Podcasts about micron

This week on Market Mondays, we break down the biggest stories shaping the markets and answer the investing questions everyone is asking.We discuss whether DRAM has finally bottomed, if Micron's pullback is an opportunity, when gold could become a buy again, and why Apple and Eli Lilly continue to outperform. We also examine Oracle, SpaceX, Netflix, McDonald's, and the stocks we'd hold for the next 10–15 years.We also cover trading strategy, market bottoms, futures trading, portfolio construction, oil prices, AI competition from China's Kimi K3 model, and the biggest investing lessons of the year. Plus, MG The Mortgage Guy joins us to break down the latest in real estate, mortgages, housing affordability, interest rates, and what buyers and investors should be watching in today's market.If you're serious about building long-term wealth through investing and real estate, this is an episode you don't want to miss.#MarketMondays #Investing #StockMarket #RealEstate #MGTheMortgageGuy #IanDunlap #EarnYourLeisure #Stocks #WealthBuilding #Finance #Trading #Gold #Micron #SpaceX #Apple #OracleAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Plus: Oil tops $90 a barrel as Middle East conflict continues. And shares of Utz Brands soar after the snack-food maker agrees to go private. Imani Moise hosts. Sign up for WSJ's free What's News newsletter. An artificial-intelligence tool assisted in the making of this episode by creating summaries that were based on Wall Street Journal reporting and reviewed and adapted by an editor. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Big Tech once again ruled the options market as NVIDIA, Tesla, Apple, Microsoft, Amazon, Alphabet, Micron, and Intel accounted for the day's most active contracts despite a lighter overall trading session. On this episode of The Hot Options Report, Mark Longo breaks down the biggest options movers of Monday's session, including: NVIDIA once again claims the top spot on the options leaderboard Tesla extends its recent slide as traders swarm same-day expiration calls Apple's impressive rally finally cools off Microsoft, Amazon, Micron, Intel and Alphabet all see heavy 0DTE activity SpaceX continues trading below its IPO price while the mysterious 330 calls remain active IREN surges nearly 20% as options traders chase the move QuikOptions' latest Congressional and Senate trading scans Plus, we examine how many of today's hottest same-day expiration options briefly traded in the money before reversing by the closing bell. For institutional-quality options flow, unusual activity, dark pool analytics, Congressional trading data, and much more, visit TheHotOptionsReport.com, powered by QuikOptions.

Dan Nathan and Guy Adami break down a wild week across markets — memory stocks in freefall, Google's brutal two-day selloff, and why China's AI models are closing the gap without top-tier Nvidia chips. Micron cratered from its all-time high of $1,255 to $800 in a matter of weeks, and the DRAM ETF whipsawed right along with it. Guy and Dan dig into whether this is a routine flush or something bigger. Google sold off 10% after Gemini news broke the hyperscaler rotation narrative, and they debate whether the moat still holds given decelerating margins and ballooning capex. On the China front: DeepSeek raised $7B at a $52B valuation, Alibaba announced $50B in capex, and Moonshot's new model is narrowing the gap with US labs — all without access to Nvidia's most advanced chips. Dan and Guy also revisit the Nvidia-vs-component-suppliers margin debate, unpack Apple's surprising rally into earnings, and call the technical line in the sand on Netflix after its cash flow miss cut the stock in half. Plus: a quick check on crude, refiners hitting new highs, and what's next for energy names like Exxon and ConocoPhillips. Show Notes Google Gemini Launch Delayed as Tech Falls Short of Internal Goals (Bloomberg) Chinese Models vs. Frontier Models (The Daily Spark) China's Moonshot Unveils AI Model That Narrows Gap With US Firms (Bloomberg) —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

Derek Moore is joined by Mike Snyder and Shane Skinner to discuss SpaceX now being below its IPO price and whether it can grow into its current value. Plus, Apple sues OpenAI as the stock continues to run into earnings. Later, did anything fundamentally change with the memory stocks like Micron or people just don't want to be the last out. Finally, the Strait of Hormuz didn't matter to markets until the market decided it mattered again.   Apple sues OpenAI Micron and the memory stocks dip again Trading behavior psychology losing hurt more than winning feels good? Strait of Hormuz tanker tracker ticks lower More sellers than buyers in memory stocks SpaceX moves below its IPO price Looking at the Mag7 stocks and how they grew into their valuations over a decade    Were the Mag7 stocks expensive or undervalued in hindsight 10 years ago? SpaceX forward valuation Interest rates and oil prices Inflation surprises to the downside   Mentioned in this Episode     Derek Moore's book Broken Pie Chart https://amzn.to/3S8ADNT   Jay Pestrichelli's book Buy and Hedge https://amzn.to/3jQYgMt   Derek's book on public speaking Effortless Public Speaking https://amzn.to/3hL1Mag   Contact Derek derek.moore@zegainvestments.com  

Avichal Garg explains the critical difference between "missionary" and "mercenary" founders, why major networks like Ethereum, Solana, and Near still hold massive long-term value despite bear market conditions, and how bringing real-world assets on-chain creates unprecedented operational efficiencies and yield opportunities for global investors, unlocking trillions of dollars in market value.~~~~~⁠⁠⁠⁠⁠⁠⁠

In this episode of Money Matters, brought to you by Greenberg Financial Group, Dave, Todd, and Dylan run the show while the rest of the team is traveling, breaking down a rough week where the chip stocks led the market lower. We get into what Dave dubbed the "chip wreck," with the semiconductor group dipping into bear market territory for the month, and why we look at that kind of volatility as part of the territory when an entire industry is going through price discovery rather than a reason to abandon good companies. We talk through the names in the middle of it, from Micron and SanDisk to Taiwan Semi's blowout earnings and its plan to pour another $100 billion into Phoenix, and why we still believe the demand story for AI is only getting started. We also dig into some encouraging news on inflation, with both CPI and PPI posting their biggest monthly drops in years as lower oil worked its way through, even as tensions in the Strait of Hormuz pushed crude back up sharply on the week. And we spend real time on the shift at the top of the market, with Apple retaking the largest company crown from Nvidia, and what that rotation is telling us. The back half of the show is all about planning, and the levers that actually matter in retirement. We walk through Roth conversions and the sweet spot in your early sixties, why QCDs are the underappreciated cousin of tax planning, how RMDs work and when to take them, and why owning individual bonds, treasuries, and fee-based annuities can serve the more conservative saver who wants off the rollercoaster. It all ties back to our fiduciary, fee-based, financial-planning-first approach, and the difference between being a true advisor and just a money manager. We are also thrilled to introduce the newest member of the Greenberg Financial team, Hailey Glick, who is helping us build out an in-house tax practice launching in 2027. It is the next step in Dean's vision of a true family office, everything you need under one roof, and we get into how bringing tax planning alongside investments, estate work, and financial planning lets us look at your whole picture over decades rather than one filing season at a time. If you have been thinking about taking us up on the free financial plan, this is exactly the kind of clarity it can bring. If you would like to contact us to learn more about our firm, our seminars, and our process - call us at 520.544.4909 or go to our website at www.Greenbergfinancial.com or email us at Contact@Greenbergfinancial.com Disclaimer: This show discusses different investment products and strategies. Every product and strategy has some type of inherent risk and we strongly encourage our listeners to properly understand these risks. Past performance is no guarantee of future performance. The information presented on this program is believed to be factual and up-to-date, but we do not guarantee its accuracy and it should not be regarded as a complete analysis of the subjects discussed.  The material covered on this program does not involve the rendering of personalized investment advice, but is for general information purposes only.  A professional advisor should be consulted before implementing any of the options presented.   Greenberg Financial Group is registered as an investment advisor with the SEC and only transacts business in states where it is properly registered, or is excluded or exempted from registration requirements.

