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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

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

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

SpaceX  – Price almost $135 – full retracement. Earnings season in on –  here we go! Inflation – choppy. War back on! Straits Open? Or? New Diet? CYCLOSPORIASIS PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? PayPal.Donation.Button({ env:'production', hosted_button_id:'JJJHP2GDEJC7J', image: { src:'https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif', alt:'Donate with PayPal button', title:'PayPal - The safer, easier way to pay online!', } }).render('#donate-button'); Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter Warm-Up - CTP for SpaceX  - Price almost hit $135 yesterday - Earnings season 0 here we go! - Inflation - choppy - War back on! Straits Open? Or? New Diet? CYCLOSPORIASIS Markets - Inflation - Some Relief - IBM's Pre-Annnouncment - What does this say? - Bank earnings and an earnings cheat sheet - Some interesting chart data Health Update: Meniscus Repair next Thursday.... Today IBM stood for 'I Be Melting' as Big Blue turned into Big Blew Up... IBM: It's Been Murdered IBM: I Bought Misery IBM: Investment Board Malfunction Big Blue looked more like Deep Red today The only cloud around IBM today was hanging over the stock chart IBM investors got a free software update: Version 2.0 of disappointment. IBM'S AI FACE-PLANT - IBM shares plunged as much as 25%, putting the stock on pace for its worst session on record. - Preliminary revenue was $17.2 billion versus expectations near $17.9 billion. - Adjusted earnings were projected at $2.93 per share versus roughly $3.01 expected. - Management said customers redirected spending toward AI servers, memory and hardware while delaying software purchases. - The drop removed roughly 375 points from the price-weighted DJIA. - JCD and AH were both right and wrong for the weekly stock picks TRUTH? - President Trump says U.S. blockade will apply only to ships from Iranian ports; says Strait of Hormuz is open to all traffic except for Iran; says "Based on highly productive conversations with Middle East leadership, I have decided to replace the 20% United States reimbursement fee with trade and investment deals that the various Gulf States will be making into the United States" - Can never be proven - no real numbers here.... Clearly needed to walk back the 20% item - Hormuz still hobbled - best estimates are that the ships passing are 20% of pre-war levels INFLATION RELIEF - FOR NOW - June CPI rose 3.5% year over year, down from 4.2% in May and below the 3.8% consensus. - Core CPI held at 2.6%. - Falling gasoline and energy prices drove much of the improvement. - Traders sharply reduced expectations for a July Fed rate increase. - Treasury yields fell and the Nasdaq advanced. WALL STREET BANKS PRINT MONEY - JPMorgan posted $21.2 billion in net income, helped by special items. - Markets revenue rose 35% to $12.1 billion, including an 86% jump in equities revenue. - Bank of America earned $9.1 billion as equities trading revenue increased 70%. - Goldman Sachs reported earnings of $20.98 per share and a 23.5% annualized return on common equity. - Jamie Dimon described conditions as close to "as good as it gets." THE $26.5 BILLION AI-MEMORY IPO - $ GRAB - SK Hynix raised $26.5 billion through a Nasdaq ADR listing. - The offering priced at $149 and finished approximately 13% higher the day of the offering, but sunk the next. - Demand reportedly exceeded the available shares by more than seven times. - SK Hynix supplies high-bandwidth memory used in Nvidia-powered AI systems. - The listing gives U.S. investors direct access to one of the largest beneficiaries of AI infrastructure spending. - - Samsung is on tap to do the same thing... Some Interesting Charts Best Quarters Plus One Equal vs Cap-Weight OIL'S CEASEFIRE WHIPLASH - Brent crude jumped 5.2% to $78.02 a barrel after the U.S.-Iran ceasefire broke down. - WTI rose 4.4% to $73.52. - Markets repriced the risk of interrupted tanker traffic through the Strait of Hormuz. - Energy stocks gained while airlines and the broader market weakened. - Rising oil prices could quickly reverse the energy-related improvement seen in the June inflation report. APPLE SUES OPENAI - AI PARTNERS TURN RIVALS - Apple sued OpenAI and two former Apple employees on July 10, alleging coordinated theft of hardware trade secrets. - The defendants include OpenAI hardware chief Tang Tan and technical employee Chang Liu, both former Apple employees. - Apple claims confidential product designs and manufacturing information were taken to accelerate OpenAI's consumer-device program. - More than 400 former Apple employees reportedly now work at OpenAI, highlighting the scale of the talent migration between the companies. IPO AND MEGADEAL FEVER RETURNS - Global deals valued above $10 billion reached record levels during the first half of 2026. - Mega-deals represented approximately 43% of newly announced M&A volume. - Investment-banking revenue surged across JPMorgan, Bank of America and Goldman Sachs. - Large offerings from SpaceX and SK Hynix added momentum to underwriting activity. - The boom depends on high equity valuations, strong AI demand and large transactions continuing. BANKS BANKS BANKS JPMORGAN CHASE - RECORD PROFIT - Profit reached a record $16.9 billion, or $6.14 per share, versus $5.59 expected. - Managed revenue totaled $58 billion, with every major business reporting record revenue. - Markets revenue rose 35%, led by an 86% surge in equities trading. - Investment-banking revenue increased 30% to its highest level since 2021. - Jamie Dimon warned that geopolitical tensions, sticky inflation, fiscal deficits and elevated asset prices remain major risks. BANK OF AMERICA - TRADING RECORD - Net income rose 27% to $9.1 billion, or $1.21 per share, versus $1.13 expected. - Revenue increased 15% to $31.6 billion. - Sales and trading revenue jumped 33% to a record $7.1 billion; equities revenue rose 70%. - Investment-banking fees increased 50% to $2.1 billion. - Full-year net-interest-income growth is now expected near the upper end of the previous 6% to 8% range. GOLDMAN SACHS - DEAL BOOM - Profit reached $6.63 billion, or $20.98 per share, versus $14.48 expected. - Revenue totaled $20.3 billion. - Equities revenue surged 72% to a record $7.42 billion. - Fixed-income, currency and commodities revenue increased 32% to $4.59 billion. - Investment-banking fees jumped 55%, helping send Goldman shares to a record high. CITIGROUP - DECADE-HIGH REVENUE - Net income jumped 45% to $5.8 billion, or $3.15 per share, versus roughly $2.74 expected. - Revenue rose 14% to $24.8 billion, the bank's highest quarterly revenue in a decade. - Investment-banking revenue increased 44% to $1.55 billion. - Equities trading revenue rose 45%, while fixed-income trading increased 7%. - Return on tangible common equity reached 13%, matching the upper end of management's target range. WELLS FARGO - BACK ON OFFENSE - Net income rose 22% to $6.4 billion, or $2.00 per share. - Revenue reached $22.6 billion and topped expectations. - Investment-banking revenue increased 20%. - Markets revenue rose 24% as volatility boosted client activity. - Management said the removal of regulatory growth restrictions is allowing the bank to deploy capital and expand its balance sheet. Bank Returns Post Earnings (July 14, 2026) Bank Stocks Earnings Cheat Sheet - JULY 15 - ASML: Expected EPS around $7.92 on $10.25 billion revenue; bookings, EUV demand and updated AI-chip equipment guidance will matter most. - JULY 15 - JOHNSON & JOHNSON: Expected EPS around $2.86 on $25.02 billion revenue; watch pharmaceutical growth, medical-device demand and full-year guidance. - JULY 15 - MORGAN STANLEY: Expected EPS around $2.89 on $19.38 billion revenue; trading, investment banking and wealth-management inflows are the key numbers. - JULY 15 - BLACKROCK: Expected EPS around $12.59 on $6.80 billion revenue; assets under management, ETF flows and private-market fundraising will be in focus. - JULY 16 - TSMC: Expected EPS around $3.76-$3.77 on roughly $40 billion revenue; AI demand, gross margin and any increase to capital-spending guidance are critical. - JULY 16 - UNITEDHEALTH: Expected EPS around $4.84 on $110.8 billion revenue; medical-cost trends and the durability of full-year guidance are the main issues. - JULY 16 - GE AEROSPACE: Expected EPS around $1.85 on $11.8 billion revenue; engine deliveries, service revenue and supply-chain constraints will drive the reaction. - JULY 16 - NETFLIX: Expected EPS around $0.79 on $12.58 billion revenue; advertising growth, engagement and operating-margin guidance will matter more than subscribers. - JULY 17 - TRAVELERS: Expected EPS around $5.33 on roughly $11 billion revenue; catastrophe losses, insurance pricing and reserve development are the key swing factors. - JULY 17 - FIFTH THIRD: Expected EPS around $0.98 on $3.25 billion revenue; net-interest income, deposit costs and credit quality will be closely watched. WAYFAIR GOES PHYSICAL - Wayfair opened its second large-format store in Atlanta on March 31, following the 2024 debut of its 150,000-square-foot Wilmette, Illinois flagship. - The company is building a national store network, with Denver expected later in 2026 and Yonkers, Cincinnati and Princeton locations planned for 2027. - The stores combine furniture, decor, appliances and home-improvement products, giving customers a chance to test large purchases before ordering. - The strategy is a major reversal for an online-first retailer and is designed to increase brand awareness, reduce dependence on digital advertising and capture shoppers returning to physical stores. AI SOVEREIGN WEALTH FUND - PUBLIC OWNERSHIP DEBATE - Bernie Sanders introduced legislation requiring the largest AI companies to transfer 50% of their equity into a U.S. sovereign wealth fund. - The fund is projected by supporters to hold roughly $7 trillion and could finance annual public dividends and government services. - OpenAI has separately floated a much smaller proposal in which major AI companies would voluntarily contribute about 5% of their equity. - Supporters call it a way to share AI-created wealth; critics warn government ownership could discourage investment, distort regulation and reduce innovation. PSA  - That's what we do....CYCLOSPORIASIS OUTBREAK - CASES SURGE - Cyclosporiasis cases are rising across more than 30 states, with Michigan, Ohio and New York reporting particularly large increases. - The CDC reported at least 843 confirmed cases and 86 hospitalizations by July 9, but state totals and reporting delays suggest the real count is substantially higher. - Lettuce, salad greens and other fresh produce are being investigated, although officials have not identified a specific product, supplier or national recall. - The parasite causes prolonged or recurring watery diarrhea; washing produce may reduce risk, and persistent symptoms should prompt medical testing and treatment. HOW TO REDUCE CYCLOSPORIASIS RISK (FWIW) - Wash hands with soap before preparing food and after using the bathroom. - Rinse fresh fruits, vegetables and herbs thoroughly under running water; scrubbing helps but cannot guarantee removal. - Keep raw produce separate from unwashed items, dirty utensils and preparation surfaces. - When traveling in tropical or subtropical areas, use safe water and avoid raw produce you cannot peel yourself; routine chemical sanitizers may not kill Cyclospora. Love the Show? Then how about a Donation? PayPal.Donation.Button({ env: 'production', hosted_button_id: 'JJJHP2GDEJC7J', image: { src: 'https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif', alt: 'Donate with PayPal button', title: 'PayPal - The safer, easier way to pay online!' } }).render('#donate-button-2'); THE CLOSEST TO THE PIN for SpaceX (SPCX) Winners will be getting great stuff like the new "OFFICIAL" DHUnplugged Shirt!     FED AND CRYPTO LIMERICKS   See this week's stock picks HERE Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter

ASML heeft een extreem goed kwartaal achter de rug. De omzet steeg met 21 procent, de winst ging met 26 procent omhoog. Beide veel beter dan gedacht. Maar belangrijker, voor de tweede keer dit jaar gaat de omzetverwachting omhoog. Ook die verhoging was niet verwacht. Met al dat goede nieuws zou je denken: het aandeel gaat door het dak. Aanvankelijk leek het erop. Aan het begin van de beurshandel stond het aandeel zo'n 7 procent hoger, maar de koers zakte als een oude onderbroek. Deze aflevering kijken we waar dat pessimisme vandaan komt. Ook lopen we uitgebreid door de cijfers en de verwachtingen heen. Je hoort meer over het opschalen van de productie, over de orders en over de mogelijke aandelensplitsing. Gaat het ook over Stripe, de betaalverwerker. Dat is een Amerikaanse concurrent van Adyen die groeit door overnames. Nu willen ze de grootste overname uit hun geschiedenis doen: voor ruim 50 miljard het kwakkelende PayPal opkopen. Het lijkt een bedreiging voor Adyen, maar toch gaat dat aandeel opvallend goed op het nieuws. De Zuid-Koreaanse beurs komt ook langs. De beurs doet het dit jaar erg goed, maar heeft ook wat manische periodes. De koers schommelt nogal. Het gaat zo hard dat zelfs de president van het land ingrijpt! Te gast: Jordy Beuving van De Aandeelhouder BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie.See omnystudio.com/listener for privacy information.

ASML heeft een extreem goed kwartaal achter de rug. De omzet steeg met 21 procent, de winst ging met 26 procent omhoog. Beide veel beter dan gedacht. Maar belangrijker, voor de tweede keer dit jaar gaat de omzetverwachting omhoog. Ook die verhoging was niet verwacht. Met al dat goede nieuws zou je denken: het aandeel gaat door het dak. Aanvankelijk leek het erop. Aan het begin van de beurshandel stond het aandeel zo'n 7 procent hoger, maar de koers zakte als een oude onderbroek. Deze aflevering kijken we waar dat pessimisme vandaan komt. Ook lopen we uitgebreid door de cijfers en de verwachtingen heen. Je hoort meer over het opschalen van de productie, over de orders en over de mogelijke aandelensplitsing. Gaat het ook over Stripe, de betaalverwerker. Dat is een Amerikaanse concurrent van Adyen die groeit door overnames. Nu willen ze de grootste overname uit hun geschiedenis doen: voor ruim 50 miljard het kwakkelende PayPal opkopen. Het lijkt een bedreiging voor Adyen, maar toch gaat dat aandeel opvallend goed op het nieuws. De Zuid-Koreaanse beurs komt ook langs. De beurs doet het dit jaar erg goed, maar heeft ook wat manische periodes. De koers schommelt nogal. Het gaat zo hard dat zelfs de president van het land ingrijpt! Te gast: Jordy Beuving van De Aandeelhouder BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie.See omnystudio.com/listener for privacy information.

