Podcasts about Riches

durée : 00:31:32 - Les pieds sur terre - par : Sonia Kronlund - Marlene Engelhorn a grandi dans une famille multimillionnaire à Vienne, en Autriche. Elle apprend à dissimuler le patrimoine que possède sa famille. Quand elle apprend qu'elle va hériter de plusieurs dizaines de millions d'euros, elle décide de redistribuer cet héritage via une assemblée citoyenne. - équipe : Valentin Rémy, Adèle Tocquet Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

Colombia tiene recursos extraordinarios, pero lahistoria del país revela que lo que sostiene o derrumba una estructura no es la riqueza, sino el carácter de quienes la dirigen. La Biblia muestra este contraste entre Saúl y Daniel: uno tenía poder, pero no estabilidad; el otro tenía convicción, aunque no tuviera posición. Este episodio aborda cómo elcarácter sostiene lo que los recursos no pueden mantener.Colombia has extraordinary resources, but its history shows that what sustains or collapses a structure is not wealth—it is the character of those who lead it. Scripture presents thiscontrast through Saul and Daniel: one had power but lacked stability; the other had conviction without position. This episode examines how character sustainswhat resources alone cannot.

Jul. 18 & 19, 2026 - The Unsearchable Riches of ChristPastor Ed TaylorEphesians 3:8-13 | Study #14915EPHESIANS

We learn from the meditation and colloquy from Divine Intimacy for the eighth Sunday after Pentecost.Please support the Our Lady of Fatima Podcast:http://buymeacoffee.com/TerenceMStantonLike and subscribe on YouTube:https://m.youtube.com/@OurLadyOfFatimaPodcastFollow us on X:@FatimaPodcastThank you!

Gospel Riches (Ephesians 1:13-21)

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

Audio reading: Num 26:1-32, 2 Chron 1:1-3:17, Rom 6;1-23, Psalm 16:1-11, Prov 19: 20-21   This episode explores 2 Chronicles 1–3 and the opening of Solomon's reign, highlighting his humble request for wisdom to govern Israel, and God's promise of wisdom, wealth, and glory. We also unpack the Tree of Life motif—linking the Torah from Genesis to Revelation, the cherubim in the Holy of Holies, and how the living Word points to Jesus as the path to life. Connect With Us - Website: https://1yearAudioBiblePodcast.com   Spotify: Https://Open.Spotify.Com/Show/7zuyulxhnkthbgmnxu6q5t Apple Podcasts: Https://Podcasts.Apple.Com/Us/Podcast/1-Year-Audio-Bible-Podcast/Id1562405086 YouTube: https://www.youtube.com/@1YearAudioBiblePodcast Podbean: https://bridgeconnector.podbean.com/

In this episode, Landon Dory, CEO of North Star Brokerage, shares insights on scaling outdoor hospitality properties, including RV parks and mobile home communities. Discover strategies for market growth, operational excellence, and building long-term client relationships.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true 'white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a "mini-mastermind" with Mike and his private clients on an upcoming "Retreat", either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas "Big H Ranch"? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

It never hurts to ask — the worst you'll hear is “no,” and even that doesn't equal failure. In today's episode, “How Many Times Did You Courageously Ask for Something Today? What Happened?” Jacquette shares why she treats asking as a practice, not a performance. She breaks down how every ask is an act of courage and a moment to honor your true desires. Remember: you stand to lose far more by staying silent than you ever will by asking for what you really want.Pricing Made Human is back! Pricing Made Human® Masterclass | Price Confidently — Jacquette TimmonsWant More? Check Out:www.jacquettetimmons.comwww.jacquettetimmons.com/digital-productswww.instagram.com/jacquettemtimmonsBuyMeACoffee.com/JacquetteSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Repousser la mort, se faire cryogéniser, avoir un igloo privatisé ou encore construire un bunker pour survivre à l'apocalypse... Chez les ultra-riches, l'argent ne sert plus seulement à vivre confortablement. Dans le monde des milliardaires, l'argent sert à assouvir de nombreuses obsessions souvent au détriment de l'environnement ou de la recherche scientifique. Pourquoi les riches se font-ils faire construire un igloo ? Pourquoi les milliardaires cherchent-ils à repousser la mort ? Comment s'y prennent-ils ? Écoutez la suite de cet épisode de "Maintenant Vous Savez". Un podcast Bababam Originals écrit et réalisé par Ludivine Morales. À écouter ensuite : ⁠Cryogénie : pourra-t-on bientôt ressusciter les morts ?⁠ ⁠Être riche rend-il vraiment snob ?⁠ ⁠Qui est l'homme le plus riche de l'histoire ?⁠ Retrouvez tous les épisodes de ⁠"Maintenant vous savez".⁠ Pour rester informé et recevoir le meilleur de “Maintenant vous savez” chaque semaine, abonnez-vous à notre⁠ newsletter⁠. Suivez Bababam sur ⁠Instagram⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

Angie Gillenwater from the Kanawha Charleston Humane Association with this week's "Adopt Me Please" Pet of the Week, Jim Strawn, Steve Animal and John Inghram on Live on the Levee, and Shawn Hardman from Trojan Landing Marine and Powersports on Ride and Riches giveaway tomorrow.

