known since the Industrial Revolution, very clearly, but now have it even more greatly elucidated in the age of artificial intelligence: if a human being equates their value to production, they are obsolete.

Today, a look at what the further acceleration in US- and other market volatility means, particularly for the highly speculative chip stocks that have seen the greatest gains this year, even on a day when the broader market and median stock closed in the green. Elsewhere, gold is melting down and faces a critical support level soon if the selling continues. This and much more on macro and FX also on today's pod, which is hosted by Saxo Global Head of Macro Strategy John J. Hardy. Links Today's only link is to an excellent FT op-ed "Why are we still arguing about the Industrial revolution?" that complains about the attempt to use poor quality 19th century data that provides few insights on how the Industrial Revolution transformed society and the types of available jobs as we attempt to anticipate how AI will transform our current society and the job market. Instead, we should consult the best fiction writers of the time, who provide excellent qualitative documentation of the impact of the industrial revolution.  About twice per week (in normal times, hopefully soon to resume), you will find links discussed on the podcast and a chart-of-the-day over at the John J. Hardy substack. Read daily in-depth market updates from the Saxo Market Call and the Saxo Strategy Team here. Please reach out to us at marketcall@saxobank.com for feedback and questions. Click here to open an account with Saxo. Intro music by AShamaluevMusic DISCLAIMER This content is marketing material. Trading financial instruments carries risks. Always ensure that you understand these risks before trading. This material does not contain investment advice or an encouragement to invest in a particular manner. Historic performance is not a guarantee of future results. The instrument(s) referenced in this content may be issued by a partner, from whom Saxo Bank A/S receives promotional fees, payment or retrocessions. While Saxo may receive compensation from these partnerships, all content is created with the aim of providing clients with valuable information and options.  

Naval in conversation with three frontier founders on the new means of production: Guillermo Rauch (Vercel), Blake Scholl (Boom Supersonic), and Max Hodak (Science). Software factories, vertical integration, the regulatory frontier, and the autonomous company. Part 1: Waste Tokens, Save Time 0:00 Intro — Three Frontier Founders 1:27 AI Software Factories 4:15 Waste Tokens, Save Time 5:47 Models Instructing Humans 9:29 Is Pure Software Dead? 12:03 You Don't Get Stuck Anymore Part 2: Vibe Coding Hardware 14:39 Vibe Coding a Turbine Blade 18:07 Open Source Compounds China's Advantage 20:15 You Always Want the Smartest Model 22:44 Software Still Needs Hands 24:43 Humans Are Becoming Verifiers   Part 3: The Regulatory Frontier 27:53 The Regulatory Red Queen Race 32:32 Why There's No Innovation in Healthcare 36:49 We Need a True 50-State Experiment 40:31 China's FDA Is Beating Ours 43:37 Healthcare Is a Communist Society Inside Capitalism 45:57 Sid's Story: N-of-1 Medicine Part 4: The Autonomous Company 47:49 Autonomous Infrastructure 51:25 Your Job Is to Train the Agent 54:54 The Next Lord of the Rings 59:08 What's Your Definition of Art? 1:05:00 Can AI Have New Ideas? 1:07:03 A Very Large Number of Small Teams Transcript: http://nav.al/industrial

This is The Briefing, a daily analysis of news and events from a Christian worldview.On today's edition of The Briefing, I discuss the stall on the proposal for Smithsonian Women's Museum because Democrats will not define women as biologically female and the strange legacy of Barney Frank. I answer questions about the historical parallels of the A.I. revolution, who has the authority to perform baptisms, if investing can turn into gambling, and why Jesus didn't stop King Herod from killing John the Baptist. Part I (00:14 – 08:01)A Women's Museum That Doesn't Know What a Woman Is? Proposal Stalls for Smithsonian Women's Museum on National Mall Because Democrats Will Not Define Women as Biologically FemaleHow a bipartisan women's history museum became a political football by The Washington Post (Jonathan Edwards)Let Democrats kill the women's history museum by Washington Times (Editorial Board)Part II (08:01 – 13:46)The Death of Barney Frank: The Strange Legacy of the First Self-Identified Gay Member of CongressPart III (13:46 – 18:37)Is the A.I. Revolution More Akin to the Revolution of the Printing Press Than to the Industrial Revolution? — Dr. Mohler Responds to Letters From Listeners of The BriefingPart IV (18:37 – 22:26)Who Has Authority to Perform Baptisms? — Dr. Mohler Responds to Letters From Listeners of The BriefingPart V (22:26 – 24:50)When Does Investing Become Gambling? — Dr. Mohler Responds to Letters From Listeners of The BriefingPart VI (24:50 – 27:03)Why Didn't Jesus Stop King Herod From Killing John the Baptist? — Dr. Mohler Responds to a Letter From a 7-Year-Old Listener of The BriefingSign up to receive The Briefing in your inbox every weekday morning.Follow Dr. Mohler:X | Instagram | Facebook | YouTubeFor more information on The Southern Baptist Theological Seminary, go to sbts.edu.For more information on Boyce College, just go to BoyceCollege.com.To write Dr. Mohler or submit a question for The Mailbox, go here.

