Podcasts about Colossus

Today, we are breaking down Applied Intuition. Our guests are co-founders Qasar Younis and Peter Ludwig, who started the company in 2017 with a mission to make a billion machines intelligent. The simplest way to understand Applied Intuition is that it builds the brains for machines, and the tools other companies use to build those brains. If a manufacturer wants its tractor, truck, or mining vehicle to drive itself, it can buy the intelligence from Applied Intuition or use its platform to develop its own. The analogy the founders use is Nvidia. Just as Nvidia sells chips into everyone else's machines, Applied Intuition sells intelligence into everyone else's machines, across automotive, defense, mining, agriculture, and robotics, without building any single machine itself. We discuss why the most important companies of the next 25 years will all be physical AI companies, Dana, their new agentic platform for developing and deploying these systems, and how the company raised a billion dollars without spending any of it. Please enjoy this Breakdown of Applied Intuition. For the full show notes, transcript, and links to the best content to learn more, check out the episode page⁠⁠⁠⁠⁠⁠⁠ here.⁠⁠⁠⁠⁠⁠⁠ ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- This episode is brought to you by⁠⁠⁠⁠ ⁠Portrait Analytics⁠⁠⁠⁠⁠ - your centralized resource for AI-powered idea generation, thesis monitoring, and personalized report building. Built by buy-side investors, for investment professionals. We work in the background, helping surface stock ideas and thesis signposts to help you monetize every insight. In short, we help you understand the story behind the stock chart, and get to "go, or no-go" 10x faster than before. Sign-up for a free trial today at ⁠⁠⁠⁠⁠portraitresearch.com⁠⁠⁠⁠⁠ ----- Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps (00:00:00) Welcome to Business Breakdowns (00:02:16) Intro: Applied Intuition (00:03:26) State of the Physical AI Market (00:07:19) Why Physical AI Will Be Bigger Than Digital AI (00:09:14) What Applied Intuition Builds & Sells (00:12:36) Staying Flexible Across Technologies & Verticals (00:13:16) Founding Story & Strategic Choices (00:17:10) Evolution of the Business: Tools → OS → Autonomy Stack (00:20:59) Introducing Dana: The Agentic Platform (00:23:40) Building Dana: Customer Demand vs. Vision (00:24:51) Why Applied Intuition Is Uniquely Positioned to Build Dana (00:28:29) The Cross-Vertical Data Flywheel (00:33:25) Rate Limiters to Physical AI Adoption (00:35:41) Business Model & Revenue (00:37:06) Customer Base & Global Reach (00:39:22) Competitive Landscape (00:44:21) Capital Allocation & Financial Strategy (00:47:15) The Future of Physical AI

My guest today is Matthew Smith. Matthew is the founder and CIO of Chronometer Partners, which invests in energy, industrials, materials, power and utilities, and related infrastructure. For the last 18 months he and his team have modeled nearly every natural gas well, pipeline, and processing asset in the United States. He's reached a conclusion most of the market doesn't share.  Starting in 2028, AI data centers and LNG exports will need more gas than the country can produce and deliver. By his math, the US could exhaust its working natural gas storage by 2030. In his words, the upside risk to prices becomes unbounded and convex. We talk about why this was set in motion long before AI arrived, why the US can't just turn off exports, who wins and loses among producers, nuclear, solar, and the hyperscalers, and what he sees as the only long-term solution. Please enjoy my conversation with Matthew Smith. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- In June, Matthew wrote a letter to a small group of confidants laying out the full case behind his natural gas forecast. He has allowed us to publish it. You can read the full letter here. ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like the Best (00:02:02) Episode Intro: Matt Smith (00:03:33) The Conclusion After 18 Months (00:04:56) The Die Was Cast Before AI (00:07:24) Sizing AI's Gas Demand (00:09:33) Why Not Just Stop Exporting? (00:11:38) Is the Gas Even There? (00:13:53) The Timing Problem, Not Supply (00:15:15) Flow Versus Stock (00:19:10) What Slows Gas to Market (00:22:21) If Nothing Changes by 2030 (00:26:11) Could Prices Hit Twenty Dollars? (00:27:00) Gas Producers Poised to Win (00:28:54) Utility-Scale Solar's Windfall (00:30:08) What About Nuclear? (00:32:40) SMRs (00:34:29) The US Consumer Pays (00:36:37) Turbine Makers Building Too Late (00:37:57) Are Hyperscalers Exposed Too? (00:44:25) Kickstarting the Nuclear Build (00:46:20) Put Solar on Every Roof (00:46:52) Implications for the World (00:49:26) No One's Securing Supply (00:52:57) The Challenge for Energy CEOs

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

En este especial de Rejugando nos metemos de lleno con una de esas obras que, para mí, están en la historia más grande del videojuego: Shadow of the Colossus. Y lo hacemos dentro de nuestro recorrido por la obra de Fumito Ueda, después de haber hablado de ICO, para analizar no solo el juego que todos conocemos, sino también todo lo que hubo detrás: su desarrollo, sus ideas descartadas, los colosos que nunca llegaron, la tecnología que llevó a PlayStation 2 al límite y las decisiones creativas que hicieron que este juego siga siendo tan especial tantos años después. Empezamos hablando de nuestras propias experiencias con el juego y de esa sensación tan difícil de explicar que produce la primera vez que llegas a las Tierras Prohibidas. No hay tutoriales interminables, no hay un mundo lleno de personajes diciéndote qué hacer y no hay una gran interfaz explicándote cada paso. Estás tú, Agro, una espada, un arco y un territorio gigantesco que parece esconder algo. Y entonces aparece el primer coloso. A partir de ahí hablamos de cómo Shadow of the Colossus consigue convertir cada enfrentamiento en algo completamente distinto. Cada coloso es prácticamente un nivel entero, un puzle y un jefe final al mismo tiempo. No se trata simplemente de golpear a un enemigo enorme, sino de observarlo, entender su comportamiento y descubrir cómo puedes subirte a él. La progresión jugable es una auténtica barbaridad. El juego no necesita explicarte constantemente las mecánicas porque cada coloso te enseña algo nuevo. Aprendes a agarrarte, a gestionar la resistencia, a utilizar el arco, a aprovechar el escenario y a interpretar el comportamiento de cada criatura. Y todo eso lo hace sin convertir a Wander en un superhéroe. De hecho, una de las cosas que más comentamos es precisamente la vulnerabilidad del protagonista. Wander es pequeño, torpe y parece completamente insignificante al lado de los colosos. Y eso es fundamental para que cada combate tenga esa sensación de escala y de aventura épica. Pero también para que empieces a hacerte preguntas. Porque a medida que avanzamos, cada vez resulta más difícil pensar que estamos haciendo lo correcto. Los colosos no parecen simples monstruos. Algunos son tranquilos, otros parecen incluso ignorarnos hasta que nosotros les atacamos. Y después de cada victoria no tenemos una fanfarria de triunfo, sino una música triste y melancólica. La propia evolución de Wander nos va dando pistas. Cada vez está más deteriorado, más oscuro, más alejado del personaje con el que empezamos. Y llega un momento en el que la pregunta es inevitable: ¿quién es realmente el malo de esta historia? También repasamos el desarrollo del juego y la enorme cantidad de ideas que se barajaron. Uno de los conceptos más sorprendentes fue la posibilidad de hacer un Shadow of the Colossus multijugador, con varios personajes colaborando para derrotar a las criaturas. Una idea que terminó descartándose, pero que demuestra que el proyecto fue cambiando muchísimo durante su desarrollo. Y después llegamos a uno de los temas más fascinantes: los colosos descartados. El juego llegó a plantearse originalmente con 48 colosos. Después fueron 36, luego 24 y finalmente se quedaron en los 16 que conocemos. Y por el camino quedaron diseños, ideas, escenarios y criaturas que durante años alimentaron la imaginación de los fans. Hablamos de algunos de ellos, de sus nombres internos, de sus posibles mecánicas y de cómo algunos llegaron a aparecer en imágenes, vídeos o materiales previos al lanzamiento. Hay diseños que son absolutamente increíbles y que hacen que te preguntes cómo habría sido el juego si se hubiera mantenido todo ese contenido. Aunque, siendo sinceros, con 48 colosos probablemente el juego habría sido una experiencia completamente distinta. Otro de los aspectos que más me gustan del desarrollo es la manera en la que trabajó el equipo. El diseño de los colosos no parece haber sido un proceso completamente jerarquizado y cerrado. Gente de escenarios, animación, combate y otras áreas aportaba ideas para las criaturas. Era casi un enorme brainstorming colectivo. Y creo que eso se nota muchísimo en el resultado final, porque cada coloso tiene una personalidad, un comportamiento y una forma de enfrentarse completamente diferente. También hablamos de la tecnología. Porque lo que hizo el Team ICO con PlayStation 2 es absolutamente demencial. Las criaturas, el tamaño de los escenarios, las animaciones, la física, el pelo, las colisiones y la forma en la que Wander se agarra a los cuerpos de los colosos hicieron que el hardware sufriera de lo lindo. El equipo utilizó técnicas muy avanzadas para conseguir que las criaturas parecieran realmente orgánicas y vivas. Y, sobre todo, para que no fueran simplemente enemigos gigantes, sino seres que parecieran formar parte de su propio ecosistema. También nos detenemos en el diseño de las Tierras Prohibidas, en la importancia del vacío, de la soledad y de los silencios. Porque en Shadow of the Colossus el silencio es casi tan importante como la música. Y hablando de música, la banda sonora de Kow Otani merece un capítulo propio. La mezcla de épica, orquesta, melancolía y silencio es absolutamente perfecta. Y comentamos también la historia de la música que suena después de derrotar a los colosos: una pieza que, según se cuenta, sorprendió inicialmente al propio equipo porque no era la típica fanfarria de victoria. Pero precisamente por eso funciona tan bien. Has ganado. Pero no tienes claro que debas estar celebrándolo. También repasamos el impacto comercial y crítico del juego. Después del éxito de crítica, pero las ventas más modestas de ICO, Shadow of the Colossus consiguió llegar a mucha más gente y vender aproximadamente 1,1 millones de copias, multiplicando enormemente el alcance de su predecesor. Y, curiosamente, también hizo que mucha gente descubriera posteriormente ICO. Hablamos de las versiones posteriores, los modos de dificultad, los desafíos, los objetos desbloqueables, los secretos, las teorías de los fans y los detalles que han hecho que la comunidad siga investigando el juego durante años. Incluso comentamos un final descartado en el que Wander y Mono podrían haber sobrevivido juntos en las Tierras Prohibidas. Y, por supuesto, terminamos reivindicando lo que para mí es una evidencia: Shadow of the Colossus es una de las grandes obras maestras de la historia del videojuego. Un juego que no necesita llenar cada rincón de contenido para hacerte sentir que estás explorando un mundo gigantesco. Un juego que convierte a sus jefes en niveles completos. Un juego que te hace sentir pequeño, vulnerable y, en ocasiones, directamente culpable. Y una obra que, tantos años después, sigue teniendo algo que muchos juegos modernos no consiguen: una identidad absolutamente propia. En este especial hablamos de Fumito Ueda, Team ICO, el desarrollo de Shadow of the Colossus, los 48 colosos originales, los diseños descartados, el multijugador cancelado, la tecnología de PS2, Wander, Mono, Agro, las Tierras Prohibidas, la banda sonora de Kow Otani, la evolución de la jugabilidad, los secretos, el impacto comercial y el legado de una de las mayores obras maestras del videojuego. Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Today my guest is John Kim. John is one of the world's top and most prolific fundraisers.  He was chief client officer at General Catalyst, where he helped raise many of the firm's flagship funds. He is now chairman and president of corporate development at Lila Sciences, a company building scientific superintelligence, where he has helped raise several hundred million dollars.  He is also the author of The Tao of Fundraising.  This conversation is really a guide on how to raise money from someone who has done it at the highest level. We talk about why persuasion equals desire minus fear, the difference between belief and trust, the laws of fundraising, and how to build the consensus that moves big pools of capital.  Please enjoy my conversation with John Kim. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:02) Introduction of John Kim (00:02:39) Money Moves at the Speed of Trust (00:05:06) How to Start a Fundraising Campaign (00:08:03) Persuasion Equals Desire Minus Fear (00:12:20) How to Raise a Few Billion Dollars (00:15:58) The Benchmark Story (00:18:36) The Law of Differentiation (00:24:13) Law of Tradeoffs and Law of Pipeline (00:27:52) The Karpman Drama Triangle (00:30:42) Oprah Winfrey (00:33:49) Most Common Fundraising Mistakes (00:38:35) Secretary of State (00:45:40) The Inner Game (00:47:38) The Kindest Thing