Did you ever wonder why so many people didn't get out before the dot-com crash? It's an important question to ask yourself, especially if you believe you'll know exactly when to get out before any potential correction in today's AI and semiconductor stocks.   The reality is that the dot-com bubble burst only 25 years ago. Human nature hasn't changed since then. Investors today are no smarter than investors were back then, and the same emotions that drove the bubble are showing up again. There were four major reasons so many people lost money during the tech bust.   The first was that investors stopped focusing on earnings and price-to-earnings ratios. Instead, they justified sky-high valuations by looking at metrics like website traffic, page views, click-through rates, and the number of "eyeballs" on a screen. The assumption was that if revenue kept growing, profits would eventually follow. Many ignored the reality that businesses also have expenses, competition, and execution risk.   The second reason was FOMO or the fear of missing out. Between 1995 and 2000, the Nasdaq surged roughly 400%. As people watched friends, coworkers, and investors make fortunes on tech stocks and IPOs, more and more money poured into the market. Institutional investors and retail investors alike stopped worrying about valuations. They simply saw stocks going up and didn't want to miss the ride.   The third reason was the belief that "this time is different." You heard it everywhere: "You just don't get it. This is the new economy." Investors argued that traditional valuation metrics no longer mattered because the only thing that counted was gaining market share. Profitability could always come later.   The fourth reason was the assumption that capital would never dry up. Few investors paid attention to where companies were getting their money. Many businesses were surviving on venture capital rather than sustainable profits. When funding slowed and investors became more selective, those companies had no profitable business model to fall back on. Many quickly went bankrupt.   At the peak of the bubble, investors stopped asking basic questions. What am I paying for this company's earnings? What am I paying for its cash flow? In many cases, there weren't any. Yet investors convinced themselves the speculative frenzy would continue indefinitely.   The biggest lesson is a humbling one. We like to believe we'll recognize the top and get out before everyone else. But investors in 2000 believed the same thing. Human psychology hasn't changed, which is why bubbles continue to repeat throughout history.   Don't Build That Data Center in My Backyard The race to build AI infrastructure is running into an obstacle that many investors probably didn't see coming: local communities.   Across the country, residents are protesting and filing lawsuits to stop new AI data centers from being built in their neighborhoods. One of the biggest concerns is something most people never think about, the constant noise. Data centers operate around the clock, with cooling fans, chillers, and backup generators creating a continuous hum 24 hours a day. That may not sound like a major issue until you have to live next to it.   New York has become one of the focal points of this debate. While the state has plenty of available land for development, many communities are pushing back. Governor Kathy Hochul is even considering legislation that would place a moratorium on the construction of large data centers in certain areas.   Public opinion reflects that growing resistance. According to recent polling, 44% of Americans oppose additional data center construction, while only 21% support it. When the question becomes more personal and whether people would support a data center being built in their own community, opposition jumps to 57%, while support falls to just 14%.   Residents also question the long-term economic benefits. Building a data center may create thousands of construction jobs, but once the facility is complete, permanent employment may fall to just 100 to 200 workers. At the same time, these facilities consume enormous amounts of electricity. In some regions served by smaller utilities, a single data center could account for as much as 25% of total power demand, raising concerns about higher electricity costs and increased strain on the grid.   The political landscape is becoming more challenging. Lawmakers in states including Arizona, Illinois, and Ohio have restricted or eliminated tax incentives that were previously used to attract data center investment.   Even the companies building this infrastructure recognize the growing risk. The hyperscalers are expected to spend nearly $1 trillion on AI infrastructure this year, but increasing public opposition could slow those plans. Nebius Group, for example, warned in its 2025 annual report that rising resistance to data center projects in certain communities could become a headwind for future expansion.   Investors have spent a great deal of time focusing on AI demand, chips, and software. However, another risk is emerging that deserves attention: if communities continue saying, "Not in my backyard," the pace of AI infrastructure growth may not be as smooth as many expect.   Is Crypto Weakening One of America's Most Powerful Weapons? One of the United States' greatest geopolitical advantages isn't its military, it's the U.S. dollar.   Roughly 90% of global foreign exchange transactions involve the U.S. dollar. That dominance gives the United States enormous leverage. When the U.S. imposes financial sanctions and cuts countries off from the dollar-based financial system, it becomes far more difficult for them to conduct international trade, finance military operations, or access global markets.   That advantage is beginning to erode. Countries that have long opposed the United States such as Russia, Iran, and North Korea are increasingly turning to cryptocurrencies to bypass traditional financial channels. According to reports, their use of virtual currencies for cross-border transactions surged from roughly $12.5 billion in 2024 to more than $100 billion in 2025.   Crypto gives sanctioned nations another way to move money. It can be used to purchase drones, weapons, military components, and fuel, while also helping finance operations such as smuggling oil and paying suppliers outside the traditional banking system.   North Korea has become one of the world's most aggressive crypto thieves, using hacking and other cybercrimes to steal digital assets that can then be converted into funding for its military and weapons programs.   Part of the challenge is that cryptocurrency wallets are identified by long strings of letters and numbers rather than names. While blockchain transactions are publicly visible, identifying the person or organization controlling a wallet can be extremely difficult without additional intelligence. That makes enforcement of financial sanctions much harder.   Even terrorist organizations such as Hamas have, at times, solicited donations in cryptocurrency, illustrating how digital assets can be used to circumvent traditional financial controls.   This is why I believe cryptocurrency has become more than just an investment story, it has become a national security issue.   If Bitcoin and other cryptocurrencies were to experience a significant decline in value, it would reduce the purchasing power of those holding large crypto reserves, including sanctioned actors that rely on digital assets. While it would not eliminate their ability to use crypto, it could make this alternative financial system less effective and increase the relative importance of the dollar-based financial system.   The stronger the role of the U.S. dollar in global commerce, the more effective financial sanctions remain as a non-military tool of foreign policy. With cryptocurrencies becoming more widely adopted, policymakers will need to consider the risk of weakening one of America's most effective forms of economic leverage.   Even with oil off its recent peak, you still may not see cheaper airline tickets. You might assume that with the decline in oil prices, jet fuel costs are also declining, and airlines will pass those savings on to travelers through lower ticket prices. Oil and jet fuel prices have indeed come down, but don't expect airlines to slash fares anytime soon.   The reason is simple: demand remains strong. Even after airlines raised fares eight times since the start of the conflict in the Middle East, analysts say the average round-trip domestic ticket climbed roughly 19% to about $638 yet demand barely changed. In other words, consumers have shown they are willing to pay higher prices to travel. If people keep buying tickets, airlines have little incentive to lower fares and give up those higher profit margins.   Supply is also likely to remain constrained. Airlines aren't rushing to add flights because keeping capacity tight helps support higher ticket prices. The bankruptcy and downsizing of low-cost carriers such as Spirit Airlines has also reduced competition on many routes, making it easier for the remaining airlines to maintain pricing power.   To be fair, airline pricing should be viewed over a longer time horizon. From 2019 through 2025, overall consumer prices rose about 26%, while average airfares actually declined roughly 3.5%. So, despite the recent increases, airline tickets are still relatively inexpensive compared with the broader rise in inflation over the past six years.   The bottom line is that lower fuel costs alone don't guarantee lower ticket prices. As long as travel demand remains healthy and airlines keep capacity in check, consumers may not see much relief at the checkout screen.   Letting Air Out of the Investment Portfolio Balloon Before It Pops At one point or another, we've all seen a balloon inflated until it finally bursts. The same thing can happen to an investment portfolio.   Watching your portfolio grow is exciting, but every investor knows that markets don't go up forever. The challenge is that no one knows exactly when a portfolio has become too inflated. One of the biggest reasons investors refuse to sell is simple: they hate paying taxes. Believe me, I dislike paying taxes just as much as anyone else. But you should never let the tax bill dictate your investment decisions.   Sometimes the smartest move is to relieve some of the pressure in your portfolio before the market does it for you. There are two simple ways to accomplish this: trim oversized positions and sell investments that have become significantly overvalued.   The first strategy is reducing concentration risk. If you review your portfolio and discover that a single stock has grown to 10% or 12% of your total assets, it may be time to trim that position back to 7% or 8%. Yes, you'll likely owe capital gains taxes, but you'll also be reducing the risk that one investment can have an outsized impact on your portfolio if it suddenly declines.   The second strategy is selling investments that have exceeded your target price and can no longer be justified based on their fundamentals. If the valuation has become stretched and the company's earnings outlook no longer supports the stock price, it may be time to take profits. Again, you'll probably owe taxes on the gain, but remember that capital gains are generally taxed at favorable rates. More importantly, paying a 20% or 25% tax on your profit is often far less painful than watching the entire investment lose 20% or more in value. That 20% decline occurs on the entire position rather than just the gain.   No strategy is perfect. You may trim a position only to watch it continue climbing for another year or two. That's part of investing. Risk management isn't about perfectly timing the top, it's about ensuring that no single investment or sector can seriously damage your long-term financial plan.   Consistently following a disciplined, conservative approach won't always maximize returns during bull markets, but it can significantly reduce risk over a full market cycle. When the next major correction inevitably arrives, your portfolio should be positioned to withstand it. That makes it far easier to stay invested, avoid emotional decisions, and continue building wealth instead of panic-selling after the damage has already been done.   Successful investing isn't just about finding great investments. It's also about knowing when to reduce risk. Sometimes, letting a little air out of the balloon today is the best way to keep it from popping tomorrow.   Is AI creating the next memory boom... or setting up the next bust? SK Hynix just pulled off the largest foreign ADR listing in U.S. history, pricing its American depositary receipts at $149 and raising $26.5 billion. That isn't just a fundraising event, it is fuel for one of the most aggressive semiconductor expansion plans the industry has ever seen.   The company is pouring money into new factories, equipment, and advanced packaging capacity around the world. In the United States, SK Hynix is building its first manufacturing facility, a $4 billion advanced packaging plant in West Lafayette, Indiana, expected to be completed in 2028.   Back home in South Korea, the spending is even more staggering. SK Hynix plans to invest up to $720 billion expanding memory production, including a $390 billion semiconductor cluster in Yongin. The company has also committed roughly $7.8 billion by the end of 2027 for additional extreme ultraviolet (EUV) lithography machines, the highly specialized tools needed to manufacture cutting-edge HBM chips. These machines cost as much as $400 million each, are in extremely limited supply, and are only produced by ASML. The company is even accelerating its expansion timeline by more than a decade, with four new fabrication plants now expected to be completed by 2033.   The question investors should be asking isn't whether AI demand is real. It clearly is. The real question is whether the industry is repeating a familiar pattern. Memory has always been one of the most cyclical businesses in technology. Every major technology revolution from the dot-com boom, to smartphones, to cloud computing created a surge in demand for memory chips. Manufacturers responded by rapidly expanding production. Eventually supply caught up, prices collapsed, profits disappeared, and investors who arrived late learned just how brutal the memory cycle can be.   Today feels different... but that is often what every cycle feels like while it is happening.   SK Hynix's market value has increased more than sevenfold over the past year as AI infrastructure spending has created a shortage of HBM. Revenue nearly tripled between 2023 and 2025 to roughly $65 billion, and Wall Street expects sales to surge again to approximately $235 billion in 2026.   Those are incredible numbers. But when major memory producers start announcing massive capacity expansions, history suggests investors should at least consider what happens when today's shortage eventually becomes tomorrow's surplus. AI may create years of strong demand for memory, but the semiconductor industry has a long history of building too much capacity just as demand begins to normalize. The opportunity is enormous, but so is the risk if history repeats itself.   Financial Planning: Simple vs Compounding Interest Loans Many people assume that choosing a simple interest loan over a compound interest loan will dramatically reduce the amount of interest they pay, but in most real-world lending situations, the difference is minimal. The reason is that the power of compounding only becomes significant when a balance grows over time because interest is being added to the principal. With most consumer loans, borrowers either make interest-only payments that keep the principal balance unchanged or make payments that reduce the principal over time. In either case, the interest charged during each payment period is based on the outstanding loan balance at that time, not on an ever-growing balance. Since the loan balance is remaining the same or steadily declining rather than increasing, there is little opportunity for “interest on interest” to accumulate. While compounding can become important if unpaid interest is capitalized and added to the loan balance, that is the exception rather than the rule. For most mortgages, HELOCs, auto loans, personal loans, and similar debt, borrowers should focus far more on the interest rate than on whether the loan is described as using simple or compound interest.   Too Many People Are Using Target Date Funds in Their 401(k) For years, we've discussed the drawbacks of target date funds, including their higher fees and one-size-fits-all approach. Despite those concerns, they remain incredibly popular because they are simple and require very little effort from the investor. According to Vanguard, 61% of 401(k) participants invest in target date funds.   On the surface, they sound like the perfect solution. If you plan to retire around 2045, you simply choose the 2045 Target Date Fund and let it manage your investments. The fund automatically adjusts your portfolio over time, gradually reducing your exposure to stocks and increasing your allocation to bonds as you approach retirement.   Many investors don't realize how significant that shift can be. By the target retirement date, a target date fund may hold around 50% of its assets in bonds. The adjustments don't stop there. Reaching the target year doesn't mean the fund is liquidated or that you receive your money. Instead, the fund continues along its glide path and could increase its bond allocation to 70% or even 80% over the following years.   That approach may have made sense decades ago, but retirement looks very different today. Many people will spend 20 years or more in retirement. Over that length of time, maintaining enough exposure to stocks can be critical to helping your portfolio grow and keep pace with inflation. A portfolio that becomes too conservative too quickly may struggle to provide the long-term growth many retirees need.   Another limitation is that target date funds only manage the assets inside your 401(k). They don't take into account your IRAs, brokerage accounts, pensions, real estate, or other investments. As a result, your overall portfolio allocation could end up being far different than what is appropriate for your financial goals.   The convenience of target date funds is appealing, but convenience shouldn't replace planning. A successful retirement requires understanding how your money is invested, estimating what your portfolio could be worth when you retire, and developing a strategy for how those assets will be invested throughout retirement, not just until you reach it.   Is That Really Your Son or Daughter Calling You? You know your children's voices. You talk to them regularly. Then one day you get a frantic phone call from your son or daughter. They tell you they've just been in a serious accident. They need $15,000 immediately or they're going to jail. They tell you exactly how to send the money. Without hesitation, you wire the funds because you want to help your child.   Unfortunately, you have just been scammed by AI. AI-powered scams are exploding. Reports show AI-related fraud surged more than 1,200% in 2025, and at the current pace, losses from AI scams in the United States could reach $40 billion annually by 2027. Another study found that one in four adults has already experienced an AI voice scam.   Your first reaction may be, "That could never happen to me. I don't post anything on social media." But the problem may not be your online presence. It's your children.   Many people regularly post videos on social media, and today's AI only needs about three seconds of someone's voice to create a convincing clone. Once scammers have that sample, they can make it sound like your son or daughter is saying almost anything.   So how do you protect yourself? If you receive an emergency call asking for money, don't panic. Before sending anything, ask a question that only you and your child would know the answer to. Make it something that has never been shared publicly.   For example, ask about a funny childhood memory that only the two of you remember. Don't use information like birthdays, graduation dates, wedding dates, or other facts that could be found online or in public records. Remember with all these data centers there is so much information that is being obtained and saved but used for the wrong purposes.   Even better, establish a family safe word or passphrase today. Choose something simple that everyone can remember but that would never appear online.   If you ever receive one of these calls, ask for the safe word. If they can't provide it, assume it's a scam until you can verify the situation by calling your child directly or contacting another trusted family member.   As AI continues to improve, these scams will only become more convincing. The same technology powering innovation is also giving criminals new tools to exploit unsuspecting families. Stay alert. Verify before you trust. A few extra minutes could save you thousands of dollars and a great deal of heartache.   Is It Boom or Bust for Micron? It is hard to argue with Micron's incredible stock performance. Through July 2, the shares were up 242% year to date and an astonishing 701% over the previous 12 months. Even after recently falling about 22% from their peak, investors are still debating whether the company has much more room to run.   The good news is that Micron has locked in 15 new customers under long-term supply agreements, with some contracts extending as long as five years. Many of these agreements include customer deposits, giving the company excellent revenue visibility and reducing uncertainty over future sales. For investors, that is exactly the kind of stability they like to see.   But every smart investor should also ask: What is the downside?   While those contracts provide a strong foundation, they do not guarantee that demand will remain as strong over the long term. Unless a customer goes bankrupt, the contracts are largely locked in, but technology changes quickly. High prices and limited supply often encourage innovation, and the AI memory market is no exception.   Several companies are developing new architectures that reduce or even eliminate the need for high-bandwidth memory (HBM), which has been one of Micron's biggest growth drivers. As companies search for lower-cost and more efficient alternatives, demand for HBM could eventually soften.   Nvidia also signaled in June that it is redesigning portions of its upcoming Vera Rubin AI platform to use memory more efficiently. While Nvidia remains a major customer for HBM, improvements in memory efficiency could reduce the amount of HBM required per AI system over time.   Meanwhile, newly public chipmaker Cerebras has taken an entirely different approach. CEO Andrew Feldman has said the company's wafer-scale AI chips do not use HBM at all, arguing that it is too expensive and supply constrained. If other AI hardware companies pursue similar designs, it could create additional competition for HBM.   None of this means Micron's growth story is over. The company's long-term contracts provide meaningful protection, and AI demand remains exceptionally strong today. However, investors should remember that today's shortages and premium pricing often inspire tomorrow's technological breakthroughs.   The question for Micron investors is whether HBM remains the industry standard for years to come or whether innovation eventually reduces the need for it. If demand for HBM begins to slow, Micron's remarkable growth could also begin to moderate.   Companies Discussed: Caterpillar Inc. (Ticker: CAT)

We cover some of the best Faith-Based investments available right now, and review some of their astonishing year-to-date returns.  We also review the president's Quantum Computing Executive Order and list some companies and ETFs that may benefit.  We also take a look at some space related stocks that may be on sale after a recent pullback.