1 Timothy 6:11-16 (ESV)Andrew, Isack, and Edwin discuss why the good fight is one of faith.Read the written devo that goes along with this episode by clicking here.    Let us know what you are learning or any questions you have. Email us at TextTalk@ChristiansMeetHere.org.    Join the Facebook community and join the conversation by clicking here. We'd love to meet you. Be a guest among the Christians who meet on Livingston Avenue. Click here to find out more. Michael Eldridge sang all four parts of our theme song. Find more from him by clicking here.   Thanks for talking about the text with us today.________________________________________________If the hyperlinks do not work, copy the following addresses and paste them into the URL bar of your web browser: Daily Written Devo: https://readthebiblemakedisciples.wordpress.com/?p=26206The Christians Who Meet on Livingston Avenue: http://www.christiansmeethere.org/Facebook Page: https://www.facebook.com/TalkAboutTheTextFacebook Group: https://www.facebook.com/groups/texttalkMichael Eldridge: https://acapeldridge.com/ 

I expected "The Future Is Peace" by Aziz Abu Sarah and Maoz Inon to challenge me. I didn't expect it to make me uncomfortable. The book asks something extraordinarily difficult of every reader: listen carefully to another person's story, even when every instinct inside you wants to argue, correct, or reject it. You don't have to agree. You don't have to abandon your convictions. But if we cannot hear one another's deepest experiences, we will never build anything beyond endless conflict. Peacemaking is not naïve. It is demanding. Those who pursue it are often mocked, dismissed, or accused of betraying their own people. Yet, as the authors write, "Ultimately, every conflict ends. It's up to us to decide how much blood must be spilled first." This episode explores the courage that peace requires - and why listening may be its hardest first step. Michael Whitman is the senior rabbi of ADATH Congregation in Hampstead, Quebec, and an adjunct professor at McGill University Faculty of Law. ADATH is a modern orthodox synagogue community in suburban Montreal, providing Judaism for the next generation. We take great pleasure in welcoming everyone with a warm smile, while sharing inspiration through prayer, study, and friendship. Rabbi Whitman shares his thoughts and inspirations through online lectures and shiurim, which are available on: YouTube: https://www.youtube.com/channel/UC5FLcsC6xz5TmkirT1qObkA Instagram: https://www.instagram.com/adathmichael/ Podcast - Mining the Riches of the Parsha: Apple Podcasts: https://podcasts.apple.com/ca/podcast/mining-the-riches-of-the-parsha/id1479615142?fbclid=IwAR1c6YygRR6pvAKFvEmMGCcs0Y6hpmK8tXzPinbum8drqw2zLIo7c9SR-jc Spotify: https://open.spotify.com/show/3hWYhCG5GR8zygw4ZNsSmO Please contact Rabbi Whitman (michael@adath.ca) with any questions or feedback, or to receive a daily email, "Study with Rabbi Whitman Today," with current and past insights for that day, video, and audio, all in one short email sent directly to your inbox.

I recently visited Professor Ben Mollov's Jewish-Arab Dialogue class at Bar-Ilan University, and it left me more hopeful about Israel than almost anything else I experienced during my trip. The class begins with something unexpected - conversational Arabic. From there, Jewish and Arab students learn, laugh, argue, and build genuine relationships. For many, it is the first time they have truly come to know one another as people rather than as representatives of opposing groups. Professor Mollov has spent years measuring the impact of this work, and his research shows that these encounters have a lasting impact long after graduation. He has created an environment in which stereotypes become impossible because real relationships replace them. I had the privilege of speaking to the students myself. Their openness, curiosity, and warmth convinced me that peace is not built only through diplomacy or political agreements. It is built one classroom, one conversation, and one relationship at a time. If Israel is ever to know lasting peace - and I am convinced it will, it will need courageous initiatives like this one - and they deserve far more attention than they receive. Michael Whitman is the senior rabbi of ADATH Congregation in Hampstead, Quebec, and an adjunct professor at McGill University Faculty of Law. ADATH is a modern orthodox synagogue community in suburban Montreal, providing Judaism for the next generation. We take great pleasure in welcoming everyone with a warm smile, while sharing inspiration through prayer, study, and friendship. Rabbi Whitman shares his thoughts and inspirations through online lectures and shiurim, which are available on: YouTube: https://www.youtube.com/channel/UC5FLcsC6xz5TmkirT1qObkA Instagram: https://www.instagram.com/adathmichael/ Podcast - Mining the Riches of the Parsha: Apple Podcasts: https://podcasts.apple.com/ca/podcast/mining-the-riches-of-the-parsha/id1479615142?fbclid=IwAR1c6YygRR6pvAKFvEmMGCcs0Y6hpmK8tXzPinbum8drqw2zLIo7c9SR-jc Spotify: https://open.spotify.com/show/3hWYhCG5GR8zygw4ZNsSmO Please contact Rabbi Whitman (michael@adath.ca) with any questions or feedback, or to receive a daily email, "Study with Rabbi Whitman Today," with current and past insights for that day, video, and audio, all in one short email sent directly to your inbox.

Ephesians 3:14-21 When have you decorated a room? The Person of the Father Who is God the Father? The Riches of the Father What does the Father give us? The Enjoyment of the Father Why does the Father give this?

Rachel Goldberg-Polin has become more than the grieving mother of Hersh Goldberg-Polin. She has become the voice and vocabulary of October 7th and its aftermath. In her remarkable new book, "When We See You Again," she gives language to grief, faith, hope, love, and resilience with a precision that helps us understand not only what happened, but what it has done to us. In this episode, I share why I believe this is one of the most important books to emerge from October 7th. Rachel does not offer easy answers or comforting clichés. Instead, she offers something rarer: words that help us carry pain without surrendering to it, search for God without pretending to understand His plan, and embrace her daily conviction that "hope is mandatory." This is a book to read slowly, to underline, and to return to again and again. It deserves to be read not only because of Rachel's story, but because of the extraordinary way she tells it. Michael Whitman is the senior rabbi of ADATH Congregation in Hampstead, Quebec, and an adjunct professor at McGill University Faculty of Law. ADATH is a modern orthodox synagogue community in suburban Montreal, providing Judaism for the next generation. We take great pleasure in welcoming everyone with a warm smile, while sharing inspiration through prayer, study, and friendship. Rabbi Whitman shares his thoughts and inspirations through online lectures and shiurim, which are available on: YouTube: https://www.youtube.com/channel/UC5FLcsC6xz5TmkirT1qObkA Instagram: https://www.instagram.com/adathmichael/ Podcast - Mining the Riches of the Parsha: Apple Podcasts: https://podcasts.apple.com/ca/podcast/mining-the-riches-of-the-parsha/id1479615142?fbclid=IwAR1c6YygRR6pvAKFvEmMGCcs0Y6hpmK8tXzPinbum8drqw2zLIo7c9SR-jc Spotify: https://open.spotify.com/show/3hWYhCG5GR8zygw4ZNsSmO Please contact Rabbi Whitman (michael@adath.ca) with any questions or feedback, or to receive a daily email, "Study with Rabbi Whitman Today," with current and past insights for that day, video, and audio, all in one short email sent directly to your inbox.

