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This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.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

Helge Heynold liest: Abschied - von Nanaja Meropis.

“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...

Menú de la Semana - 2a semana de repaso a toda la actividad en los despachos de la NBA A Fuego Lento - Noticias: Agencia libre y Traspasos equipo por equipo, y mucho más. Grabado el Domingo Comentarios de los Oyentes y más: Pasamos por nuestra página de Facebook, los comentarios de Ivoox, Twitter, iTunes, Skype y comentarios@raciondenba.com Más información en raciondenba.com. Ración de NBA es un programa que trata el baloncesto NBA en español poniendo énfasis en los jugadores hispanos. Nuestra web: raciondenba.com . Mandar preguntas/comentarios: comentarios@raciondenba.com. Dejadnos un mensaje de voz en Skype: Racion de NBA. Publicamos avisos por Twitter al publicar los episodios para que sepáis cuando podéis ir a descargarlos: - Twitter - Chechu: @astrochechu - Twitter - Javier: @Racion_de_NBA_J Música: Ración de NBA - Jaime Limit Black Samba - Juanitos   http://freemusicarchive.org/music/Juanitos/Soul_Africa/03_-_Black_Samba  Waitin´ - Betsy Olson  https://freemusicarchive.org/music/Betsy_Olson/Betsy_Olson_-_Live__KEXP_1142009/Waitin_1139/ 

In this transmission of our attempt at imaginary radio, we bring you part two of the mailbag episode with BJA Pro, Nickels as he and I try an tackle more questions that were sent in from the listeners. NOTE:Some of this was originally recorded and published in 2021 as Episode 15 of the Podcast. Updated in July 2026. We added some things here and there as well. Our apologies for the poor audio quality as it was recorded via Skype and before we had a clue about audio production! We fixed it the best we could, and it absolutely sounds better, we think. But, audio aside- there is still some entertaining and useful AP content here! We'll see you down the felt! - Mike AP

Ajay Kulkarni grew up in tech, as his father was a tech entrepreneur selling PC's in the early 80's. He went to college in MIT, and eventually founded a startup that was acquired by GroupMe (while it was being acquired by Skype... while they were being acquired by Microsoft). He's always been attracted to building things, so startups are right up his alley. Outside of tech, he is married with 2 young kids. He is a big exercise guy... he loves to run, swim and track his steps. Additionally, he loves music - to listen, and to play guitar, piano and drums.Ajay and his co-founder met 30 years ago at MIT. They reconnected after years of doing their own thing, starting to dig into the iOT world. In doing this, they built a database because they the best solution to store this data... and in doing so, they unlocked their next venture out of this necessity.This is the creation story of Tiger Data.SponsorsUnblockedTECH DomainsMezmoBraingrid.aiLinkshttps://www.tigerdata.com/https://www.linkedin.com/in/ajaykulkarni/Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Send us Fan MailThe COVID-19 pandemic proved vaccine platforms can move at unprecedented speed. It also revealed how dangerously narrow the world's response really was, built almost entirely on a single technology.Dr. Dan Barouch and Kris Brown, Co-Founders of Vector Sciences, join host David E. Williams to discuss why durable pandemic preparedness depends on having multiple vaccine platforms rather than just one, how their Rheovax platform uses mucosal immunity delivered through the nose to block transmission rather than just prevent severe disease, and why public trust in vaccines depends on being honest about what each technology can and cannot actually do.

Menú de la Semana: A Fuego Lento - Noticias: Draft, Agencia libre y Traspasos equipo por equipo, y mucho más. Grabado el Domingo Comentarios de los Oyentes y más: Pasamos por nuestra página de Facebook, los comentarios de Ivoox, Twitter, iTunes, Skype y comentarios@raciondenba.com Más información en raciondenba.com. Ración de NBA es un programa que trata el baloncesto NBA en español poniendo énfasis en los jugadores hispanos. Nuestra web: raciondenba.com . Mandar preguntas/comentarios: comentarios@raciondenba.com. Dejadnos un mensaje de voz en Skype: Racion de NBA. Publicamos avisos por Twitter al publicar los episodios para que sepáis cuando podéis ir a descargarlos: - Twitter - Chechu: @astrochechu - Twitter - Javier: @Racion_de_NBA_J Música: Ración de NBA - Jaime Limit Black Samba - Juanitos   http://freemusicarchive.org/music/Juanitos/Soul_Africa/03_-_Black_Samba  Waitin´ - Betsy Olson  https://freemusicarchive.org/music/Betsy_Olson/Betsy_Olson_-_Live__KEXP_1142009/Waitin_1139/ 

