Podcasts about Gul

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president could still fire commissioners, as the Court now permits, but if those firings broke quorum, the agency would be unable to proceed until replacements were confirmed. The guardrail would

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

“A place that doesn't have great philosophers will not have great technologists either.” — Mehran Gul on Europe's inexplicable underperformance The digital revolution, we were promised, would mean the end of geography. From Beijing to Birmingham to Berlin to Barcelona, anyone could invent anything anywhere, and so the geography of innovation would no longer matter. But that's not the way it has worked out. At least according to the Geneva-based innovation geographer Mehran Gul. Gul's acclaimed The New Geography of Innovation is a travelogue of innovation. But what he finds on his journey around the world in search of innovation is the end of the end of geography. Yes, Gul reports, there's innovation in Beijing and in Birmingham (USA) — but not in Birmingham (England), Berlin or Barcelona. All the important invention is in China and the US. There simply isn't much radical stuff going on anywhere else. Gul began his journey expecting to find ten or twelve countries able to innovate competitively with the United States and China. But what he discovered is either niche players or, in the case of South Korea, Israel, and India, just an extension of the US-centric system. Europe — as renters rather than owners of American technology — comes off worst. When PayPal went public, it minted 160 millionaires who went on to help build SpaceX, Tesla, LinkedIn and Palantir; when Skype exited at about the same value, it minted 11. And if you put London aside, the rest of the UK is now poorer per capita than Mississippi. And the AI boom has only compounded all this, with half of last year's key research papers coming from China, 40% from America, and just 4% from Europe. So really the new geography of innovation is the old geography. Only with China replacing Europe as the only serious competitor to American innovation. Oh lord, oh lord. As a Mississippi bluesman might summarize Europe's predicament. Five Takeaways •       Golden Shares: The Two Systems Are Converging. OpenAI offering Washington a 5% stake, the US government owning Intel — these are Chinese moves, and Gul argues the two models are becoming more alike than either admits. But he pushes back on the lazy version of the China story: its tech sector rose despite the state, not because of it. Jack Ma exiled to Japan, Didi hit with a billion in fines, entire sectors decapitated overnight in 2021 under the banner of common prosperity. In a country with no independent media and no opposition parties, the only rival to centralized power is the tech sector — and the party knows it. •       Two Countries — and Everyone Else. Gul started writing expecting to find ten or twelve countries punching at America's level; the honest answer turned out to be two. Only China has broad-based competence across technologies and a genuinely competitive relationship with the US. The middle powers — South Korea, Israel, India — are extensions of the American system, not rivals to it. That finding surprised the author as much as anyone: it's not the book he set out to write. •       Europe: Renters, Not Owners. After the Fable 5 and Mythos bans, Europe woke up to being a renter of American technology — foundation models, NVIDIA GPUs, all of it. Its best companies keep leaving: DeepMind to Google, Arm to a New York listing, Hugging Face from Paris to Manhattan — while Volvo, Supercell, and KUKA sold to China. Gul's diagnosis is institutional, not cultural: European employees own half as much of their startups as American ones, so there is no European PayPal mafia. His fixes: a European Nasdaq to replace 41 competing capital markets, and pension funds unleashed into venture capital. •       The Question Nobody Is Asking. Since 1990, America's share of global GDP has held at 25% while China's multiplied tenfold — the loser is Europe. The top ten American tech companies are worth $27 trillion, more than the GDP of every country on earth except America itself. Tech is not one industry among many; it is the foundation of all of them — the new cars came from Tesla, not GM. Gul's message to the skeptical Spaniard enjoying long lunches: the last sixty years of American platform dominance skewed power across the Atlantic, and the next sixty will add China to the bill. •       The Rest of the Map: Anti Case Studies. Japan tops the freedom indexes, has the technical schools, and still never escaped the keiretsu — disproving Matt Ridley's claim that innovation is simply the child of freedom. Taiwan's relevance comes down to one company and Morris Chang's missed promotion at Texas Instruments. Singapore is an inspiration, not a model — a one-party city-state that invoices NVIDIA's chips and banks ASEAN's venture capital. India underperforms while Indians excel — 56 notable American foundation models last year, 35 Chinese, barely one Indian. And Switzerland reminds us innovation isn't only venture-backed: a train network running on renewables since the 1960s. About the Guest Mehran Gul writes about technology and business. He is the winner of the Financial Times/McKinsey Bracken Bower Prize, from which The New Geography of Innovation grew. He attended Yale as a Fulbright Scholar, Fox International Fellow, and Teaching Fellow, has been a Lead for the Digital Transformation of Industries at the World Economic Forum in Geneva, and served as an expert on entrepreneurship and industrial policy at the United Nations Industrial Development Organization in Vienna. Born in Pakistan, he lives in Switzerland. The New Geography of Innovation: The Global Contest for Breakthrough Technologies (Avid Reader Press/Simon & Schuster), a Financial Times Book of the Year, is his first book, out in paperback this month in the US and UK. References: •       The New Geography of Innovation: The Global Contest for Breakthrough Technologies by Mehran Gul (Avid Reader Press/Simon & Schuster). The Wall Street Journal: “An ambitious tour of technological innovation.” •       Sebastian Mallaby — author of The Power Law, which argues China's tech rise owes more to American-style risk capital arriving in Shanghai and Shenzhen than to the state; recently on the show discussing his biography of Demis Hassabis. •       Kai-Fu Lee — author of AI Superpowers, cited by Gul as the classic account of tech written through a Chinese lens. •       Matt Ridley — author of How Innovation Works, whose thesis that innovation is “the child of freedom and the parent of prosperity” Gul tests against the anti case study of Japan. •       Andrew Keen — author of How t...

Pakistan's role as a mediator during the recent Gulf tensions earned international recognition. However, analyst Abdullah Gul argues that this diplomatic success has not translated into meaningful economic relief for ordinary Pakistanis. He criticised what he described as the government's "irresponsible" approach, saying it missed a golden opportunity to secure agreements with Iran for cheaper gas, electricity and oil. He also noted that Pakistan failed to press long-standing demands, such as seeking debt relief from the United States. According to Gul, the current wave of international goodwill is only temporary, and Pakistan should focus on securing concrete, long-term national interests. - خلیج کی حالیہ کشیدگی میں پاکستان کے ثالثی کردار نے عالمی سطح پر پذیرائی حاصل کی لیکن تجزیہ کار عبداللہ گل کے مطابق یہ سفارتی کامیابی عوام کے لیے حقیقی معاشی ریلیف میں تبدیل نہیں ہو سکی۔ انہوں نے حکومت کی "غیر ذمہ دارانہ" پالیسی پر افسوس ظاہر کیا کہ ایران سے سستی گیس، بجلی اور تیل کے معاہدے کرنے کا سنہری موقع ضائع کر دیا گیا، اور امریکہ سے قرضوں کی معافی جیسے دیرینہ مطالبات پر بھی خاموشی اختیار کی گئی۔ ان کے بقول موجودہ عالمی ہمدردی وقتی ہے اور پاکستان کو ٹھوس، دیرپا مفادات کے حصول پر توجہ دینی چاہیے۔_________Explore more ways to listen to the Urdu program here: Listen to SBS Urdu. Install our mobile app “SBS Audio” on Apple (iPhone) or Android devices: SBS Audio App. Download on Apple App Store, Download on Google Play. Follow us on social media: SBS Urdu on Facebook and on SBS Urdu on Instagram. - _______________جانئے کس طرح ایس بی ایس اردو کے مرکزی صفحے کو بُک مارک کریں ہر بدھ اور جمعہ کا پورا پروگرام اس لنک پرسنئے, اردو پرگرام سننے کے دیگر طریقے, “SBS Audio”کےنام سےموجود ہماری موبائیل ایپ ایپیل (آئی فون) یااینڈرائیڈ , ڈیوائیسزپرانسٹال کیجئے