Ephesians 1:7 Today we sit with Ephesians 1:7, and with the word that has echoed through every broken life that dared to receive it: redeemed. Does Encounter help you make a difference?  Please consider giving! What would change for you today if you truly believed your debt had already been paid in full?

As a Christian, you are called to faithfully preserve and promote what God has entrusted to you until Christ returns.

Summary: The Book of Ephesians has been called the Swiss Alps of the New Testament as we have scaled great heights of God's love and calling upon our lives. And as the Epistle began, so does it end, with “Grace”. God's story, and your story, all begins and ends with Grace…. God's-Riches-at-Christ's-Expense. Living in Grace and giving Grace to one another……. We will live a life that takes us into the heavenlies, where Christ is seated! Join us as we end where we began….. In Grace.

This morning we discuss three models of what a synagogue's purpose is. The model that is common in North America has a surprising origin. It was conceived and initiated by Rabbi Mordechai Kaplan at The Jewish Center in 1918, who later founded the Reconstructionist Movement. We at ADATH build on that model but with a critical difference. Michael Whitman is the senior rabbi of ADATH Congregation in Hampstead, Quebec, and an adjunct professor at McGill University Faculty of Law. ADATH is a modern orthodox synagogue community in suburban Montreal, providing Judaism for the next generation. We take great pleasure in welcoming everyone with a warm smile, while sharing inspiration through prayer, study, and friendship. Rabbi Whitman shares his thoughts and inspirations through online lectures and shiurim, which are available on: YouTube: https://www.youtube.com/channel/UC5FLcsC6xz5TmkirT1qObkA Instagram: https://www.instagram.com/adathmichael/ Podcast - Mining the Riches of the Parsha: Apple Podcasts: https://podcasts.apple.com/ca/podcast/mining-the-riches-of-the-parsha/id1479615142?fbclid=IwAR1c6YygRR6pvAKFvEmMGCcs0Y6hpmK8tXzPinbum8drqw2zLIo7c9SR-jc Spotify: https://open.spotify.com/show/3hWYhCG5GR8zygw4ZNsSmO Please contact Rabbi Whitman (michael@adath.ca) with any questions or feedback, or to receive a daily email, "Study with Rabbi Whitman Today," with current and past insights for that day, video, and audio, all in one short email sent directly to your inbox.

Job 28:1-28. Man searches the earths crust for its hidden treasures, but “where is wisdom found?” Its price is far above rubies and gold, for wisdom speaks of someone illuminated to the reality of God in their life of a daily basis. The fear of the living God, that is where wisdom is found! 

Martin Sixmith was the BBC's man in Moscow as the Soviet Union collapsed and Vladimir Putin came to power. In today's podcast he talks about his time in Russia, the fight for the billions lost and his time working on The Lost Child of Philomena Lee. Hosted on Acast. See acast.com/privacy for more information.

1 A false balance is an abomination to Yahweh, but accurate weights are his delight. 2 When pride comes, then comes shame, but with humility comes wisdom. 3 The integrity of the upright shall guide them, but the perverseness of the treacherous shall destroy them. 4 Riches don't profit in the day of wrath, but righteousness delivers from death. 5 The righteousness of the blameless will direct his way, but the wicked shall fall by his own wickedness. 6 The righteousness of the upright shall deliver them, but the unfaithful will be trapped by evil desires. 7 When a wicked man dies, hope perishes, and expectation of power comes to nothing. 8 A righteous person is delivered out of trouble, and the wicked takes his place. 9 With his mouth the godless man destroys his neighbor, but the righteous will be delivered through knowledge. 10 When it goes well with the righteous, the city rejoices. When the wicked perish, there is shouting. 11 By the blessing of the upright, the city is exalted, but it is overthrown by the mouth of the wicked. 12 One who despises his neighbor is void of wisdom, but a man of understanding holds his peace. 13 One who brings gossip betrays a confidence, but one who is of a trustworthy spirit is one who keeps a secret. 14 Where there is no wise guidance, the nation falls, but in the multitude of counselors there is victory. 15 He who is collateral for a stranger will suffer for it, but he who refuses pledges of collateral is secure. 16 A gracious woman obtains honor, but violent men obtain riches. 17 The merciful man does good to his own soul, but he who is cruel troubles his own flesh. 18 Wicked people earn deceitful wages, but one who sows righteousness reaps a sure reward. 19 He who is truly righteous gets life. He who pursues evil gets death. 20 Those who are perverse in heart are an abomination to Yahweh, but those whose ways are blameless are his delight. 21 Most certainly, the evil man will not be unpunished, but the offspring of the righteous will be delivered. 22 Like a gold ring in a pig's snout, is a beautiful woman who lacks discretion. 23 The desire of the righteous is only good. The expectation of the wicked is wrath. 24 There is one who scatters, and increases yet more. There is one who withholds more than is appropriate, but gains poverty. 25 The liberal soul shall be made fat. He who waters shall be watered also himself. 26 People curse someone who withholds grain, but blessing will be on the head of him who sells it. 27 He who diligently seeks good seeks favor, but he who searches after evil, it shall come to him. 28 He who trusts in his riches will fall, but the righteous shall flourish as the green leaf. 29 He who troubles his own house shall inherit the wind. The foolish shall be servant to the wise of heart. 30 The fruit of the righteous is a tree of life. He who is wise wins souls. 31 Behold, the righteous shall be repaid in the earth, how much more the wicked and the sinner! Listen Donate Subscribe: Proverbs Daily Podcast Psalms Daily Podcast

In her follow‑up episode, “You've Answered the Question — ‘What Do You Want?' — Now What?” Jacquette gets real about being tired and how that fatigue can be a sign you're in what she calls “the waiting space.” A space that often arrives right on the edge of your breakthrough. She unpacks the swirl of emotions that live there (confidence, fear, courage, doubt, anxiety) and shares how to move through the waiting space without speeding up, shutting down, or giving up on what you want.Want More? Check Out:www.jacquettetimmons.comwww.jacquettetimmons.com/digital-productswww.instagram.com/jacquettemtimmonsBuyMeACoffee.com/JacquetteSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Arizona Attorney General Kris Mayes settled a lawsuit on the state's ESA voucher program, easing oversight requirements for parents. We talk to Goldwater Institutes Jon Riches about the victory. 