This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlstBritain's most capable coding model can't be exported, and that ban is the whole reason Cosine set out to build one from scratch. Alistair Pullen, CEO and co-founder of Cosine, sits down with Tim Scarfe to explain how a frontier system he calls Fable, locked behind US export controls, became the founding case for a UK sovereign model trained on the Isambard supercomputer in Bristol.The bet underneath it is economic. Pullen argues that an inference company, rather than a training-first lab, doesn't need billions to compete: millions, a national compute allocation, and a consortium feedback loop can be enough. From there it gets into the machinery, why open-weight models still trail the frontier on size, active parameters and data, the mixture-of-experts versus dense trade-off and why active params dominate how a model actually feels, and the edge that real coding trajectories confer.The back half is about making agents trustworthy. Pullen makes the case for beating "slop" by rewarding the process instead of the final answer, reframes code review as runtime proof (spin the bug up in a VM and force the agent to actually exploit it), and walks through Swarm, Cosine's system running hundreds of sub-agents in one shot. It ends on why memory is still an unsolved hack, how synthetic graders let you run RL on tasks with no built-in test, and why Pullen reads US export controls as an accidental gift, with a supply-chain sting in the tail.---TIMESTAMPS:00:00:00 The sovereign mandate and the Fable ban00:04:02 Millions vs billions: the inference-company model00:07:19 The consortium feedback loop00:07:40 Why open models lag the frontier00:14:59 MoE vs dense, and why active params matter00:16:29 Trajectories: the process-data advantage00:19:48 Beating slop: reward the process, not the answer00:26:06 Reusable abstractions and the epistemic wall00:29:56 Code review becomes runtime proof00:37:32 Do agentic harnesses still matter?00:40:35 Swarm: orchestrating hundreds of sub-agents00:45:14 Why memory is still unsolved00:48:25 Synthetic data and graders for RL00:53:09 The US export gift and supply-chain risk---REFERENCES:organization:[00:01:15] Cosinehttps://cosine.sh[00:04:14] Mistral AIhttps://mistral.ai[00:05:50] Anthropichttps://www.anthropic.com[00:07:42] Coherehttps://cohere.com[00:08:36] DeepSeekhttps://www.deepseek.comtool:[00:02:52] Isambard-AIhttps://isambard.ac.uk[00:05:56] Colossus (xAI)https://en.wikipedia.org/wiki/Colossus_(supercomputer)[00:07:52] GLM (Z.ai)https://z.ai[00:11:52] NVIDIA B300https://www.nvidia.com/en-us/data-center/dgx-b300/[00:15:37] gpt-oss-120bhttps://huggingface.co/openai/gpt-oss-120b[00:15:52] Devstral 2https://mistral.ai/news/devstral[00:16:01] Llama 70bhttps://www.llama.com[00:17:05] Claude Codehttps://www.anthropic.com/claude-code[00:26:23] ARC-AGI (Francois Chollet)https://arcprize.org[00:40:38] Swarm (Cosine)https://cosine.sh[00:40:50] OpenAI Codexhttps://github.com/openai/codex[00:41:16] Lumen Outpost (Cosine)https://cosine.sh[00:41:18] Kimi K2 (Moonshot)https://huggingface.co/moonshotai/Kimi-K2-Instruct[00:49:55] SWE-benchhttps://www.swebench.com[00:52:40] SystemVeriloghttps://en.wikipedia.org/wiki/SystemVerilogperson:[00:23:40] Andrej Karpathyhttps://karpathy.aipaper:[00:27:10] GRPO (DeepSeekMath)https://arxiv.org/abs/2402.03300[00:27:13] GSPOhttps://arxiv.org/abs/2507.18071Incompressible Knowledge Probes, Bojie Lihttps://arxiv.org/pdf/2604.24827Estimating the Size of Claude Opus 4.5/4.6https://unexcitedneurons.substack.com/p/estimating-the-size-of-claude-opus---ReScript:https://app.rescript.info/session/5852d2b884c4ce4b?share=10b9799160845bb11779f8ac6cd3124f

Kenny Hallaert has lived almost every version of a poker life.Player.Tournament director.Casino marketer.Festival builder.PokerStars live events advisor.WSOP November Niner.Before the $1.4M score…before the Main Event final table…before he was helping shape live poker festivals around the world…Kenny was an electrician in Belgium who found poker through an online banner in 2004.Then a soccer injury forced him indoors.So he played.A lot.Limit Hold'em. Full ring. Online tournaments. Freerolls. Tiny bankroll discipline. The whole old-school grind.But Kenny's story doesn't stop at becoming a player.He helped build live poker in Belgium.Worked around weird legal restrictions.Launched tournament festivals.Ran floors.Designed structures.Learned the game from both sides of the table.Then he went on one of the most ridiculous WSOP stretches ever:Deep in the first Colossus.Deep in the Main Event.Then the next year…November Nine.In this episode of The Table 1 Podcast, Kenny joins Art Parmann and Justin Young to talk about the full ride:♠️ Growing up in Belgium♥️ Finding online poker in 2004♦️ Going from electrician to casino marketer♣️ Building live poker festivals in Belgium♠️ Knocking out Daniel Negreanu at EPT Monaco♥️ His first WSOP trip♦️ The first Colossus final table♣️ The 2016 November Nine run♠️ Preparing with Fedor Holz's team♥️ Why tournament structures are broken♦️ PokerStars, AI, smart glasses, solvers, and the future of live pokerThis one is part poker origin story, part WSOP time capsule, and part masterclass on how live tournaments actually get built.

Uncanny X-Men #290 (1992)The immense drama that is the UNCANNY X-MEN continues as Storm takes her time contemplating Forge's marriage proposal, Iceman's date continues to go sideways and Colossus has a day on the town with his newly-resurfaced brother Mikhail.Highlights include:Storm and Bishop go full Natasha BedingfieldJean Grey literally throws in the towelKrang's bodyMystique gets crazy eyesArchangel just went out for croissantsForge needs to cool the heck outAlso, if you're in the Loveland, CO area this Saturday July 11th, come check out Jen & Shawn at the RetroMania event at the Ranch Events Complex. Your hosts will be slinging comic books, action figures and saying hello to y'all.Details can be found right here: https://www.heritageeventcompany.com/loveland-retromania-comiccon-2026.html*** PROPER COMIC BOOK DISCUSSION STARTS AT 00:15:51 ***Promo: BACK TO THE BINS (https://twotruefreaks.com/podcast/qt-series/back-to-the-bins/)Continue the conversation with Shawn (@AngryHeroShawn) and Jen (@JenStansfield) on Twitter / Instagram / Facebook / Threads / Bluesky or email the show at worstcollectionever@gmail.com Also, get hip to all of our episodes on YouTube in its own playlist! https://bit.ly/WorstCollectionEverYTDownload the podcast on Spotify, Apple Podcasts and wherever you get your favorite shows. Please rate, review, subscribe and tell a friend!

Team. Welcome. Back at it. Thanks for your support. Dale's playing Starfield. Chuck is back on that Criterion stuff.LinksCheck out or Ko-fi at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ko-fi.com/batandspider⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠DISCORD⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get your ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bat & Spider STICKERS here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Steve Barkett Rules t-shirts!!!⁠⁠⁠⁠⁠⁠Get a sweet Bat & Spider ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠t-shirt here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠! All sale proceeds go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Movement For Black Lives⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Technical Adviser: Slim of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠70mm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Theme song composed and performed by Tobey Forsman of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Whipsong Music⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Follow ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bat & Spider on Instagram ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Chuck⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Dale⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ on Letterboxd.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bat & Spider on Letterboxd⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bat & Spider Watchlist⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Send us an email: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠batandspiderpod@gmail.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Leave us a voice message: (315) 544-0966Artwork by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Charles Forsman⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠batandspider.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bat & Spider is a ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TAPEDECK⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ podcast, along with our friends at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠70mm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Letterboxd Show⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Escape Hatch⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Will Run For...⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twin Vipers⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Movie Mixtape⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Yeti is Still Broken⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Austin Danger Pod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Lost Light⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. ★ Support this podcast ★

¡Apoya Reconectados, decide y participa en todos los sorteos! ✅ Patreon: https://www.patreon.com/reconectados Ya estamos llegando al final de la temporada, pero incluso al final tenemos una batería de temas y de juegos que casi son más dignas del mes de octubre que de junio. Empezamos hablando del reinicio de Xbox, de lo que Asha Sharma ha dicho que va a pasar con ciertos estudios muy importantes de la compañía. Menos mal que tenemos videojuegos para hablar, como es el caso del esperado Assassin's Creed Black Flag Resynced, que trae nostalgia y novedades muy interesantes y a partes iguales. Además, hemos podido jugar ya durante 4 horas a The Blood of Dawnwalker para poder dejaros los dientes casi tan largos como los de su protagonista. Y eso sin olvidarnos de Rythm Paradise Groove, del que podríamos decir que es el juego del momento en Nintendo Switch 2 y un nuevo referente en accesibilidad. Además, analizamos el mejorado y muy ampliado Grandblue Fantasy Relink: Endless Ragnarok, las batallas extremas en el universo Sword Art Online de Echoes of Aincrad, o la estupenda versión de Digimon Story: Time Stranger que ha llegado a Switch 2. Cerramos el programa con el re-análisis de Shadow of the Colossus, un videojuego que rompe con la concepción más tradicional del medio tratando de provocar emociones que pocas veces sentimos en otras propuestas y nos siguen haciendo vibrar. ¡Nos vemos la semana que viene en el último programa de la novena temporada! Time stamps: (00:00:00) - Introducción y Xbox deshaciéndose de estudios y miles de empleados (00:13:51) - Assassin's Creed Black Flag Resynced, análisis (00:42:14) - The Blood of Dawnwalker, impresiones (01:06:08) - Rythm Paradise Groove, análisis (01:15:05) - Grandblue Fantasy Relink: Endless Ragnarok, análisis (01:20:20) - Echoes of Aincrad, análisis (01:28:33) - Digimon Story: Time Stranger en Switch 2 (01:33:15) - Re-análisis de Shadow of the Colossus (02:07:52) - Despedida y hacia el último episodio de la temporada ¡Apoya Reconectados, decide y participa en todos los sorteos! ✅ Patreon: https://www.patreon.com/reconectados ️ ¡Sigue nuestro canal de Twitch! ️ ✅ Suscríbete a Twitch: https://www.twitch.tv/reconectados ¡Únete a nuestro grupo de Telegram de ofertas! ✅ Canal de ofertas: https://t.me/ofertasvideojuegosreco ️ ¡Escucha Reconectados cada semana: Jueves 07:00am! ️ Ivoox: https://www.ivoox.com/podcast-reconectados-videojuegos_sq_f1467878_1.html Spotify: https://open.spotify.com/show/0TzgUfUZppavUlKeRreIXL Apple: https://podcasts.apple.com/es/podcast/reconectados-videojuegos/id1304330116 ¡Síguenos en redes sociales! X-Twitter: @ReconectadosPod Jabote: @Jabote22 Manu: @ManuGmn Paula: @paulacroft02 Borja: @borjaruete TikTok: https://www.tiktok.com/@reconectadospod Facebook: https://www.facebook.com/ReconectadosPodcast/ Instagram: https://www.instagram.com/reconectadospod/

Welcome to Dev Game Club, where this week we continue our series on Psychonauts. We dive into (ha) the lungfish levels and the Milkman Conspiracy and talk about the tension between systems and individually scripted experiences, before turning to user questions. Dev Game Club looks at classic video games and plays through them over several episodes, providing commentary. Sections played: To Milkman Issues covered: a special announcement, turkeys everywhere, long digression into weird Spelunky, setting up the Lungfish, hunting down all the collectibles, an underwater bubble and moving around, consistency in levels, coming in with different player options, involving the empathy of the player, the limits of other genres in engaging empathy mechanically, the difficulty of reading the environment, the unfortunate PS4 port from the PS2, avoiding oysters, a direct lineage of creativity, becoming Godzilla, reuse of abilities when you return, every level being a genre, leading the way creatively, parallel to indie film, re-entering the levels and the tongue-in-cheek, going every direction and the costs, relearning rules each level, the cost of testing boundaries due to other mechanics, jumping between gravity spaces, describing the Milkman level, conspiracy mad-libs, being in an environment where you will fail again and again, memorable levels here and elsewhere, zingers of stingers, lines in movies vs lines in games, reinforcing them, a typical day in the life for a game designer, iterating to solve problems, getting people on board and carrying vision, people who show what's going on with the project, building consensus.  Games, people, and influences mentioned or discussed: Spelunky, Andy Nealen, mysterydip, Beyond Good & Evil, Crash Bandicoot, King's Quest, Costume Quest, Headlander, Keeper, Stacking, Trenched/Iron Brigade, Tim Schafer, Daron Stinnett, LucasArts, Community, Velvet Underground, Four Weddings and a Funeral, Psychodyssey, Nintendo, Majora's Mask, Microsoft, Shadow of the Colossus, Ico, Outer Wilds, Portal, Duke Nuke'em, John Carpenter, Them, Roddy Rowdy Piper, Sasha/scarytiger, Jonno, Starfighter, Kirk Hamilton, Aaron Evers, Mark Garcia.  Next time: Finish the game? Twitch: timlongojr and twinsunscorp YouTube Discord DevGameClub@gmail.com 