Menú de la Semana: Noticias: Fechas clave, draft, Splitter, Herro, McCollum, y mucho más A Fuego Lento: Punto y Final a la temporada con Tito Hahn y más Knicks Comentarios de los Oyentes y más: Pasamos por nuestra página de Facebook, los comentarios de Ivoox, Twitter, iTunes, Skype y comentarios@raciondenba.com Más información en raciondenba.com. Ración de NBA es un programa que trata el baloncesto NBA en español poniendo énfasis en los jugadores hispanos. Nuestra web: raciondenba.com . Mandar preguntas/comentarios: comentarios@raciondenba.com. Dejadnos un mensaje de voz en Skype: Racion de NBA. Publicamos avisos por Twitter al publicar los episodios para que sepáis cuando podéis ir a descargarlos: - Twitter - Chechu: @astrochechu - Twitter - Javier: @Racion_de_NBA_J Música: Ración de NBA - Jaime Limit Black Samba - Juanitos   http://freemusicarchive.org/music/Juanitos/Soul_Africa/03_-_Black_Samba  One Two Three - Ilona Akimova - Juice Big City  https://www.jamendo.com/track/1004899/one-two-three Waitin´ - Betsy Olson  https://freemusicarchive.org/music/Betsy_Olson/Betsy_Olson_-_Live__KEXP_1142009/Waitin_1139/ 

A Hawaiian goes all-in on the popular instrument of the islands, touring Europe and teaching virtual lessons by Skype. Side Hustle School features a new episode EVERY DAY, featuring detailed case studies of people who earn extra money without quitting their job. This year, the show includes free guided lessons and listener Q&A several days each week.Show notes: SideHustleSchool.comEmail: team@sidehustleschool.comBe on the show: SideHustleSchool.com/questionsConnect on Instagram: @193countriesVisit Chris's main site: ChrisGuillebeau.comRead A Year of Mental Health: yearofmentalhealth.comIf you're enjoying the show, please pass it along! It's free and has been published every single day since January 1, 2017. We're also very grateful for your five-star ratings—it shows that people are listening and looking forward to new episodes.

Product leaders often measure success in terms of retention, revenue, and growth. In this episode of Product Momentum, Nesrine Changuel explains that the most successful products achieve those outcomes by creating meaningful emotional connections with users. Nesrine's experience includes leadership roles at Skype, Spotify, Google Meet, and Google Chrome. But these days, she is perhaps best known for her book, Product Delight. Nesrine will explore these and other topics at the ITX Product + Design Conference, June 24-25 in Rochester NY. On conference Day 1, she will conduct a workshop that delves into her Product Delight framework, guiding attendees on how to design products that users will not only use – but will come to love, remember, and recommend to others. During this conversation, Nesrine discusses what she has learned from product teams since the book's release and explains why delight is not simply a design tactic, but a business strategy that transcends industries. Product Delight Extends Beyond Design One of Nesrine's biggest discoveries during her global book tour was the widespread misunderstanding of what product delight actually means. Many practitioners associate delight with visual design, animations, or clever interface surprises. This view is too narrow, she says. Nesrine defines delight as the overall feeling users experience while interacting with a product. “Product delight is the entire experience,” she adds. “It’s actually the feeling that the user enjoys while using the product.” It extends well beyond aesthetics to focus on creating experiences that improve how users feel while accomplishing their goals. Emotional Connection Creates Measurable Outcomes Among Nesrine's key challenges was helping product and business leaders recognize the fact that user emotions directly influence business performance. Emotional design has long been familiar to designers and marketers, but many product organizations still prioritize operational metrics above customer sentiment. Nesrine was able to connect the dots between delight and business performance by demonstrating that “the delight itself can actually double retention, referral and revenues.” By framing emotional connection through the lens of retention, referrals, and revenue growth, she demonstrated that delight is not a soft concept. Rather, it is a practical approach that can strengthen both customer loyalty and business results. Adding Delight to Your AI Product – Emphasize the Human Aspect On conference day 2 – Keynote Day – attendees will be treated to brand new content from Nesrine: how to add delight into your AI product. “I’ve been very quiet about AI over the last couple of months,” Nesrine shared. “I've always believed in the humanization of product and the human aspect, but now the product is the conversation. So how can we add delight in these kinds of experiences? That’s something new that I’m going to be talking about” at the ITX Product + Design Conference. As product teams continue navigating AI-driven technological change, core product principles remain unchanged: products that make people feel understood, respected, and supported are more likely to earn user trust, loyalty, advocacy – along with long-term success. [04:25] How writing a book is like a box of chocolates. When I was writing the book, there were some uncertainties about how it would be perceived….kind of like eating a chocolate egg, you have no idea what’s inside.  [05:33] Emotional connection is a universal topic that spans industries. When the book came out, I received a lot of feedback that actually related to the topic and the framework — even from completely diverse industries. [12:21] The ITX Product + Design Conference, June 24-25. I’m really excited to be in Rochester, NY for this event to come. It’s always a pleasure to teach the Product Delight method. It’s going to be special because I want to make it as actionable as I can; I want to make sure that I inspire people. [13:54] Nesrine’s day 2 keynote to include brand new content. I’m going to talk for the very first time about something I have not spoken about before — how to add delight and the human aspect into your AI product. [15:56] Just because it’s harder for mature, well-established companies to integrate delight into their products doesn’t mean they shouldn’t try. Please don’t ignore delight; rather, to remain established and successful, you need to add those elements of delight in your product anyway. [17:58] Even busy PMs should make time for delight. Even when I was a “regular” PM sitting on a backlog on fire with so many conflicting priorities, I tried not to let that stop us from trying to incorporate any element of delight into our roadmap. The post 190 / Nesrine Changuel: How Product Delight Drives Business Outcomes appeared first on ITX Corp..