VOV1 - Ngày 22/06, Liên minh Châu Âu (EU) đã chính thức quyết định hoãn hội nghị thượng đỉnh song phương với Vương quốc Anh, đồng thời tiến hành đánh giá lại toàn bộ kế hoạch hợp tác sau thông báo từ chức bất ngờ của Thủ tướng Anh Keir Starmer. Theo thông tin mới nhất, Hội nghị thượng đỉnh EU - Vương quốc Anh lần thứ hai, vốn vừa được lên lịch ấn định vào ngày 22/7 tới tại Brussels, chắc chắn sẽ không thể diễn ra đúng hạn. Quyết định hoãn được đưa ra ngay sau khi Thủ tướng Anh Keir Starmer tuyên bố từ chức lãnh đạo Đảng Lao động cầm quyền, mở đường cho việc nước Anh chuẩn bị đón vị Thủ tướng thứ 7 chỉ trong vòng một thập kỷ.Tại cuộc họp báo, Chủ tịch Hội đồng Châu Âu Antonio Costa đã xác nhận việc hoãn hội nghị này là điều chắc chắn, đồng thời cho biết phía EU đang đánh giá lại các cơ hội để tổ chức sự kiện vào một thời điểm thích hợp hơn. Bên cạnh đó, ông Costa cũng bày tỏ hy vọng người kế nhiệm của ông Starmer sẽ tiếp tục duy trì lộ trình đang thiết lập và làm ấm bầu không khí quan hệ giữa Brussels và London sau những năm tháng đầy sóng gió thời hậu Brexit.Về phía Ủy ban Châu Âu, Chủ tịch Ursula von der Leyen đã gửi lời tri ân sâu sắc tới nhà lãnh đạo Anh sau bài phát biểu từ biệt của ông tại số 10 phố Downing. Bà Leyen nhấn mạnh ông Starmer đã có những đóng góp to lớn giúp an ninh của châu Âu và Ukraine trở nên vững chắc hơn trong hai năm nhiệm kỳ vừa qua.Thủ tướng Keir Starmer từ chức trong bối cảnh tỷ lệ ủng hộ sụt giảm mạnh và áp lực nội bộ gia tăng, khi cử tri cho rằng ông chưa mang lại những thay đổi thực chất như kỳ vọng. Ông Starmer sẽ tạm thời giữ vai trò Thủ tướng lâm thời cho đến khi Đảng Lao động bầu ra lãnh đạo mới (dự kiến hoàn tất vào cuối tháng 7). Ông Andy Burnham, người vừa được bầu làm nghị sĩ khu vực Makerfield, hiện đang là ứng cử viên sáng giá nhất có thể tiếp quản chiếc ghế Thủ tướng Anh mà không gặp phải sự cạnh tranh lớn nào. Việc thay đổi nhân sự cấp cao đột ngột này đã khiến tiến trình đàm phán về các thỏa thuận kinh tế, thương mại nông nghiệp, quyền đánh cá và chương trình di chuyển của giới trẻ giữa Anh và EU tạm thời rơi vào trạng thái đình trệ.Anh Tuấn/VOV-ParisViệc thay đổi nhân sự cấp cao đột ngột này đã khiến tiến trình đàm phán về các thỏa thuận kinh tế, thương mại nông nghiệp, quyền đánh cá và chương trình di chuyển của giới trẻ giữa Anh và EU tạm thời rơi vào trạng thái đình trệ - Ảnh: AP

Hoy hablamos de la alarma en Estados Unidos por una posible máquina EUV de ASML en China, el carril rápido eléctrico para datacenters de IA, los fichajes estratégicos de OpenAI antes de salir a bolsa, la ronda gigante de Baseten y la niebla de neutrinos que complica la búsqueda de materia oscura.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

ASML heeft het aan de stok met Howard Lutnick, de Amerikaanse minister van Handel. Één van de beste ASML-machines zou in China zijn beland ondanks de Amerikaanse exportbeperkingen. Dat meldt Bloomberg. Het gaat om een EUV-machine. Meerdere hoge regeringsfunctionarissen zeggen bewijs te hebben dat erop wijst dat ASML zich niet aan de gemaakte afspraken houdt. Donner Bakker vertelt erover in deze Tech Update. Verder in deze Tech Update: Een aantal bedrijven hebben toch nog toegang tot Anthropic's AI-model Mythos ondanks het verbod door de Amerikaanse overheid. See omnystudio.com/listener for privacy information.

你會怕嗎？最近台股美股波動超級大，你FOMO了嗎？ 護國神山台積電為什麼無懼三星電子英特爾馬斯克TeraFab追趕？股東會上魏哲家金句連發，關鍵財富密碼是什麼？ 美國聯準會是否升息將決定全球資金走勢，通膨要失控了嗎？ Emmy本集完全詳解新金融上帝Kevin Warsh！ 含金量

รู้มั๊ยครับว่างานวิศวกรรมที่โหดหินและยากที่สุดในโลก อาจไม่ใช่การสร้างสถานีอวกาศ แต่มันคือ “การสร้างกระจก” กระจกที่ว่านี้คือหัวใจสำคัญของเครื่องทำชิป EUV ที่ช่วยกอบกู้วิกฤตการณ์ของโลกเทคโนโลยีไม่ให้ถึงทางตัน มันต้องการความสมบูรณ์แบบระดับที่นักวิทยาศาสตร์เคยหัวเราะเยาะว่า “ไม่มีทางเป็นไปได้” ลองจินตนาการดูว่า ถ้ากระจกบานนี้ใหญ่เท่าประเทศเยอรมนี ยอดเขาที่สูงที่สุดบนนั้นจะสูงไม่เกิน 0.1 มิลลิเมตรเท่านั้น ความผิดพลาดเพียงเสี้ยวเปอร์เซ็นต์หมายถึงเงินลงทุนนับพันล้านดอลลาร์ต้องสูญเปล่า และอาจทำให้เทคโนโลยี AI หรือสมาร์ตโฟนเรือธงที่เราใช้กันอยู่ทุกวันนี้ไม่มีทางเกิดขึ้นจริง มนุษยชาติฝืนขีดจำกัดของฟิสิกส์ สร้างสิ่งที่เล็กกว่าโมเลกุลของน้ำเพื่อผลักดันโลกสู่อนาคตได้อย่างไร เลือกฟังกันได้เลยนะครับ อย่าลืมกด Follow ติดตาม PodCast ช่อง Geek Forever's Podcast ของผมกันด้วยนะครับ #EUV #ASML #CarlZeiss #Semiconductor #เซมิคอนดักเตอร์ #เทคโนโลยี #ชิปคอมพิวเตอร์ #วิทยาศาสตร์ #วิศวกรรมศาสตร์ #ความรู้เทคโนโลยี #นวัตกรรม #ชิปAI #ประวัติศาสตร์เทคโนโลยี #กฎของมัวร์ #อุตสาหกรรมชิป #ผลิตชิป #EUVLithography #ไอทีน่ารู้ #geekstory #geekforeverpodcast

沒來台灣，沒在台北Computex亮相，就不算世界科技巨頭？ 台灣再次震撼全球，為什麼輝達高通Marvell英特爾AMD都要齊聚台北Computex？連三星李在鎔sk海力士崔泰源都要來朝聖？ 2026台北電腦展究竟有哪些震撼重大發表，黃仁勳這次到底講了什麼，為何讓廣達華碩宏碁緯創仁寶鴻海最近紛紛大爆發？ 華為又來吹牛跟習近平騙補貼了，韜定律到底是什麼，真的可以取代摩爾定律，彎道超車台積電嗎？別被唬了，Emmy打臉給你看！ 趕快分享給每一個關心台灣AI供應鏈的朋友吧！ 全台獨家的世界經濟追劇深入報導，精彩萬分，持續連載中！ （現在就加入會員支持我們，還可以看到更多專屬影片～） https://www.youtube.com/@emmytw/join

This special episode was recorded at Tsinghua University in Beijing, generally regarded as the top university in China. Our guests are 3 Americans studying and working at Tsinghua: Gabriel (undergrad), Justin (PhD student in AI), and Alex (Professor in AI research). Topics discussed include: Tsinghua University and elite human capital, AI in China, US-China competition, and the flow of human capital between the US and ChinaHan Feizi, columnist at Asia Times and the guest from the previous "Letter from Beijing" episode, is also in the room. Letter from Beijing with Han Feizi: https://www.manifold1.com/episodes/letter-from-beijing-with-han-feizi-72Chapter Markers:(00:00) - Welcome to Tsinghua University (02:47) - Gabriel's Undergrad Journey (12:35) - Justin's PhD (25:10) - Professor Alex on AI and Rankings (42:51) - Second Chances and Status Signals (46:48) - China's Exam Ladder Explained (50:20) - Infrastructure and Tech Competition (01:17:18) - Semiconductors, EUV, and Wrap Up –Steve Hsu is Professor of Theoretical Physics and of Computational Mathematics, Science, and Engineering at Michigan State University. Previously, he was Senior Vice President for Research and Innovation at MSU and Director of the Institute of Theoretical Science at the University of Oregon. Hsu is a startup founder (SuperFocus.ai, SafeWeb, Genomic Prediction, Othram) and advisor to venture capital and other investment firms. He was educated at Caltech and Berkeley, was a Harvard Junior Fellow, and has held faculty positions at Yale, the University of Oregon, and MSU. Please send any questions or suggestions to manifold1podcast@gmail.com or Steve on X @hsu_steve.

Se former et s'engager en chrétiens... Un peu d'histoire : poursuivant l'histoire des chrétiens aux prises avec les grands bouleversements du XXe siècle, Jérôme Cordelier nous offre chez Calmann-Lévy « Les grandes fractures - Les chrétiens face aux défis du siècle (1954-1968) ». De la guerre d'Algérie à Mai 68, en passant par Vatican II, le journaliste met en lumière l'implication - parfois conflictuelle - des chrétiens face aux changements économiques, politiques, culturels et sociaux des Trente Glorieuses. Un peu de théologie, traditionnelle et mise à jour : le père jésuite François Euvé publie chez Mame « Croire au XXIe siècle : la foi catholique face aux défis contemporains » ; il propose une relecture du Credo chrétien à l'aune des défis du XXIe siècle. En confrontant les piliers de la foi aux enjeux de l'intelligence artificielle, de la crise climatique et des mutations identitaires, il dessine une voie entre repli et relativisme. Au coeur de la librairie de la rue de Mézières, Jean-Marie Guénois interroge ces choix. Un accompagnement pour aujourd'hui : Thibaud Collin pulie, avec les pères Thibaud Guespereau et Henri Vallançon, « Renaître et vivre, Comment aider les nouveaux chrétiens à persévérer », chez Artège. De plus en plus d'adultes demandent le baptême. Ce signe d'espérance est aussi un appel pressant pour les pasteurs et les accompagnateurs : aider ces nouveaux croyants, souvent jeunes, à enraciner leur foi afin qu'elle grandisse et porte du fruit jusqu'à la vie éternelle. Comment soutenir ces commencements fragiles ? Une émission mensuelle coproduite par KTO, Le Jour du Seigneur et La Procure.