My guest today is Jeremy Giffon.  Jeremy has been on the show before as one of our most popular guests, and this conversation is every bit as enjoyable as the first. Over the last 18 months, Jeremy has had hundreds of conversations with founders and with the capital behind their companies. I don't know many investors with such a high rep count in the most interesting corners of private markets, so I asked him what he has learned. We talk about what those lessons mean for founders and investors, why everyone has become subservient to the poster class, the hidden intellectual history behind Silicon Valley and much more. Please enjoy my conversation with my friend, Jeremy Giffon. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:02) Jeremy Giffon (00:02:34) Lessons from 18 Months of Founder Conversations (00:07:01) The Billion-Dollar PDF (00:08:13) The Unifeed & Rise of the Timeline (00:17:02) Power Law & Breakout Content (00:18:48) AI Algorithms Driving Content (00:20:38) Timeline-Native White House (00:21:09) Traits of Great Posters (00:25:27) Peak Guy & the Billionaire Priest Class (00:32:13) Billionaires Now Defer to Posters (00:34:52) Freedom vs. Relevance (00:38:53) AI & White-Collar Job Displacement (00:40:53) Stewarding Your Gifts as Moral Duty (00:43:18) Next Wave of Finance: Equity-First Firms (00:53:26) East Coast vs. West Coast Finance (00:55:34) Beating the Market Is Not That Hard (01:00:40) SPV Feudalism & Allocation (01:02:10) Egregious SPV Fee Structures (01:04:50) Simplicity vs. Complexity in Investing (01:07:15) Hiring: Attracting Differentiated Talent (01:11:00) Silicon Valley's Hidden Intellectual Traditions

This edition of Foreshadowing Tech considers how so many AI speculations from Colossus: The Forbin Project have come true in both surprising and subtle ways. Regular panelists Chuck Joiner, Marty Jencius, Jeff Gamet, are joined by guest AI enthusiast Jill McKinley to look at how the film predicted things like machine autonomy, surveillance, privacy and self-improving systems. Wrapped in a thriller that is as much political as it is tech, there are questions about freedom and security that we have yet to address today. The panel mixes film trivia, tech analysis, and unsettling parallels to today's AI debates in a thoughtful discussion.  Show Notes: Chapters: 00:00 Introduction to Colossus: The Forbin Project and the Foreshadowing Tech premise 01:17 First impressions, panel introductions, and why the film still matters 03:55 Plot overview: Colossus, Guardian, and the loss of control 05:07 The original novels, sequels, and abandoned remake plans 07:59 Cast trivia, Eric Braeden, Susan Clark, and familiar TV faces 10:26 The film's technology, monitors, hardware, and production design 13:02 How 1970s computer imagery created mystery and menace 16:40 The real-world Colossus name and connections to codebreaking history 20:13 Forbin, ego, AI creators, and modern tech-bro parallels 24:50 Human arrogance, political power, and underestimating machines 26:53 Colossus and Guardian develop their own language 28:40 Predictive AI, medical promise, and solving problems beyond human speed 30:10 Self-improving systems and comparisons to current AI development 32:04 Would Colossus have taken control without Guardian? 35:40 Nuclear threats, punishment, and ruthless machine logic 39:28 Was Colossus protecting humanity or threatening it? 45:01 Public reactions, acceptance, fear, and the Colossus T-shirt moment 47:54 Surveillance, cameras, microphones, and today's self-built monitoring state 50:36 The human millennium: peace, control, and the illusion of freedom 54:36 AI, military efficiency, Flock cameras, and real-world surveillance debates 59:53 Creativity, totalitarianism, and stories of humans versus machines 1:00:54 Links to Dr. Strangelove, Fail Safe, 2001, WarGames, and Harlan Ellison 1:03:39 Forbin as a resistance figure and the “Wolverines” comparison 1:04:46 Who was most naive: the government, Forbin, Colossus, or the public? 1:07:13 Technology, unintended consequences, and AI that can advance itself 1:09:14 Guest wrap-up and where to find Jill, Jeff, and Marty 1:12:19 Closing credits and support information Links: Colossus: The Forbin Project  (Wikipedia entry): https://en.wikipedia.org/wiki/Colossus:_The_Forbin_Project Colossus: The Forbin Project [Blu-ray] https://amzn.to/4vKqGYk Colossus: The Forbin Project [Prime Video] https://amzn.to/3SD9LZ3 Guests: Jeff Gamet is a technology blogger, podcaster, author, and public speaker. Previously, he was The Mac Observer's Managing Editor, and the TextExpander Evangelist for Smile. He has presented at Macworld Expo, RSA Conference, several WordCamp events, along with many other conferences. You can find him on several podcasts such as The Mac Show, The Big Show, MacVoices, Mac OS Ken, This Week in iOS, and more. Jeff is easy to find on social media as @jgamet on X and Instagram, jeffgamet on LinkedIn., @jgamet@mastodon.social on Mastodon, and on his YouTube Channel at YouTube.com/jgamet. Marty Jencius, Ph.D.,is a counselor educator and technology pioneer who has spent 30 years bringing emerging tech into his field — from founding one of the first professional listservs (CESNET-L) to podcasting, virtual reality, and now AI and AR. He is the founder of ThePodTalk.net, where he produces Vision ProFiles, The Old Mac Gang, A.I. Productivity Workflow, The Tech Savvy Professor, 15 Minute Bytes, The Neo Notebook, and Fade to Chat: Golden Age Cinema. He is also a regular panelist on MacVoices Live!, In Touch with iOS, and The Mac Show. Find him on Bluesky and Mastodon. Jill McKinley is a Health IT professional, lifelong learner, and Northwoods dweller who believes wisdom hides in plain sight — in Scripture, in nature, in the habits we build and the questions we dare to ask. She publishes a variety of podcasts on those topics as well as productivity, AI, and tech at JillFromTheNorthWoods.com.   Support: Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect: Web:      http://macvoices.com Twitter: http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices Mastodon:      https://mastodon.cloud/@chuckjoiner Facebook:      http://www.facebook.com/chuck.joiner MacVoices Page on Facebook:      http://www.facebook.com/macvoices/ MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice LinkedIn:      https://www.linkedin.com/in/chuckjoiner/ Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher: Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

This edition of Foreshadowing Tech considers how so many AI speculations from Colossus: The Forbin Project have come true in both surprising and subtle ways. Regular panelists Chuck Joiner, Marty Jencius, Jeff Gamet, are joined by guest AI enthusiast Jill McKinley to look at how the film predicted things like machine autonomy, surveillance, privacy and self-improving systems. Wrapped in a thriller that is as much political as it is tech, there are questions about freedom and security that we have yet to address today. The panel mixes film trivia, tech analysis, and unsettling parallels to today's AI debates in a thoughtful discussion.  Show Notes: Chapters: 00:00 Introduction to Colossus: The Forbin Project and the Foreshadowing Tech premise 01:17 First impressions, panel introductions, and why the film still matters 03:55 Plot overview: Colossus, Guardian, and the loss of control 05:07 The original novels, sequels, and abandoned remake plans 07:59 Cast trivia, Eric Braeden, Susan Clark, and familiar TV faces 10:26 The film's technology, monitors, hardware, and production design 13:02 How 1970s computer imagery created mystery and menace 16:40 The real-world Colossus name and connections to codebreaking history 20:13 Forbin, ego, AI creators, and modern tech-bro parallels 24:50 Human arrogance, political power, and underestimating machines 26:53 Colossus and Guardian develop their own language 28:40 Predictive AI, medical promise, and solving problems beyond human speed 30:10 Self-improving systems and comparisons to current AI development 32:04 Would Colossus have taken control without Guardian? 35:40 Nuclear threats, punishment, and ruthless machine logic 39:28 Was Colossus protecting humanity or threatening it? 45:01 Public reactions, acceptance, fear, and the Colossus T-shirt moment 47:54 Surveillance, cameras, microphones, and today's self-built monitoring state 50:36 The human millennium: peace, control, and the illusion of freedom 54:36 AI, military efficiency, Flock cameras, and real-world surveillance debates 59:53 Creativity, totalitarianism, and stories of humans versus machines 1:00:54 Links to Dr. Strangelove, Fail Safe, 2001, WarGames, and Harlan Ellison 1:03:39 Forbin as a resistance figure and the "Wolverines" comparison 1:04:46 Who was most naive: the government, Forbin, Colossus, or the public? 1:07:13 Technology, unintended consequences, and AI that can advance itself 1:09:14 Guest wrap-up and where to find Jill, Jeff, and Marty 1:12:19 Closing credits and support information Links: Colossus: The Forbin Project  (Wikipedia entry): https://en.wikipedia.org/wiki/Colossus:_The_Forbin_Project Colossus: The Forbin Project [Blu-ray] https://amzn.to/4vKqGYk Colossus: The Forbin Project [Prime Video] https://amzn.to/3SD9LZ3 Guests: Jeff Gamet is a technology blogger, podcaster, author, and public speaker. Previously, he was The Mac Observer's Managing Editor, and the TextExpander Evangelist for Smile. He has presented at Macworld Expo, RSA Conference, several WordCamp events, along with many other conferences. You can find him on several podcasts such as The Mac Show, The Big Show, MacVoices, Mac OS Ken, This Week in iOS, and more. Jeff is easy to find on social media as @jgamet on X and Instagram, jeffgamet on LinkedIn., @jgamet@mastodon.social on Mastodon, and on his YouTube Channel at YouTube.com/jgamet. Marty Jencius, Ph.D.,is a counselor educator and technology pioneer who has spent 30 years bringing emerging tech into his field — from founding one of the first professional listservs (CESNET-L) to podcasting, virtual reality, and now AI and AR. He is the founder of ThePodTalk.net, where he produces Vision ProFiles, The Old Mac Gang, A.I. Productivity Workflow, The Tech Savvy Professor, 15 Minute Bytes, The Neo Notebook, and Fade to Chat: Golden Age Cinema. He is also a regular panelist on MacVoices Live!, In Touch with iOS, and The Mac Show. Find him on Bluesky and Mastodon. Jill McKinley is a Health IT professional, lifelong learner, and Northwoods dweller who believes wisdom hides in plain sight — in Scripture, in nature, in the habits we build and the questions we dare to ask. She publishes a variety of podcasts on those topics as well as productivity, AI, and tech at JillFromTheNorthWoods.com.   Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

Welcome to Wyllin's Gulch!Join us as we check out Daggerheart and give the Colossus of the Drylands campaign frame a go.Lore Master: IzziPlayers: Cuba as Blue Belly Bill, Adam as Ash Reddick, Amanda as Kitswizzle Wingdings, and D as HelveticaContent Warnings: Mention of suicideJoin our Patreon to get fun perks and early access to the podcast/VODs: ⁠⁠⁠⁠⁠https://www.patreon.com/dicedragonsguild⁠⁠⁠⁠⁠We've got MERCH: ⁠⁠⁠⁠⁠⁠https://tinyurl.com/ddgmerch⁠⁠⁠⁠⁠--MUSIC & SFX--"Combative Strings" and "Battlefield Gulch II", as well as additional music & SFX from Monument Studios via Fantasy+ ⁠⁠⁠⁠⁠(⁠⁠⁠⁠⁠⁠⁠⁠https://www.fantasy-plus.com/)⁠⁠⁠⁠⁠ Royalty-Free License. Music by Alexandre Miller - The Boy King of Idaho (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://open.spotify.com/artist/0WvWTz5TPYOuoZ77e2iIX8?si=bhT8sX2gS_e8huPQnWd81Q⁠⁠⁠⁠⁠) Licensed under the Creative Commons 3.0: By Attribution license.Music & Ambient sounds by Michael Ghelfi. Please support him at his Patreon (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/MichaelGhelfi⁠⁠⁠⁠⁠) and like and subscribe to his YouTube channel (⁠⁠⁠⁠⁠   / @michaelghelfistudios  ⁠⁠⁠⁠⁠)"Smoking Gun", "Cowboy Sting", "Pennsylvania Rose" by Kevin MacLeod (⁠⁠⁠⁠⁠incompetech.com⁠⁠⁠⁠⁠) Licensed under Creative Commons: By Attribution 4.0 License ⁠⁠⁠⁠http://creativecommons.org/licenses/by/4.0/⁠⁠⁠⁠ "Two Guns, One Destiny" by Shane Ivers (⁠⁠⁠⁠silvermansound.com⁠⁠⁠⁠), licensed under CC BY 4.0"Banjos, Unite!" by Alexander Nakarada is under a Creative Commons BY-SA 3.0 license (⁠⁠⁠⁠https://www.creatorchords.com⁠⁠⁠⁠)Select sound effects from ⁠⁠⁠⁠ZapSplat.com⁠⁠⁠⁠ (⁠⁠⁠⁠⁠https://www.zapsplat.com⁠⁠⁠⁠⁠)