No episódio de hoje do Kiwicast, recebemos Guilherme Torrejon, engenheiro de produção, fundador da Congresse e referência em construção de negócios digitais sustentáveis e funis low ticket.Em 2017, recém-formado em engenharia de produção, trabalhava na recepção de uma academia ganhando R$800 por mês. Durante sua graduação percebeu que estudantese profissionais do interior não tinham acesso a eventos da sua área. Organizou o primeiro Congresso Online de Engenharia de Produção, investindo apenas R$100. Resultado: 3 mil inscritos e R$12 mil faturados no primeiro dia.No segundo congresso, foram 8.500 inscritos e 28 palestrantes, incluindo 3 internacionais. Um dos futuros sócios era um dos palestrantes que quase deu bolo. Quando viu o tamanho da audiência, enxergou o negócio. A Congresse nasceu numa chamada de Skype com sócios espalhados pelo Brasil.No Kiwicast, ele falou sobre:●      Como faturou R$12 mil no primeiro dia investindo apenas R$100●      Como construiu a Congresse através de lançamentos digitais●      Por que o funil low ticket é uma das estratégias mais subestimadas do mercado●      Como empreender em casal sem deixar o relacionamentopagar o preço E muito mais!Aprenda com quem vive o mercado digital na prática.Dá o play e deixe nos comentários qual foi o melhor insight que você tirou do episódio.Nosso Instagram é @Kiwify

Today on the show, we have Dr. Nesrine Changuel, founder of Product Excellence and Product Management Career Lab Director at ESSEC Business School. Prior to Product Excellence, Nesrine held senior product roles at Google, Spotify, and Microsoft. In this episode, we dig into why the technical barrier to building products has dropped so dramatically that functional quality alone can no longer differentiate — and what that means for teams that haven't yet learned to design for emotion. We explore the concept of product delight, what it actually means beyond confetti and Easter eggs, and how Nesrine's Delight Model Framework gives teams a step-by-step path to building emotional connection into any product — B2C or B2B. We discuss the real difference between discovery and delivery, why Nesrine spent her first 18 months as a PM doing the wrong job, and what shifted when she stopped babysitting engineers and started owning the why. Finally, we get into the AI feature psychosis sweeping the market right now — why shipping velocity without emotional intentionality produces Frankenstein products, and why the companies that were great before AI will be great after it, for the same reasons they always were.As always, I'd love to hear from you. You can email me directly at andrew@churn.fm, and don't forget to follow us on X.