Moore's Law has been a cornerstone of the rapid advancement of digital technology over the past decades, although it is now confronting physical limits and diminishing economic returns.过去几十年，摩尔定律一直是数字技术快速进步的基石，尽管如今它正面临物理极限和边际收益递减的挑战。For an industry conditioned to equate progress with nanometers, the Tau Scaling Law disclosed by Huawei on Monday is a challenge to the organizing logic of the semiconductor ecosystem.对于一个习惯于用纳米衡量进步的行业而言，华为5月25日公布的“τ scaling law”（陶缩放定律）无疑是对半导体生态系统运行逻辑的一次挑战。Instead of continuing the increasingly expensive race to shrink transistors, Tau Scaling proposes that future chip performance gains can come from compressing the signal propagation time through architectural and timing innovations. Huawei has set a target of reaching a chip density equivalent to 1.4 nanometers by 2031.陶缩放定律提出，摒弃代价日益高昂的晶体管微缩竞赛，转而通过架构与时序创新压缩信号传播时间，驱动未来芯片性能提升。华为已设定目标，到2031年实现相当于1.4纳米制程的芯片密度。Washington's export restrictions have attempted to cut China off from advanced lithography equipment, leading-edge foundries and portions of the global event-driven architecture software stack. Such measures were designed to slow China's progress in advanced semiconductors.美国的出口限制试图将中国排除在先进光刻设备、前沿代工厂以及部分全球事件驱动架构软件栈之外。这些措施旨在减缓中国在先进半导体领域的进步。That is where Tau Scaling enters the picture. Instead of shrinking transistor dimensions from 3 nm to 2 nm and beyond, Huawei is extracting more performance from mature process nodes such as 5 nm and 7 nm by means of architectural optimization, timing compression, logic folding and system-level coordination.这正是陶缩放定律发挥作用的地方。华为不再追求从3纳米到2纳米及更小尺寸的晶体管微缩，而是通过架构优化、时序压缩、逻辑折叠和系统级协调等手段，从5纳米、7纳米等成熟工艺节点中挖掘更多性能。Much of the underlying research — including asynchronous computing concepts, wave pipelining, and timing optimization techniques — can be traced back decades. What Huawei has done, under conditions where the traditional scaling route became inaccessible, is to revisit those ideas, combine them, enhance them and industrialize them.许多基础性研究，包括异步计算概念、波流水线技术和时序优化技术等都可以追溯到几十年前。华为所做的，是在传统微缩路径受阻的情况下，重新审视这些想法，将它们加以融合、改进并产业化。In that sense, the emergence of Tau Scaling reflects a broader historical pattern in technology. Constraints often redirect innovation rather than stop it. So, if chip performance can be improved through architecture rather than lithography alone, then the balance of competition changes. The key question becomes not simply who owns the most advanced EUV machines, but who can design the most efficient systems using available manufacturing capabilities.从这个意义上说，陶缩放定律揭示了技术发展的一条普遍规律：限制往往促使创新转向，而非将其扼杀。若芯片性能可借架构而非单纯依赖光刻技术提升，竞争的格局便将随之改变。关键问题不再是“谁拥有最先进的极紫外光刻机”，而是“谁能利用现有制造能力设计出最高效的系统”。It would be premature, though, to declare that the arrival of Tau Scaling heralds the post-Moore era. Semiconductor history is filled with elegant concepts that struggled once they encountered manufacturing economics, ecosystem inertia, or commercial realities. Huawei's proposal faces several important ceilings.不过，现在就说陶缩放定律预示后摩尔时代已经开启，未免为时过早。半导体发展史上不乏精妙构想，但一旦遭遇制造经济学、生态系统惯性或商业现实，便会步履维艰。华为的方案目前仍面临若干关键瓶颈。Architecture cannot completely replace physics. Timing optimization can reduce inefficiencies, but signals still obey physical propagation limits. As chips become larger and workloads more complex, interconnect delays and synchronization overhead remain major bottlenecks.架构终究无法替代物理规律。时序优化虽能减少低效，信号却始终受制于物理传播的极限。随着芯片尺寸不断增大、工作负载日趋复杂，互连延迟与同步开销仍是绕不开的主要瓶颈。Logic folding and time-domain optimization introduce their own complexity penalties. The more aggressively a design compresses timing, the harder verification, debugging and manufacturing become. Commercialization will determine whether Tau Scaling becomes an industry framework. For Huawei's approach to become influential, other companies must adopt it, customers must validate it and developers must build around it. That process will take years, not conference announcements.逻辑折叠与时域优化本身也需付出复杂性代价。设计越激进地压缩时序，验证、调试与制造的难度便越大。陶缩放定律能否成为行业框架，最终取决于商业化落地。华为的方案要产生影响力，必须获得其他公司的采纳、客户的验证以及开发者的生态共建。这需要数年之功，而非一场发布会所能成就。Even so, the broader lesson already stands. The semiconductor industry is entering a phase where innovation no longer relies exclusively on brute-force scaling and trillion-dollar capital expenditures. Architectural intelligence, software-hardware codesign, advanced packaging and system optimization are becoming increasingly important.即便如此，一个更宏观的启示已然显现：半导体行业正步入新阶段——创新不再单纯依赖蛮力微缩与万亿美元级的资本投入。架构智能、软硬件协同设计、先进封装与系统优化，正变得日益关键。For China, that shift creates both an opportunity and a responsibility. The country still faces major gaps in lithography, materials, EDA tools and manufacturing equipment. But Tau Scaling demonstrates something equally important: when external pressure blocks one route, researchers will look for alternative routes and solutions can emerge through persistence, engineering discipline and targeted input.对中国而言，这一转变既是机遇，也是责任。尽管在光刻、材料、EDA工具及制造设备上差距显著，但陶缩放定律揭示了一个重要道理：外部压力堵住一条路，科研人员就会开辟另一条路。凭借坚韧、工程严谨和精准投入，解决方案终将破土而出。The semiconductor race is no longer just about making things smaller. Increasingly, it is about making systems smarter. The challenge now is for more Chinese companies and engineers to push beyond incremental imitation and focus on resolving genuine choke-point technologies with the tools they already possess.半导体竞赛，已从单纯追求“更小”转向致力实现“更智能”。当务之急，是更多中国企业与工程师超越渐进式模仿，立足现有工具，攻克真正的“卡脖子”技术。Moore's Law /mʊəz lɔː/摩尔定律diminishing economic returns /dɪˈmɪnɪʃɪŋ ˌiːkəˈnɒmɪk rɪˈtɜːnz/边际收益递减conditioned to /kənˈdɪʃənd tuː/习惯于Tau Scaling Law /taʊ ˈskeɪlɪŋ lɔː/ τ缩放定律（陶缩放定律）semiconductor ecosystem /ˌsemikənˈdʌktər ˈiːkəʊsɪstəm/半导体生态系统shrink transistors /ʃrɪŋk trænˈzɪstəz/微缩晶体管advanced lithography equipment /ədˈvɑːnst lɪˈθɒɡrəfi ɪˈkwɪpmənt/先进光刻设备leading-edge foundries /ˈliːdɪŋ edʒ ˈfaʊndriz/前沿代工厂event-driven architecture /ɪˈvent ˈdrɪvən ˈɑːkɪtektʃə/事件驱动架构asynchronous computing /eɪˈsɪŋkrənəs kəmˈpjuːtɪŋ/异步计算wave pipelining /weɪv ˈpaɪplaɪnɪŋ/波流水线EUV machines /ˌiː juː ˈviː məˈʃiːnz/极紫外光刻机post-Moore era /pəʊst mʊə ˈɪərə/后摩尔时代manufacturing economics /ˌmænjʊˈfæktʃərɪŋ ˌiːkəˈnɒmɪks/制造经济学ecosystem inertia /ˈiːkəʊsɪstəm ɪˈnɜːʃə/生态系统惯性software-hardware codesign /ˈsɒftweə ˈhɑːdweə ˌkəʊdɪˈzaɪn/软硬件协同设计advanced packaging /ədˈvɑːnst ˈpækɪdʒɪŋ/先进封装system optimization /ˈsɪstəm ˌɒptɪmaɪˈzeɪʃən/系统优化lithography /lɪˈθɒɡrəfi/光刻EDA tools /ˌiː diː ˈeɪ tuːlz/电子设计自动化工具

In this episode, our host and Head of Research, Thilan Wickramasinghe, discusses how improving sentiment surrounding a potential US-Iran agreement to reopen the Straits of Hormuz is lifting regional equities and easing pressure on crude oil prices. Against this backdrop, Singapore's April NODX numbers reached a 14-year high, reinforcing the view that AI and oil-related activity remain two major drivers of the domestic market.We begin with our Analyst, Shaina Mahtani, joins the show to discuss Centurion's stronger-than-expected 1Q results, the outlook for earnings growth and why the stock continues to stand out despite expectations for some moderation in FY26 earnings growth, supporting SMIDs Analyst, Eric's BUY view on the stock.Thilan then highlights Technology and SMIDs Analyst Jarick's continued positive outlook on Frencken following its 1Q results, with the BUY call supported by expectations of a stronger 2H recovery as semiconductor orders ramp up. He notes improving demand from key customers, potential upside from new DUV and EUV product introductions, and better earnings quality as the revenue mix shifts toward higher-margin semiconductor activity.He also discusses ST Engineering's strong start to the year, with revenue growth across all three segments, margin expansion and a record order book providing strong earnings visibility. Commercial Aerospace remains a key growth driver, while Defence and Public Security continues to provide resilience amid elevated geopolitical uncertainty, supporting REITs Analyst Krishna's BUY view on the stock.Finally, our Regional Head of TMT Research, Hussaini Saifee, who breaks down the surprise collapse of the Simba-M1 consolidation and what it means for the competitive landscape in Singapore's telco sector. He also explains why Singtel is increasingly emerging as a diversified AI and infrastructure play despite recent share price weakness, while sharing his latest views on StarHub.

Ein Urteil des Europäischen Gerichtshofes entmachtet die EU-MitgliedsstaatenEin Kommentar von Tilo Gräser.Der Europäische Gerichtshof (EuGH) hat am 21. April dieses Jahres ein Urteil gefällt, das sich gegen die Souveränität der EU-Mitgliedsstaaten richtet. Es entmachtet sie hinsichtlich ihrer nationalen Gesetzgebung, wie Kritiker warnen. Einige sprechen von einer „klaren Ansage“ an die Mitgliedsstaaten, andere sogar von einem „heimlichen Putsch“. In Fachkommentaren wurde seitdem mehrfach auf die Konsequenzen hingewiesen. Doch in der allgemeinen Öffentlichkeit wird darüber kaum diskutiert – obwohl es alle angeht.Am 21. April hatte der EuGH in Luxemburg einer Klage der EU-Kommission, des EU-Parlaments sowie von 16 Mitgliedsstaaten gegen das Mitgliedsland Ungarn stattgegeben. Anlass war das ungarische Gesetz „über ein strengeres Vorgehen gegen pädophile Straftäter und zum Schutz von Kindern“ von 2021. Das verbietet für Minderjährige den Zugang zu medialen LGBTQ+-Inhalten, insbesondere im audiovisuellen Bereich oder in der Werbung. Die Europäische Kommission hatte dagegen beim Gerichtshof eine Vertragsverletzungsklage gegen Ungarn eingereicht. Der EuGH hat nun laut Pressemitteilung geurteilt, Ungarn habe „in mehrfacher Hinsicht gegen das Unionsrecht verstoßen“: „gegen das Primärrecht und das abgeleitete Recht im Bereich der Dienstleistungen im Binnenmarkt, die Charta der Grundrechte der Europäischen Union, Art. 2 EUV sowie die Datenschutz-Grundverordnung (DSGVO)“.Demnach verstößt das ungarische Gesetz „gegen die Freiheit, Dienstleistungen zu erbringen und in Anspruch zu nehmen“, also Werbung zu machen und zu konsumieren. Es soll zudem einen „besonders schwerwiegenden Eingriff“ in mehrere durch die Europäische Menschenrechts-Charta geschützte Grundrechte darstellen. Dazu wird das Verbot der Diskriminierung wegen des Geschlechts und der sexuellen Orientierung, die Achtung des Privat- und Familienlebens sowie die Meinungs- und Informationsfreiheit gezählt. Ungarn habe mit dem Gesetz „eine Gruppe von Personen, die fester Bestandteil einer durch Pluralismus gekennzeichneten Gesellschaft sind, allein wegen ihrer sexuellen Identität oder ihrer sexuellen Ausrichtung als eine Gefahr für die Gesellschaft behandelt“, so der Gerichtshof. Dem folgt, was in kritischen Kommentaren als besonders schwerwiegend angesehen wird:„Drittens stellt der Gerichtshof erstmals einen eigenständigen Verstoß gegen Art. 2 EUV fest, in dem die Werte niedergelegt sind, auf die sich die Union gründet und die allen Mitgliedstaaten gemeinsam sind. Die Aspekte des [ungarischen] Änderungsgesetzes, die sich gegen Inhalte richten, die Abweichungen von der dem Geschlecht bei der Geburt entsprechenden persönlichen Identität, Geschlechtsumwandlungen oder Homosexualität vermitteln oder darstellen, stellen nämlich ein koordiniertes Bündel diskriminierender Maßnahmen dar, die in offenkundiger und besonders schwerwiegender Weise die Rechte nicht-cisgeschlechtlicher Personen, einschließlich transgeschlechtlicher Personen, und nicht-heterosexueller Personen sowie die Werte der Achtung der Menschenwürde, der Gleichheit und der Wahrung der Menschenrechte, einschließlich der Rechte der Personen, die Minderheiten angehören, verletzen.“...https://apolut.net/eu-putsch-ohne-widerstand-von-tilo-graser/ Hosted on Acast. See acast.com/privacy for more information.

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Die EU arbeitet an einer klareren Ausgestaltung der Beistandsklausel nach Artikel 42 Absatz 7 EUV. Hintergrund sind die Kriege in der Ukraine und im Nahen Osten sowie Sorgen vor hybriden Angriffen. Kaja Kallas räumte Schwächen in Krisenabläufen ein.

Isus i-a invitat pe cei deznădăjduiți: „Veniți la Mine și Euvă voi da odihnă!” - chemare la liniște sufletească și refacere interioară. OMS: femeile sunt de 2 ori mai predispuse la anxietate și depresie decât bărbații. Sănătatea mintală înseamnă și prezența echilibrului interior. Gândul sprijinit pe Dumnezeu aduce pace - El e refugiu sigur pentru mintea zbuciumată! Citește acest devoțional și multe alte meditații biblice pehttps://devotionale.ro#devotionale #devotionaleaudio

In this episode, Ben Bajarin and Jay Goldberg dive deep into the rapidly shifting landscape of semiconductor supply chains and the unexpected "CPU renaissance" driven by agentic AI. The duo explores the "ultimate constraint" currently bottlenecking the industry, breaks down the latest earnings from ARM and AMD, and analyzes why the "Neo Cloud" players might be facing a massive strategic deficit.Key Discussion Points:The Anhydrous Hydrogen Bromine Crisis: Jay reveals the "ultimate shortage" involving a rare gas essential for EUV lithography and memory production, involving a geopolitical tangle of Japanese refining and Israeli raw materials.+4The Death of the CPU-to-GPU Ratio: Why the industry is moving away from simple hardware ratios and toward rack-level topology and workload-specific modeling.+4ARM & AMD's "Agentic" Surge: Insights into how the need to execute AI-generated code is driving massive demand for high-core-count CPUs, far exceeding previous estimates.+4Optical Networking Timing: A reality check on the "hockey stick" growth for optical interconnects, which is projected to truly inflect around 2028.+1The Neo Cloud Challenge: A critical look at CoreWeave, Nebius, and Iron, focusing on their massive CPU-install-base deficit compared to hyperscalers.+2Breaking News: Late-session discussion on the rumored foundry deal between Intel and Apple.+1

Twee chipmachinebedrijven van Hollandse bodem, met een totaal verschillende beursdag. Besi kwam met kwartaalcijfers en werd bedolven onder de complimenten, maar bij ASML hangt de vlag er totaal anders bij. Daar zijn ineens zorgen, nu de grootste klant in opstand komt.Niemand minder dan TSMC vindt de machines van ASML te duur en koopt niet. Deze aflevering hebben we het over de (nieuwe) kopzorgen van ASML en hebben we het uitgebreid over de cijfers van Besi. Daar struikel je namelijk over het goede nieuws. Wat is nog het risico voor Besi?Verder gaat het ook over Tesla. Dat overtreft bijna alle verwachtingen, maar beleggers lijken zich toch zorgen te maken over de investeringen die het bedrijf van Elon Musk wil doen. Tesla wil 25 miljard dollar investeren in onder meer kunstmatige intelligentie. Ook in deze aflevering: de cijfers van Heineken en Fugro. Beide bedrijven hebben het lastig. Al lijken de aandeelhouders van Heineken zich meer zorgen te maken om de nieuwe topman of topvrouw. Die is nog altijd niet gevonden en de huidige trekt de deur volgende maand achter zich dicht. Te gast: Jordy Beuving van De Aandeelhouder. BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie. Van Musk tot Microsoft en van Ahold tot ASML. Wij vertellen je wat beleggers bezighoudt, wie de markten in beweging zet en wat dat betekent voor jouw beleggingsportefeuille.See omnystudio.com/listener for privacy information.