Fredrik och Poki ger i vanliga fall ton kring forna års bästa spel - denna gång är det ett helt decennium som hamnar under luppen. Forna RETRO GOTY-topp 3:or förs in i vad som är den ultimata fajten! Vilka spel är 90-talets bästa - och vilket är 00-talets absolut bästa spel!Dags för Game of the Decade 00-talet!Upplägget är lite annorlunda; nedan hittar ni alla spel som är med i striden, vi ska först föra ner dessa till en topp 20, därefter topp 10 och slutligen kora vilka spel som hamnar var i topp 10-listan. En på pappret enkel uppgift, som verkligen fick oss att gnissla våra stackars tänder under tiden vi spelade in!Exempel på spel som tas upp:Baldur's Gate II: Shadows of Amn,Batman: Arkham Asylum,BioShock,Civilization IV,Deus Ex,Diablo II,Dragon Age: Origins,Fallout 3,Fire Emblem: Path of Radiance,Half-Life 2,Lost Odyssey,Mass Effect,Max Payne,Neverwinter Nights,Portal,Prince of Persia: The Sands of Time,Shadow of the Colossus,Silent Hill 2,Star Wars: Knights of the Old Republic,Star Wars: Knights of the Old Republic II: The Sith Lords,Super Mario Galaxy,Tales of Symphonia,Tales of Vesperia,The Elder Scrolls III: Morrowind,The Elder Scrolls IV: Oblivion,The Legend of Zelda: Oracle of Seasons and Oracle of Ages,The Legend of Zelda: The Minish Cap,The Legend of Zelda: The Wind Waker,The Lord of the Rings: The Battle for Middle-earth II,Warcraft III: Reign of ChaosHäng med i snacket på Discord!Kom med i vår Discord här! - Nördliv på iTunes – Nördliv på Spotify

Anthropic said Commerce lifted export controls on Fable 5 and Mythos 5, restoring access Wednesday, and launched Sonnet 5. Sony is ending PlayStation game discs in 2028, a 140-company group unveiled Open USD, and Meta's building a cloud business. Anthropic says the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5 and that it will begin restoring access Wednesday (X) Anthropic says the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5 and that it will begin restoring access Wednesday (BleepingComputer) Anthropic launches Claude Sonnet 5, saying it nears Opus 4.8 performance at lower prices and is substantially better than Sonnet 4.6 for agentic work (Anthropic) Anthropic launches Claude Sonnet 5, saying it nears Opus 4.8 performance at lower prices and is substantially better than Sonnet 4.6 for agentic work (The New Stack) Sony says all new PlayStation games from both first- and third-party developers will be sold in digital formats from January 2028, ending physical game discs (Game File) Visa, Mastercard, Stripe, BlackRock, Coinbase, and 140+ companies join Open Standard to launch Open USD, a stablecoin that shares earnings from its reserves (The Block) Sources: Meta is developing plans for a cloud infrastructure business that will sell access to AI computing power and models, to compete with AWS and Azure (Bloomberg) SpaceX cuts monthly Starlink prices in half in the Memphis area, as it endures blowback and legal challenges from opponents of its Colossus data centers (Bloomberg) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

It's a black and white throwback side story. Except it's the most important episode and the key to this entire season I guess. Also it's a Wizard of Oz pastiche so Ashley is Mad. We talk about: A Serious-ish Man, Iron Nest, Molly Listens To Adventure Time Nonsense, LOTR, Shadow of the Colossus, Work, Planet Coaster, FF7 Remake, Black and White, Can't Map It, Please No Trans People, Class Consciousness, Dog In The Cabinet, The Future Is Now, It's My Birthday, Fucked Up Public Domain, Prizes, Wizard of AU,

My guests today are Gavin Uberti and Rob Wachen, the founders of Etched.  A few years ago, when they set out to build a better AI chip than the largest companies in the world, almost everyone I called told me it could not be done. They have since done it, taping out a working chip on their first attempt and becoming the first hardware company founded after ChatGPT to do so. They already have more than a billion dollars of customer demand for their first product, and have raised eight hundred million dollars to build it.  Etched builds chips and systems designed to run AI models faster and at lower cost. They started the company in 2023, and that product is a complete rack for inference, the chip along with the boards, the power delivery, the interconnects, and the manufacturing to produce it all. We talk about the technical bets behind their architecture, how they hired industry legends and paired them with elite 22 year-olds, and why they believe inference will become one of the largest markets in the world. I think you will find the story of what they have built hard to forget. Please enjoy my conversation with Gavin and Rob. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:07) Gavin Uberti and Rob Wachen (00:03:54) Two 21-Year-Olds Taking on NVIDIA (00:07:52) The Two Technical Bets Behind Their Architecture (00:14:15) Why Inference Becomes the Biggest Market (00:20:23) Rob and Gavin's Origins Stories (00:28:38) How They Recruit Industry Legends (00:36:30) Moving a Dozen Engineers to Bangalore for Six Months (00:38:01) Speed Wins (00:43:58) Getting More Concurrency Out of Every Megawatt (00:52:44) Vertical Integration (00:57:43) Hardest Obstacles to Overcome (01:01:09) Raising The Largest AI Chip Series A Ever (01:06:29) TSMC (01:13:20) Designing Gen 2 for Gigawatt-Scale Production (01:16:42) Why Machines Don't Think Like People (01:20:03) A Year of Compute Compressed Into a Month (01:23:44) The Trillion-Dollar Data Center (01:26:19) The Kindest Thing

Well, the madcap adventures of Team Fireball were delightful while they lasted. But there's a new sheriff in town, and their name is the First Order.The Star Wars: Resistance episodes of the week—"The High Tower" and "Children of Tehar” (Season 1, Episodes 5–6)—introduce Kylo Ren and Captain Doza, and give Captain Phasma and Major Vonreg time in the spotlight. And they pull no punches…especially when we discover The First Order is doing genocide purely to hide the bodies. Even the delightful sequences of Kaz running like a noodle with a torso don't really bring light to these dark times.In our recap, we talk about classism on the Colossus, what about the First Order, and how the relationships of the characters might blossom. Big heart eyes for the Chelidae, btw.Join us next week for Episodes 6–7, "Signal from Sector Six" and "Synara's Score.”Want more Growing Up Skywalker? This is a great time to sign up for our Patreon for bonus audio content!Timestamps:00:00:00 Who Are We?00:02:21 Plot Summary00:13:59 Classism and Economics on the Colossus00:26:00 Kylo Ren and Tehar00:28:25 The Colors of The First Order00:43:29 Bae Watch00:49:42 Closing Thoughts

Gerard and Laurent welcome Michel Boutouil, co-founder and CEO of Polarise, a leading European AI infrastructure provider and NVIDIA Cloud Partner based in Berlin. After discussing about what happens outside of a datacenter, it is time to dive inside one.  Polarise is one of the few genuinely European NeoCloud companies — essentially a European counterpart to CoreWeave — specializing in GPU infrastructure for AI inference. Through its partnership with NVIDIA, Polarise designs its datacenters around the GPU rack itself, using liquid cooling from the outset rather than starting with a traditional real estate-first approach. The company has already developed AI factories in Germany, Norway and the UK.  In the conversation, we explore the growing commoditization of large language models and why the real long-term value may lie in AI factories — facilities that are fundamentally different from conventional datacenters. Given Europe's notoriously long grid-connection timelines, Polarise focuses on refurbishing brownfield sites with under 50MW of grid access instead of pursuing massive gigawatt-scale campuses. It's a pragmatic “pod” strategy: adapt to the grid's constraints rather than try to reshape the entire energy system.  We also tackle the thorny issue of digital sovereignty. With the U.S. CLOUD Act allowing U.S. authorities access to data managed by American tech companies, it is fair to ask what hyperscalers are doing with European data — and whether Europe needs its own sovereign AI infrastructure. Polarise has secured €1 billion in backing from Swiss investor SWI Stoneweg Icona, but even that is modest compared with the hyperscalers' spending power. For comparison, SpaceX has reportedly invested around $40 billion in Colossus 1 and 2 alone.  So, what does the future of the European AI ecosystem look like? Michel's answer is clear: Europe should not try to outspend China or the United States head-on. Instead, it should play to its strengths — smart execution, agility, flexibility, and the ability to learn quickly from the mistakes being made elsewhere.    “Today's show is supported by the BMW Foundation Herbert Quandt. The BMW Foundation unites leaders across sectors to develop solutions that foster an innovative economy and a future-proof society. A key focus is "Energy Transition & Climate Change," where the Foundation drives "International collaboration to accelerate the energy transition." With rising energy demands from AI and data centres, new partnerships, effective collaboration, and the exchange of science-based solutions and strategies are essential.”  