Depuis l'automne dernier, le prix de la mémoire vive ne grimpe plus : il s'envole. Les fabricants répètent que cette tension restera temporaire, le temps d'ouvrir de nouvelles usines. Mais une analyse de Bank of America, relayée le 12 juillet par le quotidien taïwanais Commercial Times, fragilise ce discours. Elle paraît alors que Samsung, SK Hynix et Micron sont visés en Californie par une plainte collective les accusant d'avoir organisé cette rareté.La Corée du Sud affiche ses ambitions. Le président Lee Jae-myung veut doubler les capacités nationales d'ici 2030, notamment grâce aux mégasites de Samsung à Gwangju et de SK Hynix dans le Jeolla. Bank of America estime qu'après déduction des anciennes lignes arrêtées pour modernisation, la capacité coréenne en wafers (les plaques de silicium servant à fabriquer les puces) progresserait de moins de 10 % par an.Un professionnel taïwanais affirme que SK Hynix ne mettrait en service qu'un sixième des ajouts prévus d'ici 2028. Construire une usine de semi-conducteurs exige du temps : cinq ans pour les fondations, trois à quatre années supplémentaires pour les salles blanches et les machines, puis près d'une décennie pour l'écosystème complet. Le patron de SK Hynix prévient d'ailleurs que 2027 sera la pire année de l'histoire du secteur. La plainte déposée le 25 juin accuse les trois groupes d'avoir profité du basculement vers la HBM, une mémoire empilée indispensable aux accélérateurs d'intelligence artificielle, pour réduire l'offre de DDR4 et de DDR5. Chaque bit de HBM mobilise environ trois fois plus de silicium qu'un bit de DDR5 : produire davantage pour l'IA signifie fabriquer moins pour le grand public.Les plaignants évoquent une hausse de la DRAM d'environ 700 % en quatre ans. Ils devront prouver une coordination, une procédure comparable ayant échoué en 2018. Le passé nourrit les soupçons : Samsung et Hynix avaient déjà été sanctionnés pour entente dans les années 2000. En France, un kit de 32 gigaoctets de DDR5-6000 est passé d'environ 75 euros à l'été 2025 à près de 300 euros en juin. Les projections annoncent encore deux fortes hausses successives, sans répit avant 2028. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Dans cette nouvelle édition du Top Ganne, Vincent fait la mise à jour habituelle de l'analyse technique du marché actions. Il attire aussi votre attention sur 5 actions qui sont intéressantes selon lui sur le long terme. Attention, ce ne sont pas des conseils en investissement, faite vos propres recherches de votre coté.

Mel Mattison joins Marty to dissect AI's impact on memory chips, the hyperscaler debt cycle, and the Fed's rate outlook under Chairman Warsh. The conversation swings from Korean memory stocks like Micron, Samsung and SK Hynix to macro themes such as fiscal deficits, debasement trades, gold, Bitcoin and the new “Trump accounts” tax vehicle. Listeners get a contrarian view on why AI demand may outlast hype, how free‑cash‑flow can erase debt fast, and what assets could dominate the market this decade. Mel on Twitter: https://x.com/MelMattison1 Mel's Book: https://www.melmattison.com/quoz Find the Home Mining Playbook here: https://www.tftc.io/home-mining-energy-playbook STACK SATS hat: https://tftcmerch.io/ Our newsletter: https://www.tftc.io/bitcoin-brief/ TFTC Elite (Ad-free & Discord): https://www.tftc.io/#/portal/signup/ Discord: https://discord.gg/yHGkvYxdqT Opportunity Cost Extension: https://www.opportunitycost.app/ Shoutout to our sponsors: Block: Cash App: For a limited time, new customers can get $21 added to their balance. Just use code TFTC10 when you sign up, and send at least $5 to a friend in the first two weeks. Terms apply. Bitcoin services by Block, Inc. See the Bitcoin disclosures at cash.app/legal/podcast. Square: Visit http://square.com/go/**tftc** for up to $200 off eligible Square hardware. Bitkey: Use code TFTC10 for 10% off the new Bitkey. Aven https://www.aven.com/bitcoin CrowdHealth https://www.joincrowdhealth.com/tftc Unchained https://unchained.com/tftc/ Salt of the Earth: https://drinksote.com/tftc Join the TFTC Movement: Main YT Channel https://www.youtube.com/c/TFTC21/videos Clips YT Channel https://www.youtube.com/channel/UCUQcW3jxfQfEUS8kqR5pJtQ Website https://tftc.io/ Newsletter tftc.io/bitcoin-brief/ Twitter https://twitter.com/tftc21 Instagram https://www.instagram.com/tftc.io/ Nostr https://primal.net/tftc Follow Marty Bent: Twitter https://twitter.com/martybent Nostr https://primal.net/martybent Newsletter https://tftc.io/martys-bent/ Podcast https://www.tftc.io/tag/podcasts/ Disclosure: Bitcoin services are provided by Block, Inc. Bitcoin services are not licensable activity in all U.S. states and territories, and not all services are available in all states. Bitkey is not available in New York. Block, Inc. operates in New York as Block of Delaware and is licensed to engage in virtual currency business activity by the New York State Department of Financial Services. Bitcoin is a non-deposit, non-bank product that is not FDIC insured and involves risk, including monetary loss. For additional information, see the Bitcoin disclosures: https://help.cash.app/btcdisclosures Get up to $200 off Square hardware when you sign up at http://square.com/go/tftc**!** #squarepartner. Offer expires December 31, 2026 at 11:59 pm PST. Offer for $40 off the cost of one Square Stand, $75 off the cost of one Square Terminal, $100 off the cost of one Square Handheld, or $200 off the cost of one Square Register, excluding applicable taxes. Limited to one discount per product type per seller account. Each code is limited to one redemption per account holder. Valid for new Square customers located in the US only. Offer not valid with guest checkout. Square reserves the right to modify, revoke or cancel the offer at any time. Offer cannot be combined with any other coupon. Void where prohibited, not redeemable for cash, and non-transferable. #squarepartner #blockpartner

Der Halbleiterindex SOX verliert 20 Prozent in einem Monat, Speicherchip-Werte wie SK Hynix und Micron stürzen um bis zu 35 Prozent ab. Und das, obwohl ASML und TSMC sensationelle Zahlen vorlegen und Planungssicherheit bis 2030 versprechen. Die beiden Wirtschaftsjournalisten Dietmar Deffner und Holger Zschäpitz streiten über die große Tech-Rotation, dieses Mal mit vertauschten Rollen: Der Bär sieht mittelfristig Chancen im abgekühlten Chip-Sektor bei einem KGV von 17, der Bulle warnt vor einer nachhaltigen Korrektur nach dem Vorbild der Rüstungswerte. Außerdem: Stripe will PayPal für 60,50 Dollar je Aktie übernehmen, SpaceX verliert eine Billion Dollar an Marktkapitalisierung und Apple stößt Nvidia vom Thron des wertvollsten Unternehmens der Welt. Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte: https://linktr.ee/deffnerundzschaepitz DEFFNER & ZSCHÄPITZ sind wie das wahre Leben. Wie Optimist und Pessimist. Im wöchentlichen WELT-Podcast diskutieren und streiten die Journalisten Dietmar Deffner und Holger Zschäpitz über die wichtigen Wirtschaftsthemen des Alltags. Schreibt uns an: wirtschaftspodcast@welt.de Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutzerklärung: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

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

Wrapping up a volatile week for stocks, Carl Quintanilla, Sara Eisen and Michael Santoli delved into the chip sector extending its global sell-off on AI jitters. The anchors reacted to Netflix shares tumbling on current quarter guidance that indicated slower growth. TD Cowen analyst John Rutledge joined the Netflix conversation. Apple gets an analyst upgrade and surpasses Nvidia to reclaim the title of the world's most valuable company. Also in focus: Shares of SpaceX slide further below their $135 IPO price after an aborted Starship launch, Micron falls out of the trillion-dollar market cap club, PayPal M&A watch, LeBron James speaks about Nike's challenges, oil rallies on U.S.-Iran tensions, President Xi's AI game plan for China.   Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Apple bucked the broader tech selloff while Netflix stumbled following earnings on an action-packed expiration Friday. On this episode of The Hot Options Report, we break down the biggest options movers of the day, including Apple, Netflix, NVIDIA, Tesla, SpaceX, Micron, Intel, Meta, Amazon, and Microsoft. We also dive into today's featured Senate Yay Scan, highlighting the latest stock purchases disclosed by Senator John Boozman, and analyze what they could mean for traders. From expiration-day gamma to unusual options activity and the hottest contracts on the tape, we cover everything active options traders need to know before heading into the weekend. For more options data, unusual activity scans, and professional tools, visit TheHotOptionsReport.com, brought to you by QuikOptions.

In this episode of the Milk Road Show, we sit down with Colin McCune, Head of Government Affairs at Andreessen Horowitz (a16z), to break down the latest developments in Washington, why the Clarity Act matters for Bitcoin, Ethereum, stablecoins, and crypto startups, and how U.S. regulation could shape the next phase of the bull market.~~~~~⁠⁠⁠⁠⁠⁠

El analista independiente y responsable de operativa DAX examina los títulos de SpaceX, Micron, Ferrari y United Health, entre otros

Met twee fervente volgers van de AI-revolutie en de chipsector aan tafel, kon het maar over één ding gaan: de meer dan goede cijfers en outlook van ASML - en wat die betekenen voor beleggers in dit aandeel.Analisten Jos Versteeg (InsingerGilissen) en Hildo Laman (IEX) gaan in de nieuwste aflevering van de IEX Beleggerspodcast dieper in op de heftige bokkesprongen van chipaandelen (wat zegt dat over de sector?), de houdbaarheid van de miljardeninvesteringen in datacenters en de technologische voorsprong van het Nederlandse chiptrio ASML, ASMI en Besi.Onderwerpen in deze aflevering:Nieuws van de week: Amerikaanse banken en nepdiamantenASML laat de terughoudendheid varen en voorspelt flinke groeiAnalisten zijn juichend, maar de koers daalt: waarom?Wat hebben de Snollebollekes met chips te maken?De technologische suprematie van ASML, ASMI en Besi  Kunnen de hyperscalers het AI-investeringstempo wel volhouden?Hoe belangrijk wordt de factor energie voor de verdere groei van AI?Dilemma van de week: Micron of Microsoft?Aandelen: CVC Capital Partners, Randstad en TomTom*Vooruitblik op het cijferseizoen: waar wordt het spannend?* Ter aanvulling/verduidelijking: De €1,30 waar Hildo Laman het over heeft bij TomTom is op jaarbasis, niet op kwartaalbasis.