From developer dependency to AI-powered ownership in 4 weeks Episode Summary: AI entrepreneurs and side hustlers often fail the same way—and it costs them. This episode breaks down the $30,000 mistake that transformed how I build AI side gigs, teach financial freedom to parents, and think about entrepreneur independence. Expect the real playbook behind failing smart so you don't repeat my errors. Parent entrepreneur Tracy Brinkmann shares the raw truth about firing his $120,000 developer and rebuilding his entire backend using Cursor AI in just 4 weeks. This episode reveals the hidden cost of outsourcing your brain, the specific prompting strategies that actually work, and why dependency might be more expensive than you think. Perfect for parents who want to own their technology instead of renting someone else's expertise. https://DarkHorseEntrepreneur.com Key Points 00:00 - Opening Cursor AI saves $90,000 01:40 - The Stupid Decision - Rebuilding entire backend alone in 4 weeks using Cursor AI 02:15 - Vibe Coding Explained - Directing AI through intent rather than instruction, Collins Dictionary Word of the Year 2025 03:00 - Why Cursor AI - Cursor Composer maintains persistent context across entire codebase 04:30 - Day 10 Shift - Realized he was learning architecture for the first time, not just rebuilding 04:55 - The Real Return - Could build features, maintain systems, make decisions without outside help 06:00 - The Hidden Cost - Lost learning by osmosis and institutional knowledge from Marcus 07:00 - Bug Reports Reality Check - Scaling problems that only show up with experience 08:50 - Parent Entrepreneur Connection - Dependency trap affects family time and business freedom 09:45 - Why This Matters - Biggest shift in work since Industrial Revolution 10:15 - New vs. Old Model - Expand zone of genius vs. hire experts and delegate 11:05 - Whiskered Wisdom - Dependency is expensive, ownership is priceless 11:55 - Closing - Goal is understanding everything well enough to make smart decisions   Key Topics Covered: The $30,000 Dependency Trap Why hiring exceptional talent can make you incompetent in your own business The difference between buying expertise and renting ignorance How every day of outsourcing critical functions reduces your own capabilities The Cursor AI Rebuild Strategy "Vibe coding" vs. traditional prompting approaches Why Cursor Composer's persistent context changes everything The constraint-based prompting framework that eliminates AI hallucinations Context-Rich Prompting System Standard prompt: "Build me a user dashboard" Better prompt: Complete context including database schemas, design patterns, previous failures, and specific success criteria Results: 70% usable code on first pass vs. multiple iterations The Real Cost of Expert Dependency Hourly rate: $150 per hour True cost: Infinite dependency and arrested business evolution The moment when you realize you can't make decisions without external approval Ownership vs. Access Paradigm Old model: Hire experts, delegate complexity, focus on zone of genius New model: Use AI to expand your zone of genius to include previously outsourced functions Why the entrepreneurs who thrive will own capabilities, not just access them Key Quotes: "The hourly rate of a developer might be $150. But the cost of dependency is infinite." "Every time you hand off a critical piece of your business to someone else, you're making a bet that their knowledge will always be available to you." "Dependency is expensive, but ownership is priceless." Action Steps: Identify one area where you're completely dependent on outside expertise Spend 30 minutes learning the basics using AI as your teaching assistant Focus on becoming conversational, not expert-level Start owning your business evolution again Tools Mentioned: Cursor AI (Cursor Composer) Claude Sonnet for architectural decisions PostgreSQL for database management Visual Studio Code (Cursor is a fork) Resources: AI Escape Plan Newsletter: Practical AI-powered strategies for parent entrepreneurs https://DarkHorseInsider.com Focus: Building systems you own, understand, and control while protecting family time