This evening we analyze the requirement to not only speak truthfully but meaningfully. We explore the extent to which losing one's temper is harmful to oneself, even when it is justified. And we see the practical application of a not applicable Mitzvah, to make us more careful and less negligent. Michael Whitman is the senior rabbi of ADATH Congregation in Hampstead, Quebec, and an adjunct professor at McGill University Faculty of Law. ADATH is a modern orthodox synagogue community in suburban Montreal, providing Judaism for the next generation. We take great pleasure in welcoming everyone with a warm smile, while sharing inspiration through prayer, study, and friendship. Rabbi Whitman shares his thoughts and inspirations through online lectures and shiurim, which are available on: YouTube: https://www.youtube.com/channel/UC5FLcsC6xz5TmkirT1qObkA Instagram: https://www.instagram.com/adathmichael/ Podcast - Mining the Riches of the Parsha: Apple Podcasts: https://podcasts.apple.com/ca/podcast/mining-the-riches-of-the-parsha/id1479615142?fbclid=IwAR1c6YygRR6pvAKFvEmMGCcs0Y6hpmK8tXzPinbum8drqw2zLIo7c9SR-jc Spotify: https://open.spotify.com/show/3hWYhCG5GR8zygw4ZNsSmO Please contact Rabbi Whitman (michael@adath.ca) with any questions or feedback, or to receive a daily email, "Study with Rabbi Whitman Today," with current and past insights for that day, video, and audio, all in one short email sent directly to your inbox.

One of the most surprising details in this week's Parsha is easy to miss. Moshe doesn't simply tell the tribes of Gad and Reuven to fight alongside the rest of the Jewish people before returning to their own land. He tells them to go as Chalutzim - pioneers at the front of the army, leading the charge and strengthening everyone else's resolve. Why? Because serving the Jewish people means thinking not only about what is best for you, but also about the effect you have on those around you. Through this Torah insight, together with a story about Rabbi Tzvi Yehuda Kook and an unforgettable encounter at a bank, I explore a lesson that reaches far beyond the battlefield. Our responsibility includes more than our actions and words. It includes our attitude, our presence, our body language, and the atmosphere we create for others. Sometimes the greatest act of leadership is simply helping the people around us become stronger. Most of us think responsibility ends with doing the right thing. The Torah says responsibility also includes the spirit in which we do it. Michael Whitman is the senior rabbi of ADATH Congregation in Hampstead, Quebec, and an adjunct professor at McGill University Faculty of Law. ADATH is a modern orthodox synagogue community in suburban Montreal, providing Judaism for the next generation. We take great pleasure in welcoming everyone with a warm smile, while sharing inspiration through prayer, study, and friendship. Rabbi Whitman shares his thoughts and inspirations through online lectures and shiurim, which are available on: YouTube: https://www.youtube.com/channel/UC5FLcsC6xz5TmkirT1qObkA Instagram: https://www.instagram.com/adathmichael/ Podcast - Mining the Riches of the Parsha: Apple Podcasts: https://podcasts.apple.com/ca/podcast/mining-the-riches-of-the-parsha/id1479615142?fbclid=IwAR1c6YygRR6pvAKFvEmMGCcs0Y6hpmK8tXzPinbum8drqw2zLIo7c9SR-jc Spotify: https://open.spotify.com/show/3hWYhCG5GR8zygw4ZNsSmO Please contact Rabbi Whitman (michael@adath.ca) with any questions or feedback, or to receive a daily email, "Study with Rabbi Whitman Today," with current and past insights for that day, video, and audio, all in one short email sent directly to your inbox.

The tribes of Reuven and Gad asked Moshe for permission to settle east of the Jordan River rather than enter the Land of Israel with the rest of the Jewish people. Moshe reluctantly agreed, but only after insisting on conditions designed to preserve their connection to the nation and to the Land. History tells a sobering story. Those tribes were the first Israelites exiled by Assyria, disappearing from Jewish history long before the others. Their downfall was not simply geography, but their separation reminds us that distance from the spiritual center of Jewish life carries real risks. Most of us live outside Israel for good and legitimate reasons. This week's parashah challenges us with a timeless question: if we live far from Israel, what are we doing to keep our connection to the Land, the Jewish people, and our spiritual identity alive? That question mattered in Moshe's day. It matters no less in ours. Michael Whitman is the senior rabbi of ADATH Congregation in Hampstead, Quebec, and an adjunct professor at McGill University Faculty of Law. ADATH is a modern orthodox synagogue community in suburban Montreal, providing Judaism for the next generation. We take great pleasure in welcoming everyone with a warm smile, while sharing inspiration through prayer, study, and friendship. Rabbi Whitman shares his thoughts and inspirations through online lectures and shiurim, which are available on: YouTube: https://www.youtube.com/channel/UC5FLcsC6xz5TmkirT1qObkA Instagram: https://www.instagram.com/adathmichael/ Podcast - Mining the Riches of the Parsha: Apple Podcasts: https://podcasts.apple.com/ca/podcast/mining-the-riches-of-the-parsha/id1479615142?fbclid=IwAR1c6YygRR6pvAKFvEmMGCcs0Y6hpmK8tXzPinbum8drqw2zLIo7c9SR-jc Spotify: https://open.spotify.com/show/3hWYhCG5GR8zygw4ZNsSmO Please contact Rabbi Whitman (michael@adath.ca) with any questions or feedback, or to receive a daily email, "Study with Rabbi Whitman Today," with current and past insights for that day, video, and audio, all in one short email sent directly to your inbox.