Charles Payne is joined by Jessica Inskip, Director of Investor Research at Stockbrokers.com, to discuss how post-earnings sell-offs and overseas leverage are driving current market volatility. They explore buying opportunities for long-term investors amid pullbacks in semiconductor leaders like ASML, Micron, and Taiwan Semiconductor, while also assessing shifting software and hardware demands and noting potential headline risks in upcoming mergers and acquisitions. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Is the tech correction finally giving options traders a bargain hunter's paradise? On this episode of The Option Block, host Mark Longo is joined by Uncle Mike Tosaw of St. Charles Wealth Management and Cboe's Henry Schwartz (The Flowmaster) to dissect a choppy, red-tinged market. Together, the panel searches for deals, analyzes massive unusual blocks, and discusses whether the AI/chip trade has finally peaked. On The Trading Block Market Round-up: Mark Longo guides the team through the SPX flirts with the 7,600 magnet, a slipping Nasdaq, and VIX cash vacillating around the 16 handle. The Chip Sell-Off: Mike Tosaw breaks down why blockbuster earnings from Taiwan Semiconductor (TSM) triggered a massive wave of "sold to you" across AMD, Nvidia, and Micron. SpaceX Breakdown: Trading well below its IPO price, Henry Schwartz details the bizarre 330 call options activity and explains how double-levered ETFs use these deep out-of-the-money options for risk management. The New Number Three: Apple solidifies its lock on the #3 spot in options volume. Plus, a preview of earnings for Netflix and Alcoa. The Odd Block (Unusual Options Activity) Papa John's (PZZA): A massive roll-up in the August 40/45 vertical call spreads. Is someone picking the bottom in pizza volatility? Henry Schwartz dives deep into the Cboe data. Cerebras Systems (CBRS): Mark Longo points out heavy volume in the July 31st 360 calls for this newly listed AI/semi player. DNOW Inc (DNOW): A customer unloads over 5,900 of the September 14 calls. Why writing these juicy calls might be a genius "line in the sand" play. The Mail Block & Around the Block Extended Options Hours: Henry Schwartz gives an exclusive update on the pending rollout of pre-market single-name options trading. Mike Tosaw shares what he's keeping on his trading radar for the rest of the week, from bank earnings reactions to the next wave of mega-cap tech reports.

Here are the descriptions written out in standard text format, ready to copy and paste without any of the HTML coding. 1. Libsyn Show Description (Podcast Directories) Wrap up your trading day with host Mark Longo on The Hot Options Report for Thursday, July 16, 2026. Tech giants and chipmakers are feeling the heat as we head into a massive Friday expiration. Whether it's traders hunting for late-stage juice in Micron or managing risk in SpaceX, the options tape was absolutely on fire today. Inside This Episode: Capitol Hill Trades: We dive into recent stock purchases from House members including Amphenol Corp (APH), Ellington Financial (EFC), and Hilton (HLT). The Tech & Chip Sell-off: Chip names get hammered despite strong Taiwan Semi numbers. Intel (INTC) drops nearly 6% while Nvidia (NVDA) slides to close at $207.40, pacing the leaderboard with 2.76M contracts. The Wild West of Micron (MU): Down $51 to close at $853.20. Are traders abandoning the $1,000 strikes? We look at the heavy action on the $900 calls going for over $7.00 in premium. SpaceX Back in the Top 10: Trading below its $135 IPO price, SpaceX draws 774,000 contracts. We decipher the massive block of next-week $330 calls—is this ETF risk management at play? Earnings & Expiration Action: Post-earnings movement on Netflix (NFLX) plus short-dated plays on Apple (AAPL), Tesla (TSLA), Alphabet (GOOGL), Amazon (AMZN), and Microsoft (MSFT). Resources & Links: Run Your Own Scans: Check out TheHotOptionsReport.com to find unusual activity, scan the flow, and track the data yourself.

The AI trade is under pressure again as semiconductor stocks swing sharply, gas prices climb, and investors prepare for a crucial stretch of earnings reports.Chuck Zodda and Mike Armstrong break down why recent moves in Micron, SanDisk, Taiwan Semiconductor, and Korean chip stocks show how volatile the AI trade has become. They also discuss why Taiwan Semiconductor's strong earnings were not enough to lift the stock, how rising oil prices and record crack spreads are pushing gas and diesel costs higher, why renewed tensions around the Strait of Hormuz could pressure global energy supplies, how GLP-1 weight loss drugs may be affecting grocery sales, and why a new cholesterol-lowering pill could be part of a major shift in health care.

Are you looking to save time, make money, and start winning with less risk? Then head to https://www.ovtlyr.com.Learn more about OVTLYR: https://youtu.be/TUCbD5KovlcThe market keeps pushing higher... but there's one problem standing in the way. Every time the S&P 500 gets close to breaking out, it runs straight into overhead resistance. And that's exactly why blindly chasing stocks right now could end up costing traders a lot more than they think.This conversation dives into why order blocks matter so much, what the latest market breadth is really saying, and why strong-looking markets can still hide weakness underneath the surface. There's also a great breakdown of semiconductors after Micron's sharp selloff, why some of the hottest themes eventually come to an end, and how to spot those warning signs before everyone else sees them.One of the biggest lessons in this episode has nothing to do with picking stocks. It's about having a plan before emotions take over. Whether it's avoiding the temptation to "buy the dip," managing losses without panic, rolling option positions to reduce risk, or simply turning off your P&L so it doesn't control your decisions... the discussion keeps coming back to one idea: disciplined traders survive, emotional traders don't.There's also a full walkthrough of active trades, earnings season preparation, GameStop and Apple option rolls, why Netflix could still have more downside despite looking "cheap," and how to use market conditions to decide when it's better to sit in cash instead of forcing trades.✅ SPY, Nasdaq, market breadth, bond yields, and dollar analysis✅ Micron, semiconductors, and Netflix earnings breakdown✅ Order blocks, overhead resistance, and buying the rip vs. buying the dip✅ Options rolling strategies and risk management techniques✅ Real portfolio updates, trade management, and disciplined investingIf you've ever wondered why following a trading plan is more important than finding the next hot stock... this episode is packed with lessons you'll be able to use immediately.Subscribe to OVTLYR for disciplined trading strategies that actually make sense.

Has Bitcoin already found its bottom? Former Coinbase Head of Institutional Research David Duong joins the Milk Road Show to explain why the macro outlook for Bitcoin is becoming increasingly bullish. We break down why cooling inflation, the Federal Reserve's next moves, improving ETF flows, and shifting market sentiment could create a strong setup for Bitcoin in the months ahead. David also shares his thoughts on the AI trade, Robinhood's new blockchain, the Clarity Act, institutional adoption, and why he believes investors are focusing on the wrong headlines.~~~~~⁠⁠⁠⁠⁠

Is the tech correction finally giving options traders a bargain hunter's paradise? On this episode of The Option Block, host Mark Longo is joined by Uncle Mike Tosaw of St. Charles Wealth Management and Cboe's Henry Schwartz (The Flowmaster) to dissect a choppy, red-tinged market. Together, the panel searches for deals, analyzes massive unusual blocks, and discusses whether the AI/chip trade has finally peaked. On The Trading Block Market Round-up: Mark Longo guides the team through the SPX flirts with the 7,600 magnet, a slipping Nasdaq, and VIX cash vacillating around the 16 handle. The Chip Sell-Off: Mike Tosaw breaks down why blockbuster earnings from Taiwan Semiconductor (TSM) triggered a massive wave of "sold to you" across AMD, Nvidia, and Micron. SpaceX Breakdown: Trading well below its IPO price, Henry Schwartz details the bizarre 330 call options activity and explains how double-levered ETFs use these deep out-of-the-money options for risk management. The New Number Three: Apple solidifies its lock on the #3 spot in options volume. Plus, a preview of earnings for Netflix and Alcoa. The Odd Block (Unusual Options Activity) Papa John's (PZZA): A massive roll-up in the August 40/45 vertical call spreads. Is someone picking the bottom in pizza volatility? Henry Schwartz dives deep into the Cboe data. Cerebras Systems (CBRS): Mark Longo points out heavy volume in the July 31st 360 calls for this newly listed AI/semi player. DNOW Inc (DNOW): A customer unloads over 5,900 of the September 14 calls. Why writing these juicy calls might be a genius "line in the sand" play. The Mail Block & Around the Block Extended Options Hours: Henry Schwartz gives an exclusive update on the pending rollout of pre-market single-name options trading. Mike Tosaw shares what he's keeping on his trading radar for the rest of the week, from bank earnings reactions to the next wave of mega-cap tech reports.