Galambos Ádám a G-ADAM Kiadó alapítója és a Magyar Könyvkiadók Érdekvédelmi Szövetségének elnöke. Beszélgettünk a sport iránti szenvedélyéről, a vízilabdáról és a Vasasról, valamint arról, hogyan vezetett az útja a reklámszakmából a könyvkiadás világába. Megismerhetjük a G-ADAM Kiadó történetét, az első könyvkiadások kihívásait, valamint olyan sikersztorikat, mint a Phil Taylor-könyv, a Michael Jordan-életrajz vagy a Gulácsi Péterről szóló kötet. Szó esik a Touchdown Magazin indulásáról, a Sportkönyvek.hu megvásárlásáról, a magyar, osztrák és német könyvpiac különbségeiről, a testvérével közös munkáról, valamint az Antistigma-díjas könyvkiadási projektről is. beszélgetés második felében a Magyar Könyvkiadók Érdekvédelmi Szövetségének munkájáról, a könyvszakma jelenlegi helyzetéről és a könyvek jövőjéről beszélgettünk.Fejezetek:00:00:00 Beköszönés00:01:00 Sport szeretete, vízilabda, Vasas 00:03:00 Profi sport?00:04:00 Sport es tudatossag00:09:00 Gyerekek es a sport00:13:00 Könyvek szeretete00:16:00 Mix magazin00:18:00 Reklámügynökség00:19:00 Aréna 2000, sport kiadvány: Ládonyi László00:22:00 G-ADAM kiadó00:24:00 Első könyvkiadás00:26:00 Masodik könyvkiadás: 2004-es olimpiához kapcsolódó Aranyút Athénba00:28:00 Touchdown Magazin 00:38:00 Phil Taylor - Erőpróba (2021) 00:40:00 Michael Jordan életrajz (2020)00:43:00 Sportkönyvek.hu megvásárlása (2018)00:45:00 Rossz döntések00:47:00 Ausztria00:51:00 Osztrák és a német könyvpiac00:55:00 Gulácsi Peter00:56:00 Galambos Dániel - Testvérrel együtt dolgozni00:59:00 Gulácsi könyv01:03:00 Antistigma-díj: Bipoláris zavar – Útmutató önnek és szeretteinek01:11:00 Olvasd a játékot (2022) - Lies das Spiel (2026)01:14:00 Foci világbajnokság, Könyvhét01:17:00 Magyar Könyvkiadók Érdekvédelmi Szövetsége01:24:00 Könyv jövője01:28:00 Tervek 01:30:00 Elköszönés#könyv #kiadó #sportolóhttps://gadam.hu/

アーティストインタビュー『奥深きシールの世界』（2026.5.29）mogragRADIO vol,470パーソナリティ：沖冲.×おおたアーティストゲスト：GULアーティストインタビュー『奥深きシールの世界』（2026.5.29）　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　..

Ritka betegségek egyéni és társadalmi terhei az innovációk korszakában - ezzel a címmel szervezett szakmai kerekasztal-beszélgetést a Magyar Közgazdasági Társaság Egészség- és Egészségügy-gazdaságtani Szakosztálya 2026. április 28-án, kedden.A rendezvény előadói: Gulácsi László egyetemi tanár, az MTA doktora, az Óbudai Egyetem tudományos rektorhelyettese; Péntek Márta, az MTA doktora, az Óbudai Egyetem Egészségügyi Közgazdaságtan Kutatóközpontjának egyetemi tanára; Hölgyesi Áron, a kutatóközpont tudományos főmunkatársa; valamint Pogány Gábor, a Ritka Betegségek Országos Szövetségének elnöke.

Miegs var būt salds kā medus, bet var arī izrādīties caurs kā nelāpīta zeķe. Kā miega traucējumi izpaužas dažādos vecumos un kā ārstēt miega traucējumus, pētām raidījumā Kā labāk dzīvot. Skaidro Latvijas Miega medicīnas biedrības prezidente Marta Celmiņa un sertificēta neiroloģe ar specializāciju miega medicīnā Madara Mičule. Ierakstā uzklausām Natāliju Bērziņu. Viņa ir ārste psihiatre un medicīnas zinatņu doktore. Ārste vērtē, ka pēc palīdzības pie psihiatra jāvēršas, ja bezmiegs ilgst vairāk nekā trīs mēnešus un parādās noteikti simptomi. "Pamošanās epizodēm nakts laikā ir diezgan fiksētas, cilvēks principā kāpēc pulkstenis mostas divos vai trijos," skaidro Madara Mičule. "Šo mēs saistām ar to, ka cilvēkam aizejot gulēt, joprojām ir diezgan aktīva simpātiskā nervu sistēma, kura mūs dienas laikā dzen uz priekšu, darbina. Bet, lai mēs varētu kvalitatīvi gulēt, no simpātiskās ir jāpārslēdzas uz to parasimpātisko [nervu sistēmu], kas ir mūsu mierīgā daļa, kura palīdz atgūt atgūt resursus. Ja pāreja pārāk veiksmīgi nenotiek, jo ir trauksme, esam varbūt pārguruši, sastresojušies, kā jau mūsdienās notiek. Tad vienā brīdī šī mūsu simpātiskā nervu sistēma liek par sevi manīt un mēs uzmostamies. Kādēļ tas notiek vairāk ap pulksten trijiem vai četriem? Tuvojas rīts, vēl tīri fizioloģiski sāk pastiprināti izdalīties arī kortizols, un reizēm tas sakrīt ar šo kortizola pīķi. Ja tas notiek ilgstoši un sāk ietekmēt cilvēka pašsajūtu, un viņš pēc tam nevar aiziet gulēt, tad skaidrs, ka ir jāmeklē jāmeklē risinājumi, lai miega kvalitāti atkal stabilizētu." "Optimālā miega nepieciešamība ir atkarīga no vecuma, bet, ja mēs runājam par pieaugušu cilvēku, kā pietiekamu uzskatām nakts miegu, kas ir 7 līdz 9 stundas. Gulēt mazāk kā septiņas stundas lielākajai daļai no mums būs nepietiekami," norāda Madara Mičule. Tāpat gulēt vairāk nekā deviņas stundas arī nav labi. Vēl jāņem vērā, ka senioriem nepieciešamība pēc miega samazinās, un tad nevar gaidīt, ka cilvēks vairāk nekā 80 gadu vecumā gulēs astoņas stundas. Tas bieži vien tīri fizioloģiski nav iespējams.

Lêpirsîna li ser wundakirina keça Kurd Gulîstan Doku hefteyek e didome. Kurê walîyê berê yê Dersîmê (Tuncelî) Mustafa Tuncel Sonel bi kuştina Gulîstanê tê tawanbarkirin û hate girtin. Bavê wî yê walî jî ji tarîkirina delîlan, bikaranîna erka dewletê tê tawanbarkirin. Walî Sonel, sê rojan berê li bajarê Elezîzê, bi nasnameyyeke sexte hate girtin. Lêpirsîna wî li bajarê Erzîromê destpêkir û roja Sêşemê (21/04/2026) ew dersixstin pêş dozger bi dexwaza girtinê. Heta niha 15 kes di vê lêpirsînê hatine girtin.

Šajā intervijā ar psihoterapeitu Dr. Artūru Miksonu runājam par to, kā cilvēku finanšu lēmumus ietekmē emocijas, ieradumi un priekšstati par naudu. Es dakterim jautāju arī, kāpēc uzkrājumi un ieguldījumi daudziem joprojām šķiet sarežģīti, nesasniedzami vai “ne jau man domāti”. Es uzskatu, ka ir svarīgi uzlabot spējas prasmīgi apieties ar naudu, jo finansiālā nedrošība ietekmē cilvēka veselību, attiecības un iespējas.Saskaņā ar SEB bankas un Norstat aptauju, kas veikta 2026. gada februārī, 22.1% cilvēku uzkrājumu nav vispār. Tikai 21.8% aptaujāto cilvēku naudas pietiktu ilgāk nekā 3 mēnešiem, bet 15.1% - mazāk nekā vienam mēnesim. Tas nozīmē, ka liela daļa dzīvo bez īsta “rezerves plāna”.Pētījumā konstatēts, ka stress par naudu ir daudzu cilvēku ikdiena: 23.2% bieži vai ļoti bieži izjūt trauksmi par naudu, vēl 32% to jūt laiku pa laikam (gandrīz katru mēnesi). Cilvēku, kuri par naudu vispār nesatraucas, ir tikai 5.7%.Šo sarunu veidojām ar SEB bankas atbalstu, jo gan bankai, gan Cilvēkjaudai rūp, lai Latvijā pieaugtu cilvēku pārticība un sabiedrības turības līmenis. SEB bankai ir svarīgi palīdzēt cilvēkiem izprast uzkrājumu un ieguldījumu nozīmi, lai viņi varētu veidot labākas attiecības ar naudu un justies pārliecinātāki par savu nākotni.Saruna palīdz paskatīties uz finanšu tēmām vienkāršāk, cilvēcīgāk un bez liekas spriedzes, lai katram ir iespēja izdevīgāk rīkoties ar finanšu izvēlēm savai nākotnei.SARUNAS PIETURPUNKTI:00:00 Ievads: kāpēc mēs zinām, bet nedarām02:45 SEB un Norstat pētījumu dati par latviešu finansēm06:37 Kā veidojas cilvēka attiecības ar naudu09:27 Vajadzības vai vēlmes: kāpēc robeža saplūst13:21 Padomju mantojums un "tagad varu atļauties"18:53 Vai krāt nesanāk, jo tiešām nav naudas?20:20 Gulētiešanas analoģija, kad mazais lēmums vakarā ietekmē visu nākamo dienu24:33 Pensija šķiet pārāk abstrakts jēdziens, ko smadzenes neuztver nopietni31:01 Par ilūziju, kad atbildību uzkraujam savam "nākotnes es"39:25 Ja skatīties savos tēriņos šķiet biedējoši43:50 Kāpēc zinām, ka vajag, bet nedarām48:14 Sāpju slieksnis, iemācītā bezpalīdzība un draugu loka ietekme uz cilvēka finansēm59:27 Skaidras vērtības kā pamats veselīgām attiecībām ar naudu01:08:20 Kā atrast savu motivācijas sistēmu, lai izdotos01:17:18 Neveselīgas attiecības ar naudu: pazīmes un spektrs01:26:03 Arī gudri un veiksmīgi cilvēki pieņem muļķīgus finanšu lēmumus01:29:57 Kā nesamaitāt nākamās paaudzes attiecības ar naudu01:41:35 Kā mainīt iesakņojušos finanšu paradumus01:43:16 Šī problēma nav tikai par naudu.