*For our Summer week off, we're replaying some of our favorite RHOC recaps to celebrate the upcoming Season 20 premiere. Enjoy, and we will see you next week!This episode originally aired March 2022Heather Dubrow hosts a dinner party with Jen and Ryne that results in one of the funniest, awkwardest scenes we've seen in a long time. It's safe to say we will always remember this spectacular moment.Find bonus episodes at patreon.com/watchwhatcrappens and follow us on Instagram @watchwhatcrappens @ronniekaram @benmandelker Hosted on Acast. See acast.com/privacy for more information.

We are all special to God and he has put something special in all of us. We are not to waste our time trying to become someone esle. Because we are who God has created us to be God does everything for a reason and even when we don't understand the reasons. He doesWe have a God who says he has created us for is own purpose and good works we will never understand everything. But we can rest assure that God has created everything for our good and he will turn everything around for our goodI want to thank you all for taking the time to listen to our podcast.blessed and free 63 Where Jesus christ is Lord Godbless you all. I pray blessings and you and all your familes

A year ago, I was convinced AI had little to offer me. I was wrong. Today I use ChatGPT every day, and it consistently helps me think more clearly, and improves my work beyond what I could accomplish on my own. Precisely because it is such a powerful tool, I believe it demands thoughtful and ethical use. In this video I share the three principles that now guide my use of AI: transparency, retaining human judgment and responsibility, and careful verification. Drawing on examples from my own work - including questions of Halacha and kashrut - I argue that AI can make us more productive and better thinkers, but only if we never surrender the one thing it cannot replace: our own judgment. Michael Whitman is the senior rabbi of ADATH Congregation in Hampstead, Quebec, and an adjunct professor at McGill University Faculty of Law. ADATH is a modern orthodox synagogue community in suburban Montreal, providing Judaism for the next generation. We take great pleasure in welcoming everyone with a warm smile, while sharing inspiration through prayer, study, and friendship. Rabbi Whitman shares his thoughts and inspirations through online lectures and shiurim, which are available on: YouTube: https://www.youtube.com/channel/UC5FLcsC6xz5TmkirT1qObkA Instagram: https://www.instagram.com/adathmichael/ Podcast - Mining the Riches of the Parsha: Apple Podcasts: https://podcasts.apple.com/ca/podcast/mining-the-riches-of-the-parsha/id1479615142?fbclid=IwAR1c6YygRR6pvAKFvEmMGCcs0Y6hpmK8tXzPinbum8drqw2zLIo7c9SR-jc Spotify: https://open.spotify.com/show/3hWYhCG5GR8zygw4ZNsSmO Please contact Rabbi Whitman (michael@adath.ca) with any questions or feedback, or to receive a daily email, "Study with Rabbi Whitman Today," with current and past insights for that day, video, and audio, all in one short email sent directly to your inbox.

There are people whose cruelty makes us question humanity. But there are also people whose goodness raises an equally profound question. How is it possible for ordinary human beings to show such extraordinary compassion, forgiveness, and selflessness? Today I tell two remarkable stories. One is the ethical commitment of Magen David Adom, whose medics are taught to save every life - including those who have just committed acts of terror. The other is the heartbreaking story of eight-year-old Charlotte Herzberg of Monsey, New York. After Charlotte was tragically killed by her father's closest friend in a terrible accident, her parents publicly forgave him and transformed their grief into a movement encouraging forgiveness and reconciliation. Through the Shalom for Charlotte initiative, thousands of people have already shared stories of letting go of anger and choosing peace. If evil can spread from one person to another, perhaps goodness can too. Today, I invite you to honor Charlotte's memory in the same way her family has asked thousands of others to do: forgive someone, let go of a grudge, make peace where you can, or perform an unexpected act of kindness. The world has enough examples of hatred. It always needs another example of extraordinary goodness. Michael Whitman is the senior rabbi of ADATH Congregation in Hampstead, Quebec, and an adjunct professor at McGill University Faculty of Law. ADATH is a modern orthodox synagogue community in suburban Montreal, providing Judaism for the next generation. We take great pleasure in welcoming everyone with a warm smile, while sharing inspiration through prayer, study, and friendship. Rabbi Whitman shares his thoughts and inspirations through online lectures and shiurim, which are available on: YouTube: https://www.youtube.com/channel/UC5FLcsC6xz5TmkirT1qObkA Instagram: https://www.instagram.com/adathmichael/ Podcast - Mining the Riches of the Parsha: Apple Podcasts: https://podcasts.apple.com/ca/podcast/mining-the-riches-of-the-parsha/id1479615142?fbclid=IwAR1c6YygRR6pvAKFvEmMGCcs0Y6hpmK8tXzPinbum8drqw2zLIo7c9SR-jc Spotify: https://open.spotify.com/show/3hWYhCG5GR8zygw4ZNsSmO Please contact Rabbi Whitman (michael@adath.ca) with any questions or feedback, or to receive a daily email, "Study with Rabbi Whitman Today," with current and past insights for that day, video, and audio, all in one short email sent directly to your inbox.

What does it really take to build a business that thrives - not just financially, but sustainably?In this episode of A Life of Greatness, Sarah Grynberg sits down with Verne Harnish, founder of Entrepreneurs' Organization and bestselling author of Scaling Up. After decades spent advising some of the world's fastest-growing companies, Verne shares the principles that separate businesses that simply survive from those that truly flourish.But this conversation goes far beyond business strategy. Verne reflects on growing up in a family that went from wealth to hardship, the lessons he learned watching his father's entrepreneurial journey unravel, and how those experiences shaped his life's work. Together, Sarah and Verne explore leadership, resilience, curiosity, personal growth, and why success is built just as much from who you become as what you achieve.Whether you're leading a business, building a career, or simply trying to create a more meaningful life, this conversation offers practical wisdom that extends far beyond the boardroom.You'll learn:Why every great business starts with a bold vision and relentless clarity.The leadership habits that create high-performing, connected teams.How curiosity, resilience and lifelong learning become your greatest competitive advantage.Why relationships and mentorship can change the entire trajectory of your life.The difference between growing a business and truly scaling one.How to navigate setbacks, uncertainty and failure without losing sight of your purpose.Why peace, love and meaningful connection may be the greatest measures of success.Ultimately, this episode is a reminder that greatness isn't built overnight. It's created through thousands of small decisions, a willingness to keep learning, and the courage to pursue something bigger than yourself. Because the businesses that endure, and the lives that matter most, are built with purpose, curiosity and heart.Purchase Sarah's book: Living A Life Of Greatness here.To purchase Living A Life of Greatness outside Australia here or here.Watch A Life of Greatness Episodes On Youtube here.Sign up for Sarah's newsletter (Greatness Guide) here.Purchase Sarah's Meditations here.Instagram: @sarahgrynberg Website: https://sarahgrynberg.com/Facebook: facebook.com/sarahgrynbergTwitter: twitter.com/sarahgrynberg Hosted on Acast. See acast.com/privacy for more information.