ASML may have delivered the most important earnings report of the week for semiconductor investors.The company raised its full-year sales guidance again, reported a sharp increase in memory-related revenue, and announced plans to expand EUV lithography production by around 30% annually in 2027 and 2028.While the headlines focused on ASML, the implications extend far beyond one company.In this episode, we break down why ASML's latest results reinforce the broader AI infrastructure story—and what they mean for companies across the semiconductor supply chain.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comKey topics we explore:– Why ASML is one of the most important companies in the AI ecosystem– What the latest earnings reveal about global semiconductor demand– Why memory-related revenue surged and what it says about HBM demand– How strong EUV orders reinforce the long-term AI infrastructure buildout– The read-through for Nvidia, TSMC, Micron, SK hynix, and other semiconductor leaders– Why investors should watch semiconductor equipment companies as closely as AI chip designersThe bigger question:If the AI infrastructure boom were slowing, would ASML be raising guidance and expanding production capacity?For investors, ASML's earnings provide another important data point that demand for advanced semiconductor manufacturing—and the AI infrastructure powering it—remains robust.LINKSPrashant Choubey - ⁠https://www.linkedin.com/in/choubeysahab⁠Subscribe to VC10X newsletter - ⁠https://vc10x.beehiiv.com⁠Subscribe on YouTube - ⁠https://youtube.com/@VC10X⁠Subscribe on Apple Podcasts - ⁠https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986⁠Subscribe on Spotify - ⁠https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQ⁠VC10X website - ⁠https://vc10x.com⁠For sponsorship queries reach out to prashantchoubey3@gmail.comThis channel is for asset managers, allocators, and investors who want analysis that holds up—not headlines dressed as insight.Subscribe for weekly data-driven breakdowns of the forces reshaping capital markets.#ASML #Semiconductors #AI #ArtificialIntelligence #Nvidia #TSMC #Micron #SKHynix #HBM #EUV #ChipStocks #Investing #TechStocks #VC10X #Finance #VentureCapital #DataCenters #SemiconductorEquipment #WallStreet #Markets

Business news rarely gets examined through the lens of category design, but when it does, the insights are striking. From charitable investing to the economic impact of the World Cup and the marketing brilliance of Black Rifle Coffee Company, a new way of thinking about business is emerging. At the center of one of the most compelling stories is Micron, a company that just made the largest corporate commitment of its kind to the Invest America program, seeding up to one million children’s investment accounts with $250 million. This is just one of the topics that Pirates Christopher Lochhead, Eddie Yoon and Bri Clark discuss on this episode of Pirate Street Journal. Each week, the Category Pirates pick three headlines worth paying attention to and break down the category underneath. 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.   Micron and the Birth of Charitable Investing For over a century, philanthropy followed a predictable pattern. A billionaire writes a check, a foundation cuts a grant, and the money gets spent. Micron’s $250 million commitment to Invest America accounts breaks that pattern entirely. Instead of routing funds through a nonprofit or NGO, Micron is directly seeding investment accounts for up to one million children, turning them into shareholders in the S&P 500 from a very young age. What makes this genuinely different is the alignment of incentives. When Micron contributes stock into these accounts, every child who benefits now has a reason to care about Micron’s success. Both the company and the child are pulling in the same direction, which creates a virtuous cycle that traditional charitable giving has never been able to produce. This is charitable investing, and it is an entirely new category. The long-term implications are profound. If those dollars sit in an index fund and compound over 18 years at the S&P 500’s historical average of approximately 10% per year, the financial transformation for underprivileged communities could be generational. Micron is not handing out fish. It is teaching an entire generation how to fish.   Why the Old Model of Charitable Giving Is Broken Charitable giving, as a category, has deep structural problems that most people do not discuss openly. As organizations grow, they often become more focused on their own survival than on delivering value to the people they intend to help. Administrative overhead, bureaucratic inefficiency, and misaligned incentives mean that only a fraction of donated dollars actually reach those who need them most. The peer-to-peer structure of Invest America accounts eliminates that problem entirely. There is no NGO taking a cut along the way. Contributions go directly into governed investment accounts with clear rules about how and when the funds can be accessed. This direct model, made possible by the internet, is a harbinger of what charitable investing can look like at scale. Beyond efficiency, the greatest flaw in traditional charitable giving is that it creates dependency rather than capability. Micron’s approach forces financial literacy by making children stakeholders in the market itself. The account becomes a lived lesson in compounding, patience, and long-term thinking, skills that are rarely taught in schools, colleges, or even households.   What Micron’s Move Tells Us About the Future of Corporate Philanthropy Micron did not stumble into this decision. As a category king in the memory chip space, Micron understands that the most durable competitive advantages are built on ecosystem relationships, not just product performance. By seeding one million children’s investment accounts, Micron is building a generation of stakeholders who are emotionally and financially connected to the company’s future. This is a model that other major corporations are likely to follow. When the incentives are aligned this clearly, and when the marketing and goodwill benefits are this visible, it becomes increasingly difficult for other companies to justify staying on the sidelines. The prediction is straightforward: the category of charitable investing will grow steadily as the limitations of traditional charitable giving become harder to ignore. The Invest America program, championed by Brad Gerstner and now powered by commitments from Micron and others like SpaceX president Gwynne Shotwell, is showing the country what it looks like when capital is deployed with purpose and precision. Micron’s $250 million is not just a donation. It is a category-defining move that could reshape the entire landscape of corporate philanthropy for decades to come. To hear about the other topics in this week's 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!

In this mid-year outlook episode of Facts vs Feelings, Ryan Detrick, Chief Market Strategist at Carson Group, and Sonu Varghese, Chief Macro Strategist at Carson Group, revisit their 2026 forecast and explain why they've raised their S&P 500 target from 12-15% to 15-18% for the year, while holding bonds steady at 3-5%. They walk through how AI capex has become a macroeconomic story as much as a market one, contributing roughly 90 basis points per quarter to real GDP growth, and why hyperscaler spending plans for 2026 and 2027 keep getting revised sharply higher.The conversation covers the labor market's quiet resilience, why business creation data suggests confidence rather than desperation, an inflation picture that isn't going away despite market expectations for Fed rate hikes, and a sector rotation story where former "value" stocks like Micron have become momentum plays almost overnight. Ryan and Sonu also dig into earnings estimate revisions, midterm-year volatility patterns, diversifiers like gold and managed futures, and swap stories from their World Cup travels before previewing next week's guest.[Key Takeaways]Carson raised its 2026 S&P 500 target from 12-15% to 15-18% at the midpoint of the year, with the index already up 11% total return year-to-date; bonds remain forecast at 3-5%.AI-related hardware and software investment (excluding data centers) has contributed about 45% of real GDP growth over the last five quarters, roughly 90 basis points per quarter.Hyperscaler capex estimates keep climbing: the five largest tech spenders were projected to spend $470 billion in 2026 back in November; that figure is now $740 billion, with 2027 estimates rising from $530 billion to nearly $900 billion.S&P 500 2026 EPS estimates have risen from $308 to $339 a share (up 10%) since the start of the year, with 2027 estimates up 12%, led by technology, energy, and materials.The labor market shows underlying strength despite headline softness, with unemployment at 4.2%, average payroll growth around 110,000 a month, and falling continuing claims.Inflation remains sticky due to incomplete tariff pass-through, reshoring-related cost increases, and rising computer/software prices, a reversal from the deflationary tech trends of the 1990s.Jump to:0:00 - Welcome And The Midyear Setup1:45 - Why We Raised The Stock Target5:38 - AI Spending Shows Up In GDP9:44 - The Consumer Looks Better Than Feels14:20 - Business Creation As A Confidence Signal17:08 - The Real Leaders Inside “Tech”18:53 - Earnings Keep Getting Revised Higher27:03 - The Inflation Problem Isn't Gone31:06 - The Fed Pause Versus Hike Pricing35:00 - Second-Half Equity Playbook And Rotation42:19 - Volatility, Breadth, And Midterm Patterns49:06 - Bonds, Oil Headlines, Gold, Diversifiers52:55 - World Cup Travel Notes And Wrap-Up57:08 - DisclosuresConnect with Ryan:• LinkedIn: https://www.linkedin.com/in/ryandetrick/• X: https://x.com/RyanDetrickConnect with Sonu:• LinkedIn: https://www.linkedin.com/in/sonu-varghese-phd/• X: https://x.com/sonusvarghese?lang=enQuestions about the show? We'd love to hear from you! factsvsfeelings@carsongroup.com

Charles Schwab's Nate Peterson explains the bearish case for the memory market and why Micron's (MU) performance could shape the industry's outlook. He also discusses Apple's (AAPL) partnerships with AI research labs to examine how advances in AI model compression could affect future memory demand. David also shares his outlook for the Magnificent Seven and what these developments could mean for investors.======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