On Episode 310 of The Six Five Pod, Patrick Moorhead and Daniel Newman unpack the biggest stories from the week, including insights from Qualcomm Investor Day 2026, OpenAI and Broadcom's Jalapeño AI chip, Anthropic's Micron partnership, SpaceX's massive Reflection AI compute deal, Sakana AI's new Fugu orchestrator, and why memory is emerging as a critical layer of AI infrastructure. Plus, Bulls & Bears covers NVIDIA's $25B bond offering, Apple's MacBook price increases, Micron's record quarter, and Cerebras' first earnings as a public company. The handpicked topics for this week are: Qualcomm Investor Day 2026 — The Data Center Debut: Pat and Dan break down Qualcomm's push into the data center after the company took the stage with Microsoft's Satya Nadella and Meta's Mark Zuckerberg as named customers. They unpack the new Dragonfly platform, including the C1000 250-core data center CPU with PCIe Gen 7 and CXL, the AI200 and AI250 inference accelerators, and a novel High Bandwidth Compute (HBC) architecture that stacks compute under LPDDR memory at dramatically lower cost than HBM. They highlight Qualcomm's ambitious growth targets: $15B data center revenue target for FY 2029, an increased total non-handset revenue goal from $22B to  $40B, and a shortened timeline for automotive revenue by two years. They also debate the identity of Qualcomm's unnamed hyperscaler customer and why its robotics opportunity may be flying under the radar. (The Decode) OpenAI and Broadcom Unveil Jalapeño, OpenAI's First Custom Chip: A photo of Sam Altman and Hock Tan holding a wafer and packaged die kicked off OpenAI's reveal of Jalapeño, a custom inference chip built with Broadcom and slated for late-2026 deployment. The chip reached tape-out in roughly nine months, which is an aggressive cycle for an ASIC of this size, and uses HBM3E memory. Pat takes a victory lap on his long-standing heterogeneous compute thesis: every hyperscaler and now every model lab is building accelerators, and the XPU efficiency argument has played out as predicted. Dan frames OpenAI's broader move as existential: they cannot serve frontier models at premium margins if compute remains constrained. He flags that OpenAI is trying to do everything from chips and fabs to social networks and browsers, and that its IPO is now delayed. (The Decode)   Anthropic and Micron Sign a Strategic Multi-Year Memory Agreement: Anthropic and Micron announced a multi-year supply agreement for HBM, DRAM, and SSDs, including co-designed next-generation memory for AI workloads, along with a strategic investment by Anthropic in Micron. The pattern mirrors Samsung and SK Hynix's pre-funding Anthropic in May, and follows OpenAI's Jalapeño as another frontier lab moving to lock in supply chain control. Dan frames it as the same circular financing playbook NVIDIA ran two to three years ago, but with the ball now in the memory triopoly's court. Pricing-floor agreements with no ceilings, customized rather than commoditized memory architecture, and demand running well past the previously assumed 2027-2028 horizon. Pat notes that the rumored 14% free cash flow margin at Anthropic makes the strategic investment math work cleanly for both sides. (The Decode)   SpaceX Signs $6.3B Compute Deal with Reflection AI: SpaceX inked a $6.3B compute lease with open-source AI lab Reflection AI, at $150M per month from July 2026 through 2029, giving Reflection access to NVIDIA GB300 chips inside the Colossus infrastructure. Combined with the $920M-per-month Google compute contract and existing xAI commitments, SpaceX now has a contracted backlog larger than most public AI startups' entire revenue base, with some calling it the largest commercial AI infrastructure provider at $80B in contracted revenue. Pat reads it as XAI failing to land with developers, consumers, or enterprises, leaving SpaceX with a pot of gold worth far more as wholesale capacity than as XAI's own training compute. Dan flags that Google owning 7% of SpaceX ahead of an IPO is not accidental, and the open question is whether this becomes a Nebius-style infrastructure trade or a full-stack Google-equivalent platform. (The Decode)   Japan's Agentic Orchestrator Sakana AI Ships Fugu Plus and Fugu Ultra: Japan's Sakana AI released Fugu Plus and Fugu Ultra, an agentic orchestrator built on a multi-agent MOE approach that routes workloads across multiple underlying models rather than training a new frontier base model. Sakana claims agentic capabilities on par with or better than top frontier models at significantly lower input/output token costs, similar to the DeepSeek and GLM cost-undercut narrative. Pat compares the architecture to OpenRouter and notes the developer-facing parallel to Perplexity Computer's model-routing approach. Both agree that models themselves are no longer moats, and suggests the real moat is the harness, tooling, connectivity, looping, agentic stack, and total compute availability. Expect more sovereign agentic plays from Japan, the Middle East, and elsewhere on the same template. (The Decode)   The Flip — Is the Era of Memory as a Commodity Over? Daniel takes the FOR side: memory has moved from commodity to strategic AI infrastructure, citing 16 multi-year agreements covering $22B in committed volume booked through 2027, 84.9% gross margins higher than NVIDIA's, the technology barriers of HBM yield/stacking/packaging that only three companies can clear, and demand drivers tied to HBM as the binding constraint on every AI accelerator rather than to elastic consumer cycles. Patrick takes the AGAINST side: long-term agreements and SCAs signal a commodity in a strong cycle, not a structural rerating; nearly every relevant memory standard — DDR5, MRDIMM, HBM3/3E/4, LPDDR5X/6, GDDR6/7, LPCAM2 — is JEDEC-standard and therefore commodity at the pin; and CXMT's China DDR5 production ramps in 2H 2026 with Lenovo already shipping and HP and Dell qualifying. Custom HBM4 and Qualcomm-style HBC are where strategic memory genuinely lives. (The Flip)   NVIDIA's $25B Investment-Grade Bond Offering: NVIDIA priced a $25B multi-tranche bond offering on June 15, its first investment-grade debt sale since 2021, with seven tranches maturing between 2028 and 2056 and $85B in orders against an initial $20B target. Dan reads it as raising when capital is cheap, and oversubscription is real. NVIDIA doesn't need the money, it has a gold balance sheet, and is establishing a credit benchmark rather than funding CapEx. Pat agrees the optics are clean, but flags the irony of NVIDIA, with negative debt, borrowing while the stock trades like dead money at a sub-20x forward P/E. Both note that NVIDIA's underperformance reflects the market's skepticism on memory-as-strategic and on NVIDIA's own capex pace relative to the buildout opportunity ahead. (Bulls & Bears)   Tim Cook Calls Apple's Memory Crunch Price Raises on MacBook and iPad "Unsustainable": Apple announced MacBook and iPad price increases of up to $300, with Tim Cook telling the WSJ the memory cost environment is unsustainable. AAPL fell ~5% on the news, the broader rally was momentarily wiped out before Micron held the gains by close. Dan frames it as a moment when the market saw who is going to pay for the AI buildout: the consumer. He notes Apple's pricing power and inelasticity test is now live. Pat traces the backstory to Apple's negative-margin pricing pressure on Micron during the 2022-2023 memory downturn. The question is whether consumer-price blowback will eventually flow back to the memory vendors. (Bulls & Bears)   Micron Blows the Doors Off Fiscal Q3 — $41.46B Revenue, 84.9% Gross Margin: The memory story continues as Micron reported its largest beat in company history with fiscal Q3 revenue of $41.46B versus a $35.69B consensus, EPS of $25.11, year-over-year growth of more than 340%, and a record 84.9% gross margin that is roughly 10 points above NVIDIA's. Q4 guidance came in at a $50B midpoint against a $43B consensus. The 16 multi-year strategic customer agreements add up to $22B in committed volume, with most contracts containing pricing floors but no ceilings on most of the volume — a structurally asymmetric setup. Pat notes 95% of the beat came from price, not units, which reinforces his commodity argument; Dan flips it as the early innings of an NVIDIA-style run that puts Micron's 2027 profit on par with Google. (Bulls & Bears)   Cerebras' First Earnings Report Since IPO — Revenue Doubles, Margins Compress: Cerebras (CBRS) reported its first earnings as a public company, doubling year-over-year revenue and beating the top line while missing EPS, but the stock sold off hard amid gross margin deterioration. Core revenue came in at $191M, up 12% sequentially, with a $194M Q2 guide that is essentially flat, core gross margins at 47% guiding to 36-38% and 38-41% for the year, and operating margins flipping from positive 2% to a guided -30% to -32%. Customer concentration is shifting from Core42 and G42 (86% of FY25 revenue) to OpenAI, which loaned Cerebras $1B and gets paid quarterly in warrants. Pat flags that Cerebras' uncontested speed claim is no longer uncontested with Groq, TPU v8i, and Tenstorrent putting up real numbers. Cathie Wood is down 52% on her position. (Bulls & Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. The Decode Qualcomm Investor Day Lands the Data Center Pivot — Microsoft Deploying Qualcomm HBC XPUs in Azure (Per Satya Nadella) + Meta MOU on Three New Qualcomm Datacenter CPUs (Per Zuckerberg); $3.9B Modular Acquisition; Dragonfly Brand + AI200/AI250 Roadmap; HUMAIN 200MW Ramp; Qualcomm to Become Largest Automotive Silicon Company; Targets $3B Datacenter Revenue FY27, $35B by FY31 https://finance.yahoo.com/markets/stocks/articles/qualcomm-investor-day-detail-data-163247063.html  OpenAI Begins Vertical Integration — First Custom Inference Chip "Jalapeño" Unveiled With Broadcom June 24 (Hock Tan: As Good as Blackwell + TPU; ~50% Cost Savings; Late-2026 Microsoft Deployment, 10GW Multi-Gen Roadmap); Daybreak Cyber Stack (June 22) Confirms the Platform Shift https://x.com/OpenAI/status/2069770172802773292  Frontier AI Labs Are Now Financing Their Own Supply Chains — Anthropic Locks In Multi-Year Micron HBM/DRAM/SSD Supply + Micron Becomes Series H Investor; Same Pattern as Samsung + SK hynix Pre-Funded Anthropic in May; $965B Post-Money, $47B Revenue Run-Rate, October IPO Target https://investors.micron.com/news-releases/news-release-details/micron-and-anthropic-announce-strategic-agreement-scale-next  SpaceX Signs $6.3B Compute Deal With Reflection AI — $150M/Month July 2026 → End of 2029; NVIDIA GB300 + Colossus 2 Capacity; SpaceX Now Largest Commercial AI Infrastructure Provider With $80B+ Committed Compute Revenue Through 2029 https://finance.yahoo.com/technology/ai/articles/spacex-reportedly-grant-reflection-ai-162749237.html  The Sovereign AI Stack Lands — Japan's Sakana Ships Fugu + Fugu Ultra Multi-Agent System (June 22) That Beats Opus 4.8, GPT-5.5, and Gemini 3.1 Pro on 10 of 11 Benchmarks; Designed Around US Export-Control Risk; Completes the Three-Bloc Sovereign-AI Map With Mistral Compute (Europe) + DeepSeek $7.4B (China) https://www.datacamp.com/blog/sakana-fugu  The Flip Is the Era of Memory as a Commodity Over? FOR: Memory is now strategic AI infrastructure with multi-year supply lock-ins. The cycle dynamics that defined the last 30 years no longer apply. https://www.benzinga.com/markets/tech/26/06/60062500/micron-earnings-could-echo-nvidias-2023-moment-says-futurum-ceo  AGAINST: Memory is cyclical and priced for perfection. This print is either step change or top of the cycle, and the second one is more likely. https://www.cnbc.com/2026/06/25/apple-macbook-ipad-price-hike-memory.html Bulls & Bears NVIDIA (NVDA) $25B Bond Sale Anchors the AI Debt-Finance Boom — First Bond Offering Since 2021; Joins Alphabet $80B, Amazon $27.5B, Meta $30B, Oracle Stack; Dan: "Locking In Cheap Capital While It Can" https://finance.yahoo.com/technology/ai/articles/nvidia-record-us-25-billion-131039687.html  Apple (AAPL) Falls −5%+ Thursday June 25 on Confirmed MacBook + iPad Price Hikes — Tim Cook RAM "Unsustainable" Comment Lands as Real Price Action; Apple Hikes Erase Micron-Driven Tech Rally Mid-Session; Memory Beneficiaries (SanDisk, Micron) Surge; Analysts "Mostly Nonplussed" https://tickerspark.ai/market/apple-inc-aapl-drops-5-3-as-price-hikes-spook-investors-1782399950638  Micron (MU) Q3 FY26 ACTUALS — Largest Beat in Company History; Revenue $41.46B (+346% YoY) Crushes $35.69B Consensus; Non-GAAP EPS $25.11 (+1,215% YoY) Beats $20.49; Record 84.9% Gross Margin (Higher Than NVIDIA); Q4 Guide $50B Midpoint vs $43B Consensus; Stock +18-19% Overnight to $1,242 https://www.nasdaq.com/articles/nvda-who-micron-blows-doors-q3-earnings-revs  Cerebras Systems (CBRS) Q1 ACTUALS — First Earnings Post-IPO; Revenue $193.4M Nearly Doubled YoY; 2026 Guide $855-$865M Beats $824M; BUT Gross Margins Forecast 38-41% (Down From 45% Q1, Half of NVIDIA + Micron); Stock −20% AH on Margin Compression; Sets Up Inference-Tier Margin Debate https://investors.cerebras.ai/news-releases/news-release-details/cerebras-systems-announces-strong-first-quarter-2026-results  

In this second of three episodes on xAI's data center buildout in Memphis, Tennessee and Southaven, Mississippi, Justin Hendrix speaks with Amanda Garcia, senior attorney and data center project leader at the Southern Environmental Law Center (SELC), about the fight over Colossus and Colossus 2 and what it means for disputes over the AI infrastructure boom across the country.

AI-driven automation of cognitive labour is not merely another technological transition but a structural discontinuity that will end, sooner or later, the central role of wages in how society operates. This discontinuity can be called “The End of Postwar Capitalism”. That's the conclusion of a tightly argued set of essays, “The Discontinuity Thesis”, written by our guest in this episode, Ben Luong.The essays look at a range of arguments that all try to make the case that wages paid for cognitive labour will remain significant for the majority of people, so that capitalism can continue in place, even with AI having greatly expanded capabilities. According to Ben, each of these arguments fail. These back-and-forth debates are what we explore in this episode.Selected follow-ups:"The Discontinuity Thesis: A Sequence of Seven Essays on Why Postwar Capitalism Ends" by Ben Luong"Mark Zuckerberg just declared war on the entire advertising industry" - The Verge"P versus NP problem" - Wikipedia"KPMG Pulls AI Report After Hallucinated Claims About Major Organisations" - AI Insider "GDPval-AA v2 Leaderboard" - Artificial Analysis"OSWorld: 369 real computer tasks across Windows, macOS, and Ubuntu requiring GUI interaction... Much harder than web-only benchmarks""Sorites paradox" - Wikipedia"Young people not in education, employment or training (NEET), UK: May 2026" - Office of National Statistics"Five Years" - Song by David Bowie"How ZEISS and ASML Enable the Modern Chip Industry" - Rob Hoeijmakers"Mistral AI's $830 Million Debt Financing: Inside the European Bet on 13,800 Nvidia GPUs and a Paris AI Data Center" - Marcus Chen"Colossus (data center)" - Wikipedia"Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence" - Stanford Digital Economy Lab"Appendix I: What Would Refute the Thesis" - by Ben Luong"The Cope Index: Tracking who's coping hardest about the end of work" - by Ben Luong"The AI Alignment Problem" - from "The Singularity Principles"Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain DeclarationC-Suite PerspectivesElevate how you lead with insight from today's most influential executives.Listen on: Apple Podcasts Spotify