“Through wisdom a house is built, And by understanding it is established; By knowledge the rooms are filled With all precious and pleasant riches.” (Proverbs 24:3-4) Society has sought to redefine the happy home. Scripture shows us the truth.

The Torah's response to negligent homicide is remarkably humane. Rather than imposing a fixed prison sentence, it sends the offender to an Ir Miklat - a City of Refuge - where rehabilitation is the goal and release can come unexpectedly with the death of the Kohen Gadol. Yet our Parsha contains a remarkable detail. Rabbi Meir Simchah of Dvinsk explains that for fourteen years, this entire system could not operate. Why would the Torah suspend one of its own institutions? His answer reveals a profound principle: the Torah would not consign a person to a life without hope. Along the way, I explore this striking Torah insight through the words of Rabbi Nachman of Breslov and another inspiring voice. Their message is as relevant today as it was then: hope is not merely a comforting emotion. It is an essential part of living a human life. Michael Whitman is the senior rabbi of ADATH Congregation in Hampstead, Quebec, and an adjunct professor at McGill University Faculty of Law. ADATH is a modern orthodox synagogue community in suburban Montreal, providing Judaism for the next generation. We take great pleasure in welcoming everyone with a warm smile, while sharing inspiration through prayer, study, and friendship. Rabbi Whitman shares his thoughts and inspirations through online lectures and shiurim, which are available on: YouTube: https://www.youtube.com/channel/UC5FLcsC6xz5TmkirT1qObkA Instagram: https://www.instagram.com/adathmichael/ Podcast - Mining the Riches of the Parsha: Apple Podcasts: https://podcasts.apple.com/ca/podcast/mining-the-riches-of-the-parsha/id1479615142?fbclid=IwAR1c6YygRR6pvAKFvEmMGCcs0Y6hpmK8tXzPinbum8drqw2zLIo7c9SR-jc Spotify: https://open.spotify.com/show/3hWYhCG5GR8zygw4ZNsSmO Please contact Rabbi Whitman (michael@adath.ca) with any questions or feedback, or to receive a daily email, "Study with Rabbi Whitman Today," with current and past insights for that day, video, and audio, all in one short email sent directly to your inbox.

White Lotus, Succession, Real Housewives... Depuis plusieurs années, les ultra-riches ont envahi nos écrans. Alors que les inégalités se creusent, des personnages réels ou fictionnels étalent leurs privilèges pour notre divertissement. Alors pourquoi on regarde ?Ameziane :Son compte Instagram : https://www.instagram.com/hahamez/?hl=frLe Summer Tour de la Random Family : https://www.billetweb.fr/random-summer-tourSon spectacle Seum & Spleen à La Nouvelle Seine : https://www.billetreduc.com/spectacle/ameziane-dans-seum-spleen-393302Sources : Emma Garland, "Why Do We Love Watching Rich Arseholes on TV?", Vice (2021)Sophie Gilbert, "Money Is Ruining Television", The Atlantic (2025)Emily J. Smith, "When Did TV Stop Worrying About Money?", Substack (2025)Amelia Eqbal, "After years of 'eat the rich' television, are we finally full?", CBC (2025)Mia Sato, "Streaming services really want you to buy stuff while you watch TV", The Verge (2024)Erica Sweeney, "Wealth on TV Could Change Attitudes Toward Low-Income People, Study Finds", Teen Vogue (2018)"Rich People on TV: Satire or Good PR?", Wisecrack (2022)Kaleigh Werner, "Are TikTok Influencers Mainstreaming the Birkin?", WWD (2026)Rob Grams, Bourgeois Gaze, La domination de classe au cinéma, Editions Les liens qui libèrent (2026)Abonnez-vous à la newsletter sur Substack : chaque mois, je publie un article sur un sujet de la pop culture !Suivez Star System sur les réseaux :Instagram : @starsystempodcastTikTok : @starsystempodcastIllustration : Ines Basille. Musique : Naaha. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

POWER FOR TODAY is intended to equip the believers with the supernatural dimension of God through the teaching of the unadulterated word of God.

Remember God loves you so much he sent his Son Jesus Christ to take the punishment for your sins. You are of great value. Jesus loves you and He is just a prayer away! 