Stephen Sopko and Brendan Burke break down ASML Holding's (ASML) strong earnings beat and why the stock slipped as the company changed its reporting approach. They explain how a wafer‑fab equipment super cycle is taking shape, led by surging memory demand and massive orders from SK Hynix, Micron (MU) and Samsung. Despite near‑term noise and export concerns, the long‑term outlook remains tied to the essential role of EUV machines in the next phase of AI infrastructure buildout.======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe 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

ASML is known for one thing: the most advanced EUV lithography machines on the planet. But there's a quieter story developing in their advanced packaging division that's worth paying attention to.While the Fab Five have seen a recent sell-off, the second half of 2026 is shaping up to be a revenue inflection point for the semiconductor equipment sector.In this episode, we dig into how ASML is iterating on decades-old i-line technology with the Twinscan XT:260 — and why that "ancient" tool is suddenly relevant again for solving critical modern problems like wafer warpage and 3D chip integration.We cover:The 2026 revenue outlook for semiconductor equipmentFront-end wafer development vs. advanced packagingCompetitor spotlight: Onto, Lam Research, and BESI rumorsASML's Twinscan XT:260 and the Carl Zeiss precision optics partnershipWhether packaging can actually move ASML's bottom lineSemiconductor Insider (Discord + deeper analysis): chipstockinvestor.com/pricingFiscal.ai 15% discount: fiscal.ai/csiFree Newsletter: mailchi.mp/b1228c12f284/sign-up-landing-page-short-formNick and Kasey own shares of ASML.Content is for general information and entertainment only — not individual investment advice. All investing involves risk.

Pat Hindle talks with Herbie Smith of TAU Systems about the the world's first commercial laser-driven compact particle accelerator technology from TAU Systems that could fundamentally transform semiconductor manufacturing economics while enabling the next generation of chip fabrication. It would replace the light source on very large and expensive EUV systems currently defining the smallest geometries for high-performance ICs.

UCL researchers are using lasers and drones to scan forests in 3D — turning climate arguments into hard numbers. Then we zoom out to the UK's latest digital sector stats, before heading global as ASML pushes forward the EUV tech that underpins the chips in basically everything. After the break, there's a fascinating “super agers” brain clue — and in gaming, Xbox hits the big reset button at the top. More on all of it at standard.co.uk, and follow Tech and Science Daily from The Standard for your weekday briefing. Hosted on Acast. See acast.com/privacy for more information.