Across the country, developers are racing to build huge new buildings to house computers to fuel the AI boom, creating an explosive demand for new energy. While some hyperscalers seek renewable energy, others are turning to fossil fuels. But concerns around high electric bills, air and noise pollution and water depletion have generated widespread community pushback against these giant facilities, and it seems opposing data centers is a bipartisan issue. Many cities and states are working to rapidly update zoning and other local regulations to respond to the dual pressures of developer interest and constituent backlash. Since data center development isn't slowing down, what policies or creative strategies can lessen the impacts for local communities and ratepayers? Guests:  KeShaun Pearson, Executive Director, Memphis Community Against Pollution  Rebecca Egan McCarthy, Freelance Journalist Jason Plautz, Reporter, E&E News and Politico Astrid Atkinson, CEO, Camus Highlights: 00:00 Introduction 3:15 KeShaun Pearson on updates to the Colossus data center pollution 6:18 KeShaun Pearson on state regulators allowing an expansion of gas turbines  8:08 KeShaun Pearson on the effect of the pollution on the community 16:24 KeShaun Pearson on what he hopes the lawsuits can achieve  19:38 Rebecca Egan McCarthy on Archbald and data center development  22:26 Rebecca Egan McCarthy on who has the power to regulate data center projects 28:16 Rebecca Egan McCarthy on data center development outside of Archbald 30:21 Jason Plautz on changing attitudes toward data centers 34:32 Jason Plautz on where there is meaningful regulation happening 39:27 Jason Plautz on state level regulatory changes  41:26 Jason Plautz on the pace of data center development 44:45 Astrid Atkinson on the effects of data center energy load on the grid 46:19 Astrid Atkinson on what flexibility means in the energy world 50:39 Astrid Atkinson on hyperscalers paying for their energy 55:22 Astrid Atkinson on how some policy changes can help communities  For show notes and related links, visit ⁠our episode page⁠ at climateone.org --- Join Climate One for an induction cooking demonstration night on July 21, at 6 p.m. at the Commonwealth Club in San Francisco. Come enjoy delicious food and wine, and learn about why cooking with magnets beats cooking with gas. Tickets available at ⁠climateone.org/events⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Across the country, developers are racing to build huge new buildings to house computers to fuel the AI boom, creating an explosive demand for new energy. While some hyperscalers seek renewable energy, others are turning to fossil fuels. But concerns around high electric bills, air and noise pollution and water depletion have generated widespread community pushback against these giant facilities, and it seems opposing data centers is a bipartisan issue. Many cities and states are working to rapidly update zoning and other local regulations to respond to the dual pressures of developer interest and constituent backlash. Since data center development isn't slowing down, what policies or creative strategies can lessen the impacts for local communities and ratepayers? Guests:  KeShaun Pearson, Executive Director, Memphis Community Against Pollution  Rebecca Egan McCarthy, Freelance Journalist Jason Plautz, Reporter, E&E News and Politico Astrid Atkinson, CEO, Camus Highlights: 00:00 Introduction 3:15 KeShaun Pearson on updates to the Colossus data center pollution 6:18 KeShaun Pearson on state regulators allowing an expansion of gas turbines  8:08 KeShaun Pearson on the effect of the pollution on the community 16:24 KeShaun Pearson on what he hopes the lawsuits can achieve  19:38 Rebecca Egan McCarthy on Archbald and data center development  22:26 Rebecca Egan McCarthy on who has the power to regulate data center projects 28:16 Rebecca Egan McCarthy on data center development outside of Archbald 30:21 Jason Plautz on changing attitudes toward data centers 34:32 Jason Plautz on where there is meaningful regulation happening 39:27 Jason Plautz on state level regulatory changes  41:26 Jason Plautz on the pace of data center development 44:45 Astrid Atkinson on the effects of data center energy load on the grid 46:19 Astrid Atkinson on what flexibility means in the energy world 50:39 Astrid Atkinson on hyperscalers paying for their energy 55:22 Astrid Atkinson on how some policy changes can help communities  For show notes and related links, visit our episode page at climateone.org --- Join Climate One for an induction cooking demonstration night on July 21, at 6 p.m. at the Commonwealth Club in San Francisco. Come enjoy delicious food and wine, and learn about why cooking with magnets beats cooking with gas. Tickets available at climateone.org/events Learn more about your ad choices. Visit megaphone.fm/adchoices

Announcing the CTP for SpaceX. MahJong Craze gone wild. Goodbye to Alan Greenspan – The Maestro. Have you seen RAM prices? PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? PayPal.Donation.Button({ env:'production', hosted_button_id:'JJJHP2GDEJC7J', image: { src:'https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif', alt:'Donate with PayPal button', title:'PayPal - The safer, easier way to pay online!', } }).render('#donate-button'); Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter Warm-Up - Announcing the CTP for SpaceX - MahJong Craze - Goodbye to Alan Greenspan - The Maestro - Have you seen RAM prices? Markets - Economic Collapse Imminent? - Breathe is narrowing again - chips chips chips are the only play - Spacex coming back down to earth? What is that sucking sound? -- Markets getting weird..... 3% down for NASDAQ 100 today - 8% for SMH and 14% for Memory ETF - Just announced - Alphabet (Google) will replace Verizon in DJIA DEDICATION: Alan Greenspan - Died Monday at age 100 Google Enters DJIA - High priced shares - Moves tech to 22% of DJIA from 17% or so - very meaningful move - Every $1 move for Google = $7 move on DJIA - Tech:  S&P 500 (~30%+), Nasdaq (~50%+) Computer Pricing - What as $2,000 a year ago for a nice desktop is not like $4,000 - Dell not holding pricing quotes - and even if they do, back ordered so prices could go up after order - Will IPOs put more money in the pocket of tech companies to buy gear at any price? Endless - SpaceX recently finalized two massive, multibillion-dollar artificial intelligence contracts: a $6.3 billion computing power agreement with Reflection AI and a $60 billion acquisition of the AI coding startup Cursor. - AI Compute Deal with Reflection AI - - - - The Terms: Reflection AI agreed to pay SpaceXAI $150 million per month from July 2026 through the end of 2029. - - -- - - The Infrastructure: The startup will tap into hardware and GB300 chips housed at SpaceX's Colossus 2 data center in Memphis, Tennessee. More SpaceX - SpaceX shares were as high as $220 post IPO. - Sharea ahve been down over the past 3 days. - Most that got in POST IPO probably bought in at about $162-$165 - Newsline: SpaceX shares slipped for a third straight day, shedding hundreds of billions of dollars in market value, after the company said it is selling investment-grade bonds for the first time. - The stock fell 16% Monday to close at $154.60, the lowest level since the company's first day of trading, pushing its three-day loss to 23% and erasing over $600 billion in value over that period. - SpaceX is seeking to raise at least $20 billion from the first bond offering to fund its artificial-intelligence ambitions. Missed Opportunity - Short the Mattress companies he said...... ----- Got squeezed out....Never to return Swing and a Miss Maybe Because this can happen... - Shares of Getty Images Holdings Inc. soared as much as 145% on Monday after it announced a licensing deal with OpenAI. - Getty said that images from its library will appear in the search and discovery features of ChatGPT, marking a key reversal for the firm. - The partnership with OpenAI could improve “licensing optics” and shift the narrative on the stock, according to analyst Mark Zgutowicz. - Getty shares were up 118% to $1.32 as of 12:44 p.m. in New York, putting them on track for the best session since July 2022. The stock had fallen about 55% this year to close at 61 cents on Thursday before the Juneteenth holiday weekend began. KOREA - SK Hynix - New #1 in South Korea: SK Hynix surpassed Samsung Electronics on Monday to become the country's most valuable listed company. - Remarkable turnaround: A striking reversal for a chipmaker that nearly collapsed under heavy debt roughly two decades ago. (CYCLES) - AI memory leader: Now the dominant supplier of high-bandwidth memory (HBM) chips powering AI systems. - Marquee customers: Key buyers include Nvidia (NVDA) and Alphabet's Google (GOOGL). - Massive 2026 rally: Shares are up more than 340% year-to-date, fueled by the global AI boom. - Market cap milestone: Valuation now exceeds both Samsung and Micron (MU). Markets Get Chopped - Questions being asked about if AI spend boom producing fast enough return - Back to earth on valuation scare - (all of a sudden?) - KOSPI down 11% - Chips getting hit - 12% for Memory ETF - MU down 9%, Intel 4%, ASML 7% RAM Prices... - Looking at some additional RAM today for some office computers .... --- ARE THEY KIDDING? RAM Prices Imminent Collapse???? - President Donald Trump said the prospect of global economic collapse was a big reason he signed an interim peace deal with Iran. - According to sources, the deal reopened the Strait of Hormuz and set in motion waivers for sanctions on Iran's oil sales to the international market, with the effect being an immediate drop in oil prices and a rise in US stocks. - The agreement has been seen as skewed in Iran's favor, giving the country broad gains before the next round of talks, and has prompted pushback and anger from Republican lawmakers. - MOU signed lat Wednesday - also now more waivers of sanctions on sale of Iranian oil - 60 day reprieve. China - Weak economic conditions - H Shares about to enter bear market - Hong Kong - Close to a technical bear market, dragged down by weak domestic consumption, a struggling property sector, and an exodus of funds fleeing "old tech" for AI plays elsewhere in Asia. - A-shares are listed in mainland China (Shanghai/Shenzhen) and primarily target domestic investors. H-shares are listed in Hong Kong and are freely available to international investors More China - Retail sales declined for the first time since December 2022, dropping 0.6% from a year earlier. - China's urban fixed-asset investment contracted 4.1% as of end-May, dragged by real estate and manufacturing. - Manufacturing fixed-asset investment contracted for the first time since December 2020. - Industrial output was the lone bright spot, rebounding from April's near three-year low. - The national unemployment rate fell to 5.1% in May, compared with 5.2% in April. Marrrr Jonggg - Mahjong can be highly addictive due to its rewarding blend of strategy, luck, and social interaction. The rapid tile-drawing, need for pattern recognition, and "just one more round" mentality trigger dopamine releases. If compulsive play disrupts your finances or daily life, it can become a behavioral addiction requiring intervention. - Tactile and Auditory Appeal: Many users on community forums like Reddit agree that the physical weight, texture, and distinct clinking sound of shuffling tiles provide soothing, sensory satisfaction. - There has been a 70% surge in mahjong content on TikTok in the past year - Yelp recently named the Chinese tile game a top trend of 2026, noting that searches for mahjong clubs surged 4,467% year over year for the period from September 2024 to August 2025 and that searches for mahjong lessons rose 819%. Alphabet - WHAT>????*&*^ - Alphabet shares slid 7%, on track for the search giant's worst day in a year. - Alphabet's Google has seen consecutive high-profile researchers leave in the last several days. - The company also has exposure to the market's concerns around commoditized AI and ballooning capital expenditures. - The share slide also came on the heels of a Sunday Wall Street Journal interview with Microsoft CEO Satya Nadella, who called for less dependence on “AI Giants” and said the AI market was commoditized. Back to Oracle - Oracle reduced workforce by 21,000 employees over past twelve months. - Cuts broader than previously disclosed, driven by artificial intelligence adoption. - Global headcount fell from 162,000 to 141,000 full-time employees year-over-year. - Workforce reductions generated $1.8 billion in restructuring costs, company reported. - Company warned AI deployment may continue resulting in workforce reductions. NVDA - Underperforming - Nvidia shares slipping recently despite remaining up about 12% in 2026. - Stock down roughly 3% past month, underperforming semiconductor peers. - SMH ETF surged 84% year-to-date, gaining 15% last month. - Traders predict Nvidia chip pricing power is beginning to decline. - Wall Street focus shifting toward memory and infrastructure AI buildout. - Micron and Sandisk shares jumped nearly 60% over past month. Gloom and Doom - JCD sent interesting take from Chris Bloomstran - Traditionally asset light companies with all sorts of revenue, high margins now.... ---- Converting into asset heavy with no real understanding of what the profitability or even revue will be in the future ----- Here are the highlights of his commentary we can explre: ------------AI buildout shifting markets from asset-light toward capital-intensive infrastructure cycle - Hyperscaler capex surge reflects move into heavy, long-duration asset base - Massive capital requirements challenge economics versus prior asset-light models - Depreciation burden rising sharply as infrastructure scales across AI ecosystem - Returns depend on utilization of expensive, long-lived physical compute assets - Asset-heavy cycles historically lead to overbuild, weak returns, eventual consolidation - Infrastructure spending absorbing nearly all operating cash flow for hyperscalers - Off-balance-sheet financing masking true scale of capital intensity shift - AI economics hinge more on physical capacity than software-driven scalability - Echoes of past asset-heavy booms with eventual oversupply and value destruction Amazon Day - Today - June 26th - US consumers will spend $26.3 billion online at Amazon and other retailers during the four-day sale, up 9% from last year's event in July, according to Adobe Inc. - About 201 million Amazon shoppers in the US were Prime subscribers as of March, up about 3% from a year earlier - Amazon will capture about 60% of all US online spending during Prime Day, its highest market share since 2019, according to estimates from EMarketer Inc. Chevron and Microsoft - Chevron Corp signed 20-year deal with Microsoft for data center power. - Agreement supplies natural-gas fired generation for massive West Texas facility. - Project Kilby expected online 2028, ramping to 2.67 gigawatts. - Full output enough to power more than 530,000 Texas homes. - Chevron partnering Engine No. 1, final investment decision planned later. - Deal follows prior reports of exclusive long-term power negotiations. More Oil News - Drill baby Drill - Interior Department cutting federal drilling bonds by 95% to spur exploration. - Required bond drops from $500,000 to $25,000 for leases. - Bonds ensure cleanup costs don't fall on taxpayers if wells abandoned. - Policy change aims to encourage more oil and gas development. - Proposal subject to 60-day public comment after Federal Register publication. FedEx Earnings - FedEx posted strong fiscal fourth-quarter earnings on Tuesday in the company's last quarter that included the freight business before its spin off. - FedEx Freight spun off into a separate publicly traded company on June 1. - The company said it saw a 3% year-over-year increase in domestic volume. - Stock down 6% A/H   Love the Show? Then how about a Donation? PayPal.Donation.Button({ env:'production', hosted_button_id:'JJJHP2GDEJC7J', image: { src:'https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif', alt:'Donate with PayPal button', title:'PayPal - The safer, easier way to pay online!', } }).render('#donate-button'); ANNOUNCING the THE CLOSEST TO THE PIN for SpaceX (SPCX) Winners will be getting great stuff like the new "OFFICIAL" DHUnplugged Shirt!     FED AND CRYPTO LIMERICKS   See this week's stock picks HERE Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter

My guest today is Vlad Barbalat, the Chief Investment Officer of Liberty Mutual Investments, the $120 billion investment platform that sits within one of the largest insurance companies in the world.  Vlad grew up in Soviet Moldova, came to America in 1990, and built a career that eventually led him to one of the most distinctive capital allocator seats anywhere in finance.  Today we talk about how the mutual insurance structure creates a unique investment platform, what Liberty looks for in a new deal or partner, and what it means to build a career and a life in a country that gave you opportunities you never would have had anywhere else.  Please enjoy my conversation with Vlad Barbalat. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Timestamps: (00:00:00) Welcome to Invest Like The Best (00:00:53) Vlad Barbalat (00:01:28) The Most Interesting Seat in the Market (00:05:53) Breaking Down the $120B (00:10:41) How the Portfolio Is Constructed (00:11:00) The House View (00:13:49) What Liberty Looks for in a GP (00:16:32) Why Not Just Buy Bonds (00:18:30) Benefits of the Mutual Structure (00:23:40) The Luxury of the American Citizen Through Immigrant Eyes (00:30:26) How Immigration Shaped His Worldview (00:32:45) Direct Deals vs. GP Allocations (00:35:23) Branded Capital (00:39:07) Geopolitics & Investing (00:43:48) AI's Impact on Investing (00:46:22) The Valuation Debate (00:50:47) Public vs. Private Markets (00:53:53) Lessons from Goldman (00:54:41) Why Excellence Matters (00:57:30) Managing Permanent Capital (01:03:54) The Kindest Thing 

This week’s Pirate Street Journal episode covered three topics that, on the surface, seem unrelated: the SpaceX IPO and its acquisition of AI coding startup Cursor, the rise of plug-in solar panels for everyday consumers, and KFC’s ambitious brand overhaul. But at the end, each story carries a deeper lesson about how categories are born, how they grow, and what separates winners from everyone else. The Pirate Street Journal is a business show with a simple but provocative premise: the Wall Street Journal does not know how business really works. Not because its journalists are incompetent, but because mainstream business media obsesses over companies, products, and technologies while almost completely ignoring market categories. Hosted by Christopher Lochhead alongside Eddie and Bri, the show takes three major business stories each week and examines them through the category design lens. The result is a sharper, more useful read on what is actually happening in the economy and why it matters. You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go.   SpaceX Did Not Just Buy a Startup, It Bought a Category SpaceX went public last Friday, and by Tuesday it had become one of the five most valuable companies in America, surpassing Amazon with a market cap of roughly $2.5 trillion. Days later, SpaceX agreed to acquire Cursor, an AI coding startup founded by four MIT students in 2022, for $60 billion in stock. Cursor had been valued at around $29 billion just months earlier, so SpaceX effectively paid double almost overnight. Most coverage focused on the eye-popping price tag and the fact that Cursor has roughly 20 employees. But Christopher argues that framing misses the point entirely. SpaceX did not make a consolidation play, where a company in a mature market acquires a competitor to cut costs and grab market share. This was an acceleration play. What SpaceX purchased was the category king position in a brand new and rapidly growing software category: AI tools for building software with AI. Cursor’s founder called it a new type of software, and he meant it. SpaceX, which already owns the bottom of the AI infrastructure stack through its Colossus supercomputer and orbital data center ambitions, just bought its way into the top of that stack through applications.   Plug-In Solar Is Not a Green Hobby, It Is a New Category Forming in Real Time Over a million households in Germany have installed plug-in solar panels that hang from a balcony and connect directly to a wall outlet in under an hour. Each unit is capped at around 800 watts and costs roughly $500. In states like California and Hawaii, where electricity runs 30 to 40 cents per kilowatt-hour, the panels pay for themselves in three years or less. Nine US states have already legalized the technology, with more than 20 others working on similar legislation. Eddie points out that traditional rooftop solar remained a luxury product because of permitting costs and installation complexity. Stripping those barriers away creates a fundamentally different category: distributed, consumer-owned power sold at Costco prices. The real power here is the network effect. One household with solar panels feeding back into the grid is a novelty. One million households doing it is a functioning power plant. Ten million changes the entire economics of the American grid, reduces peak demand costs, and buys the country time while large-scale nuclear and orbital solar infrastructure are developed. As Christopher notes, when a category is designed to produce radical abundance and includes a network effect, the compounding impact becomes truly transformational.   KFC Is Trying a New Look, But the Real Problem Is the Category Model Underneath KFC operates more than 3,600 locations in the United States, which is actually more than Chick-fil-A. And yet Chick-fil-A generates roughly $7.5 million per store each year while KFC pulls in under $2 million, despite being closed every Sunday. KFC’s response is a sweeping rebrand: new sauces, a boba and shakes drink line, immersive restaurant screens, a new logo, and a redesigned loyalty program. Eddie explains that the three things that actually drive success in quick service restaurants are beverages, speed of service, and the drive-through. Some of KFC’s moves make sense on the beverage side, since margins on drinks are far higher than on food. But expanding the menu risks slowing down service, which undermines the entire premise of the category. The deeper issue is structural. KFC is owned by Yum Brands, which for years co-located KFC with Taco Bell, confusing both the consumer and the category. Chick-fil-A, by contrast, is private, has an extraordinarily selective operator model, and charges just $10,000 for a franchise because it is looking for missionaries rather than mercenaries. That ownership clarity and cultural alignment is what produces four times the revenue per store, and no amount of boba or new signage is likely to close that gap without addressing what is happening underneath the brand. To hear more from The Pirate Street Journal, download and listen to this episode. You can also read more Pirate Street Journal entries in the Category Pirates newsletter.   We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, X (formerly Twitter), LinkedIn, and subscribe on Apple Podcast / Spotify!

The madcap adventures of the Team Fireball continue! In the third and fourth episodes of Star Wars: Resistance (The Triple Dark + Fuel for the Fire), Kaz is struggling with double duty and managing to fail forward—and sometimes backwards—on everything. In this week's episode, we talk about how fitting in is hard for Kaz, but being a good person seems to come easy. We speculate on Yeager's backstory as a Rebel hero, threats to the Colossus, and how the First Order is maneuvering to establish a foothold in the galaxy.Join us next week for episodes 4–5, "The High Tower" and "Children of Tehar.”Timestamps:00:00:00 Who Are We?00:01:57 Plot Summary00:10:29 Threats to the Colossus 00:17:03 Kaz's Struggle00:30:11 Yeager, Former Rebel Hero00:37:14 The Colossus00:51:10 Bae Watch00:59:14 Closing ThoughtsWant more Growing Up Skywalker? This is a great time to sign up for our Patreon for bonus audio content!

It's Brittney bitch. And we are back.Best BooksAbsolute Green Arrow #2Superman Unlimited #14Book BlurbsX-Men United #4, New Titans #36, X-Men #31, Avengers Armageddon #1, D'orc #5, Absolute Batman #21, Magik and Colossus #5, Uncanny X-Men #30, The Trillion Dollar Kid #1, Hornsby and Halo #0Honorable Mentions Graphic NovelsSupergirl: Woman of TomorrowAll-Star SupermanB- SegmentNintendo Direct News! Elden Ring? Legion of Superheros! What is the problem with Marvel Comics?Uncle's One More ThingAnti-One More Thing (One Less Thing?)- Maternal Instincts on NetflixSleep Token - Even in Arcadia

2:07 News16:28 Masters of the Universe spoilers16:50 End of MotU spoilers29:00 Comic reviews30:24 New Titans #3634:42 What If Thor #137:57 Imperial Guardians #439:35 Magik & Colossus #540:54 Nightwing #13942:45 GI Joe #2346:39 End of Life #547:34 Deadly Hands of K'un-lun #550:02 AtLA Kyoshi Warriors #250:10 Mumm-Ra Ever Living #350:37 Sonic the Hedgehog #8750:59 Odin #251:35 Deathstroke #458:08 Absolute Green Arrow #21:00:00 What we're excited for1:01:15 Fantastic Four

SpaceX shares saw a third straight day of losses on Monday, tumbling 16% and erasing $400 billion in market value — the second most in a single day for any company, per the Financial Times. Also on Monday, SpaceX signed a deal worth up to $6.3 billion to provide computing power to AI startup Reflection AI. Under the agreement, Reflection will pay $150 million a month from July through 2029 for access to hardware at SpaceX's Colossus 2 data center.

While Anthropic and the U.S. Government continued to try and make amends, there was another seismic shift quietly taking place: open source surged. Between Microsoft reportedly testing Open Source models for Copilot and the powerful new GLM-5.2, there was a clear trend this week in AI world. Missed it all? Don't worry, we'll catch you up so you can make the informed decisions for your company. Anthropic Continues Fable Fight, Microsoft Goes Open Source, Midjourney's Big Pivot and More AI News That Matters -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Fable 5 and Mythos 5 Export BanTrump Labels Anthropic a National Security ThreatMicrosoft Copilot CoWork Open Source Model SwitchMicrosoft Considers DeepSeek-V4 for AI Cost ReductionChinese GLM 5-2 Sets Open Source BenchmarkGLM 5-2 Challenges Proprietary AI ModelsMidJourney Hardware Pivot: AI Medical Imaging ScannerCursor Building 1.5T Parameter Model, GitHub CompetitorAI CEO Summit: G7 Pushes US-Led AI CoalitionOpenAI Prepares GPT-5.6 ReleaseAnthropic, OpenAI, Google Face Geopolitical AI ScrutinyAdvancements in Token Efficiency and Cost ControlTimestamps:00:00 Trump's comments on Anthropic06:17 Microsoft exploring lower-cost AI models09:07 Microsoft exploring DeepSeek amid tensions13:45 AI model performance and efficiency trends15:59 AI leaders meet at G7 Summit21:22 Midjourney unveils first hardware product23:26 MidJourney's innovative spa technology28:50 Discussing Cursor's evolution and impact32:24 Talking about AI use cases33:27 Rumors and upcoming AI model releases37:20 OpenAI's major new hiresKeywords: Anthropic, Fable Five, Mythos Five, export controls, national security threat, Dario Amodei, Amazon, supply chain risk, Defense Production Act, Copilot CoWork, Microsoft, usage based pricing, open source AI, DeepSeek V4, Chinese AI model, token costs, Azure, agentic AI, enterprise AI billing, data security, compliance filters, GLM 5-2, Zhipu AI, 753 billion parameter model, MIT open source license, long context window, autonomous coding, Hugging Face, benchmark performance, text only model, multimodal capabilities, token efficiency, AI spend, G7 summit, AI governance, AI coalition, AI standards, cybersecurity risks, bioterrorism, chip trade, Sam Altman, OpenAI, Claude Opus 4.8, Gemini 3.5 Pro, MidJourney, medical imaging, MidJourney scanner, full body ultrasound, Butterfly Network, MRI alternative, spa launch, SpaceX, Cursor, 1.5 trillion parameter model, code hosting, GitHub competitor, code generation, AI super apps, Colossus compute, technical prompts, context window expansion, GPT 5.6, Claude Conway agent, Grok Imagine, Firefly AI, code artifacts, Google Ad Manager AI, Open Knowledge Format, Noam Shazeer, Dean Ball, Andrej Karpathy.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