Music from: Tartanic, Cheeks and Phoenix, Cheeks and Phoenix, Mc Dades, Jim Hancock, Craig O'Farrington, Jackdaws, Pictus, Brian "Tinker" Leo, Cheeks and Phoenix, Angus McHugh, Scottish Pirate, Blame Not the Bard, Better Than Nun, Cast in Bronze, Fiddler's Tales VISIT OUR SPONSORS Bawdy Podcast Happy To Be Coloring Pages RESCU The Patrons of the Podcast The Ren List SONGS Song 01: Mad Dogs Triple by Tartanic from Universal www.facebook.com/tartanicofficial Song 02: Hellbound Train by Tartanic from Universal www.facebook.com/tartanicofficial Song 03: Nancy Whiskey [06] by Cheeks and Phoenix from Coddiwomple www.cheeksandphoenix.com/ Song 04: Dark Lady [03] by Cheeks and Phoenix from Any Requests www.cheeksandphoenix.com/ Song 05: Miri_s Reel_The Foolie_The Onlies_ Reel by Mc Dades from Thread the Light www.themcdades.com Song 06: Gaelic Waltz Part 1 by Mc Dades from Thread the Light www.themcdades.com Song 07: Gaelic Waltz Part 2 by Mc Dades from Thread the Light www.themcdades.com Song 08: Dance Around the Spinning Wheel by Mc Dades from Thread the Light www.themcdades.com Song 09: Haul Away Joe [09] by Jim Hancock from Rolling Home www.jimhancock.com Song 10: The Magician [01] by Craig o'Farrington from 20 Years of Sweet Delights www.facebook.com/cmbroers Song 11: Waves by Jackdaws from Amuse www.thejackdaws.com/ Song 12: The Green Man by Pictus from Air www.pictusmusic.com Song 13: Whisky, Stout & Beer by Brian "Tinker" Leo from Tinker's Rest www.facebook.com/tinkersings/ Song 14: Riches, Woman, and Beer by Cheeks and Phoenix from Any Requests www.cheeksandphoenix.com/ Song 15: Haul Away Joe [02] by Angus McHugh, Scottish Pirate from Rebels Pirates and Cutthroats www.matthughesmusic.com Song 16: Greenland Whale Fisheries - Whiskey Before Breakfast by Blame Not the Bard from Soundcheck www.blamenotthebard.com/ Song 17: Ding Dong! by Better Than Nun from Devilish www.betterthannun.bandcamp.com/ Song 18: Original Sin II by Better Than Nun from Devilish www.betterthannun.bandcamp.com/ Song 19: Definition 4 Part by Better Than Nun from Our Lady of Immaculate ~Vibes~ www.betterthannun.bandcamp.com/ Song 20: Will ye buy a fine dog by Better Than Nun from Our Lady of Immaculate ~Vibes~ www.betterthannun.bandcamp.com/ Song 21: When Johnny Comes Marching Home by Cast in Bronze from The Voyage www.castinbronze.net/ Song 22: The Terk [01] by Fiddler's Tales from Anatidaephobia UNKNOW WEBSITE Song 23: Spill the Tea by The Harlot Queens from Tea and Strumpets www.theharlotqueens.com Song 24: Dandy Candymaker by The Harlot Queens from Tea and Strumpets www.theharlotqueens.com HOW TO CONTACT US Please post it on Facebook https://www.facebook.com/renfestmusic Please email us at renfestpodcast@gmail.com OTHER CREDITS Thee Bawdy Verson https://renfestbawdypodcast.libsyn.com/ The Minion Song by Fugli www.povera.com Valediction by Marc Gunn https://marcgunn.com/ HOW TO LISTEN Patreon https://www.patreon.com/RenFestPodcast Apple https://podcasts.apple.com/us/podcast/renaissance-festival-podcast/id74073024 Spotify https://open.spotify.com/show/76uzuG0lRulhdjDCeufK15?si=obnUk_sUQnyzvvs3E_MV1g Listennotes http://www.listennotes.com/podcasts/renaissance-festival-podcast-minions-1Xd3YjQ7fWx/

What do you want? And even more importantly, what do you want that you're not asking for? It's one of the most powerful questions you can pose to yourself. In today's episode, “The Question Before the Question: What Do You Want?” host Jacquette breaks down how knowing your true answer can keep you from paying for other people's wants. Stay tuned!Want More? Check Out:www.jacquettetimmons.comwww.jacquettetimmons.com/digital-productswww.instagram.com/jacquettemtimmonsBuyMeACoffee.com/JacquetteSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today in the business of podcasting:Podcast Movement has received nearly 800 submissions for the upcoming New York City edition of the conference and business summit.Next Audio co-founder Lemya Soltani detailed on LinkedIn how her MENA audio network hosted a Cannes Lions networking event without VC funding, cutting costs through shared boat rentals, local catering, and budget flights. Sounds Profitable's Bryan Barletta shared a similar low-cost Cannes playbook, showing brands don't need big budgets to make an impact at the festival.A Sounds Profitable panel from Cannes Lions, "Riches vs Niches," is now live on YouTube, featuring executives from SoundStack, Red Seat Ventures, ART19, and ADOPTER Media. The group makes the case that niche podcasts can outperform broad-reach shows on engagement, ad rates, and subscription revenue.Deep Blue Sports + Entertainment and Mondo Metrics launched the Women's Sports Index, a real-time platform benchmarking media value across women's sports using social data. The tool aims to fill gaps left by traditional ratings metrics as new women's sports leagues build fan bases largely through social media.Nielsen's May 2026 survey found PM drive listening now leads AM drive across the top 10 U.S. radio markets, with afternoon share continuing to grow year over year. The trend challenges long-standing assumptions about morning drive's dominance in radio ad planning.To find links to these, and every article covered in today's episode, click here. You can also subscribe to The Download's newsletter to receive the full issue straight to your email inbox every day.

What gives this life meaning? We discuss this and more on this weeks episode covering Matthew 19:16-30.

Today in the business of podcasting:Podcast Movement has received nearly 800 submissions for the upcoming New York City edition of the conference and business summit.Next Audio co-founder Lemya Soltani detailed on LinkedIn how her MENA audio network hosted a Cannes Lions networking event without VC funding, cutting costs through shared boat rentals, local catering, and budget flights. Sounds Profitable's Bryan Barletta shared a similar low-cost Cannes playbook, showing brands don't need big budgets to make an impact at the festival.A Sounds Profitable panel from Cannes Lions, "Riches vs Niches," is now live on YouTube, featuring executives from SoundStack, Red Seat Ventures, ART19, and ADOPTER Media. The group makes the case that niche podcasts can outperform broad-reach shows on engagement, ad rates, and subscription revenue.Deep Blue Sports + Entertainment and Mondo Metrics launched the Women's Sports Index, a real-time platform benchmarking media value across women's sports using social data. The tool aims to fill gaps left by traditional ratings metrics as new women's sports leagues build fan bases largely through social media.Nielsen's May 2026 survey found PM drive listening now leads AM drive across the top 10 U.S. radio markets, with afternoon share continuing to grow year over year. The trend challenges long-standing assumptions about morning drive's dominance in radio ad planning.To find links to these, and every article covered in today's episode, click here. You can also subscribe to The Download's newsletter to receive the full issue straight to your email inbox every day.