In this episode of The Milk Road Show, we sit down with William Quigley, co-founder of Tether and an early investor in Coinbase, Kraken, and dozens of other crypto projects, to discuss the biggest shifts happening in crypto today. We break down why Tether chose to leave Europe under MiCA, what the U.S. Clarity Act could mean for digital assets, why Big Tech companies like Apple, Amazon, and Meta may eventually launch their own digital currencies, and whether regulation is strengthening or weakening the crypto ecosystem.~~~~~⁠⁠⁠⁠

Face à la flambée des prix de la mémoire, Apple cherche de nouvelles marges de manœuvre. Selon le Financial Times, le groupe a commencé à tester les puces DRAM du fabricant chinois ChangXin Memory Technologies, plus connu sous le nom de CXMT. La DRAM est une mémoire vive utilisée dans les smartphones, les ordinateurs et de nombreux appareils électroniques. Apple ne se contente donc plus de discuter avec le fournisseur chinois : l'entreprise aurait engagé une véritable phase de qualification technique. Ces essais doivent déterminer si les composants de CXMT répondent à ses exigences de performance, de fiabilité et de production à grande échelle.Cette évaluation ne signifie pas qu'un contrat a déjà été signé. Elle permet néanmoins à Apple de préparer une solution de remplacement. En cas de pénurie ou de nouvelle hausse des prix, le groupe pourrait mobiliser plus rapidement ce fournisseur, sans devoir reprendre l'ensemble des tests depuis le début. CXMT occupe désormais une place importante sur le marché mondial. L'année dernière, l'entreprise représentait environ 11 % des capacités de production de wafers DRAM, ces grandes plaques de silicium sur lesquelles sont fabriquées les puces. Elle se classait derrière Samsung, SK Hynix et Micron. Sa part pourrait atteindre 15 % d'ici 2028 grâce à de nouvelles usines à Hefei, Shanghai et Pékin.Pour Apple, diversifier ses sources devient d'autant plus stratégique que les prix contractuels de la DRAM standard auraient progressé de 55 à 60 % au début de 2026. Les serveurs dédiés à l'intelligence artificielle absorbent une part croissante des capacités autrefois réservées à l'électronique grand public. Mais le dossier est aussi géopolitique. CXMT figure sur une liste du Pentagone recensant des entreprises soupçonnées de liens avec l'armée chinoise. Cette inscription n'interdit pas aux groupes américains d'acheter ses puces, contrairement au régime plus contraignant appliqué à YMTC, autre fabricant chinois placé sur l'Entity List du département du Commerce.Apple chercherait donc à éviter que CXMT subisse le même sort. Tim Cook aurait défendu auprès de l'administration américaine l'idée d'utiliser ces mémoires uniquement dans les appareils vendus en Chine. Cela permettrait de réserver davantage de composants Samsung, SK Hynix et Micron aux autres marchés. La stratégie rappelle une tentative menée en 2022 avec YMTC, finalement abandonnée sous la pression politique. Cette fois encore, aucun accord n'est garanti. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Wrap up your trading day for Tuesday, July 14, 2026, with Mark Longo on The Hot Options Report. It was a steady day on the tape, with the top 10 threshold landing just shy of the half-million contract mark at 497,000. Mark breaks down the social scans over at TheHotOptionsReport.com—highlighting what's trending up on X, including Luckin Coffee (LKNCY), Amkor Technology (AMKR), and a wild day for Carvana (CVNA). Then, we count down the top 10 most active equity options dominating the chains: Microsoft (MSFT): Paper leans toward selling the short-term 390 calls expiring Wednesday. Terawulf (WULF) & Oracle (ORCL): Massive action in WULF's August 25 calls, while Oracle hits a new 52-week low. Amazon (AMZN) & Micron (MU): Tech chips in as Amazon eyes the 250 strike and Micron rallies nearly 5% to flirt with par ($1,000). Intel (INTC) & Palantir (PLTR): Out-of-the-money lotto prints in Intel, and Palantir climbs the ranks to number 4. Apple (AAPL), Tesla (TSLA), & Nvidia (NVDA): The big dogs take center stage as Nvidia regains yesterday's losses, lighting up the Wednesday 210 calls. Find out where the smart money is leaning, which strikes are creating the most sweat equity, and how to scan the unusual activity for yourself at TheHotOptionsReport.com

In this episode of the Milk Road Show, we sit down with Matt Crosby, Director of Research & Analytics at Bitcoin Magazine Pro, to break down the latest Bitcoin onchain data, macro trends, ETF flows, and market cycles. Matt explains why a rare confluence of indicators, including MVRV, Realized Price, long-term holder cost basis, mining production costs, and global liquidity, suggests Bitcoin may be entering one of its most attractive buying opportunities since the 2022 bear market.~~~~~⁠⁠⁠

Carl Quintanilla, Jim Cramer and David Faber kicked off a new week with the AI trade: Shares of SK Hynix tumbled 15% for its worst-ever day in the South Korean markets. The stock also fell sharply on Wall Street after surging 13% Friday in its U.S. trading debut — and dragged down memory names including Micron and Sandisk. The anchors reacted to the Sam Altman-Elon Musk's social media spat in wake of Apple's lawsuit against OpenAI. Also in focus: Oil prices jump on U.S.-Iran tensions, SpaceX shares fall closer to their $135 IPO price, Cramer on the non-tech names worthy of your attention, Disney's live-action "Moana" stumbles, Meta and data centers, American Express upgraded, "Faber Report" on State AGs vs. Paramount-WBD deal.   Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Whether you're starting a business, growing one, or preparing to exit, the financial decisions you make today can have long-term tax and estate planning consequences. In Hour 1, Brian Wiley and Jeremiah Bates explain the differences between LLCs and S Corporations, when an S Corp election may make sense, how business structure affects taxes and liability, and why business owners should coordinate their entity planning with their estate plan. They also discuss cost basis, inherited assets, and avoiding costly mistakes before they happen. Hour 2 shifts to investing, covering concentrated stock positions, strategies for managing highly appreciated investments like Micron, investor psychology, and ways to think through market volatility, geopolitical events, and portfolio risk without letting headlines dictate your decisions. The final hour answers listener questions on business succession planning, selling a closely held business when family members don't want to take over, Qualified Opportunity Zones and the upcoming 2026 tax deadline, evaluating annuities within a comprehensive financial plan, and why fiduciary advice should focus on your entire financial picture—not just one investment account.   Listen, Watch, Subscribe, Ask! https://www.therealmoneypros.com ————————————————————— Ataraxis PEO https://ataraxispeo.com Tree City Advisors of Apollon: https://www.treecityadvisors.com Apollon Wealth Management: https://apollonwealthmanagement.com/ —————————————————————

In this episode of the Milk Road Show, we sit down with Nick Roberts-Huntley, CEO of Blueprint Finance, to unpack why Wall Street is quietly building the infrastructure that could power crypto's next major growth cycle. We discuss why Ethereum and Solana aren't necessarily competitors, how institutions are thinking about blockchain adoption, and why the biggest opportunity in crypto may be happening behind the scenes.~~~~~⁠⁠⁠

Jason Ware, Chief Investment Officer at Albion Financial Group, returns to break down one of the biggest shifts happening in the stock market today: the move away from the Magnificent Seven and toward AI infrastructure winners like Micron and other semiconductor companies. We discuss why memory stocks have become the hottest trade on Wall Street, whether hyperscalers are spending too much on AI, and what Microsoft's recent struggles really mean for investors. Jason also explains what he's watching heading into earnings season, why free cash flow matters more than ever, and how the new Federal Reserve under Kevin Warsh could shape markets in the second half of the year.

Valve offers Windows drivers now for the Steam Machine, Intel feels like they need to join to price rising brigades, Micron is fab, MiniPC's with last gen gear, and Nvidia will lend you money on easy terms!  Adobe has another vulnerability and Microsoft has a couple fresh ones too!So much more in the actual show ... especially the live version, which you probably missed.Timestamps:00:00 Intro01:22 Patreon02:53 Food with Josh 05:41 Steam Machine coverage continues (again)08:52 Intel Nova Lake and AVX-51210:04 Intel raises MSRPs on their best processors11:02 Intel restarts 13th and 14th Gen Core production - for China12:23 Lian Li B4 mATX makes another appearance15:01 Micron breaking ground on a new fab expansion16:01 Get ready for Mini PCs with previous-gen Intel CPUs and DDR417:33 Internal sound cards in 2026?20:25 Cover story - RTX 3060 12GB returns in 202623:54 An NVIDIA story about AI cloud26:24 Jeremy leads us into a story about Samsung's obscene profits28:22 Josh's Racing Corner32:38 (In)Security Corner50:07 Gaming Quick Hits57:12 Picks of the Week1:08:22 Outro ★ Support this podcast on Patreon ★

Charles is joined by Valtrion CEO Rob Luna to discuss how big tech companies like Meta are spending money now to make massive profits later, why popular tech tools like Micron, CrowdStrike, and Snowflake are still great long-term buys, and how to find hidden gems in small healthcare stocks. Learn more about your ad choices. Visit podcastchoices.com/adchoices

On this episode: Micron's earnings sparked excitement, but market volatility remains. Greg explains why retirement planning shouldn't depend on stock picking or chasing the next hot investment. From 401(k)s to advisory accounts, fees can quietly drain retirement assets. Greg breaks down the true cost of financial advice and why value matters. An advisor suggested borrowing instead of spending retirement money. Greg examines the math, tax consequences, and why some retirement advice may not serve clients. Delayed dreams, working too long, tax mistakes, and outdated estate plans. Greg shares lessons retirees wish they had learned sooner. Subscribe or follow so you never miss an episode! Check out Fire Your Financial Advisor on YouTube! Learn more at GoldenReserve.com or follow on social: Facebook & LinkedIn.See omnystudio.com/listener for privacy information.