Editor's note: CuspAI raised a $100m Series A in September and is rumored to have reached a unicorn valuation. They have all-star advisors from Geoff Hinton to Yann Lecun and team of deep domain experts to tackle this next frontier in AI applications.In this episode, Max Welling traces the thread connecting quantum gravity, equivariant neural networks, diffusion models, and climate-focused materials discovery (yes, there is one!!!).We begin with a provocative framing: experiments as computation. Welling describes the idea of a “physics processing unit”—a world in which digital models and physical experiments work together, with nature itself acting as a kind of processor. It's a grounded but ambitious vision of AI for science: not replacing chemists, but accelerating them.Along the way, we discuss:* Why symmetry and equivariance matter in deep learning* The tradeoff between scale and inductive bias* The deep mathematical links between diffusion models and stochastic thermodynamics* Why materials—not software—may be the real bottleneck for AI and the energy transition* What it actually takes to build an AI-driven materials platformMax reflects on moving from curiosity-driven theoretical physics (including work with Gerard ‘t Hooft) toward impact-driven research in climate and energy. The result is a conversation about convergence: physics and machine learning, digital models and laboratory experiments, long-term ambition and incremental progress.Full Video EpisodeTimestamps* 00:00:00 – The Physics Processing Unit (PPU): Nature as the Ultimate Computer* Max introduces the idea of a Physics Processing Unit — using real-world experiments as computation.* 00:00:44 – From Quantum Gravity to AI for Materials* Brandon frames Max's career arc: VAE pioneer → equivariant GNNs → materials startup founder.* 00:01:34 – Curiosity vs Impact: How His Motivation Evolved* Max explains the shift from pure theoretical curiosity to climate-driven impact.* 00:02:43 – Why CaspAI Exists: Technology as Climate Strategy* Politics struggles; technology scales. Why materials innovation became the focus.* 00:03:39 – The Thread: Physics → Symmetry → Machine Learning* How gauge symmetry, group theory, and relativity informed equivariant neural networks.* 00:06:52 – AI for Science Is Exploding (Not Emerging)* The funding surge and why AI-for-Science feels like a new industrial era.* 00:07:53 – Why Now? The Two Catalysts Behind AI for Science* Protein folding, ML force fields, and the tipping point moment.* 00:10:12 – How Engineers Can Enter AI for Science* Practical pathways: curriculum, workshops, cross-disciplinary training.* 00:11:28 – Why Materials Matter More Than Software* The argument that everything—LLMs included—rests on materials innovation.* 00:13:02 – Materials as a Search Engine* The vision: automated exploration of chemical space like querying Google.* 01:14:48 – Inside CuspAI: The Platform Architecture* Generative models + multi-scale digital twin + experiment loop.* 00:21:17 – Automating Chemistry: Human-in-the-Loop First* Start manual → modular tools → agents → increasing autonomy.* 00:25:04 – Moonshots vs Incremental Wins* Balancing lighthouse materials with paid partnerships.* 00:26:22 – Why Breakthroughs Will Still Require Humans* Automation is vertical-specific and iterative.* 00:29:01 – What Is Equivariance (In Plain English)?* Symmetry in neural networks explained with the bottle example.* 00:30:01 – Why Not Just Use Data Augmentation?* The optimization trade-off between inductive bias and data scale.* 00:31:55 – Generative AI Meets Stochastic Thermodynamics* His upcoming book and the unification of diffusion models and physics.* 00:33:44 – When the Book Drops (ICLR?)TranscriptMax: I want to think of it as what I would call a physics processing unit, like a PPU, right? Which is you have digital processing units and then you have physics processing units. So it's basically nature doing computations for you. It's the fastest computer known, as possible even. It's a bit hard to program because you have to do all these experiments. Those are quite bulky, it's like a very large thing you have to do. But in a way it is a computation and that's the way I want to see it. You can do computations in a data center and then you can ask nature to do some computations. Your interface with nature is a bit more complicated. But then these things will have to seamlessly work together to get to a new material that you're interested in.[01:00:44:14 - 01:01:34:08]Brandon: Yeah, it's a pleasure to have Max Woehling as a guest today. Max has done so much over his career that I've been so excited about. If you're in the deep learning community, you probably know Max for his work on variational autocoders, which has literally stood the test of prime or officially stood the test of prime. If you are a scientist, you probably know him for his like, binary work on graph neural networks on equivariance. And if you're a material science, you probably know him about his new startup, CASPAI. Max has a long history doing lots of cool problems. You started in quantum gravity, which is I think very different than all of these other things you worked on. The first question for AI engineers and for scientists, what is the thread in how you think about problems? What is the thread in the type of things which excite you? And how do you decide what is the next big thing you want to work on?[01:01:34:08 - 01:02:41:13]Max: So it has actually evolved a lot. In my young days, let's breathe, I would just follow what I would find super interesting. I have kind of this sensor. I think many people have, but maybe not really sort of use very much, which is like, you get this feeling about getting very excited about some problem. Like it could be, what's inside of a black hole or what's at the boundary of the universe or what are quantum mechanics actually all about. And so I follow that basically throughout my career. But I have to say that as you get older, this changes a little bit in the sense that there's a new dimension coming to it and there's this impact. Going in two-dimensional quantum gravity, you pretty much guaranteed there's going to be no impact on what you do relative, maybe a few papers, but not in this world, this energy scale. As I get closer to retirement, which is fortunately still 10 years away or so, I do want to kind of make a positive impact in the world. And I got pretty worried about climate change.[01:02:43:15 - 01:03:19:11]Max: I think politics seems to have a hard time solving it, especially these days. And so I thought better work on it from the technology side. And that's why we started CaspAI. But there's also a lot of really interesting science problems in material science. And so it's kind of combining both the impact you can make with it as well as the interesting science. So it's sort of these two dimensions, like working on things which you feel there's like, well, there's something very deep going on here. And on the other hand, trying to build tools that can actually make a real impact in the world.[01:03:19:11 - 01:03:39:23]RJ: So the thread that when I look back, look at the different things that you worked out, some of them seem pretty connected, like the physics to equivariance and, yeah, and, uh, gravitational networks, maybe. And that seems to be somewhat related to Casp. Do you have a thread through there?[01:03:39:23 - 01:06:52:16]Max: Yeah. So physics is the thread. So having done, you know, spent a lot of time in theoretical physics, I think there is first very fundamental and exciting questions, like things that haven't actually been figured out in quantum gravity. So that is really the frontier. There's also a lot of mathematical tools that you can use, right? In, for instance, in particle physics, but also in general relativity, sort of symmetry space to play an enormously important role. And this goes all the way to gauge symmetries as well. And so applying these kinds of symmetries to, uh, machine learning was actually, you know, I thought of it as a very deep and interesting mathematical problem. I did this with Taco Cohen and Taco was the main driver behind this, went all the way from just simple, like rotational symmetries all the way to gauge symmetries on spheres and stuff like that. So, and, uh, Maurice Weiler, who's also here, um, when he was a PhD student, he was a very good student with me, you know, he wrote an entire book, which I can really recommend about the role of symmetries in AI and machine learning. So I find this a very deep and interesting problem. So more recently, so I've taken a sort of different path, which is the relationship between diffusion models and that field called stochastic thermodynamics. This is basically the thermodynamics, which is a theory of equilibrium. So but then formulated for out of equilibrium systems. And it turns out that the mathematics that we use for diffusion models, but even for reinforcement learning for Schrodinger bridges for MCMC sampling has the same mathematics as this theoretical, this physical theory of non-equilibrium systems. And that got me very excited. And actually, uh, when I taught a course in, um, Mauschenberg, uh, it is South Africa, close to Cape Town at the African Institute for Mathematical Sciences Ames. And I turned that into a book site. Two years later, the book was finished. I've sent it to the publisher. And this is about the deep relationship between free energy, diffusion models, basically generative AI and stochastic thermodynamics. So it's always some kind of, I don't know, I find physics very deep. I also think a lot about quantum mechanics and it's, it's, it's a completely weird theory that actually nobody really understands. And there's a very interesting story, which is maybe good to tell to connect sort of my PZ back to where I'm now. So I did my PZ with a Nobel Laureate, Gerard the toft. He says the most brilliant man I've ever met. He was never wrong about anything as long as I've seen him. And now he says quantum mechanics is wrong and he has a new theory of quantum mechanics. Nobody understands what he's saying, even though what he's writing down is not mathematically very complex, but he's trying to address this understandability, let's say of quantum mechanics head on. And I find it very courageous and I'm completely fascinated by it. So I'm also trying to think about, okay, can I actually understand quantum mechanics in a more mundane way? So that, you know, without all the weird multiverses and collapses and stuff like that. So the physics is always been the threat and I'm trying to apply the physics to the machine learning to build better algorithms.[01:06:52:16 - 01:07:05:15]Brandon: You are still very involved in understanding and understanding physics and the worlds. Yeah. And just like applications to machine learning or introducing no formalisms. That's really cool.[01:07:05:15 - 01:07:18:02]Max: Yes, I would say I'm not contributing much to physics, but I'm contributing to the interface between physics and science. And that's called AI for science or science or AI is kind of a super, it's actually a new discipline that's emerging.[01:07:18:02 - 01:07:18:19]Speaker 5: Yeah.[01:07:18:19 - 01:07:45:14]Max: And it's not just emerging, it's exploding, I would say. That's the better term because I know you go from investments into like in the hundreds of millions now in the billions. So there's now actually a startup by Jeff Bezos that is at 6.2 billion sheep round. Right. Insane. I guess it's the largest startup ever, I think. And that's in this field, AI for science. It tells you something that we are creating a new bubble here.[01:07:46:15 - 01:07:53:28]Brandon: So why do you think it is? What has changed that has motivated people to start working on AI for science type problems?[01:07:53:28 - 01:08:49:17]Max: So there's two reasons actually. One is that people have been applying sort of the new tools from AI to the sciences, which is quite natural. And there's of course, I think there's two big examples, protein folding is a big one. And the other one is machine learning forest fields or something called machine learning inter-atomic potentials. Both of them have been actually very successful. Both also had something to do with symmetries, which is a little cool. And sort of people in the AI sciences saw an opportunity to apply the tools that they had developed beyond advertised placement, right, or multimedia applications into something that could actually make a very positive impact in society like health, drug development, materials for the energy transition, carbon capture. These are all really cool, impactful applications.[01:08:50:19 - 01:09:42:14]Max: Despite that, the science and the kind of the is also very interesting. I would say the fact that these sort of these two fields are coming together and that we're now at the point that we can actually model these things effectively and move the needle on some of these sort of science sort of methodologies is also a very unique moment, I would say. People recognize that, okay, now we're at the cusp of something new, where it results whether the company is called after. We're at the cusp of something new. And of course that always creates a lot of energy. It's like, okay, there's something, it's like sort of virgin field. It's like nobody's green field. Nobody's been there. I can rush in and I can sort of start harvesting there, right? And I think that's also what's causing a lot of sort of enthusiasm in the fields.[01:09:42:14 - 01:10:12:18]RJ: If you're an AI engineer, basically if the people that listen to this podcast will be in the field, then you maybe don't have a strong science background. How does, but are excited. Most I would say most AI practitioners, BM engineers or scientists would consider themselves scientists and they have some background, a little bit of physics, a little bit of industry college, maybe even graduate school that have been working or are starting out. How does somebody who is not a scientist on a day-to-day basis, how do they get involved?[01:10:12:18 - 01:10:14:28]Max: Well, they can read my book once it's out.[01:10:16:07 - 01:11:05:24]Max: This is basically saying that there is more, we should create curricula that are on this interface. So I'm not sure there is, also we already have some universities actual courses you can take, maybe online courses you can take. These workshops where we are now are actually very good as well. And we should probably have more tutorials before the workshop starts. Actually we've, I've kind of proposed this at some point. It's like maybe first have an hour of a tutorial so that people can get new into the field. There's a lot out there. Most of it is of course inaccessible, but I would say we will create much more books and other contents that is more accessible, including this podcast I would say. So I think it will come. And these days you can watch videos and things. There's a huge amount of content you can go and see.[01:11:05:24 - 01:11:28:28]Brandon: So maybe a follow-up to that. How do people learn and get involved? But why should they get involved? I mean, we have a lot of people who are of our audience will be interested in AI engineering, but they may be looking for bigger impacts in the world. What opportunities does AI for science provide them to make an impact to change the world? That working in this the world of pure bits would not.[01:11:28:28 - 01:11:40:06]Max: So my view is that underlying almost everything is immaterial. So we are focusing a lot on LLMs now, which is kind of the software layer.[01:11:41:06 - 01:11:56:05]Max: I would say if you think very hard, underlying everything is immaterial. So underlying an LLM is a GPU, and underlying a GPU is a wafer on which we will have to deposit materials. Do we want to wait a little bit?[01:12:02:25 - 01:12:11:06]Max: Underlying everything is immaterial. So I was saying, you know, there's the LLM underlying the LLM is a GPU on which it runs. In order to make that GPU,[01:12:12:08 - 01:12:43:20]Max: you have to put materials down on a wafer and sort of shine on it with sort of EUV light in order to etch kind of the structures in. But that's now an actual material problem, because more or less we've reached the limits of scaling things down. And now we are trying to improve further by new materials. So that's a fundamental materials problem. We need to get through the energy transition fast if we don't want to kind of mess up this world. And so there is, for instance, batteries. That's a complete materials problem. There's fuel cells.[01:12:44:23 - 01:13:01:16]Max: There is solar panels. So that they can now make solar panels with new perovskite layers on top of the silicon layers that can capture, you know, theoretically up to 50% of the light, where now we're at, I don't know, maybe 22 or something. So these are huge changes all by material innovation.[01:13:02:21 - 01:13:47:15]Max: And yeah, I think wherever you go, you know, I can probably dig deep enough and then tell you, well, actually, the very foundation of what you're doing is a material problem. And so I think it's just very nice to work on this very, very foundation. And also because I think this is maybe also something that's happening now is we can start to search through this material space. This has never been the case, right? It's like scientists, the normal way of working is you read papers and then you come up with no hypothesis. You do an experiment and you learn, et cetera. So that's a very slow process. Now we can treat this as a search engine. Like we search the internet, we now search the space of all possible molecules, not just the ones that people have made or that they're in the universe, but all of them.[01:13:48:21 - 01:14:42:01]Max: And we can make this kind of fully automated. That's the hope, right? We can just type, it becomes a tool where you type what you want and something starts spinning and some experiments get going. And then, you know, outcome list of materials and then you look at it and say, maybe not. And then you refine your query a little bit. And you kind of do research with this search engine where a huge amount of computation and experimentation is happening, you know, somewhere far away in some lab or some data center or something like this. I find this a very, very promising view of how we can sort of build a much better sort of materials layer underneath almost everything. And also more sustainable materials. Our plastics are polluting the planet. If you come up with a plastic that kind of destroys itself, you know, after, I don't a few weeks, right? And actually becomes a fertilizer. These are things that are not impossible at all. These things can be done, right? And we should do it.[01:14:42:01 - 01:14:47:23]RJ: Can you tell us a little bit just generally about CUSBI and then I have a ton of questions.[01:14:47:23 - 01:14:48:15]Speaker 5: Yeah.[01:14:48:15 - 01:17:49:10]Max: So CUSBI started about 20 months ago and it was because I was worried about I'm still worried about climate change. And so I realized that in order to get, you know, to stay within two degrees, let's say, we would not only have to reduce our emissions to zero by 2050, but then, you know, another half century or even a century of removing carbon dioxide from the atmosphere, not by reducing your emissions, but actually removing it at a rate that's about half the rate that we now emit it. And that is a unsolved problem. But if we don't solve it, two degrees is not going to happen, right? It's going to be much more. And I don't think people quite understand how bad that can be, like four degrees, like very bad. So this technology needs to be developed. And so this was my and my co-founder, Chet Edwards, motivation to start this startup. And also because, you know, we saw the technology was ready, which is also very good. So if you're, you know, the time is right to do it. And yeah, so we now in the meanwhile, we've grown to about 40 people. We've kind of collected 130 million investment into the company, which is for a European company is quite a lot. I would say it's interesting that right after that, you know, other startups got even more. So that's kind of tells you how fast this is growing. But yeah, we are we are now at the we've built the platform, of course, but it's for a series of material classes and it needs to be constantly expanded to new material classes. And it can be more automated because, you know, we know putting LLMs in as the whole thing gets more and more automated. And now we're moving to sort of high throughput experimentation. So connecting the actual platform, which is computational, to the experiments so that you can get also get fast feedback from experiments. And I kind of think of experiments as something you do at the end, although that's what we've been doing so far. I want to think of it as what I would call a sort of a physics processing unit, like a PPU, right, which is you have digital processing units and then you have physics processing units. So it's basically nature doing computations for you. It's the fastest computer known as possible, even. It's a bit hard to program because you have to do all these experiments. Those are quite, quite bulky. It's like a very large thing you have to do. But in a way, it is a computation. And that's the way I want to see it. So I want to you can do computations in a data center and then you can ask nature to do some computations. Your interface with nature is a bit more complicated. But then these things will have to seamlessly work together to get to a new material that you're interested in. And that's the vision we have. We don't say super intelligence because I don't quite know what it means and I don't want to oversell it. But I do want to automate this process and give a very powerful tool in the hands of the chemists and the material scientists.[01:17:49:10 - 01:18:01:02]Brandon: That actually brings up a question I wanted to ask you. First of all, can you talk about your platform to like whatever degree, like explain kind of how it works and like what you your thought processes was in developing it?[01:18:01:02 - 01:20:47:22]Max: Yeah, I think it's been surprisingly, it's not rocket science, I would say. It's not rocket science in the sense of the design and basically the design that, you know, I wrote down at the very beginning. It's still more or less the design, although you add things like I wasn't thinking very much about multi-scale models and as the common are rated that actually multi-scale is very important. And the beginning, I wasn't thinking very much about self-driving labs. But now I think, you know, we are now at the stage we should be adding that. And so there is sort of bits and details that we're adding. But more or less, it's what you see in the slide decks here as well, which is there is a generative component that you have to train to generate candidates. And then there is a digital twin, multi-scale, multi-fidelity digital twin, which you walk through the steps of the ladder, you know, they do the cheap things first, you weed out everything that's obviously unuseful, and then you go to more and more expensive things later. And so you narrow things down to a small number. Those go into an experiment, you know, do the experiment, get feedback, etc. Now, things that also have been more recently added is sort of more agentic sort of parts. You know, we have agents that search the literature and come up with, you know, actually the chemical literature and come up with, you know, chemical suggestions for doing experiments. We have agents which sort of autonomously orchestrate all of the computations and the experiments that need to be done. You know, they're in various stages of maturity and they can be continuously improved, I would say. And so that's basically I don't think that part. There's rocket science, but, you know, the design of that thing is not like surprising. What is it's surprising hard to actually build it. Right. So that's that's the thing that is where the moat is in the data that you can get your hands on and the and actually building the platform. And I would say there's two people in particular I want to call out, which is Felix Hunker, who is actually, you know, building the scientific part of the platform and Sandra de Maria, who is building the sort of the skate that is kind of this the MLOps part of the platform. Yeah. And so and recently we also added sort of Aaron Walsh to our team, who is a very accomplished scientist from Imperial College. We're very happy about that. He's going to be a chief science officer. And we also have a partnerships team that sort of seeks out all the customers because I think this is one thing I find very important. In print, it's so complex to do to actually bring a material to the real world that you must do this, you know, in collaboration with sort of the domain experts, which are the companies typically. So we always we only start to invest in the direction if we find a good industrial partner to go on that journey with us.[01:20:47:22 - 01:20:55:12]Brandon: Makes a lot of sense. Over the evolution of the platform, did you find that you that human intervention, human,[01:20:56:18 - 01:21:17:01]Brandon: I guess you could start out with a pure, you could imagine two directions when you start up making everything purely automatic, automated, agentic, so on. And then later on, you like find that you need to have more human input and feedback different steps. Or maybe did you start out with having human feedback? You have lots of steps and then like kind of, yeah, figure out ways to remove, you know,[01:21:17:01 - 01:22:39:18]Max: that is the second one. So you build tools for you. So it's much more modular than you think. But it's like, we need these tools for this application. We need these tools. So you build all these tools, and then you go through a workflow actually in the beginning just manually. So you put them in a first this tool, then run this to them or this with sithery. So you put them in a workflow and then you figure out, oh, actually, you know, this this porous material that we are trying to make actually collapses if you shake it a bit. Okay, then you add a new tool that says test for stability. Right. Yeah. And so there's more and more tools. And then you build the agent, which could be a Bayesian optimizer, or it could be an actual other them, you know, maybe trained to be a good chemist that will then start to use all these tools in the right way in the right order. Yeah. Right. But in the beginning, it's like you as a chemist are putting the workflow together. And then you think about, okay, how am I going to automate this? Right. For one very easy question you can ask yourself is, you know, every time somebody who is not a super expert in DFT, yeah, and he wants to do a calculation has to go to somebody who knows DFT. And so could you start to automate that away, which is like, okay, make it so user friendly, so that you actually do the right DFT for the right problem and for the right length of time, and you can actually assess whether it's a good outcome, etc. So you start to automate smaller small pieces and bigger pieces, etc. And in the end, the whole thing is automated.[01:22:39:18 - 01:22:53:25]Brandon: So your philosophy is you want to provide a set of specific tools that make it so that the scientists making decisions are better informed and less so trying to create an automated process.[01:22:53:25 - 01:23:22:01]Max: I think it's this is sort of the same where you're saying because, yes, we want to automate, yeah, but we don't see something very soon where the chemists and the domain expert is out of the loop. Yeah, but it but it's a retreat, right? It's like, okay, so first, you need an expert to tell you precisely how to set the parameters of the DFT calculation. Okay, maybe we can take that out. We can maybe automate that, right? And so increasingly, more of these things are going to be removed.[01:23:22:01 - 01:23:22:19]Speaker 5: Yeah.[01:23:22:19 - 01:24:33:25]Max: In the end, the vision is it will be a search engine where you where somebody, a chemist will type things and we'll get candidates, but the chemist will still decide what is a good material and what is not a good material out of that list, right? And so the vision of a completely dark lab, where you can close the door and you just say, just, you know, find something interesting and then it will it will just figure out what's interesting and we'll figure out, you know, it's like, oh, I found this new material to blah, blah, blah, blah, right? That's not the vision I have. He's not for, you know, a long time. So for me, it's really empowering the domain experts that are sitting in the companies and in universities to be much faster in developing their materials. And I should say, it's also good to be a little humble at times, because it is very complicated, you know, to bring it to make it and to bring it into the real world. And there are people that are doing this for the entire lives. Yeah. Right. And it's like, I wonder if they scratch their head and say, well, you know, how are you going to completely automate that away, like in the next five years? I don't think that's going to happen at all.[01:24:35:01 - 01:24:39:24]Max: Yeah. So to me, it's an increasingly powerful tool in the hands of the chemists.[01:24:39:24 - 01:25:04:02]RJ: I have a question. You've talked before about getting people interested based on having, you know, sort of a big breakthrough in materials, incremental change. I'm curious what you think about the platform you have now in are sort of stepping towards and how are you chasing the big change or is this like incremental or is there they're not mutually exclusive, obviously, but what do you think about that?[01:25:04:02 - 01:26:04:27]Max: We follow a mixed strategy. So we are definitely going after a big material. Again, we do this with a partner. I'm not going to disclose precisely what it is, but we have our own kind of long term goal. You could call it lighthouse or, you know, sort of moonshot or whatever, but it is going to be a really impactful material that we want to develop as a proof point that it can be done and that it will make it into the into the real world and that AI was essential in actually making it happen. At the same time, we also are quite happy to work with companies that have more modest goals. Like I would say one is a very deep partnership where you go on a journey with a company and that's a long term commitment together. And the other one is like somebody says, I knew I need a force field. Can you help me train this force field and then maybe analyze this particular problem for me? And I'll pay you a bunch of money for that. And then maybe after that we'll see. And that's fine too. Right. But we prefer, you know, the deep partnerships where we can really change something for the good.[01:26:04:27 - 01:26:22:02]RJ: Yeah. And do you feel like from a platform standpoint you're ready for that or what are the things that and again, not asking you to disclose proprietary secret sauce, but what are the things generally speaking that need to happen from where we are to where to get those big breakthroughs?[01:26:22:02 - 01:28:40:01]Max: What I find interesting about this field is that every time you build something, it's actually immediately useful. Right. And so unlike quantum computing, which or nuclear fusion, so you work for 20, 30, 40 years and nothing, nothing, nothing, nothing. And then it has to happen. Right. And when it happens, it's huge. So it's quite different here because every time you introduce, so you go to a customer and you say, so what do you need? Right. So we work, let's say, on a problem like a water filtration. We want to remove PFAS from water. Right. So we do this with a company, Camira. So they are a deep partner for us. Right. So we on a journey together. I think that the breakthrough will happen with a lot of human in the loop because there is the chemists who have a whole lot more knowledge of their field and it's us who will help them with training, having a new message. And in that kind of interface, these interactions, something beautiful will happen and that will have to happen first before this field will really take off, I think. And so in the sense that it's not a bubble, let's put it that way. So that's people see that as actual real what's happening. So in the beginning, it will be very, you know, with a lot of humans in the loop, I would say, and I would I would hope we will have this new sort of breakthrough material before, you know, everything is completely automated because that will take a while. And also it is very vertical specific. So it's like completely automating something for problem A, you know, you can probably achieve it, but then you'll sort of have to start over again for problem B because, you know, your experimental setup looks very different in the machines that you characterize your materials look very different. Even the models in your platform will have to be retrained and fine tuned to the new class. So every time, you know, you have a lot of learnings to transfer, but also, you know, the problems are actually different. And so, yes, I would want that breakthrough material before it's completely automated, which I think is kind of a long term vision. And I would say every time you move to something new, you'll have to start retraining and humans will have to come in again and say, okay, so what does this problem look like? And now sort of, you know, point the the machine again, you know, in the new direction and then and then use it again.[01:28:40:01 - 01:28:47:17]RJ: For the non-scientists among us, me included a bit of a scientist. There's a lot of terminology. You mentioned DFT,[01:28:49:00 - 01:29:01:11]RJ: you equivariance we've talked about. Can you sort of explain in engineering terms or the level of sophistication and engineering? Well, how what is equivariance?[01:29:01:11 - 01:29:55:01]Max: So equivariance is the infusion of symmetry in neural networks. So if I build a neural network, let's say that needs to recognize this bottle, right, and then I rotate the bottle, it will then actually have to completely start again because it has no idea that the rotated bottle. Well, actually, the input that represents a rotated bottle is actually rotated bottle. It just doesn't understand that. Right. If you build equivariance in basically once you've trained it in one orientation, it will understand it in any other orientation. So that means you need a lot less data to train these models. And these are constraints on the weights of the model. So so basically you have to constrain the way such data to understand it. And you can build it in, you can hard code it in. And yeah, this the symmetry groups can be, you know, translations, rotations, but also permutations. I can graph neural network, their permutations and then physics, of course, as many more of these groups.[01:29:55:01 - 01:30:01:08]RJ: To pray devil's advocate, why not just use data augmentation by your bottle is in all the different orientations?[01:30:01:08 - 01:30:58:23]Max: As an option, it's just not exact. It's like, why would you go through the work of doing all that? Where you would really need an infinite number of augmentations to get it completely right. Where you can also hard code it in. Now, I have to say sometimes actually data augmentation works even better than hard coding the equivariance in. And this is something to do with the fact that if you constrain the optimization, the weights before the optimization starts, the optimization surface or objective becomes more complicated. And so it's harder to find good minima. So there is also a complicated interplay, I think, between the optimization process and these constraints you put in your network. And so, yeah, you'll hear kind of contradicting claims in this field. Like some people and for certain applications, it works just better than not doing it. And sometimes you hear other people, if you have a lot of data and you can do data augmentation, then actually it's easier to optimize them and it actually works better than putting the equivariance in.[01:30:58:23 - 01:31:07:16]Brandon: Do you think there's kind of a bitter lesson for mathematically founded models and strategies for doing deep learning?[01:31:07:16 - 01:31:46:06]Max: Yeah, ultimately it's a trade-off between data and inductive bias. So if your inductive bias is not perfectly correct, you have to be careful because you put a ceiling to what you can do. But if you know the symmetry is there, it's hard to imagine there isn't a way to actually leverage it. But yeah, so there is a bitter lesson. And one of the bitter lessons is you should always make sure your architecture is scale, unless you have a tiny data set, in which case it doesn't matter. But if you, you know, the same bitter lessons or lessons that you can draw in LLM space are eventually going to be true in this space as well, I think.[01:31:47:10 - 01:31:55:01]RJ: Can you talk a little bit about your upcoming book and tell the listeners, like, what's exciting about it? Yeah, I should read it.[01:31:55:01 - 01:33:42:20]Max: So this book is about, it's called Generative AI and Stochastic Thermodynamics. It basically lays bare the fact that the mathematics that goes into both generative AI, which is the technology to generate images and videos, and this field of non-equilibrium statistical mechanics, which are systems of molecules that are just moving around and relaxing to the ground state, or that you can control to have certain, you know, be in a certain state, the mathematics of these two is actually identical. And so that's fascinating. And in fact, what's interesting is that Jeff Hinton and Radford Neal already wrote down the variational free energy for machine learning a long time ago. And there's also Carl Friston's work on free energy principle and active entrance. But now we've related it to this very new field in physics, which is called stochastic thermodynamics or non-equilibrium thermodynamics, which has its own very interesting theorems, like fluctuation theorems, which we don't typically talk about, but we can learn a lot from. And I think it's just it can sort of now start to cross fertilize. When we see that these things are actually the same, we can, like we did for symmetries, we can now look at this new theory that's out there, developed by these very smart physicists, and say, okay, what can we take from here that will make our algorithms better? At the same time, we can use our models to now help the scientists do better science. And so it becomes a beautiful cross-fertilization between these two fields. The book is rather technical, I would say. And it takes all sorts of things that have been done as stochastic thermodynamics, and all sorts of models that have been done in the machine learning literature, and it basically equates them to each other. And I think hopefully that sense of unification will be revealing to people.[01:33:42:20 - 01:33:44:05]RJ: Wait, and when is it out?[01:33:44:05 - 01:33:56:09]Max: Well, it depends on the publisher now. But I hope in April, I'm going to give a keynote at ICLR. And it would be very nice if they have this book in my hand. But you know, it's hard to control these kind of timelines.[01:33:56:09 - 01:33:58:19]RJ: Yeah, I'm looking forward to it. Great.[01:33:58:19 - 01:33:59:25]Max: Thank you very much. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