In this Interview with wealth expert Kuldeep Madan, we break down how whole life insurance premium financing works for ultra-wealthy family's and when it's actually a viable strategy. We then compare using whole life insurance and IUL's for premium financing and which one wins in the end.Watch the Interview on Youtube for Visuals - https://youtu.be/ljkaP_J7ZAkWant a Whole Life Insurance Policy? Go Here: https://bttr.ly/bw-yt-aa-clarityBuy Your Tickets to the Life Insurance Summit! Click Here: https://betterwealth.com/summitConnect with Kuldeep's Team: https://madanplus.com/team/kuldeep-madan/Learn More About BetterWealth: https://betterwealth.comChapters:00:00 - Introduction to Whole Life Premium Finance 01:08 - When Whole Life Premium Financing Works 02:30 - Risks and Failures in Indexed Universal Life (IUL) 04:17 - Solving Liquidity Problems for Ultra-Wealthy Families 06:44 - Estate Planning and Opportunity Cost 08:30 - Using External Leverage 09:03 - Client Profiles and Estate Freezing 10:13 - Educating Family Offices 12:00 - Whole Life vs. IUL and GUL 15:31 - Challenges of Financing GUL 16:03 - Closing Remarks and Event AnnouncementDISCLAIMER: https://bttr.ly/aapolicy*This video is for entertainment purposes only and is not financial or legal advice. Financial Advice Disclaimer: All content on this channel is for education, discussion, and illustrative purposes only and should not be construed as professional financial advice or recommendation. Should you need such advice, consult a licensed financial or tax advisor. No guarantee is given regarding the accuracy of the information on this channel. Neither host nor guests can be held responsible for any direct or incidental loss incurred by applying any of the information offered.

I denne udgave af Gul fredag tager Morten Olsen sammen med Toke Theilade fra Vilfortpark.dk og Magnus Nissen fat i en af de mest besynderlige Brøndby-historier i lang tid. Panelet går tæt på sagen om Benjamin Tahirovic, efter påstanden om, at Steve Cooper skulle have holdt ham ude for at hjælpe Wales i en VM-kvalkamp mod Bosnien. Hvad er op og ned, og er Tahirovics tid i Brøndby reelt ved at være forbi? Der bliver også talt om Sean Klaibers Instagram-opslag fra golfbanen. Er det ligegyldigt, eller sender det et uheldigt signal i en periode, hvor Brøndby kæmper med resultaterne? Til sidst tager panelet debatten om Jacob Ambæk. Er det for hårdt at lægge så meget ansvar på en 17-årig angriber på et hold i modvind — eller er det netop sådan, unge spillere vokser? Der bliver også set frem mod næste kamp mod FC Nordsjælland, kigget tilbage på historiske datoer i Brøndbys historie og rundet af med godt nyt om Marcus Younis. Vært: Morten OlsenMedvirkende: Toke Theilade, chefredaktør på Vilfortpark.dk, Magnus Nissen, Brøndby-fan og fast mand ved stadion, når transfervinduet lukker i denne episodeTahirovic-sagen og den vilde påstand om Steve CooperBrøndbys afvisning af historienEr Tahirovics Brøndby-tid slut?Klaibers golf-opslag: ligegyldigt eller provokerende?Jacob Ambæk: for meget ansvar for tidligt?Optakt til FC NordsjællandTilbageblik i Brøndbys historiePositiv update på Marcus YounisLinksArtikel om Marcus YounisArtikel om sydsidens historie Støt Den Gamle HytteFølg os på InstagramFølg os på FacebookFølg os på X

Orbán Viktor ukránozással, Magyar Péter janicsározással ünnepelte március 15-ét – az ünnep képekben "Azt üzenem a magyarság ellenségeinek, hogy jogos a félelmük" – Toroczkai László a Pilvax közből üzent a népnek Történelmi csúcsra emelkedtek a nagy nyugati olajvállalatok részvényei "Ha eladtok neki 100 millióért, beszállok" – Balogh Levente rá akarta sózni üzleti ajánlatát Bojinka Miklósra 444: Tervezett provokáció során feszíthettek ki egy ukrán zászlót a tiszás Nemzeti Meneten Az MKKP pártigazgatója szerint új helyzet alakult ki a magyar politikában Teherán üldözi Benjamin Netanjahut, a megölése a cél Az iráni háború háttérbe szoríthatja Donald Trump és Hszi Csin-ping csúcstalálkozóját Nagymamára támadt rá egy kóbor németjuhász Dunaszerdahelyen, a nő máltai selyemkutyáját is elvitte Nyitott sorompónál ment át egy vonat a Budapest–Belgrád-vonalon Szoboszlai: Megértem, ha kifütyülnek minket Gulácsi riválisának égbekiáltó hibáján hüledeznek, Orbán a kapufát találta el Hidegfront indítja a hetet, de a télikabátra nem lesz szükség A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Orbán Viktor ukránozással, Magyar Péter janicsározással ünnepelte március 15-ét – az ünnep képekben "Azt üzenem a magyarság ellenségeinek, hogy jogos a félelmük" – Toroczkai László a Pilvax közből üzent a népnek Történelmi csúcsra emelkedtek a nagy nyugati olajvállalatok részvényei "Ha eladtok neki 100 millióért, beszállok" – Balogh Levente rá akarta sózni üzleti ajánlatát Bojinka Miklósra 444: Tervezett provokáció során feszíthettek ki egy ukrán zászlót a tiszás Nemzeti Meneten Az MKKP pártigazgatója szerint új helyzet alakult ki a magyar politikában Teherán üldözi Benjamin Netanjahut, a megölése a cél Az iráni háború háttérbe szoríthatja Donald Trump és Hszi Csin-ping csúcstalálkozóját Nagymamára támadt rá egy kóbor németjuhász Dunaszerdahelyen, a nő máltai selyemkutyáját is elvitte Nyitott sorompónál ment át egy vonat a Budapest–Belgrád-vonalon Szoboszlai: Megértem, ha kifütyülnek minket Gulácsi riválisának égbekiáltó hibáján hüledeznek, Orbán a kapufát találta el Hidegfront indítja a hetet, de a télikabátra nem lesz szükség A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Bronzový recept roku 2018, žandárský guláš Jaroslavy Bernatíkové, uvařil šéfkuchař Roman Paulus. „Kotlíkové guláše vařím velice rád. Abych tam vnesl takovou tu kouřovost, použiju uzenou papriku a trochu whisky.“ Guláš má světlejší barvu, spíše dočervena. Maso se rozpadá, hustotu omáčky zajistí brambory a cibule.

Bronzový recept roku 2018, žandárský guláš Jaroslavy Bernatíkové, uvařil šéfkuchař Roman Paulus. „Kotlíkové guláše vařím velice rád. Abych tam vnesl takovou tu kouřovost, použiju uzenou papriku a trochu whisky.“ Guláš má světlejší barvu, spíše dočervena. Maso se rozpadá, hustotu omáčky zajistí brambory a cibule.

Bronzový recept roku 2018, žandárský guláš Jaroslavy Bernatíkové, uvařil šéfkuchař Roman Paulus. „Kotlíkové guláše vařím velice rád. Abych tam vnesl takovou tu kouřovost, použiju uzenou papriku a trochu whisky.“ Guláš má světlejší barvu, spíše dočervena. Maso se rozpadá, hustotu omáčky zajistí brambory a cibule.

Bronzový recept roku 2018, žandárský guláš Jaroslavy Bernatíkové, uvařil šéfkuchař Roman Paulus. „Kotlíkové guláše vařím velice rád. Abych tam vnesl takovou tu kouřovost, použiju uzenou papriku a trochu whisky.“ Guláš má světlejší barvu, spíše dočervena. Maso se rozpadá, hustotu omáčky zajistí brambory a cibule.