Live July 9, 2026 | Yaron Brook Show(Season 12, Episode 119)War?; ICE; Cronyism; Soccer; Homeschooling; Chat Control; Costco; Achievement | Yaron Brook ShowIran on the Brink, ICE Backlash, Crony Capitalism & the Real Drivers of Human AchievementIs the world moving closer to another major war—or are politicians making every crisis worse? Why is ICE becoming increasingly controversial? Is "crony capitalism" really capitalism? And what actually creates human achievement?In this wide-ranging live episode, Yaron Brook tackles the latest developments in Iran and the Strait of Hormuz, critiques Trump's foreign policy, examines the growing political controversy surrounding ICE, exposes government-created cronyism in autonomous vehicle regulation, and discusses everything from equal pay in soccer and homeschooling to Europe's proposed chat-control laws.He also explores major developments in AI, nuclear energy, SpaceX, Micron's massive U.S. investment, Argentina's nuclear ambitions, and what these trends reveal about innovation, freedom, and the future of capitalism.The live Q&A dives into some of today's biggest philosophical and political questions—including loneliness, property rights, religion and morality, AI competition between China and the West, housing affordability, entrepreneurship, Peterson Academy, family obligations, Europe's economic decline, and whether eliminating income taxes could dramatically increase prosperity.If you enjoy serious analysis grounded in reason, individual rights, capitalism, and Objectivism, subscribe and join us live every week.Watch now: https://youtube.com/live/RGyErkMtXFsMain Topics00:00 Introduction01:10 World Cup update & announcements06:25 Iran, recent events & the Strait of Hormuz12:54 Attacks inside Iran & Gulf state involvement15:23 Trump's Iran strategy under scrutiny16:45 ICE raids, politics & public reaction29:46 Why is ICE becoming so unpopular?31:16 Robotaxis, New Jersey & cronyism34:19 U.S. Soccer's equal pay debate40:28 Brazil homeschooling victory45:20 Europe's "Chat Control" controversy49:54 The remarkable Costco employee story56:57 SpaceX's nuclear-powered satellite plans59:03 Micron's massive U.S. investment1:01:36 Argentina embraces nuclear energyLive Audience Questions1:17:21 Is collectivism the real cause of the loneliness epidemic?1:20:06 Do property rights include your view and sunlight?1:23:34 How heavily will Costco retirement savings be taxed?1:24:34 How is Yaron designing his Peterson Academy course?1:29:04 Would a self-hosted AI assistant benefit Yaron?1:29:53 Does ARI have a plan if America becomes authoritarian?1:30:44 Can morality exist without God?1:31:34 Is China winning the AI race through less regulation?1:32:09 Has statism crippled Europe's tech sector?1:35:20 Is inflation—not housing supply—the real housing crisis?1:42:56 What does Trump skipping his son's wedding say?1:43:56 What's the best order for Peterson Academy courses?1:45:39 Has Jordan Peterson watched Yaron's lectures?1:46:21 Would ending income taxes create millions more millionaires?1:47:06 Which poses the bigger threat: Catholic integralism or evangelical politics?1:47:17 Does Europe's welfare state discourage innovation?1:48:26 Can free banking support long-term investment?Subscribe for daily analysis on economics, politics, philosophy, technology, investing, and current events.#Iran #ICE #Capitalism #Objectivism #Trump #ai #ArtificialIntelligence #SpaceX #nuclearenergy #EconomicsThe Yaron Brook Show is Sponsored by[The Ayn Rand Institute](https://www.aynrand.org/starthere)[Energy Talking Points, featuring AlexAI, by Alex Epstein](https://alexepstein.substack.com/)[Express VPN](https://www.expressvpn.com/yaron)[Hendershott Wealth Management](https://www.youtube.com/watch?v=X4lfC...) &(https://hendershottwealth.com/ybs/)[Michael Williams & The Defenders of Capitalism Project](https://www.DefendersOfCapitalism.com)[Support the Show]( / yaronbrookshow )[Sponsor the Show](askyaron@yaronbrookshow.com/)[One-time donation](https://bit.ly/2RZOyJJ)Join the [Yaron Brook Show YouTube channel]( / @yaronbrook )Like what you hear? Like, share, and subscribe to stay updated on new videos and help promote the [Yaron Brook Show](https://bit.ly/3ztPxTx)Continue the discussion by following Yaron on [Twitter](https://bit.ly/3iMGl6z) and [Facebook](https://bit.ly/3vvWDDC )Want to learn more about Ayn Rand and Objectivism? Visit the [Ayn Rand Institute](https://bit.ly/35qoEC3)Become a supporter of this podcast: https://www.spreaker.com/podcast/yaron-brook-show--3276901/support.Yaron is the executive chairman of the Ayn Rand Institute and a world class speaker. He is the coauthor of the national best-seller Free Market Revolution: How Ayn Rand's Ideas Can End Big Government, Equal is Unfair: America's Misguided Fight Against Income Inequality and In Pursuit of Wealth: The Moral Case for Finance. He speaks around the world on a variety of topics including the morality of capitalism, Ayn Rand and her philosophy, finance and economics, and the value of inequality.