Welcome to Wyllin's Gulch!Join us as we check out Daggerheart and give the Colossus of the Drylands campaign frame a go.Lore Master: IzziPlayers: Cuba as Blue Belly Bill, Adam as Ash Reddick, Amanda as Kitswizzle Wingdings, and D as HelveticaContent Warnings: Mention of suicideJoin our Patreon to get fun perks and early access to the podcast/VODs: ⁠⁠⁠⁠https://www.patreon.com/dicedragonsguild⁠⁠⁠⁠We've got MERCH: ⁠⁠⁠⁠⁠https://tinyurl.com/ddgmerch⁠⁠⁠⁠--MUSIC & SFX--"Combative Strings" and "Battlefield Gulch II", as well as additional music & SFX from Monument Studios via Fantasy+ ⁠⁠⁠⁠(⁠⁠⁠⁠⁠⁠https://www.fantasy-plus.com/)⁠⁠⁠⁠ Royalty-Free License. Music by Alexandre Miller - The Boy King of Idaho (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://open.spotify.com/artist/0WvWTz5TPYOuoZ77e2iIX8?si=bhT8sX2gS_e8huPQnWd81Q⁠⁠⁠⁠) Licensed under the Creative Commons 3.0: By Attribution license.Music & Ambient sounds by Michael Ghelfi. Please support him at his Patreon (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/MichaelGhelfi⁠⁠⁠⁠) and like and subscribe to his YouTube channel (⁠⁠⁠⁠   / @michaelghelfistudios  ⁠⁠⁠⁠)"Smoking Gun", "Cowboy Sting", "Pennsylvania Rose" by Kevin MacLeod (⁠⁠⁠⁠incompetech.com⁠⁠⁠⁠) Licensed under Creative Commons: By Attribution 4.0 License ⁠⁠⁠http://creativecommons.org/licenses/by/4.0/⁠⁠⁠ "Two Guns, One Destiny" by Shane Ivers (⁠⁠⁠silvermansound.com⁠⁠⁠), licensed under CC BY 4.0"Banjos, Unite!" by Alexander Nakarada is under a Creative Commons BY-SA 3.0 license (⁠⁠⁠https://www.creatorchords.com⁠⁠⁠)Select sound effects from ⁠⁠⁠ZapSplat.com⁠⁠⁠ (⁠⁠⁠⁠https://www.zapsplat.com⁠⁠⁠⁠)

The Music of America Podcast Season 3 winds down this week with Wyoming and wow! What a way to end a season. We begin a duo who goes by the name Sparrow Bones. Mattias Russell and Alyssa kick the week off from Casper with songs that include Colossus, Anesthesia, I Am Sure and Take Me To the Water

In June 2024 the Greater Memphis, Tennessee Chamber of Commerce announced Elon Musk's artificial intelligence company, xAI, would build its "Colossus" data center in an old Electrolux factory. Two years on, the story continues to expand alongside the company's growing footprint, with a second campus, Colossus II, across the state line in Southaven, Mississippi; a contested gray water recycling plant; an ever-rising count of gas turbines; multiple lawsuits; and communities in South Memphis still pressing for straight answers.Few people have tracked all of it more closely than Neil Strebig, a reporter with The Commercial Appeal in Memphis who has covered the xAI story daily from the beginning. He's attended community meetings and hearings, filed right-to-know requests, parsed the differing interpretations of the Clean Air Act by the EPA, the Shelby County Health Department and the Mississippi Department of Environmental Quality, counted turbines, and spent time with residents living alongside the facilities. The result is a level of detail that few can match.In this conversation, Strebig brings us up to speed on the latest developments — including a newly updated lawsuit citing unpermitted turbines in Southaven, the implications of the SpaceX IPO and the impending IPOs of other AI firms, and the stalled water recycling plant Memphis leaders had counted on. And, he reflects on what it has been like to chase facts as the story spread across two states and a thicket of jurisdictions.

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In the 977th episode of the PokerNews Podcast, which is sponsored by FanDuel Poker, Chad Holloway and Mike Holtz are joined at Level 9 Studio in Las Vegas by a pair from the UK in Grosvenor Poker's Katie Swift and Philip "The Tower" Heald. The quartet discusses the recent high-profile bet between Phil Hellmuth and Shaun Deeb, one involving the former's son, Phillip "P3" Hellmuth III, and the 2026 World Series of Poker (WSOP) Main Event. Deeb stands to win $14,000 max, while Hellmuth could potentially win $10,000,000! It's an extremely long shot, but what do you think? From there, the crew looks at a pair of game-changing hands. In the first, Dario Sammartino shared on social media that an automatic shuffler had apparently sorted the cards, which resulted in two very similar hands, and in the other, a dealer error resulted in the final two players in the COLOSSUS being dealt the wrong cards in the first hand of heads-up play. No one noticed in real time, and the tournament ended as a result. Other topics include changes to the Poker Hall of Fame, The Tower advocating for Barny Boatman and John Duthie to be inducted, and a look ahead to Grosvenor Poker's famed GOLIATH, which will run July 23-August 2. Finally, don't forget to order your Think Jerky here! A new PokerNews Podcast drops three times a week during the 2026 WSOP! You can expect a new episode every Tuesday, Thursday, and Saturday at 8a PT / 11a ET / 4p UK time. Remember to subscribe to our YouTube channel so you do not miss an episode! Time Stamps *Time    Topic* 00:00 Welcome to the show 00:25 Katie Swift & the Tower join The Show 01:40 Hellmuth Mark-Up Police 08:36 Hellmuth responds 11:00 Two Identical Hands 13:39 COLOSSUS Heads-Up Mistake 22:36 Poker Hall of Fame Changes 25:50 English players for the Poker Hall of Fame 29:50 A Look at the GOLIATH  34:40 Book Giveaway Winners 35:30 Think Jerky 37:15 Ladies Event at GOLIATH

Every time you run an AI query, a pump turns on somewhere. In Memphis, Tennessee, that pump draws from one of the purest aquifers in the world. That same water supply is already threatened by a century of industrial pollution. Community organizer Sarah Houston shares what xAI's five-million-gallon-a-day habit is costing the communities closest to Colossus. Then, a talk from Ayşe Coskun on who pays the price when development comes to town. Talk featuredThe Story You're Not Hearing About AI Data Centers | Ayşe Coskun Hosted on Acast. See acast.com/privacy for more information.

As director of Keyhole, Dave Lorenzini delivered the 3D Earth zooms that ran on CNN during the 2003 Iraq War — netting five million users in a month. Sergey Brin was one of them. Google bought the company and poured in billions to build, fuel, and serve maps. As Google Earth, it forever changed how we relate to space.From there: pioneering work on Google Glass, AR platforms, and running an immersive XR lab in Europe for Draw & Code exploring the future of spaces, places, and faces. Today Dave directs Quantum Studio, building World Agent and 4D ID — the "DNS for real space," an addressing layer where every place, object, and moment gets a name AI systems can agree on. His thesis: the next decade of AI won't run on better maps. AI needs an operating system for reality. Not a map. Not a database. A living, queryable foundation where every place on Earth answers for itself.AI XR News: The OpenAI vs. Musk trial continued with damaging testimony from Mira Murati and Greg Brockman. Anthropic struck an unholy alliance with xAI's Colossus compute. GameStop bid for eBay. Colin Angle is back with Familiar, an AI robot pet. Coinbase cut staff. Ask.com finally died. VRChat hit 100,000 concurrent daily users in Japan.Key Moments:[00:03:34] AWE Long Beach in 30 days: Dave on the board, Snap glasses expected, 400 speakers and 250 exhibitors[00:20:10] 30 AI glasses coming: why the near future belongs to audio-first, AI-powered smart glasses[00:25:34] Keyhole origin story: satellite imagery, $25K/sq mile, Sergey Brin, and a $500M/year acquisition[00:37:30] Google Glass, Luxottica, and why Google blinked when it could have been 10 years ahead of Meta[00:40:00] XR vs. rockets: why building for the human brain is harder than getting to MarsBrought to you by Zappar, the company behind Mattercraft — the leading visual development environment for immersive 3D web experiences. Start building at mattercraft.io.Subscribe to the AI XR Podcast wherever you listen to podcasts. Watch the full episode on YouTube: https://youtu.be/weNANIIo7EA Hosted on Acast. See acast.com/privacy for more information.

SpaceX's $1.75 trillion IPO has just created the world's first trillionaire. But for families in Morgan County, Georgia and Boxtown in South Memphis, the AI investment rush seems to look rather different: brown water, diesel fumes, and higher bills.This week, Tom Rivett-Carnac and Paul Dickinson take on the data centre boom - now one of the fastest-moving forces in the global energy system. Why exactly do so many of these buildings need to be situated so close to population centres? And why do the communities that end up hosting them so rarely get a meaningful say?We hear from Nick Reece, Lord Mayor of Melbourne, one of the most vocal city leaders addressing the challenge head-on. He explains the costs and the unrealised promises, and shares his vision for what a genuinely good deal between the tech industry and host communities could look like.What would it take for communities to actually share in the benefits of the AI boom? How do cities avoid a race to the bottom while national governments court the biggest investors? And is the world heading for the same story it has seen before: transformative technology reshaping society, with the legislation catching up 20 years too late?Learn More:

Thank you For Listening. Click here to Send us a comment if you have any thoughts on the episode! Fear of failure sounds harmless until it starts running your life. Sitting down with Jason Knowlton, an active duty Army combat medic, we get honest about what people are really afraid of when they avoid trying, and how learning to fail can become a turning point instead of a label. Jason's story moves from early childhood trauma and adoption to mentors, scouting, faith, and the long road of becoming comfortable with growth that doesn't look perfect.We also dig into repentance and accountability in a way that feels practical, not performative. We talk about how shame distorts identity, why making things right quickly restores peace, and how parenting changes when you stop pretending you're flawless. Jason shares how he approaches mistakes with his kids, what he wishes he'd learned earlier, and why comparison quietly steals joy and courage.Then the conversation shifts into the reality of military service: the responsibility a medic carries, the training culture that shapes standards, and what the public often misses about care for families after a loss. Jason also opens up about military mental health and PTSD, including a season of depression, nightmares, hypervigilance, and the moment he knew he needed real help. We walk through intensive therapy and the stellate ganglion block, sometimes called a neurosympathetic reset, plus a thoughtful look at emerging trauma treatments like ketamine and ayahuasca in controlled settings. We close with Ether 12:27, “thorns,” and why weakness can be the very thing that keeps us close to God.If you want a story-driven conversation about resilience, faith, military life, PTSD recovery, and choosing growth over fear, hit play. Subscribe, share this with a friend, and leave a review so more people can find the show.Beyond The BeaconJoin Bishop Kevin Sweeney for inspired interviews with Catholics living out our faith!Listen on: Apple Podcasts Spotify Beyond The BeaconJoin Bishop Kevin Sweeney for inspired interviews with Catholics living out our faith!Listen on: Apple Podcasts Spotify Fit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts Spotify Fit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts SpotifySupport the showThanks for listening!  Keep on Striving!Don't Forget to leave a review and rating.  Let us know your thoughts about the episode.  You can also follow on the following:YouTubehttps://www.youtube.com/@thejacksonhowellpodcastFacebookhttps://www.facebook.com/TheJacksonHowellPodcastTik Tokhttps://www.tiktok.com/@thejacksonhowellpodcastInstagramhttps://www.instagram.com/jacksonhowell5/

My guest today is Kareem Amin, co-founder and CEO of Clay. Clay has become one of the fastest-growing software companies of the last few years, valued at over four billion dollars. It helps companies find their best customers and reach them at scale. But this conversation is about a lot more than Clay. Kareem is one of the most original thinkers I know.  We talk about the statues he keeps at the center of how he runs Clay — truth, justice, and courage — and what those words demand of him in practice. We talk about risk, ambition, and what he learned about both on a ten-day silent meditation retreat.  I've had a lot of conversations with Kareem over the years. This is one I'll remember. Please enjoy this unique conversation with Kareem Amin. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Kareem Amin (00:03:07) Clay's Origin (00:10:50) Truth, Courage and Justice (00:16:09) Adulation (00:18:28) Risk, Courage & Self-Respect (00:21:14) Jony Ive & Steve Jobs (00:21:42) Role of Introspection (00:23:08) Lack to Wholeness (00:27:27) The Day Five Insight (00:29:57) Running a Startup Unusually (00:34:41) Learning from Magicians (00:36:27) Music's Role in Your Life (00:39:38) Making People Feel Something New (00:41:20) Vision in Company Building (00:44:29) Wealth & What It's Taught You (00:47:40) All Problems Are Communication Problems (00:52:14) Death Doula & Scaling (00:55:06) The Kindest Thing