Bronzový recept roku 2018, žandárský guláš Jaroslavy Bernatíkové, uvařil šéfkuchař Roman Paulus. „Kotlíkové guláše vařím velice rád. Abych tam vnesl takovou tu kouřovost, použiju uzenou papriku a trochu whisky.“ Guláš má světlejší barvu, spíše dočervena. Maso se rozpadá, hustotu omáčky zajistí brambory a cibule.

Bronzový recept roku 2018, žandárský guláš Jaroslavy Bernatíkové, uvařil šéfkuchař Roman Paulus. „Kotlíkové guláše vařím velice rád. Abych tam vnesl takovou tu kouřovost, použiju uzenou papriku a trochu whisky.“ Guláš má světlejší barvu, spíše dočervena. Maso se rozpadá, hustotu omáčky zajistí brambory a cibule.

Bronzový recept roku 2018, žandárský guláš Jaroslavy Bernatíkové, uvařil šéfkuchař Roman Paulus. „Kotlíkové guláše vařím velice rád. Abych tam vnesl takovou tu kouřovost, použiju uzenou papriku a trochu whisky.“ Guláš má světlejší barvu, spíše dočervena. Maso se rozpadá, hustotu omáčky zajistí brambory a cibule.

Bronzový recept roku 2018, žandárský guláš Jaroslavy Bernatíkové, uvařil šéfkuchař Roman Paulus. „Kotlíkové guláše vařím velice rád. Abych tam vnesl takovou tu kouřovost, použiju uzenou papriku a trochu whisky.“ Guláš má světlejší barvu, spíše dočervena. Maso se rozpadá, hustotu omáčky zajistí brambory a cibule.

Bronzový recept roku 2018, žandárský guláš Jaroslavy Bernatíkové, uvařil šéfkuchař Roman Paulus. „Kotlíkové guláše vařím velice rád. Abych tam vnesl takovou tu kouřovost, použiju uzenou papriku a trochu whisky.“ Guláš má světlejší barvu, spíše dočervena. Maso se rozpadá, hustotu omáčky zajistí brambory a cibule.

Bronzový recept roku 2018, žandárský guláš Jaroslavy Bernatíkové, uvařil šéfkuchař Roman Paulus. „Kotlíkové guláše vařím velice rád. Abych tam vnesl takovou tu kouřovost, použiju uzenou papriku a trochu whisky.“ Guláš má světlejší barvu, spíše dočervena. Maso se rozpadá, hustotu omáčky zajistí brambory a cibule.

Bronzový recept roku 2018, žandárský guláš Jaroslavy Bernatíkové, uvařil šéfkuchař Roman Paulus. „Kotlíkové guláše vařím velice rád. Abych tam vnesl takovou tu kouřovost, použiju uzenou papriku a trochu whisky.“ Guláš má světlejší barvu, spíše dočervena. Maso se rozpadá, hustotu omáčky zajistí brambory a cibule.

Bronzový recept roku 2018, žandárský guláš Jaroslavy Bernatíkové, uvařil šéfkuchař Roman Paulus. „Kotlíkové guláše vařím velice rád. Abych tam vnesl takovou tu kouřovost, použiju uzenou papriku a trochu whisky.“ Guláš má světlejší barvu, spíše dočervena. Maso se rozpadá, hustotu omáčky zajistí brambory a cibule.

Jauna realitāte - karš Tuvajos Austrumos. ASV un Izraēlas triecieni Irānā turpinās. Makrons paziņo par nepieciešamību palielināt Francijas kodolgalviņu skaitu. Pakistāna veikusu jaunu gaisa triecienu sēriju pa Afganistānas teritoriju, afgāņi atbild ar uzbrukumiem Pakistānas robežposteņiem. Aktualitātes analizē Ģeopolitikas pētījumu centra vadītājs Māris Andžāns un politologs Veiko Spolītis. Irāna rāda zobus pārspēkam Pēc tam, kad Izraēlas raķetes trāpījums 28. februārī bija laupījis dzīvību Irānas augtākajam vadonim Alī Hāmenejī, nomināli viņa funkcijas līdz jauna islāma republikas virsvadītāja iecelšanai uzņēmās Pagaidu vadības padome, kurā ietilpst Irānas prezidents Masuds Pezeškjāns, Irānas tiesu varas augstākais vadītājs Gulāmhuseins Mohsenī-Ežeī un Irānas konstitūcijas sargu padomes loceklis Alirezā Arafi. Tomēr pēdējās dienās izplatījies viedoklis, ka faktiski Irānas režīma priekšgalā jau kopš asiņainās masu protestu apspiešanas janvārī atrodas Augstākās nacionālās drošības padomes sekretārs, atvaļinātais Islāma revolūcijas sargu korpusa ģenerālis Alī Laridžāni. Tieši viņš pirmdienas vakarā, reaģējot uz Savienoto Valstu prezidenta Donalda Trampa izteikumiem par iespējamu sarunu atsākšanu, paziņoja, ka Irāna ar amerikāņiem nekādas sarunas nevedīs. Lai arī amerikāņu un izraēliešu raķetes turpina pārvērst drupu kaudzēs Irānas militāros un administratīvos objektus, un bez augstākā līdera Hāmenejī nogalināto sarakstā ir arī Islāma revolūcijas sargu korpusa komandieris, aizsardzības ministrs, bruņoto spēku ģenerālštāba priekšnieks un vēl kādi desmit augsta ranga militāristi, Teherāna, vismaz savās ārējās izpausmēs, ietur kareivīgu stāju. Irānas raķešu un aviācijas triecienu mērķi ir ne vien Izraēla un Savienoto Valstu militārie un diplomātiskie objekti reģionā, bet arī naftas un gāzes pārstrādes, transporta, arī biznesa un tūrisma infrastruktūra Apvienotajos Arābu emirātos, Saūda Arābijā, Bahreinā, Kuveitā, Katarā un Omānā, tāpat britu militārā bāze Kiprā. Aktivizējušies Irānas sabiedrotie: Libānā bāzētais grupējums „Hezbollah” sācis apšaudīt ar raķetēm Izraēlas ziemeļu rajonus, Irākas šiītu militārie grupējumi ar lidrobotiem uzbrukuši viņu valstī izvietotajām amerikāņu militārajām bāzēm, tiek ziņots, ka Jemenas hutiešu nemiernieki plānojot atsākt uzbrukumus kuģiem Adenas līcī un Sarkanajā jūrā. Pret „Hezbollah” Izraēla jau izvērsusi plaša mēroga karadarbību, kas ietver arī sauszemes operācijas. Irāna vērsusi gaisa triecienus arī pa kurdu autonomijas teritorijām Irākas ziemeļos, jo šeit bāzējas tās režīmam naidīgas kurdu nemiernieku grupas, kas apvienojušās Irānas Kurdistānas Politisko spēku koalīcijā. Kurdu minoritāte Irānas ziemeļos ir tā, kas pirmām kārtām varētu izvērst bruņotu cīņu pret Teherānas varu. Kā jau tika sagaidīts, jūras transporta pārtraukšana Hormuzā likusi pakāpties naftas un sašķidrinātās gāzes cenām globālajā tirgū, tomēr par kādu paniku šai ziņā runāt nenākas. Pirms karadarbības sākuma tirgos valdīja šo resursu pārprodukcija, un lielākie patērētāji kā ASV un Ķīna ir izveidojuši rezerves, kas šobrīd amortizē situāciju. Pakistānai savs „pārmācīšanas karš” Nu jau labu laiku spriedzi Pakistānas valdības un Afganistānā valdošā talibu režīma starpā rada teroristu grupas, kuras rod patvērumu Afganistānā, rīkojot uzbrukumus Pakistānas teritorijā. Lielākā no šīm grupām ir džihāda kaujinieku struktūra Pakistānas Talibans, kas gan nav organizatoriski vienota ar saviem vārdabrāļiem Kabulā, tomēr idejiski un vēsturiski gan. Jau pagājušā gada oktobrī Pakistāna veica gaisa triecienus pa teroristu objektiem Afganistānā, tai skaitā galvaspilsētā Kabulā, kam sekoja vairākas dienas ilgas sadursmes uz robežas. Tad viss beidzās ar trauslu pamieru, taču šī gada februārī Pakistāna piedzīvoja vairākus terora aktus gan Pakistānas Talibana, gan divu citu teroristu struktūru – Islāma valsts un beludžu nacionālistu organizācijas Beludžistānas Atbrīvošanas armija – izpildījumā. Uzbrukumos dzīvību zaudēja vairāk nekā piecdesmit cilvēku, gan militārpersonas, gan civiliedzīvotāji. 21. februārī Pakistāna īstenoja savus jau iepriekš izteiktos brīdinājumus un veica jaunu gaisa triecienu sēriju pa Afganistānas teritoriju. Oficiālā Islamabada apgalvoja, ka mērķēts tikai pa teroristu bāzēm, taču afgāņu puse un arī Apvienoto Nāciju misija vēstīja par upuriem arī starp mierīgajiem iedzīvotājiem, tai skaitā bērniem. Gluži tāpat kā iepriekšējo reizi sekoja afgāņu uzbrukumi Pakistānas robežposteņiem, pēc kam pakistāniešu gaisa un arī sauszemes spēku triecieni tika vērsti nu jau pret Afganistānas bruņoto spēku objektiem – munīcijas noliktavām, komandpunktiem, kazarmām – gan pierobežas provincēs, gan galvaspilsētā Kabulā un otrā lielākajā valsts pilsētā Kandahārā. Talibiem netrūkst kaujas spara un fanātisma, viņu spēki rūdījušies divdesmit gadus ilgā un galu galā panākumiem vaiņagotā cīņā pret amerikāņu un to sabiedroto okupācijas spēkiem. Šī pieredze ietver labas kaujas lidrobotu izmantošanas prasmes, ko jau nācies sajust uz savas ādas pierobežā dislocētajiem pakistāniešu spēkiem. Pakistānas pusē ir nospiedošs militāri tehniskais pārsvars. Marta sākumā pakistāniešu gaisa spēki vairākkārt bombardējuši nozīmīgo Bagramas aviobāzi, kuru Afganistānai savulaik uzbūvēja Padomju Savienība; neliela Afganistānas pierobežas teritorija nonākusi pakistāniešu spēku rokās. Kā tiek atzīmēts, konflikta attīstību veicina arī vispārējais starptautiskais fons, kad pasaules uzmanība koncentrēta Persijas līča rajonā un neviens īsti nav gatavs veltīt uzmanību šim karam Āzijas vidienē. Kodollietussargs „Made in France” Prezidenta Emanuela Makrona uzstāšanās šo pirmdien, 2. martā, Īllongas kodolzemūdeņu bāzē Bretaņā, Francijas rietumu piekrastē, tiek uzlūkota kā lūzuma punkts franču kodolpolitikā. Kopš aukstā kara baigām valsts atomieroču potenciāla palielināšana faktiski bijusi tabu tēma, bet pirmdien šis tabu tika lauzts. Kā paziņoja Makrons, šobrīd, kad Krievija izvērsusi klaju militāro agresiju, nozīmīgi augušas Ķīnas ģeopolitiskās ambīcijas, bet Savienoto Valstu aizsardzības prioritātes strauji mainījušās, Francijas kodolgalviņu skaita palielināšana ir nepieciešamība. Pie tam, kā uzsvēra Francijas vadītājs, franču kodolatturēšanas programma kļūst par visas Eiropas kopēju rūpi. Viņš uzskaitīja sabiedrotos, ar kuriem Francijai šai ziņā jau ir konkrēti sadarbības plāni: Vācija, Polija, Grieķija, Nīderlande, Beļģija, Dānija un Zviedrija. Nav grūti pamanīt, ka ģeogrāfiski šī valstu grupa koncentrējas Baltijas jūras un Ziemeļjūras baseinā. Visciešākā sadarbība plānota ar Vāciju, un tajā pašā 2. martā tika publiskota prezidenta Makrona un kanclera Merca kopīga deklarācija par franču–vācu kodolvadības darba grupas izveidi. Šo franču „kodollietussarga” izplešanu virs pārējo apvienotās Eiropas partneru galvām Francijas prezidents nodēvēja par „izvērsto atturēšanu”. Prezidents gan nepiemirsa norādīt, ka šī iniciatīva nekādi neesot uzlūkojama kā alternatīva NATO kodolatturēšanas spējām, bet gan kā to papildinājums. Kaut ekspertu vērtējuma Makrona uzstāšanās bija veiksmīga, labi līdzsvarojot Francijas nacionālo un Eiropas kopējo interešu motīvus, viņš, protams, saņēma paredzamu kritiku no pašmāju politiķiem, tai skaitā Nacionālās kustības līdera Žordāna Bardellas, kuru uzskata par visai reālu Makrona pēcteci prezidenta krēslā. Tas ir vēl viens faktors, kas liek prezidētam steigties, lai šī iniciatīva būt jau gana tālu attīstīta un nākamais Elizejas pils saimnieks nevarētu to viegli likvidēt. „Nākamais pusgadsimts būs kodolieroču laikmets, un Francija tajā spēlēs savu pilnvērtīgo lomu, turpinot vairot savus spēkus,” pirmdienas runas noslēgumā pauda Emanuels Makrons. Šobrīd Francijai ir apmēram trīssimt kodolgalviņas, ar kurām aprīkotas ballistiskās raķetes uz četrām „Triomphant” tipa atomzemūdenēm, kā arī spārnotās raķetes uz daudzfunkcionālajiem iznīcinātājiem „Rafale”.   Sagatavoja Eduards Liniņš.