Mark Tluszcz had the career most people would chase: a top consulting job, rapid success, and a clear path forward. But eight years in, he made a move very few would dare to make. He left his high-paying job to start his own firm, faced hundreds of rejections while raising capital, and built Mangrove Capital from scratch. That same contrarian mindset led him to back companies others overlooked, becoming an early investor in Skype and later Chairman of Wix. In this episode, Mark joins Ilana to share how he invested early in Skype, spotted Wix before it became a global company, and why he turned down a $400 million acquisition offer. He also breaks down how to build conviction, embrace rejection, and find opportunities where others see risk. Mark Tluszcz is the co-founder and CEO of Mangrove Capital Partners and chairman of Wix, one of the world's leading website-building platforms. He is widely regarded as one of Europe's most influential venture capitalists, known for his early investments in Skype and Wix. In this episode, Ilana and Mark will discuss: (00:00) Introduction (05:36) Growing Up Across Multiple Cultures (09:09) Leaving Corporate Consulting to Build His Own Firm (13:58) Building Mangrove Capital During the Internet Boom (20:54) How to Keep Going After Hundreds of Nos (27:25) How Skype Evolved Beyond Music (36:47) Staying Humble After a Massive Win (43:08) Discovering Wix and Rethinking Venture Capital (50:33) Turning Down a $400M Deal: The Wix Story (57:41) Building Resilience Through Honest Conversations (01:01:14) Spotting the Next Wave of AI Opportunity (01:10:57) The Founder Mindset That Actually Lasts Mark Tluszcz is the co-founder and CEO of Mangrove Capital Partners, a Luxembourg-based venture capital firm he built from the ground up after leaving Arthur Andersen in 2000. He was one of the first investors in Skype, where Mangrove's $2 million investment reportedly returned $200 million, and he has served as chairman of Wix, where an $8 million investment reportedly yielded $700 million. Connect with Mark: X: x.com/marktluszcz  Mark's Instagram:  instagram.com/marktluszcz/  Mark's LinkedIn: linkedin.com/in/mark-tluszcz-a024b51  Mark's Blog: https://www.daretodreambeyond.com/  Leap Academy: Ready to make the LEAP in your career? There is a NEW WAY for professionals to fast-track their careers and leap to bigger opportunities.  Check out our free training today at https://bit.ly/leap--free-training

In this episode, the now-infamous Snowman joins the show as he and I dive into the mailbag and answer your questions.What you're hearing is a significantly improved version of the original recording from November 2020, back when we were recording over Skype and doing our best to stay sane during pandemic lockdowns. If we're being honest, this entire AP Podcast project started out of pure boredom. There wasn't much to do, but I had a bunch of friends with great stories, so we started sharing them. Somehow, that little lockdown experiment grew into what Tens & Aces is today.The original audio quality of this episode made me cringe a bit when I revisited it in 2026, so I dusted off some of the ninja-level audio editing skills I've picked up over the years and cleaned it up as best I could. The sound is better now, but the real reason I wanted to bring this one back is because the content remains one of my favorite episodes we've ever done.So why not give it a listen?Thank you for all of your support over the years. We couldn't have done this without you, and we're just getting started. Here's to many more years ahead!I'm also using this episode as a test of our new podcast feed after some jackass decided to attempt to get the show's feed taken down because he wasn't a fan of certain truths being discussed. We stand by the truth with our integrity 100% intact. He remains standing in his web of lies and toxicity. He's shown his true self to this community with his continued actions. But, like we've said in the past, the best way to get rid of a troll is to ignore the. So, let's get back to doing exactly that! We'll leave it at that!Anyway... here we go...