Story of the Week (DR):Social Media's 'Big Tobacco Moment': Meta Faces $1.4 Trillion Fine for Allegedly Fueling Teen Suicide and AddictionMeta is being sued by 33 US states, led by California, Colorado, Kentucky, and New Jersey.12 blue/12 red/9 purpleThe states allege that Meta deliberately designed Facebook and Instagram to be addictive to children and teens, fueling a youth mental health crisis (including anxiety, depression, self-harm, and suicide).They also accuse Meta of violating child privacy laws by collecting data from children under 13 without parental consent.Meta warned a federal court that it could face up to $1.4T in penalties if the states prevail at the upcoming trial (set for August 18, 2026). Meta extrapolated this massive figure—which is roughly equivalent to the company's entire stock market value—based on the methodology proposed by the lead states for calculating damages.Meta calls the penalty "outlandish" and "unsubstantiated," arguing it has no precedent in consumer protection history: 'A sanction of that size has no analog in the history of consumer protection enforcement.' The company accuses the states of improperly multiplying penalties (e.g., stacking fines based on daily usage time). Meta denies the allegations, asserting its platforms have extensive safety tools and that the claims are unmoored from actual unfair practices.‘I Don't Think I'm Ever Going to Stop,' Says Mark Zuckerberg. Even With 'Infinite Money,' He Has No Plans to Retreat to His Massive Hawaii EstateMeta found to breach EU laws with 'addictive' Instagram, Facebook designsInstagram and Facebook's “addictive” designs have put Meta in breach of the European Union's digital laws, the EU concluded Friday in a preliminary report.The tech giant violated the EU's Digital Services Act by failing to adequately consider the risks associated with design features that affected the physical well-being of its users, including minors and vulnerable adults, the European Commission said.These features include infinite scroll, which constantly shows fresh content, autoplay, push notifications and highly personalized recommendation systems — feeding users' compulsion to continue using platforms and putting them into “autopilot mode.”The EU Commission also accused Meta of ignoring available information about how much time young people are spending on Instagram or Facebook at night, and how different types of content formats, from reels to stories, could lead to excessive use of its services.Meta said, “We disagree with these preliminary findings.”New Zealand Moves To Ban Climate Change Litigation. Will The U.S. Follow?New Zealand has proposed a bill to limit the ability of individuals to sue high greenhouse gas emitters over the impacts of climate change, relying instead on the enforcement measures taken by the government. The bill appears poised to pass.The women who wouldn't let climate data disappear MMAfter losing their jobs at National Oceanic and Atmospheric Administration (NOAA), Rebecca Lindsey, her sister Mary and colleague Anna Eshelman teamed up to rebuild a pivotal resource the Trump administration took offlineRebecca Lindsey, a technical writer for NASA–one of 280,000 federal workers fired by Musk/Trump, joined forces with former NOAA employees Anna Eshelman, and Mary Lindsey, her older sister, to become the core team behind the deactivated site's successor, Climate.us, preserving over 15 years of key climate data and resources.Elon Musk says he always wanted his SpaceX employees to get rich — and now thousands of them are millionairesElon Musk's 'Chainsaw for Bureaucracy' Just Left an $11 Billion Budget Hole as Trump Rehires StaffThe trove features key maps, educational materials and climate indicator reports, including the now-deleted Fifth National Climate Assessment, the government's most comprehensive analysis of climate change that was at risk of being lost to the publicJersey Mike's $12 billion IPO filing reveals a $50 million payday for the founder's stepson and a $41 million jetFamily members of founder Peter Cancro were employed Jersey Mike's in various roles and received compensation in excess of $120,000 from the Company as follows for the years ended December 28, 2025 and December 31, 2024 and 2023:John Cancro, Mr. Cancro's brother, received total compensation of approximately $20,019,231, $519,231 and $500,000, respectively;Paul J. Cancro, Mr. Cancro's son, received total compensation of approximately $8,001, $216,022 and $208,023, respectively;Robert Cancro, Mr. Cancro's son, received total compensation of approximately $38,462, $1,038,462 and $1,000,000, respectively;Tatiana Cancro, Mr. Cancro's wife, received total compensation of approximately $11,538, $311,539 and $300,000, respectively;Caroline Jones, Mr. Cancro's daughter, received total compensation of approximately $38,462, $1,038,462 and $1,000,000, respectively;Alexandra Powers, Mr. Cancro's sister-in-law, received total compensation of approximately $0, $1,165,437 and $0;Daniel Powers, Mr. Cancro's brother-in-law, received total compensation of approximately $30,213,462, $1,793,952 and $0;John Tesauro Jr., Mr. Cancro's brother-in-law, received total compensation of approximately $0, $0 and $2,429,628, respectively;Phillip Sivolobov, Mr. Cancro's stepson, received total compensation of approximately $50,011,538, $311,539 and $276,923, respectively.GRAND TOTAL: $112MStepson Phillip got $51M, brother John got $21MOther Peter Cancro schwag in 2025:Got a $41 million jet and an additional fixed amount of $166,666.66 per month in light of the business expenses incurred by Mr. Cancro related to air transportation to travel from time to time for business purposes.Lease agreements valued at $1M in rent (leases go to 2030) Controlled company: Blackstone (will control more than 50% of voting power)Board: 8 directorsBlackstone:Chair Nigel Travis (also chair of Abercrombie & Fitch)David N. KestnbaumDevon L. RinkerMichael J. StaubFounder/former CEO/chair: Peter CancroCEO Charles R. MorrisonCheryl S. Miller, director on two controlled companies:Tyson FoodsOld Dominion Freight Line (Congdon brothers)Fran Horowitz, CEO of Abercrombie & Fitch (where chair serves as chair)Goodliest of the Week (MM/DR):DR: Amazon, Walmart and Other Large Employers Could Face New Costs As New Jersey Targets Companies With Medicaid Workers— Will Other States Follow?DR: UBS says rich people will be younger, female and openly queer thanks to the Great Wealth TransferMM: Meta Buried Research Linking Instagram To Teen Harm While Facing $1.4 Trillion Penalty That Could Erase Its Entire WorthMM: ESG!Madison Square Garden Kept a List of Gay CelebritiesAn internal Madison Square Garden database of VIPs labels Joe a “medium risk,” one of roughly 400 celebrities given a risk score.If you're a celebrity and you're marked with a risk score—even as a low risk—it means “you've done something in the publicity world, the social media world, that has caught the attention of the wrong people,” the source continues.The talent database also tracks some celebrities' race, gender identity, and sexual orientation; 93 entries are marked as “LGBTQIA.”MM: California vs. Elon Musk: Tesla Snubbed as New EV Incentives Boost Rivian, Lucid MM DRAssholiest of the Week (MM):Billionaire amplificationKen Griffin says everyone is misinterpreting the AI revolution — and wishes Zohran and Bernie would ‘read a damn history book for once'“[Capitalism is] the greatest success story in the history of humanity,” Griffin said, urging the self-identified socialist politicians, “whether it's Bernie Sanders, whether it's Mamdani,” to “read a damn history book for once and then tell us how to run our country.”Jeff Bezos 'Made All of Our Lives So Much Better,' Says Billionaire Investor Tim Draper"Amazon has made all of our lives so much better," Draper said.Draper said he has benefited from what Bezos has done, and that's a part of the world economy that isn't spoken about enough."Those geniuses who create this incredible world for us are benefiting all of us."40 Epstein-Tied Billionaires Have Injected $1.6B Into US Elections, Report FindsThese are the millionaires and billionaires pledging to fund Trump accountsZuckMeta AI Data Centre Contractor Triggers Biohazard Scare After Flushing Rare Bacterium Into Public SewersMeta Platforms To Build $9 Billion A.I. Data Centre In CanadaThe $145 Billion Lie? Zuckerberg's Leaked Town Hall Audio Exposes Massive AI Failures After Mass LayoffsMeta jumps into AI coding market in effort to chase Anthropic and OpenAIWhat person in their right mind would trust Zuckerberg with their coding?Hollywood Vs Zuckerberg: CAA Warns Meta's AI Image Tool Needs A Major Privacy Overhaul‘I Don't Think I'm Ever Going to Stop,' Says Mark Zuckerberg. Even With 'Infinite Money,' He Has No Plans to Retreat to His Massive Hawaii EstateJeff Sonnenfeld - DRIn defense of Musk, SpaceX, and dual class shares“the rigid formulas of proxy advisors create a perverse, socially destructive incentive: hoard your wealth like an oligarch to maintain your good governance rating, or give it away and risk losing your company”“The proxy advisors want a world governed by rigid mathematical formulas because auditing a checklist is easy. Evaluating human character, industry dynamics, track records of success and failure, and the capacity for visionary leadership is hard. But it is exactly that hard work of judgment which is vital. When it comes to dual-class shares, it is time for the critics to step out of the theoretical vacuum and look at the real-world scoreboards”Headliniest of the WeekDR: OpenAI Wants a $1 Trillion Valuation. But College Students Are Testing At The Level Of 10-Year-Olds AND Suspecting AI cheating, Ivy League prof ordered an in-person final; scores fell 50%MM: ‘Waymo Takes Revenge, Dropping Drunk Teens Directly Into Squad of CopsMM: West Virginia spent $3M to create university program to fight ‘woke ideology.' One student is enrolledWho Won the Week?DR: Free Float's new platformAnd blowhard Jeffrey Sonnenfeld for arguing that dualclass owners are necessary because the dualclass mechanism allows them to sell shares and maintain voting power so they can “cure diseases, endow universities, and combat poverty” MM: Free Float data: Elon Musk and the age of the corporate leviathanAbove a certain size the ordinary rules of governance apparently cease to apply.Of the 16 listed firms worth more than $1trn, seven are shareholder fundamentalists.None has an elaborate statement of corporate purpose, since they are mostly content making heaps of moneyFree Float says Nvidia, Amazon, Broadcom, Micron are all TOTALITARIANNext are the corporate paternalists, who believe that the problem with shareholder democracy is that its voters do not know what is best for them.Free Float says Berkshire, Google, Meta are all TOTALITARIANThe final clan presents the strongest argument against the end of corporate history: individual shareholders consent to hand over all of their rights to the world's richest man, who then governs as he sees fit.Free Float says SpaceX, Tesla are TOTALITARIANBasically, it's nice of the economist to recognize everything Free Float says every weekPredictionsDR: Jeff Sonnenfeld writes something on Fortune that triggers meMM: Jeff Sonnenfeld writes something on Fortune that triggers Damion

The MacVoices Live! panel dives deep into Apple's across-the-board price increases, weighing rising RAM, storage, inflation, shareholder obligations, and product value. Chuck Joiner, David Ginsburg, Brian Flanigan-Arthurs, Ben Roethig, Eric Bolden, Marty Jencius, Jeff Gamet, Norbert Frassa, Jim Rea, and Web Bixby debate whether higher prices are a short-term reset or the new normal, how buyers may react, why the MacBook Neo increase stings, and whether refurbished or education pricing may become more important.  This edition of MacVoices is brought to you by the MacVoices Dispatch, our weekly newsletter that keeps you up-to-date on any and all MacVoices-related information. Subscribe today and don't miss a thing. Show Notes: Chapters: 00:00 Debating Apple's Price Increase Strategy00:58 Component Costs and Market Pressures01:32 Which Product Increases Hurt Most02:44 A Strategy for Future Hardware Pricing03:37 The New Normal for Technology Costs04:34 RAM Suppliers, Micron, and Apple's Buying Power05:56 Apple Stock Reaction and Market Recovery07:27 Why Apple May Not Absorb the Costs08:23 Long-Term Investing Versus Short-Term Reaction10:59 Inflation and Years of Held-Down Prices11:32 Will Higher Prices Stop Purchases?12:39 Upgrade Timing and Desktop Mac Decisions14:00 Apple Pricing Versus Other Ecosystems15:01 MacBook Neo Price Increase and Optics17:08 Refurbished Macs as a Price Alternative18:21 Education Discounts and Login Requirements18:57 Final Thoughts on Across-the-Board Increases Links: Apple Raises Prices for Many Products – TidBITShttps://tidbits.com/2026/06/26/apple-raises-prices-for-many-products/   Micron Executive Insinuates Apple Partially Caused Memory Price Hikeshttps://www.mactrast.com/2026/06/micron-executive-insinuates-apple-partially-caused-memory-price-hikes/ Guests: Get detailed bios and contact information about for the panel on the MacVoices Live! Panel page on our web site:https://macvoices.com/macvoiceslive/macvoices-live-panel/ 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