Episode Description: What does it truly mean to move past intellectual theology and step into a lived, practical experience of God's presence? In this episode of For Zion's Sake, hosts Shelly and June Volk continue their deep dive into the closing verses of Ephesians chapter 3. Writing from a Roman prison cell in 61 AD, the Apostle Paul paints a staggering picture of a God who is "beyond beyond"—offering unlimited riches of grace, glory, and power to the believer's inner man. June shares a profoundly raw personal testimony about a 30-year-old conflict, revealing how a striking word from the Lord completely exposed her selfishness and shifted her heart toward true, cross-like forgiveness. Together, they unpack how human effort will always fall short of the Christian calling, pointing listeners to 2 Corinthians to prove that our ultimate sufficiency comes only from above. Tune in to discover how you can become a sweet fragrance of Christ, rooted and grounded entirely in His love. Key Takeaways from This Episode: The Practical Shift: Reiterating the structure of Ephesians, where the first three chapters establish complex doctrine and theology, setting up the final three chapters for highly practical daily living. But June, I Care: June Volk opens up about a decades-long grievance and a conversation with a pastor that led to a direct, heart-stopping revelation from God about layover selfishness, bitterness, and the active choice to forgive. Sufficiency from Above: Exploring Paul’s perspective in 2 Corinthians that no one is naturally capable of living out the Christian life on their own strength—our true sufficiency must be inherited from God. A Fragrance of Life or Death: Analyzing the unique spiritual phenomenon where walking intimately in the love of Christ diffuses a distinct spiritual aroma—serving as a breath of life to those being saved, and a reminder of death to those who are perishing. Scripture References Mentioned in This Episode: The Scriptural Anchor: Ephesians 3:14-21 Unlimited Supply: Philippians 4:19 Riches of Grace: Ephesians 1:7 and Ephesians 2:7 Abundant Grace: 1 Timothy 1:14 Insurmountable Realities: 1 Corinthians 2:9 / Isaiah 64:4 The Sufficiency Clause: 2 Corinthians 3:5 The Fragrance of Knowledge: 2 Corinthians 2:14-16 Support & Connect with For Zion's Sake: For Zion's Sake is a broadcast dedicated to strengthening believers in their faith and praying for Jewish kinsmen to come to a saving knowledge of Jesus as the Messiah. Visit Us Online: Find full archives, updates, and resources at shellyandjunevolk.com. Write to Us Directly: For Zion's Sake P.O. Box 244 Kannapolis, NC 28082 This program is proudly made possible and sponsored by the Psalm 127 Fund. Support the show: https://shellyandjunevolk.com/product/partner-with-us-psalm-127-fund/See omnystudio.com/listener for privacy information.

Most machine shops judge a good day by spindle time. If the machines are cutting, we're making money. If they're sitting still, something must be wrong. But what if keeping capacity open for the right customer is actually part of the product? That question kicks off our conversation with strategist Kaihan Krippendorff, who we met at MFG 2026 in Fort Lauderdale. Kaihan's big idea is that value is moving closer to the moment and place where it is needed. For machine shops, that means reshoring, faster turnaround, stocked material, flexible capacity, automation, and being the supplier customers call when a problem cannot wait. We talk about why customers are rarely just buying a part. They are buying uptime, speed, confidence, and the ability to avoid a production nightmare. That shift changes how a shop should think about pricing, customer relationships, and where it can create value beyond the machine. We also get practical about what this could look like on the shop floor: having material ready, showing available capacity online, and recognizing when a $200 part becomes a $2,000 solution because the customer needs it immediately. The future may not belong to the biggest shop or the cheapest shop. It may belong to the shops that understand urgency, move quickly, and become harder for customers to replace. Because it is not the big that eat the small. It is the fast that eat the slow. What's Covered in this Episode (1:06) Recapping MFG 2026 and welcoming Kaihan Krippendorff (2:02) Kaihan's two worlds: strategy thought leadership and the Outthinker think tank (4:02) The game is changing, and proximity is the new playbook (5:15) Reshoring, the end of globalization, and the rising cost of distance (6:39) Take your shop high-end with DN Solutions (7:52) "Jobs to be done": sell the outcome, not the part (11:21) Storch Magnetics: one lobbyist out-sold the whole sales team (12:26) Tooling vending machines and the $8 stadium water bottle (14:06) The weekend rush job: a $200 part worth $2,000 by Monday (15:52) Distributed 3D printing, zero marginal cost, and selling uptime (18:32) A 2026 prediction: AI gets arms and legs (21:04) Reinvest in yourself first with ProShop ERP (22:43) Coca-Cola Freestyle: create the value after demand shows up (24:57) Low Country Aerospace and buying raw material smarter (27:25) Sell results, not atoms: the Uber and Domino's lesson (29:04) Putting open capacity online and a distributed network of shops (32:08) Connecting directly to customers and ProCNC's 2004 head start (35:36) Why categories are powerful and time splicing for quick response (37:15) Stop getting burned by recruiters: Use Hire MFG Leaders (38:29) Segmenting customers by who values speed most (42:09) Riches in the niches and the rise of the mega factories (44:48) What a typical shop can do now: the nine Ps checklist Resources Mentioned DN Solutions ProShop ERP Hire MFG Leaders SendCutSend Arbill Storch Magnetics Quickparts 3D Printing Low Country Aerospace The End of the World Is Just the Beginning Fast Formulator Connect with Kaihan Krippendorff Connect on LinkedIn Kaihan.net Outthinker Proximity by Kaihan Krippendorff Connect with MakingChips Website: www.MakingChips.com On Facebook On LinkedIn On Instagram On Twitter On YouTube

Date: June 21, 2026Title: The Riches of God's Grace and BlessingsSpeaker: Pete DeisonPCPC High SchoolAttributions:Used as part of an example, played a clip from the Documentary Short: Powers of Ten (1977) directed by Charles Eames and distributed by IBM

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