That AI essay I shared with you yesterday sure got Wall Street's attention. Anthropic says Chinese models are training off of Claude. A significant new breakthrough in chip production technology. And as fun as that tri-fold phone might be, you probably want to wait for later iterations of the form factor. Software Stocks Are Having Another Ugly Day (WSJ) Anthropic Accuses Chinese Companies of Siphoning Data From Claude (WSJ) Meta and AMD Agree to AI Chips Deal Worth More Than $100 Billion (WSJ) Exclusive: ASML unveils EUV light source advance that could yield 50% more chips by 2030 (Reuters) Putting Samsung's $2,899 TriFold To the Test as a Phone, Tablet and Laptop (Bloomberg) Learn more about your ad choices. Visit megaphone.fm/adchoices

This is a recap of the top 10 posts on Hacker News on February 23, 2026. This podcast was generated by wondercraft.ai (00:30): The Age Verification Trap: Verifying age undermines everyone's data protectionOriginal post: https://news.ycombinator.com/item?id=47122715&utm_source=wondercraft_ai(01:55): Ladybird adopts Rust, with help from AIOriginal post: https://news.ycombinator.com/item?id=47120899&utm_source=wondercraft_ai(03:21): Americans are destroying Flock surveillance camerasOriginal post: https://news.ycombinator.com/item?id=47127081&utm_source=wondercraft_ai(04:47): Elsevier shuts down its finance journal citation cartelOriginal post: https://news.ycombinator.com/item?id=47119530&utm_source=wondercraft_ai(06:12): Pope tells priests to use their brains, not AI, to write homiliesOriginal post: https://news.ycombinator.com/item?id=47119210&utm_source=wondercraft_ai(07:38): Binance fired employees who found $1.7B in crypto was sent to IranOriginal post: https://news.ycombinator.com/item?id=47127396&utm_source=wondercraft_ai(09:04): Hetzner (European hosting provider) to increase prices by up to 38%Original post: https://news.ycombinator.com/item?id=47121029&utm_source=wondercraft_ai(10:29): Magical Mushroom – Europe's first industrial-scale mycelium packaging producerOriginal post: https://news.ycombinator.com/item?id=47119274&utm_source=wondercraft_ai(11:55): FreeBSD doesn't have Wi-Fi driver for my old MacBook, so AI built one for meOriginal post: https://news.ycombinator.com/item?id=47129361&utm_source=wondercraft_ai(13:21): ASML unveils EUV light source advance that could yield 50% more chips by 2030Original post: https://news.ycombinator.com/item?id=47125349&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Onderzoekers van ASML zeggen een manier te hebben gevonden om het vermogen van de lichtbron in hun chipmachine te verhogen van het huidige 600 watt tot 1000 watt. Dat maakt dat ASML verwacht nog voor 2030 hun chipproductie met 50 procent te kunnen verhogen. Dit moet ASML helpen zijn voorsprong te houden op Amerikaanse en Chinese concurrenten. Dat meldt persbureau Reuters. Rosanne Peters vertelt erover in deze Tech Update. Het gaat om EUV machines, wat staat voor ultraviolette lithografie (EUV). Doordat de lichtbron in de chipmachine verhoogd kan worden, maakt dat er meer chips per uur geproduceerd kunnen worden waardoor de kosten per chip dalen. De EUV-machines van ASML zijn zo belangrijk dat er hard wordt gewerkt deze machines uit verkeerde handen te houden. Door deze nieuwe kennis heeft ASML het technologisch meest uitdagende aspect van hun chipmachines weten te verbeteren. Verder in deze Tech Update: De nieuwe S26 toestellen van Samsung krijgen ook Perplexity als een van de AI-assistenten See omnystudio.com/listener for privacy information.

From new cancer drugs to batteries and robotics – China's top-tier growth companies are forging paths of their own rather than following in the west's footsteps. Investment manager Sophie Earnshaw names companies that have caught her eye and explains why being a long-term stock picker differs in China from elsewhere. Background:Sophie Earnshaw is a decision-maker on our China Equities Strategy and joint manager of the Baillie Gifford China Growth Trust. In this conversation, she tells Short Briefings… host Leo Kelion about a select group of Chinese companies breaking new ground, supported by the state's efforts to become self-sufficient in more of today's critical technologies and a leader in some of those of the future. Earnshaw also details how the “phenomenal rate” at which companies are born, scale and die in the country makes stock-picking a challenging task – making the access we have to company leaders, academics and other local expertise core to our mission of finding the best firms to invest in on behalf of our clients. Portfolio companies discussed include:- CATL – the battery maker whose products power electric vehicles worldwide and increasingly support the renewable energy sector- BeOne and Innovent Biologics – pharmaceutical firms developing the next generation of cancer drugs - AMEC and NAURA – semiconductor equipment makers enabling China to develop increased self-reliance in computer chips - Alibaba, ByteDance and Tencent – China's ‘big tech' companies, whose artificial intelligence tools are becoming embedded into people's daily lives- MiniMax – the AI startup rolling out video and agentic tools at a fraction of the cost of western counterparts- Horizon Robotics – the automated driving tech provider with its eye on an even bigger opportunity. Resources:Baillie Gifford podcastsChina: a tale of two storiesChina investment strategy hub (institutional clients only)House of HuaweiPrivate investor forum 2025: investing in great growth companiesTrip notes: on the road with Baillie Gifford China Growth Trust  Companies mentioned include:AlibabaAMECASMLBeOneByteDanceCATLHorizon RoboticsInnovent BiologicsJiangsu HengruiHuaweiMiniMaxSamsungNAURATencentTSMCXiaohongshu Timecodes:00:00  Introduction01:55   Joining the China Equities Strategy02:40  Intense competition04:00  The government's influence06:10   CATL, the electrification champion08:45  Investing with a 5-year time horizon10:25   Shanghai office, local expertise11:45   Regulations and geopolitics14:30   China's next Five-year Plan16:15   Innovent Biologics' new cancer drugs18:10   Lower-cost clinical trials19:45   Being selective in semiconductors21:25   Investing in chip equipment makers23:00  China's ‘big tech and AI'25:10   MiniMax making AI like ‘tap water'27:45  The road to robotics29:35  A market you can't ignore30:30  Book choice Glossary of terms (in order of mention): Third plenum: a major policy meeting of China's ruling Communist Party, often used to set big economic/political direction.Sovereign bond issuance: The government raising money by selling bonds (IOUs) to investors.Opportunity set: the range of investable companies available to choose from.Capex: capital expenditure – money spent on long-term assets like factories, equipment, or data centres.Fiscal deficit target: how much more the government plans to spend than it collects in revenue (taxes plus other income), expressed as a share of the economy.GDP: gross domestic product – the total value of goods and services a country produces in a year.Market capitalisation: the total value of a company's shares (share price × number of shares).ESG: environmental, social and governance – how a company manages environmental impact, people issues, and corporate oversight.Large-form batteries: big battery packs used in things like electric vehicles and grid storage.Energy storage systems: large batteries that store electricity for later use (helping balance the grid).Generic drugs: copies of medicines whose patents have expired; usually cheaper, same active ingredient.Bi-specific (bispecific) drugs: drugs designed to bind to two targets at once (often to direct immune cells to cancer).ADC drugs: antibody–drug conjugates – antibodies that deliver a toxic payload to cancer cells.Out-licensing: selling rights to your drug/technology to another company (often for upfront + milestone payments).EUV machines: extreme ultraviolet lithography equipment used to make the most advanced chips.Foundry: a factory business that manufactures chips for other companies.Etch and deposition: steps in chipmaking – etch removes material to form patterns, deposition adds thin layers.Picks and shovels: a metaphor for companies that sell essential tools to an industry (rather than end products).Digitalisation: moving processes and services from offline to software and data-driven systems.Compute: the processing power (chips and servers) used to train/run AI.Large language model (LLM): an AI trained on lots of text to generate and understand language.Margins: how much profit a company makes per pound/dollar of revenue (after costs).Cloud business: selling computing power/storage/software over the internet instead of on a local machine.Algorithm layer: the method or software logic that makes the AI work (as distinct from the hardware).Gross margin: revenue minus direct costs (before overheads), a rough measure of product profitability.Assisted driving: features that help a driver (lane-keeping, adaptive cruise control, etc) but don't fully replace them.Autonomous driving: a car driving itself with minimal or no human input.Software attachment rate: the percentage of customers who add paid software features and/or subscriptions.

Il y a 60 ans mourait le Père Pierre Teilhard de Chardin. Ce Jésuite tout à la fois paléontologue, théologien et philosophe fut autant vénéré que décrié de son vivant, manquant même de peu une condamnation de Rome. Aujourd'hui que peut-on dire de cette grande figure du catholicisme du XXème siècle ? Qui était Pierre Teilhard de Chardin ? Quelle influence a-t-il exercé sur la science ? Et que peut-on retenir de sa pensée ? Pour nous accompagner dans cette réflexion, nous recevons cette semaine Patrice Boudignon, historien et auteur en 2008 de la biographie " Pierre Teilhard de Chardin, sa vie son oeuvre, sa réflexion (Cerf Histoire), ainsi que le Père François Euvé, lui aussi Jésuite, théologien et scientifique de formation, auteur du livre " Sauver le cosmos dans les pas de Teilhard de Chardin " qui paraîtra courant novembre aux éditions Salvatore. Il est par ailleurs membre du comité de rédaction de la revue Recherches de sciences religieuses et rédacteur en chef de la revue Etvdes Emission du 11 octobre 2015.

Wat als het meest waardevolle bedrijf van Nederland geen economische troef meer is, maar een geopolitiek drukmiddel op het bord van Donald Trump? In aflevering 138 van Studio Tegengif ontrafelen we waarom ASML plots midden in een mondiale machtsstrijd staat — en waarom Nederland zich moet afvragen of het eigenaar is van een succesverhaal, of beheerder van een gevaarlijk geo-economisch chokepoint. Journalist Diederik Baazil, auteur van De belangrijkste machine ter wereld, neemt ons mee van een bijna mislukte Philips-afsplitsing naar de kern van de wereldwijde chipoorlog. We praten over EUV, exportrestricties, Trump, Taiwan en het fascinerende ecosysteem rond ASML — van Zeiss tot TSMC, van kennisinstellingen tot kabinetten. En vooral: wat moet Nederland doen als bondgenootschappen verschuiven, afhankelijkheden wapens worden en ASML letterlijk “op het menu” komt te staan? Zoals je van Studio Tegengif verwacht proberen we complexe zaken toegankelijk te bespreken. Deze aflevering werd gemaakt met ondersteuning van Wim Brons van remotepodcast.nl. Een aanrader voor als je op afstand een podcast wil maken met fantastische geluidskwaliteit. Wil je ons steunen? Dat kan: je kunt vriend van de show worden:
https://vriendvandeshow.nl/studio-tegengif ***SHOWNOTES*** Diederik Baazil, en Cagan Koc, ‘De Belangrijkste machine ter wereld: Hoe ASML verwikkeld raakte in een internationale machtsstrijd' (2025) https://uitgeverijprometheus.nl/boeken/belangrijkste-machine-ter-wereld-paperback/ Reuters, ‘Exclusive: How China built its ‘Manhattan Project' to rival the West in AI chips' https://www.reuters.com/world/china/how-china-built-its-manhattan-project-rival-west-ai-chips-2025-12-17/ Diederik Baazil, en Cagan Koc, Financieel Dagblad, ‘Wat doet Nederland als Trump ASML op het menu zet?' https://fd.nl/opinie/1583851/wat-doet-nederland-als-trump-asml-op-het-menu-zet

TSMC: extreem goede cijfers. Samsung: explosieve verwachtingen. Intel: kan niet aan de vraag voldoen. Kortom, het lijkt in de sterren geschreven te staan dat ook het laatste kwartaal van afgelopen jaar garant staat zéér goede cijfers van de chipmachinemaker uit Veldhoven. Ook analisten zijn enthousiast. Ze verhogen stuk voor stuk hun koersdoel voor het aandeel. Het lijkt er dus op dat je superlatieven te kort komt. Woensdag weten we ook of dat terecht is, want dan komt ASML met hun cijfers. Bob Homan van ING Investment Office vertelt je in hoeverre ASML nog teleur kan stellen. En naar welk cijfertje je woensdagochtend op zoek moet. Over de podcast: In Beurs in Zicht stomen we je klaar voor de beursweek die je tegemoet gaat. Want soms zie je door de beursbomen het beursbos niet meer. Dat is verleden tijd! Iedere week vertelt een vriend van de show waar jouw focus moet liggen. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro.See omnystudio.com/listener for privacy information.