En este episodio Víctor cuenta cómo ha llegado a la conclusión de que ninguna herramienta de grabación de podcast en remoto le funciona del todo bien, y lo que ha hecho al respecto: construir la suya propia. Se llama Red Panda Club y hoy sale al mundo. Quince años de podcast, de Skype con grabación en local, de Zencastr perdiendo episodios enteros, de Riverside complicándose hasta el punto de necesitar 26 clics para bajarte un archivo. Y la solución más sencilla: separar la llamada de la grabación, y dejar cada parte en manos de quien mejor la hace. ¿Quieres saber cómo Víctor construye herramientas como esta por su cuenta? En el episodio Premium de esta semana entramos en los detalles técnicos y de negocio que no caben aquí. → Apúntate al Premium Lo que vas a escuchar La historia completa de grabación en remoto de Víctor: de Skype y QuickTime a Zencastr, de Zencastr a Riverside, y de Riverside a nada de lo anterior. Por qué Riverside se ha convertido en un problema: demasiadas funciones, demasiados clics, grabaciones que se pierden. La idea que Víctor llevaba años queriendo ejecutar: separar la herramienta de llamada de la herramienta de grabación. Red Panda Club: cómo funciona, para qué sirve y por qué Víctor ya lo usa en lugar de Riverside y Zencastr. El flujo completo: sesión, link al invitado, grabación en local en los dos ordenadores, subida al servidor, mezcla y descarga. El episodio Premium de esta semana Este episodio en abierto es la presentación pública de Red Panda Club. En el episodio Premium de esta semana Víctor va más a fondo en cosas que no caben aquí: Las decisiones técnicas y de producto que hay detrás de cómo está construida la herramienta. Cómo vibe-codear un proyecto así desde cero sin ser desarrollador. Los primeros pasos reales del lanzamiento y qué está pidiendo la gente en el beta. → Apúntate a No es Asunto Vuestro Premium para escuchar el episodio completo. Transcripción del episodio [00:00] Los primeros años grabando podcast en remoto Cuando comencé a hacer los primeros podcast, hace ya unos quince años, los primeros que hice fueron con Adrià Cuatracasas y hablábamos de cine documental. Era todo a distancia, y los problemas que teníamos para grabar eran increíbles. En aquella época empezamos con Skype y cada uno se grababa en local en su ordenador, con QuickTime o lo que tuviese a mano. Luego uno de los dos tenía que enviarle el archivo al otro. Ya sabéis cómo iba esto. Al cabo de dos o tres años empezaron a salir las primeras herramientas que hacían lo que ahora hace Zencastr o Riverside. Creo que la primera que usamos se llamaba Ringer, y iba fatal. Y ya en 2015, más o menos, salió Zencastr. La idea era buenísima y nos solucionaba mucho trabajo, pero daba un montón de problemas: se perdían grabaciones, estaban desincronizadas, te tenías que bajar los archivos por separado y volver a sincronizarlos tú. Era el mismo trabajo que haberlo hecho con Skype. Había ecos raros, problemas en Chrome, etcétera. Problemas toda la vida. [01:38] De Zencastr a Riverside, y de Riverside a nada Hasta el año pasado estuve con Zencastr y tenía problemas. Me pasé a Riverside, y en Riverside también tengo problemas. Hay gente a quien le va muy bien, hay gente que Riverside le parece lo mejor del mundo, pero lo que no me podréis negar es que se ha complicado de una manera increíble. Los últimos podcast que grabé la semana pasada, no podía ni bajarme los archivos: tenía que usar Edge porque en Chrome se quedaba tonto. Han añadido tantas cosas, vídeo, transcripciones, inteligencia artificial… que para bajarte un archivo tienes que hacer 26 clics sin exagerar. Una auténtica chapuza. Son herramientas que uso un par de veces a la semana y no deberían ser tan complicadas. Por eso llevo tiempo cagándome en Riverside y en Zencastr. Y entonces he hecho lo mío: he hecho yo un Zencastr bien, un Riverside bien. Como siempre había deseado hacerlo. [02:25] La idea que llevaba años en el cajón Esta es una idea que he tenido desde hace mucho tiempo y de hecho la he probado un par de veces, intentando desarrollarla subcontratando a desarrolladores. El razonamiento era muy sencillo: si hay herramientas que ya hacen muy bien el tema de la llamada, si Google Meet es de Google y funciona de maravilla, si Zoom funciona estupendamente… ¿por qué no hacer un sistema de grabación tipo Zencastr o Riverside que sea paralelo a esa otra herramienta y funcione por encima? Tú la llamada la haces con la herramienta que ya sabes que funciona y que funcionará siempre. Y con esta otra herramienta de grabación de podcast en local consigues lo que realmente quieres: que el sonido sea bueno, que se grabe en los dos ordenadores y que se haga la mezcla automática. Lo había intentado en un par de ocasiones y no salía bien, porque hace dos meses las cosas eran mucho más complicadas de implementar. Hoy, ya no. [03:27] Red Panda Club: así funciona Hoy nace, atención: Red Panda Club. Se llama así porque estoy reaprovechando un dominio que tenía, y además me gustan los pandas rojos. Red Panda Club. ¿Qué hace? Es un servicio web que funciona tanto en navegador de escritorio como en móvil. Tiene dos versiones: la del host (yo, en este caso) y la del invitado, la persona a quien le envío el link. El flujo es este: creo una sesión, en Google Meet, en Zoom, en Discord, en WhatsApp, donde quieras. Pones el link ahí, pones el email del invitado, le das a continuar. Cuando el invitado abre el link, tú recibes una notificación. Le das a grabar y empieza la grabación en local en los dos ordenadores. El audio se sube al servidor, se hace la mezcla, y puedes descargarlo todo junto o por pistas separadas. Listo. Funciona siempre porque la parte de la llamada, que es la complicada, ya la gestiona Google Meet. Y ese es exactamente el problema que Zencastr y Riverside nunca resolvieron del todo: mezclar la llamada y la grabación en la misma herramienta genera demasiados puntos de fallo. [04:27] Beta, primeras peticiones y lo que viene Además de bajarte el archivo, ya puedes añadirle una música de entrada y una música de salida, y hay muchas otras cosas que me están pidiendo los primeros beta testers y que voy a ir añadiendo. Estoy contentísimo. Ya está, me he olvidado de Riverside, me he olvidado de Zencastr. Tengo esta herramienta que además voy a hacer crecer con el feedback de toda la gente que sigue No es Asunto Vuestro. Si queréis probarlo: redpandaclub.com. Menciones y recursos del episodio Red Panda Club: la nueva herramienta de grabación de podcast en remoto de Víctor. redpandaclub.com Riverside: plataforma de grabación de podcast en remoto. Zencastr: plataforma de grabación de podcast en remoto. Google Meet: herramienta de videollamada usada como base para las sesiones de Red Panda Club. Zoom: alternativa de videollamada compatible con Red Panda Club. Discord: alternativa de llamada compatible con Red Panda Club. Ringer: primera herramienta de grabación en remoto que mencionó Víctor (ya desaparecida). Skype: herramienta usada en los inicios del podcast. Adrià Cuatracasas: co-conductor del primer podcast de cine documental de Víctor. QuickTime: software de grabación en local usado en los primeros tiempos. ¿Quieres saber cómo se construye algo así? En el episodio Premium de esta semana Víctor entra en los detalles reales: las decisiones de producto, cómo lo ha desarrollado y qué está aprendiendo en los primeros días del beta. Todo lo que no cabe en cinco minutos de audio. → Apúntate a No es Asunto Vuestro Premium Noesasuntovuestro.com