Grilor, Nyx och Isidor besöker Gulön för att skaffa en kunglig skrud i den förbjudna färgen. Under tiden undersöker Korf och Krax den dolda ingången till hexadromen. Här är kartan över Fumurahl

In The New Geography of Innovation: The Global Contest for Breakthrough Technologies, Mehran Gul examines how innovation works in different countries around the globe—diving deep into the ecosystems that produce great technology companies.Gul is a writer and leading technology thinker, having served as the Lead for the Digital Transformation of Industries at the World Economic Forum. His book, which was nominated as a Financial Times best business book of 2025, he discusses why the United States remains at the world's technological frontier, with only China being a true challenger.In his conversation with Nikolaus Lang, Global Leader of the BCG Henderson Institute, he talks about how innovation ecosystems are converging, the role of statecraft in fostering innovation ecosystems, and the main forces that will shift the global innovation landscape in the coming decade.Key topics discussed: 01:22 | Attributes of successful innovation ecosystems06:57 | US vs. China talent pool10:26 | What China gets right about innovation13:20 | Why Europe lags behind on innovation18:54 | The role of intentional statecraft in fostering innovation23:31 | The convergence of innovation ecosystems around the globe26:34 | Implications for businesses28:56 | How the global innovation landscape will evolve in the next decade

Der BVB muss heute gegen Atalanta ohne Nico Schlotterbeck ran. Die Dreierkette der Dortmunder stellt sich deshalb fast von alleine auf. Péter Gulácsi fehlt RB doch länger als gedacht. Die Wachablösung könnte es also früher geben als geplant und André und Podcast-Papa Flo zoffen sich über die Bedeutung des dritten Torwarts bei der WM und die Personalie Jonas Urbig.

It's back-to-back weirdos being weird about Kira, but this time it's Dukat! In an A plot that runs the length of the episode without check-ins back at the station, Kira and the ex-Gul enter a game of cat and mouse (or Bird of Prey and defrocked Cardassian?) and Matt and Andy are along for the ride.[Episode discussion begins around 45:00]

Mi újság a sportkönyvek piacán? Mostani extra adásunkban Galambos Ádámmal, a G-ADAM könyvkiadó tulajdonosával beszélgettünk a magyar, és a nemzetközi helyzetről nem csak a futball kapcsán! (0:00) Magyar sportkönyvpiaci trendek (8:00) Szerző vagy cím? (14:15) Honnan jött a könyvkiadás Ádámnak? (22:00) Könyvesbolt helyett online rendelés (28:00) Alapötlet az Amazonról (30:00) Olvasd a játékot! (33:00) Az osztrák piac (38:30) Milyen könyv lesz a következő? (41:45) Terjesztési csatornák itthon és külfödön (46:00) Gulácsi-könyv németül (47:00) Ádám kedvenc könyvei