1:57:05 – Frank in New Jersey, plus the Other Side. Topics include: Music selections, yacht rock, The Second Arrangement by Steely Dan, Tron: Ares (2025), poker chips, Stranger Things finale, Sorcerer (1977), Stones in Exile (2010), EUV microchip manufacturing, “The Ridiculous Engineering Of The World’s Most Important Machine”, Mixue pronounciation, Oh! Susanna, another Mullholland Drive synchronicity, and […]

1:57:05 – Frank in New Jersey, plus the Other Side. Topics include: Music selections, yacht rock, The Second Arrangement by Steely Dan, Tron: Ares (2025), poker chips, Stranger Things finale, Sorcerer (1977), Stones in Exile (2010), EUV microchip manufacturing, “The Ridiculous Engineering Of The World’s Most Important Machine”, Mixue pronounciation, Oh! Susanna, another Mullholland Drive synchronicity, and […]

Happy holidays from Sinica! This week, I speak with Paul Triolo, Senior Vice President for China and Technology Policy Lead at DGA Albright Stonebridge Group and nonresident honorary senior fellow on technology at the Asia Society Policy Institute's Center for China Analysis. On December 8th, Donald Trump announced via Truth Social that he would approve Nvidia H200 sales to vetted Chinese customers — a decision that immediately sparked fierce debate. Paul and I unpack why this decision was made, why it's provoked such strong reactions, and what it tells us about the future of technology export controls on China. We discuss the evolution of U.S. chip controls from the Entity List expansions under Trump's first term through the October 2022 rules and the Sullivan Doctrine, the role of David Sacks and Jensen Huang in advocating for this policy shift, whether Chinese firms will actually want to buy H200s given their heterogeneous hardware stacks and Beijing's autarky ambitions, what the Reuters report about China cracking ASML's EUV lithography code tells us about the choke point strategy, and whether selective engagement actually strengthens Taiwan's Silicon Shield or undermines it. This conversation is essential listening for understanding the strategic, technical, and political dimensions of the semiconductor competition.6:44 – What the H200 decision actually changes in the real world 9:23 – The evolution of U.S. chip controls: from Entity Lists to the Sullivan Doctrine 18:28 – How Jensen Huang and David Sacks convinced Trump 25:21 – The good-faith case for why export control advocates see H200 approval as a strategic mistake 32:12 – What H200s practically enable: training, inference, or stabilizing existing clusters 38:49 – Will Chinese companies actually buy H200s? The heterogeneous hardware reality 46:06 – The strategic contradiction: exporting 5nm GPUs while freezing tool controls at 16/14nm 51:01 – The Reuters EUV report and what it reveals about choke point technologies 58:43 – How Taiwan fits into this: does selective engagement strengthen the Silicon Shield? 1:07:26 – Looking ahead: broader rethinking of export controls or patchwork exceptions? 1:12:49 – What would have to be true in 2-3 years for critics to have been right about H200?Paying it forward: Poe Zhao and his Substack Hello China TechRecommendations: Paul: Zbig: The Life of Zbigniew Brzezinski, Amerca's Great Power Propheti by Ed Luce; Hyperdimensional Substack by Dean Ball Kaiser: Everything Is Tuberculosis by John Green; The Anthropocene Reviewed by John Green; So Very Small by Thomas LevensonSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Our 229th episode with a summary and discussion of last week's big AI news!Recorded on 12/19/2025Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Notable releases include OpenAI's GPT-5.2 Codex for advanced coding and Google's Gemini Free Flash for competitive AI application performance. Nvidia's new open-source Trion-3 models also showcase impressive benchmarks.Funding updates highlight Lovable's $330M Series B, valuing the AI coding startup at $6.6B, and Faya's $140M Series D for AI model hosting, valued at $4.5B.China makes significant strides in semiconductor technology with advances in EUV lithography machines, led by Huawei and SMIC, potentially disrupting global chip manufacturing dominance.Key safety and policy updates include OpenAI's GPT-5.2 system card focusing on biosecurity and cybersecurity risks, while Google partners with the US military to power a new AI platform with Gemini models.Timestamps:(00:00:10) Intro / Banter(00:02:09) News PreviewTools & Apps(00:02:56) Google launches Gemini 3 Flash, makes it the default model in the Gemini app | TechCrunch(00:10:13) ChatGPT launches an app store, lets developers know it's open for business | TechCrunch(00:13:35) Introducing GPT-5.2-Codex | OpenAI(00:19:23) Story about OpenAI release - GPT image 1.5(00:22:27) Meta partners with ElevenLabs to power AI audio across Instagram, Horizon - The Economic TimesApplications & Business(00:23:16) OpenAI to End Equity Vesting Period for Employees, WSJ Says(00:28:20) How China built its ‘Manhattan Project' to rival the West in AI chips(00:36:47) China's Huawei, SMIC Make Progress With Chips, Report Finds(00:41:03) OpenAI in Talks to Raise At Least $10 Billion From Amazon and Use Its AI Chips(00:43:32) Amazon has a new leader for its ‘AGI' group as it plays catch-up on AI | The Verge(00:47:27) Broadcom reveals its mystery $10 billion customer is Anthropic(00:49:12) Vibe-coding startup Lovable raises $330M at a $6.6B valuation | TechCrunch(00:50:38) Fal nabs $140M in fresh funding led by Sequoia, tripling valuation to $4.5B | TechCrunchProjects & Open Source(00:51:10) Nvidia Becomes a Major Model Maker With Nemotron 3 | WIRED(00:59:24) Meta introduces new SAM AI able to isolate and edit audio • The Register(00:59:54) [2512.14856] T5Gemma 2: Seeing, Reading, and Understanding Longer(01:03:10) Anthropic makes agent Skills an open standard - SiliconANGLEResearch & Advancements(01:03:47) Budget-Aware Tool-Use Enables Effective Agent Scaling(01:08:21) Rethinking Thinking Tokens: LLMs as Improvement Operators(01:10:50) What if AI capabilities suddenly accelerated in 2027? How would the world know?Policy & Safety(01:12:58) Update to GPdfT-5 System Card: GPT-5.2(01:18:04) Neural Chameleons: Language Models Can Learn to Hide Their Thoughts from Unseen Activation Monitors(01:20:47) Async Control: Stress-testing Asynchronous Control Measures for LLM Agents(01:24:37) Google is powering a new US military AI platform | The VergeSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

「NordVPN X M觀點」: https://nordvpn.com/miula 專屬優惠碼「miula」 透過專屬優惠連結購買兩年方案加贈4個月好禮，還有30天內退款保證，完全零風險！ --- EP262. 馬斯克拿到薪水、TikTok 交易完成、中國 EUV 能打嗎 | M觀點 --- (00:40) EP262 預告 (03:07) 業配時間：NordVPN (05:22) 閒聊時間：台北隨機攻擊事件 (08:09) 第一個話題：馬斯克拿到薪水 (23:58) 第二個話題：TikTok 交易完成 (39:56) 第三個話題：中國 EUV 能打嗎 --- M觀點資訊 --- 科技巨頭解碼: https://bit.ly/3koflbU M觀點 Telegram - https://t.me/miulaviewpoint M觀點 IG - https://www.instagram.com/miulaviewpoint/ M觀點Podcast - https://bit.ly/34fV7so M報: https://bit.ly/345gBbA M觀點YouTube頻道訂閱 https://bit.ly/2nxHnp9 M觀點粉絲團 https://www.facebook.com/miulaperspective/ 任何合作邀約請洽 miula@outlook.com -- Hosting provided by SoundOn

Burnie and Ashley discuss the great Chinese catch up, EUV chips, research milestones, and all the Marvel teasers.

Has China cracked a major puzzle for chip parity? Trump Media merges with a… Fusion Energy startup? Coinbase continues its efforts to let you trade everything. OpenAI is turning on the fundraising afterburners. And how to catch a North Korean IT infiltrator. China may have reverse engineered EUV lithography tool in covert lab, report claims — employees given fake IDs to avoid secret project being detected, prototypes expected in 2028 (Tom's Hardware) Trump media group agrees $6bn merger with Google-backed fusion energy company (FT) Coinbase adds prediction markets and stock trading in push to be one-stop trading app (CNBC) OpenAI Has Discussed Raising Tens of Billions at Valuation Around $750 Billion (The Information) Amazon Caught North Korean IT Worker By Tracing Keystroke Data (Bloomberg) Learn more about your ad choices. Visit megaphone.fm/adchoices

Shenzhen scientists develop EUV lithography prototype, the FTC probes Instacart’s AI pricing tool, and Apple is modifying its iOS app store policies in Japan. MP3 Please SUBSCRIBE HERE for free or get DTNS Live ad-free. A special thanks to all our supporters–without you, none of this would be possible. If you enjoy what you seeContinue reading "Gemini 3 Flash Replaces 2.5 as Default in Google’s AI Tools – DTH"

Het is Tesla toch wéér gelukt. Het bedrijf stunt op de beurs. Misschien wel de comeback van het jaar. In het eerste kwartaal ging er nog 36 procent van de beurswaarde af, nu tikt het bedrijf van Elon Musk een all-time-high aan.Deze aflevering kijken we of dat logisch is. Er zijn nog steeds de nodige beren op de weg, maar toch lijken beleggers die niet te zien. Sterker nog: ze denken dat Tesla helemaal binnen gaat lopen met robotaxi's. Collega Noud Broekhof (Nationale Autoshow) legt uit waarom dat nergens op slaat.Verder hebben we het over dé overnamesoap van 2025. Die van Warner Bros. Netflix lijkt dan toch aan het langste eind te trekken, want Paramount moet twee klappen verwerken. De eerste is Jared Kusner, schoonzoon van Trump. Die loopt als financieerder weg van de deal. Tweede klap is de directie van Warner Bros zelf.Ook bespreken we Chinese zorgen voor ASML. Nee, geen exportrestrictie van de Amerikanen. De Chinezen zijn dit keer zelf het probleem. Volgens persbureau Reuters hebben ze zelf een EUV-machine in elkaar geknutseld... Justin Blekemolen van onlinebroker Lynx is te gast. Met hem hebben we het ook over de waarschuwing van De Nederlandsche Bank. Dat zich nu ook zorgen maakt over een AI-bubbel.See omnystudio.com/listener for privacy information.

ASML CEO Christophe Fouquet says the company's technical knowledge is essential for its work with customers, and he spends time studying the technical details of the company's chip-making machines. Fouquet's next big test is leading a transition from extreme ultraviolet lithography technology to high numerical aperture EUV, which aims to push chip geometries below 2-nm and make chips more capable of running advanced applications in AI and other fields. He speaks with Bloomberg's Tom MackenzieSee omnystudio.com/listener for privacy information.

- Marvell in AI, Celestial AI - Co-Packaged Optics, Photonics Interconnects - Lasers for EUV, xLight FEL Lasers, ASML Cymer's LPP Lasers - ASML, Canon, Nikon - Chinese efforts in chip manufacturing: SMEE, SiCarrier - Canon's Nano Imprint Lithography (NIL) - China's Xizhi Electron Beam Lithography - Okinawa Institute of Science and Technology (OIST)'s simplified optics in EUV - SDCS Research on Parkinson's Disease [audio mp3="https://orionx.net/wp-content/uploads/2025/12/HPCNB_20251208.mp3"][/audio] The post HPC News Bytes – 20251208 appeared first on OrionX.net.

PC World's Adam Patrick Murray stops by this week to discuss the trip he and Will recently took to visit Intel's new 18A chip fabrication facility in Arizona. Settle in for a wide-ranging chat about the upcoming Panther Lake architecture, why Intel won't have a new desktop part for a while longer, the future of next-gen chiplet interconnects, the difficulty of scheduling between big and little cores, suiting up to enter the fab, 30mph FOUPs whizzing around overhead, EUV machines the size of multiple school buses, getting served beer by tiny horses (??), and more. Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

In this episode, Clay unpacks the extraordinary rise of ASML — a little-known Dutch company that quietly became the most important player in global technology. Since its IPO in 1995, ASML has compounded at 20% annually. ASML holds one of the most powerful monopolies on earth as it's the sole manufacturer of EUV lithography machines, which make the world's most advanced semiconductor chips. Without ASML, companies like Apple, NVIDIA, and TSMC couldn't power iPhones, AI data centers, or the modern digital economy. IN THIS EPISODE YOU'LL LEARN: 00:00 - Intro 04:56 - How ASML grew from a Philips spinoff into Europe's most important tech company. 13:31 - How ASML's partnership with TSMC shaped the global semiconductor industry. 32:16 - Why ASML holds a near-monopoly on EUV lithography machines. 41:01 - Why the geopolitical tension between the US and China place ASML at the center of technology power struggles. 53:30 - How investors can view ASML's growth, risks, and future opportunities. 01:00:23 - What makes ASML's moat nearly impossible for competitors to replicate. 01:05:08 - The dual leadership that propelled ASML's rise and built a culture of relentless focus. And so much more! Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Join Clay and a select group of passionate value investors for a retreat in Big Sky, Montana. Learn more ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to engage in meaningful stock investing discussions with Stig, Clay, Kyle, and the other community members. Marc Hijink's book: Focus: The ASML Way. Related Episode TIP727: 7 Powers by Hamilton Helmer. Related Episode TIVP024: TSMC: The Most Important Business in the World?. Follow Clay on ⁠⁠X⁠⁠ and ⁠⁠LinkedIn⁠⁠. Check out all the books mentioned and discussed in our podcast episodes ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy ad-free episodes when you subscribe to our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Get smarter about valuing businesses in just a few minutes each week through our newsletter, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠We Study Billionaires Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X (Twitter)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Browse through all our episodes (complete with transcripts) ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance Tool⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠⁠⁠sponsors⁠⁠⁠: ⁠SimpleMining⁠ ⁠HardBlock⁠ ⁠AnchorWatch⁠ ⁠Human Rights Foundation⁠ ⁠Cape⁠ ⁠Unchained⁠ ⁠Vanta⁠ ⁠Shopify⁠ ⁠Onramp⁠ ⁠Abundant Mines⁠ HELP US OUT! Help us reach new listeners by leaving us a ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠rating and review⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠! It takes less than 30 seconds, and really helps our show grow, which allows us to bring on even better guests for you all! Thank you – we really appreciate it! Support our show by becoming a premium member! ⁠⁠⁠https://theinvestorspodcastnetwork.supportingcast.fm⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

In this episode, Jeremie and Edouard Harris, co-founders of Gladstone AI and national security advisors, join us to break down the real score in the U.S.–China AI race. We unpack what it actually means to “win” in AI: from cutting-edge model development and compute infrastructure to data center vulnerabilities, state-sponsored espionage, and the rise of robotic warfare. The Harris brothers explain why energy is the hidden battleground, how supply chains have become strategic liabilities, and why export controls alone won't save us. This is not just a geopolitical showdown - it's a race for superintelligence, and the clock is ticking. ------