Carl Quintanilla, Jim Cramer and David Faber explored stocks rising and oil prices falling, despite the U.S. and Iran trading military strikes for a second consecutive night. Tech and the AI trade getting a big lift from a surge in semiconductor stocks, fueled in part by Micron announcing plans to boost AI spending in the U.S. to $250 billion through 2035. PepsiCo CEO Ramon Laguarta joined the program to discuss the company's mixed quarterly results and the consumer. The anchors grilled him about what it would take to boost the snack and beverage giant's business and sluggish stock performance. Also in focus: Meta AI chip buzz, Costco shares slide on June sales, Salesforce gets downgraded, SK Hynix gears up for its U.S. listing debut slated for Friday.   Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Host Mark Longo breaks down today's wild market action, starting with the 5K Club scans on TheHotOptionsReport.com featuring Keel Infrastructure, Caesars, and StubHub. Then, we dive into the Top 10 most active options chains of the day, breaking down volume, hot contracts, and what paper was doing ahead of the upcoming expirations. On today's report: Tech and Chip Volatility: Massive moves in Micron rallying over 4 percent, Meta surging past the 600 strike on AI compute news, and Intel's deep in-the-money options activity. The Trillion-Dollar Club: Breaking down heavy expiration volume in Nvidia, Tesla, Apple, and Microsoft. Market Movers: Surprising volume spikes in Oracle, Amazon, and SpaceX. Find your own unusual activity at TheHotOptionsReport.com.

Dan Nathan and Guy Adami open the podcast by framing the day's key market story as sharp weakness and heightened volatility in memory, chips, and semi equipment, citing rapid reversals in names like Micron and Applied Materials and opaque guidance from Samsung. They discuss whether the AI build-out is increasingly debt-funded—highlighting Amazon and Oracle debt issuance and private credit deals—and reference a “Groundbreaker” piece shared by Jim Chanos arguing investors miss second-derivative slowdowns and that AI resembles a credit-driven real estate cycle more like 2008 than 2000's patient equity bubble. They note a rotation bounce into software (Microsoft, Palantir, Salesforce, Adobe, ServiceNow) but question sustainability, and debate SpaceX's fast-tracked inclusion in the Nasdaq 100 after its IPO, the stock's poor trading, possible broader IPO implications, and an upcoming insider share unlock —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

Anthropic se está comiendo a sus propios clientes. Figma la acusa de robarse su tecnología después de meses de trabajar juntos. El gobierno de Estados Unidos ya no confía en tener sus modelos de IA en manos de terceros y contrata a Palantir para construir los suyos propios con hardware propio.Carlos Muñoz y Ricardo Moreno destapan el caso que tiene a Silicon Valley hablando: cómo una empresa de inteligencia artificial terminó compitiendo con quienes la contrataron, por qué esto es distinto a lo que hizo Meta o Google en su momento, y por qué el "Memory Trade" de Micron movió miles de millones en la bolsa el mismo día que nadie lo vio venir.━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

This week on Market Mondays, we tackled the biggest stories shaping the markets, technology, and investing. From the controversy surrounding Trump's investment accounts and crypto allegations to NVIDIA's bold new AI startup strategy, we broke down what matters—and what investors should ignore.We also discussed Michael Saylor's latest Bitcoin sale, the SK Hynix IPO, warnings of a potential AI bubble, Alex Karp's passionate comments on AI spending, TSM's long-term outlook, whether QQQ is still the best ETF choice, lessons from the 2026 market rally, Wall Street's biggest forecasting mistakes, which companies have the strongest competitive moats, and the one private company we'd invest in today. Plus, we answered a practical question: if you started over with $50,000, debt, and a low credit score, how would you rebuild your financial future?Whether you're investing for the long term, trading today's market, or looking to stay ahead of the biggest trends in AI, crypto, and equities, this episode is packed with actionable insights to help you make smarter investment decisions.TIMESTAMPS:00:00 Why Wealth Matters00:33 Show Disclaimer01:08 July Check In01:48 Live Week Schedule02:46 Salon Suite Spotlight04:49 Community Shoutouts05:36 Market Facts Roundup07:17 Semiconductor Volatility09:44 Invest Fest Youth Day11:21 Catering Callout14:21 Relationships Barter Play16:03 Singles Lounge Launch18:00 Trump Accounts Explained19:09 Barriers Trust Education24:17 Compounding Math Examples28:59 ETF Alternatives Plan30:19 Reaching Those In Need33:11 Website Robinhood Details34:20 Culture Responsibility Talk37:38 Spend It Culture38:33 Trump Account Alternatives39:23 Trump Meme Coin Fallout41:12 Rug Pull Mechanics43:59 Crypto Scam Culture46:02 Equities Influence Shift48:30 Presidential Trading Stats52:03 NVIDIA Startup Strategy54:57 Compute for Revenue Share58:30 NVIDIA as Venture Capital01:03:06 Relationship Capital Banter01:05:51 50K Reset Plan01:11:24 Debt Versus Market Returns01:15:37 MicroStrategy Dividend Sales01:22:12 SK Hynix ADR Debut01:23:43 Memory Bottleneck Thesis01:25:15 IPO Signals to Watch01:26:31 Micron vs Hynix Outlook01:31:11 Valuations and Patience01:35:33 AI Bubble Reality Check01:39:41 Alex Karp Safety Rant01:48:23 Who Owns the Stack01:53:01 TSM Earnings Preview01:55:27 Core Four Investing01:57:06 Events and Community01:58:19 World Cup Banter02:01:32 Final Sendoff#MarketMondays #Investing #Stocks #StockMarket #AI #ArtificialIntelligence #NVIDIA #Bitcoin #Crypto #MichaelSaylor #TSMC #QQQ #ETFs #WealthBuilding #Finance #Business #LongTermInvesting #Trading #EarnYourLeisure #MarketAnalysisAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Jordi Visser is a veteran macro investor with 30+ years of experience and the author of the VisserLabs Substack. In this conversation, we break down the AI mid-cycle slowdown, the Fed under Warsh and what rate cuts could mean for markets, the $90 trillion AI infrastructure buildout, memory and Micron's role in the AI bottleneck, and why Jordi believes bitcoin is positioned to be the best performing assets once the negatives of AI start to show up.=====================Need liquidity without selling your crypto? Take out a Figure Crypto-Backed Loan, allowing you to borrow against your BTC, ETH, or SOL with 12-month terms, 8.91% interest rates, and no prepayment penalties. Or check out Democratized Prime (https://figuremarkets.co/pomp) and earn ~9% APY on real world assets, paid hourly. Unlock your crypto's potential today at Figure! https://figuremarkets.co/pomp Figure Lending LLC dba Figure (NMLS 1717824). Loans subject to approval. Crypto collateral may be liquidated. Terms apply - see full disclosures at figure.com/disclosures/=====================Looking for a better place to trade? BloFin gives traders access to deep liquidity, advanced futures products for crypto AND TradFi assets, fast execution, and a clean, intuitive interface—all in one platform. To celebrate their partnership with us, they're giving away $100,000 in Deposit & Trade Rewards. Deposit, trade, and earn rewards based on your activity during the campaign. Check them out at ( https://partner.blofin.com/d/Pomp ).=====================This episode is brought to you by mogul ( https://www.mogul.club/pomp ). Deloitte estimates that $4 trillion of real estate will move onto the blockchain over the next decade. Through tokenized residential real estate, mogul gives investors access to professionally managed properties with targeted yields, monthly rent payouts, and potential tax benefits — all without the headaches of being a landlord. Learn more and claim a special offer at https://www.mogul.club/pomp . See important disclosures at disclaimer.mogul.club.=====================0:00 - Intro1:20 - Has bitcoin bottomed? 6:03 - Bitcoin vs the Mag 7: is the correlation breaking?9:00 - Sam Altman, OpenAI & the government getting involved11:11 - American open source vs Chinese AI models14:28 - AI compute demand & the infrastructure buildout20:37 - AI slowdown: how bad, how long?22:44 - Where do markets go from here? 27:30 - AI agents in the workplace & security risk34:50 - Memory, Micron & the AI bottleneck39:53 - The next 10-20 years: robots, AI & society44:18 - The Fed, rate cuts & inflation's impact on AI49:55 - Why bitcoin wins in the end