Elhallgatott történelem, örök sebek | Mező Gábor New Yorkban ep. 294   Mi történik akkor, amikor egy egész nemzetet évtizedeken keresztül hallgatásra kényszerítenek? Mi marad ki a tankönyvekből, a „hivatalos" történetmesélésből, és miért élnek tovább ezek a sebek generációkon át?   A MÓKA Podcast legújabb epizódjának vendége Mező Gábor újságíró, kutató, aki New Yorkban beszél arról, hogyan vált sportújságíróból a kommunista diktatúra, az állambiztonság, az ÁVH, a propaganda és a kollektív traumák egyik legismertebb kutatójává.   Ebben az adásban szó esik arról, hogy:  • hogyan élte túl a kommunista rendszer saját bűneit,  • miért maradt el a valódi elszámoltatás a rendszerváltás után,  • és hogyan hat mindez a mai fiatal generációk gondolkodására Magyarországon és Amerikában egyaránt.   Mező Gábor személyes hangon mesél arról is, milyen lelki terhekkel jár olyan témákról írni, mint a politikai pszichiátria, a délvidéki népirtás, a Gulág, vagy a szovjet hadsereg által elkövetett tömeges nemi erőszak. Ezekről a történetekről hosszú ideig nem lehetett beszélni, pedig szinte minden magyar családban jelen vannak.   Az adás egyik legerősebb gondolata, hogy a történetek kimondása önmagában terápia. Nem bosszúról, nem revansról van szó, hanem arról, hogy egy nemzet csak akkor tud továbblépni, ha végre nevén nevezi a múltját.   Különösen izgalmas része a beszélgetésnek, amikor Mező Gábor az amerikai fiatalokról beszél. Arról, miért lehet vonzó számukra a szocializmus és a kommunizmus eszméje egy olyan országban, ahol ezeknek nincs megélt történelmi tapasztalata. Miért veszélyes az idealizált múltkép, és mit tanulhatna Amerika Közép- és Kelet-Európa tapasztalataiból.   Szó esik:  • az egyetemek és értelmiségi hálózatok szerepéről,  • a propaganda „becsomagolt" üzeneteiről,  • és arról, miért fontos, hogy a magyar diaszpóra megszólaljon ezekben a kérdésekben.   Az epizódban Mező Gábor a jövőről is beszél. Egy olyan animációs sorozat tervéről, amely rövid, érthető formában mutatná be a 20. századi magyar történelem legsötétebb fejezeteit a fiatalabb generációknak. Mert a történelem nem attól ismétli önmagát, hogy beszélünk róla, hanem attól, ha nem.   Ez az adás nem könnyű, nem „háttérzaj", viszont őszinte, mély és gondolkodásra késztet. Ha érdekel a kommunizmus valódi arca, a kibeszéletlen magyar traumák története, vagy az, hogy miért nem mindegy, mit gondolnak a fiatalok a múlt rendszereiről, akkor ezt az epizódot érdemes végighallgatni.https://www.facebook.com/mezogaborhalozat   Mező Gábor oldalai: https://www.youtube.com/@hamis_gulyas https://www.youtube.com/@apolip https://youtube.com/playlist?list=PLMdU8ly_7MYFnendiJaac-NIgXSVVB28d&si=Pm4VQbiSowu3Rfu2     Mező Gábor, MÓKA Podcast, kommunizmus Magyarországon, ÁVH, állambiztonság, rendszerváltás, kollektív trauma, transzgenerációs trauma, magyar történelem, Gulág, szovjet megszállás, propaganda, kommunizmus Amerikában, magyar diaszpóra New York, szocializmus kritikája, történelmi emlékezet    

Onze spieren doen veel meer dan bewegen. Ze zorgen dat suiker uit ons eten wordt opgenomen en houden onze stofwisseling op peil. Maar wat gebeurt er als spieren plotseling stilvallen? Spierfysioloog Gul Turan (Wageningen University) zet daarvoor onze stagiaire Zinzi tijdelijk in het gips. Niet omdat ze iets gebroken heeft, maar als proefpersoon om te zien wat er gebeurt als je jouw spier absoluut niet kan bewegen. En de resultaten zijn verbluffend: al na twee dagen nemen spieren 40% minder suiker op. Gul laat zien hoe snel stilstand het hele lichaam uit balans kan brengen en waarom bewegen zoveel meer betekent dan spierkracht alleen. Oproep deelnemers voor onderzoek ► Gül is nog hard opzoek naar deelnemers voor haar vervolgonderzoek. Ze is op zoek naar mensen zonder gezondheidsproblemen en ook specifiek nog naar mensen met diabetes type 2. Wil jij meedoen? Hier vind je meer info + kan je je aanmelden: https://www.diabetesfonds.nl/onderzoeken/spieren-goed-houden-als-je-niet-kunt-bewegen Bronnen: Onderzoek naar spieren die 40% minder suiker opnemen ► https://academic.oup.com/jcem/article/105/1/276/5586896?login=false#323955710See omnystudio.com/listener for privacy information.

Ennek a podcastnak a vendége nem csupán a magyar labdarúgó-válogatott kapusaként, hanem a német bajnokság, a Bundesliga egyik meghatározó játékosaként is letette a névjegyét. Gulácsi Péter pályafutása példamutató ívet rajzolt: Liverpooltól Salzburgon át Lipcséig, miközben higgadtsága, profizmusa és emberi tartása sokak számára inspirációt jelent. Nemcsak a focikapuban állt szilárdan, hanem a közéleti kérdésekben is bátran megszólalt, amikor úgy érezte, meg kell szólalnia. Kapott is érte eleget. Ezúttal nemcsak a futballról, hanem döntésekről, értékekről és belső utazásokról is beszélgetünk vele, valamint arról is, hogy hogyan lehet megőrizni a hitet, a mentális egyensúlyt és az alázatot egy olyan világban, ahol a siker, a pénz és a hírnév gyakran eltorzítja az arányérzékeket.Hogyan támogathatja a munkánkat? - Legújabban már a Donably felületen is támogathat bennünket, itt ÁFA-mentesen segítheti munkavégzésünket: https://www.donably.com/friderikusz-podcast - De lehet a patronálónk a Patreon-on keresztül is, mert a támogatása mértékétől függően egyre több előnyhöz juthat: https://www.patreon.com/FriderikuszPodcast - Egyszerű banki átutalással is elismerheti munkavégzésünk minőségét. Ehhez a legfontosabb adatok az alábbiak: Név: TV Pictures Számlaszám: OTP Bank 11707062-21446081 Közlemény: Podcast-támogatás Ha külföldről utalna, nemzetközi számlaszámunk (IBAN - International Bank Account Number): HU68 1170 7062 2144 6081 0000 0000 BIC/SWIFT-kód: OTPVHUHB Akármilyen formában támogatja munkánkat, nagyon köszönjük!Kövessenek, kövessetek itt is:youtube: https://www.youtube.com/c/FriderikuszPodcastFacebook: https://www.facebook.com/FriderikuszPodcastInstagram: https://www.instagram.com/friderikuszpodcastSpotify: https://open.spotify.com/show/0TBImnF4bdNCvmhJwyOlRhAmazon Music: https://music.amazon.com/podcasts/a159b938-d63e-4927-9e9b-bea37bc378d3/friderikusz-podcastYoutube Music: https://music.youtube.com/playlist?list=PLu6L9HlV4-KuNOYy_rS97rP_Q-ncvF14rApple Podcasts: https://apple.co/3hm2vfiDeezer: https://www.deezer.com/hu/show/1000256535

Sean love affair with Lemix is more fickle the fel-flame. Lex laments some aspects of Delves. The crew weighs in on the must-have features for the integrated UI in Midnight. News Legion Remix: Rise of the Nightfallen The 2nd wave of content for Lemix is now live in NA and EU, after a long extended maintenance. Return to KarazhanNightholdSuramar CampaignLegion Remix Helper Links Legion Remix: Rise of the Nightfallen Now Live!World of Warcraft on Instagram: "The story continues in Legion Remix Phase 2! Relive the Suramar Campaign, return to Karazhan, and defeat Gul'dan at Nighthold!"https://www.curseforge.com/wow/addons/legion-remix-helper Turbo Boost is Now Live! The 2nd part of Turbo Boost is Mythic+ drop rates from Mythic+ dungeons and Manaforge Omega are greatly increased for Warband until Equipped gear. Valorstones may now be transferred across Warbands.Two vendors this time.First quest for 3 Puzzling Cartel Chips.Players will be able to earn a maximum of 9 Puzzling Cartel Chips per character.Weapons, trinkets, and cantrip items. Links https://worldofwarcraft.blizzard.com/en-us/news/24242856 Midnight Delves In the upcoming expansion Delves are being integrated into the leveling experience and continue to be an endgame pillar. We're taking some time to talk about Delves so far on the alpha. What did we think about Delves last tier?Experience in alpha with Delves so farWhat would make delves a joyous experience for us? Story Title Phase 4 of Midnight Alpha allows players to level up to Level 90, and includes the following content: Zone: Voidstorm Dungeons:  Voidscar ArenaNexus Point XenasMagister's Terrace Delves:  Shadowguard PointSunkiller Sanctum PvP:  Slayer's Rise PvP zoneSlayer's Rise 40v40 battleground Classes: Apex Talents Links In Development: Phase Four of the Midnight Alpha - General Discussion - World of Warcraft Forums iTunes // Bonus Roll Production Directory Thanks Special thanks to all our patrons. FAZ will always be free, but if you enjoy the content we produce, consider pledging to our Patreon at Patreon.com/FAZ Subscribe to For Azeroth!  UI Feature Shortcomings We are beyond the halfway point Raid Frames Sean: Raid frames ability to adjust tracking, position and size of debuffs CD Manager Sean #1: Tracking for Racials and TrinketsLex: Alert for resource thresholds (below or above a certain amount of a resource)Sean #2: CDM still needs some way to track debuffs with white/blacklisting   Nameplates Sean: Health bar color change based on aggroSean #2: Nameplates need a way to color mobs based on type (casters, lieutenants, etc).Sean #3: White/blacklist for buffs/debuffs tracked   Encounter Warnings Lex: Sound CustomizationSean: Ability to turn on/off warnings based on rolePlatynator: [addon] Introducing Platynator, a new customisable nameplate addon : r/WowUI Outro Be part of the conversation and join us on Discord bit.ly/fazdiscord Thank you so much for supporting the show!

Previously a Fulbright Scholar, Fox International Fellow and Teaching Fellow at Yale, Gul has also been a Lead for the Digital Transformation of Industries at the World Economic Forum in Geneva, and an Expert on Higher Education, Entrepreneurship, and Industrial Policy at the United Nations Industrial Development Organisation in Vienna. His book The New Geography of Innovation won the Financial Times/McKinsey Bracken Bower Prize for writers under 35. In this episode you will learnHow the geography of innovation is shifting and what it means for the new world order The art of connecting innovation, geography, and ambition with the help of illustrative case studiesHow to write a deeply-researched book

Ezen a hétvégén már az összes topligában játszanak!

In this episode, I speak with Gul Rukh Rahman, a woman whose life and work cross continents, cultures, and the fault lines of global politics.Born in Pakistan and raised in countries including Libya and Saudi Arabia, Gul moved to the US for university before settling in Europe 15 years ago. She grew up in conservative Peshawar in the protective bubble of a military family, yet in a region marked by instability and violence, including bomb blasts during Eid celebrations. Family expectations came with tightly controlled choices for education and relationships, leading Gul to go on a hunger strike to avoid dentistry school.Culturally hard to categorise and politically impossible to intimidate, Gul reflects on identity as a woman and a Muslim in the aftermath of 9/11, her decision to wear the hijab as a political statement, and the circumstances that prompted her to take it off. She chose activism over the safety of a corporate career, driven by a commitment to speak uncomfortable truths.Now teaching at the University of Geneva, Gul works far beyond the classroom, advising philanthropists and nonprofits, investigating where the money really goes, and exposing the darker side of “doing good.” We dive into silent philanthropy, the geopolitics shaping global giving, and how vast wealth from the Global South still flows into bank accounts in Switzerland and Dubai while the South continues to “beg” the North.This conversation blends biography, political critique, and a fearless look at philanthropy's contradictions. Gul doesn't pull her punches: and that's exactly why you should listen.Connect with Gul on LinkedIn at linkedin.com/in/gul-rukh-rahman-1b74604.Instagram: @at.the.coalfaceAnd don't forget to subscribe to At the Coalface for new episodes every two weeks.Help us produce more episodes by becoming a supporter. Your subscription will go towards paying our hosting and production costs. Supporters get the opportunity to join behind the scenes during recordings, updates about the podcast, and my deep gratitude!Support the show

For the past fifty years, Silicon Valley has led the world in developing cutting-edge technologies and spawning high-growth, billion-dollar tech companies. More recently, China has emerged as a formidable force in innovation. But is our focus on the US-China rivalry causing us to overlook the rise of new tech powerhouses elsewhere? In today's episode, Financial Times/McKinsey Bracken Bower Prize winner Mehran Gul invites us to take a wider view. Drawing from his new book, The New Geography of Innovation, Gul joins Adam McCauley to explore how innovation is playing out globally - and how emerging technologies will both influence and be influenced by shifting geopolitical dynamics in the decades ahead. If you'd like to become a Member and get access to all our full conversations, plus all of our Members-only content, just visit intelligencesquared.com/membership to find out more. For £4.99 per month you'll also receive: - Full-length and ad-free Intelligence Squared episodes, wherever you get your podcasts - Bonus Intelligence Squared podcasts, curated feeds and members exclusive series - 15% discount on livestreams and in-person tickets for all Intelligence Squared events  ...  Or Subscribe on Apple for £4.99: - Full-length and ad-free Intelligence Squared podcasts - Bonus Intelligence Squared podcasts, curated feeds and members exclusive series … Already a subscriber? Thank you for supporting our mission to foster honest debate and compelling conversations! Visit intelligencesquared.com to explore all your benefits including ad-free podcasts, exclusive bonus content and early access. … Subscribe to our newsletter here to hear about our latest events, discounts and much more. https://www.intelligencesquared.com/newsletter-signup/ Learn more about your ad choices. Visit podcastchoices.com/adchoices Learn more about your ad choices. Visit podcastchoices.com/adchoices

Lauren welcomes Gulrukh Mehboob for a discussion about the impacts of relaxed staff training and licensing requirements on nursing homes during the COVID-19 pandemic. Gul is a Fulbright PhD scholar at the University of Iowa College of Public Health, studying health workforce policy, particularly in long-term care settings. • Between March 2020 and September 2021, 19 states reduced CNA training and licensing requirements in an effort to address staffing shortages in nursing homes. • Gulrukh's study found that these policy changes did not improve CNA staffing levels in nursing homes, even though some scholars had argued that strict training and licensing requirements were a barrier to entering the field. • Gulrukh suggests that improving wages, benefits, and working conditions for CNAs may be more effective than reducing training and licensing requirements for addressing staffing shortages in nursing homes. • Her future research will explore the impact of other workforce policies, such as incentive-based programs and wage increases, on staffing and quality of care in nursing homes. A transcript of this episode is available at https://www.public-health.uiowa.edu/news-items/plugged-in-to-public-health-the-impacts-of-relaxed-staff-training-and-licensing-requirements-on-nursing-homes/ Have a question for our podcast crew or an idea for an episode? You can email them at CPH-GradAmbassador@uiowa.edu You can also support Plugged in to Public Health by sharing this episode and others with your friends, colleagues, and social networks. #publichealth #ruralhealth #nursinghomes #covid19 #pandemic

In today's post, Bruce and I (Rachel Marshall) want to bring you behind the scenes of a candid and educational conversation we had with Matt Ewald, Vice President of Life Insurance at Advisors Excel. If you've ever wondered when and why to use guaranteed universal life insurance (GUL) —especially in the context of estate planning—this one is for you. We've been having more and more conversations with families who aren't just thinking about how to grow their wealth—but how to keep it intact for the next generation. And when estate taxes enter the picture, the stakes change. It's not just about protecting income anymore—it's about protecting impact. About making sure what you've built doesn't get lost in fees, confusion, or government claims. Because when it comes to life insurance in the context of wealth transfer, you're not just planning for protection—you're planning for legacy. Let's get into it. Why This Conversation MattersFrom Infinite Banking to Estate Strategy: A Shift in FocusGuaranteed Universal Life insurance 101: What It Is (and Isn't)Estate Planning and the Tax ConversationThe Myth of “Set It and Forget It”What About Accessing Capital?Roth Conversions, IRA Taxes, and Legislative RiskThe Real Value: Peace of Mind, Not Just Rate of ReturnWhat We CoveredBook A Strategy Call Why This Conversation Matters If you're like most of our clients, you're already successful. You've created wealth, you've stewarded well—and now you're asking deeper questions. Questions like: How do I pass on what I've built with intention? How do I shield my estate from unnecessary taxation? Is whole life the only tool for this? Or is there something else I should consider? In this blog, we're breaking down exactly what guaranteed universal life insurance is, how it's different from traditional IULs and whole life, and why it could be a strategic piece in your legacy plan. From Infinite Banking to Estate Strategy: A Shift in Focus We spend a lot of time on this podcast talking about whole life and its power as a privatized banking system—a way to store capital, access liquidity, and fund your life on your own terms. But not every financial goal calls for cash accumulation. Sometimes, the goal isn't to use the money during your lifetime at all. It's to transfer wealth efficiently, minimize estate taxes, and ensure your heirs receive more—without the friction and loss. And that's where guaranteed universal life enters the scene. Guaranteed Universal Life insurance 101: What It Is (and Isn't) Matt Ewald described guaranteed universal life insurance as a permanent term contract. That phrase stuck with me. Here's what it means: GUL is designed to give you the most death benefit for the least premium. Unlike cash-rich whole life or traditional IULs used for banking or income, GUL is a protection-first strategy. The focus is not on growing cash inside the policy. The focus is on locking in a death benefit that will be there guaranteed—no matter what the market does. And what makes it guaranteed? The no-lapse guarantee rider. This rider is the linchpin. It says, “As long as you pay the premium exactly as illustrated, this policy will not lapse—no matter how the underlying market indexes perform, no matter what cap rates change, no matter what happens behind the scenes.” It's simple. It's predictable. And it's ideal for estate planning when death benefit certainty is the priority. Estate Planning and the Tax Conversation Here's the reality we're facing: The estate tax exemption today is high—around $13 million per person. But it won't stay there forever. Just 20 years ago, it was $1 million. And the political winds are already shifting toward reducing the exemption again. That means more families will face estate tax exposure in the future—even those who don't consider themselves “ultra-wealthy.” And taxes at death are not just a theoretical prob...

Elemeztük a BL-döntős esélyeket, beszéltünk Gulácsi válogatottból való visszavonulásáról, a Fradi bajnoki címéről és Xabi Alonso új munkájáról.

In this career insight episode, Anthony speaks with Mahgul Ansari, an Assistant Portfolio Manager at Premier Miton, about her unconventional path into investment management. Starting out studying modern languages at Oxford University and later qualifying as a chartered accountant, Gul shares how she pivoted into asset management, the challenges she faced along the way, and how she built an edge in the industry by combining linguistic, analytical, and accounting skills.Gul opens up about dealing with rejection, breaking into the industry without a finance degree, and why authenticity and personal branding matter more than ever.(00:00) Gul's Journey: From Languages to Finance(07:22) Navigating Career Challenges and Self-Discovery(16:56) Finding Your Edge in Investment Management(23:50) The Role of a Portfolio Manager(28:34) Personal Branding and Authenticity in Finance(37:46) Diversity and Inclusion in Asset Management(45:45) Exploring Non-Traditional Paths into Finance Hosted on Acast. See acast.com/privacy for more information.

Manu and Stefan were back for the Bundesliga preview show (a day late due to travel), and there's plenty to unpack. Here's your weekly conversation-style recap from the pod: Bayern on the Brink (Without Kane) Manu: “This could be it — if Bayern beat Leipzig, they win the title.” Stefan: “And Harry Kane won't be on the pitch, which just writes its own jokes.” Still, Kane and Eric Dier will get their first career trophies if Bayern wrap it up. Stefan joked Dier could do a John Terry and show up in full kit at full-time. Stefan: “Bayern won't delay the title for sentimental reasons. They'll get it done.” Both agree this season's been mixed for Bayern, but Kompany has done the job that matters: reclaiming the Bundesliga title. Leipzig's Squeaky-Bum Time Manu: “This game is massive for Leipzig. If they don't get a result, Champions League football could be gone.” Stefan: “They're up against it. Orban and Gulácsi are out. Without them, they're hopeless.” Leipzig are under pressure from Dortmund and Freiburg. Without Champions League qualification, they may need to sell key players like Xavi Simons. Stefan: “They've been sleepwalking for years. Leverkusen's rise is forcing them to wake up.” Manu: “The issue isn't ambition. It's their failure to find the right formula.” The coaching situation is also in focus. Manu and Stefan suggest Leipzig need to make a bold hire — maybe Sandro Wagner or Cesc Fàbregas. Freiburg's Big Chance Manu: “If Freiburg beat Leverkusen and Leipzig lose, the door to the Champions League really opens.” Stefan: “Julian Schuster has actually improved on Streich's work.” Freiburg are two points ahead of Leipzig. Despite struggling against top-four teams this season, they have a chance to change that now. Stefan: “They need to break the curse. If you want to be top four, you have to beat top-four clubs.” They've signed a replacement for Ritsu Doan and could have Noah Atubolu back in goal — a major boost. Manu also explained why Freiburg shouldn't be considered a small club anymore. Manu: “Freiburg's greater metro area is bigger than Dortmund. They're a sleeping giant with real infrastructure now.”

The Into the Emerald Dream Prerelease is almost finished, and I play Dark Gifts Warlock in the Brawl! You can find the deck import code below the following contact links.  You can follow me @blisterguy on Twitch, Bluesky, and Youtube. Join our Discord community here or at discord.me/blisterguy. You can support this podcast and my other Hearthstone work at Patreon here. # 2x (1) Armor Vendor # 2x (1) Gul'dan's Gift # 2x (1) Rotheart Dryad # 2x (2) Avant-Gardening # 2x (2) Creature of Madness # 2x (2) Defile # 2x (2) Drain Soul # 2x (3) Hellfire # 2x (3) Hopeful Dryad # 2x (3) Raptor Herald # 1x (4) Nightmare Lord Xavius # 2x (4) Treacherous Tormentor # 2x (5) Ancient of Yore # 1x (5) Mind Control Tech # 2x (5) Overgrown Horror # 1x (7) Wallow, the Wretched # 1x (100) The Ceaseless Expanse #  AAECAbWnBwTx5gaq6gbkggfDgwcNj58E56AEyOsFz54G8KkGsPUGnvkGtfoG34IHjoMHtpQH6psH9qcHAAA=

Gul, meaning "flower" in Urdu, is a symbol of beauty, love, and life. Like a flower that blooms for a short time, happy and sad moments also come and go. It reminds us of hope, strength, and the quiet power of gentle things. It teaches us to enjoy the present before it fades. Even when a gul withers, its fragrance stays in the air, just like memories. No matter how harsh the winds, a gul still finds a way to bloom again. In this episode of Urdunama, we talk about gul, its meaning in poetry, and how it represents love, loss, and hope. Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us a textIn this episode of the Psychedelic Therapy Frontiers podcast Dr. Steve Thayer and Dr. Reid Robison are joined by Dr. Gül Dölen MD, PhD. Gül is an associate professor of neuroscience at the Johns Hopkins University School of Medicine and a pioneer and world leader of psychedelics research. She earned her MD, PhD at Brown University and the Massachusetts Institute of Technology (MIT), where she carried out seminal work on critical periods, learning and memory, and the pathogenesis of autism. You can learn about her current work at dolenlab.org.In today's interview, Gul teaches us about critical learning periods and social reward learning. She clarifies what it really means when we say psychedelics cause “neuroplasticity” and explains the difference between metaplasticity and hyperplasticity. We talk about the possibility that psychedelics are the “master key” that unlocks various kinds of critical periods and what that means for treating psychiatric, neurodevelopmental, and even motor dysfunction like that found in stroke patients. She provides a neuromechanistic explanation for the phenomenology of a psychedelic trip and makes a case for the critical importance of psychotherapy in combination with psychedelic use for people who are seeking healing or behavior change.*This episode originally aired 08/15/23Learn more about our podcast at https://numinus.com/podcast/Learn more about psychedelic therapy training opportunities at https://numinus.com/training/Learn more about our clinical trials at https://www.numinus.com/clinical-trials Learn more about Numinus at https://numinus.com/Email us at ptfpodcast@numinus.com Follow us on Instagram: https://www.instagram.com/drstevethayer/https://www.instagram.com/innerspacedoctor/https://www.instagram.com/numinushealth/

As we grow up, there are little windows of time when we can learn very, very fast, and very, very deeply. Scientists call these moments, critical periods. Real, neurological, biological states when our brain can soak up information like a sponge. Then, these windows of learning close. Locking us in to certain behaviors and skills for the rest of our lives. But … what if we could reopen them? Today, we consider a series of discoveries that are reshaping our understanding of when and how we can learn. And what that could mean for things like PTSD, brain disease, or strokes. And cuddle puddles. It's a mind-bending discussion. Literally and figuratively.This is the second episode in an ongoing series hosted by Molly Webster, in conversation with scientists and science-y people, doing work at the furthest edges of what we know. You can find the first episode here. More to come! Special thanks to Gül Dölen, at the University of California, Berkeley, along with researcher Romain Nardou. Plus, Charles Philipp and David Herman.We have some exciting news! In the “Zoozve” episode, Radiolab named its first-ever quasi-moon, and now it's your turn! Radiolab has teamed up with The International Astronomical Union to launch a global naming contest for one of Earth's quasi-moons. This is your chance to make your mark on the heavens. Vote on your favorites starting in November: https://radiolab.org/moonEPISODE CREDITS: Hosted by - Molly WebsterReported by - Molly WebsterProduced by -Sindhu Gnanasambandan with help from - Timmy Broderick and Molly WebsterOriginal music and sound design contributed by - Dylan Keefewith mixing help from - Jeremy BloomFact-checking by - Emily Kriegerand Edited by  - Soren WheelerEPISODE CITATIONS:Science Articles -Gul's 2019 paper: Oxytocin-dependent reopening of a social reward learning critical period with MDMA  (https://zpr.io/wfQjeA6PGCBv) on the feel-good brain chemical oxytocin, and how it reopens social reward learning when combined with MDMA.Gul's 2023 paper: Psychedelics reopen the social reward learning critical period (https://zpr.io/TKDKEwiLwGRN) on the role of psychedelics in social reward learning. Sign-up for our newsletter. It includes short essays, recommendations, and details about other ways to interact with the show. Sign up (https://radiolab.org/newsletter)!Radiolab is supported by listeners like you. Support Radiolab by becoming a member of The Lab (https://members.radiolab.org/) today.Follow our show on Instagram, Twitter and Facebook @radiolab, and share your thoughts with us by emailing radiolab@wnyc.org.Leadership support for Radiolab's science programming is provided by the Gordon and Betty Moore Foundation, Science Sandbox, a Simons Foundation Initiative, and the John Templeton Foundation. Foundational support for Radiolab was provided by the Alfred P. Sloan Foundation.