Sarah Kellen, Jeffrey Epstein's former personal assistant, told the House Oversight Committee that she was brought into Prince Andrew's orbit, including private dinners in Andrew's Buckingham Palace apartment and Princess Beatrice's 18th birthday party at Windsor Castle. Kellen identified Andrew and Sarah Ferguson as notable figures within Epstein's network, saying Andrew had been at Epstein's New York home and that she had also been present at royal residences connected to him. Andrew has denied wrongdoing, but the testimony adds another layer to the long-running scrutiny over how deeply Epstein and his associates were able to move through elite royal spaces.Kellen's testimony is also significant because she occupies one of the most complicated positions in the Epstein story: she was named as a potential co-conspirator in Epstein's 2008 plea deal, yet she has told authorities she was also groomed, controlled, and repeatedly raped by Epstein. She described Epstein as a manipulative and dangerous figure who used his access to powerful people around the world as a tool of intimidation, and she said the abuse continued even after he was jailed, including an alleged Skype call from prison in which he ordered her to undress on camera. Her account places Andrew's palace access inside a broader pattern of Epstein using proximity to royalty, politicians, financiers, academics, and foreign leaders to project power and keep those around him trapped.to contact me:bobbycapucci@protonmail.comsource:Epstein's PA dined with Andrew in his Buckingham Palace roomsBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-moscow-murders-and-more--5852883/support.

Sarah Kellen, Jeffrey Epstein's former personal assistant, told the House Oversight Committee that she was brought into Prince Andrew's orbit, including private dinners in Andrew's Buckingham Palace apartment and Princess Beatrice's 18th birthday party at Windsor Castle. Kellen identified Andrew and Sarah Ferguson as notable figures within Epstein's network, saying Andrew had been at Epstein's New York home and that she had also been present at royal residences connected to him. Andrew has denied wrongdoing, but the testimony adds another layer to the long-running scrutiny over how deeply Epstein and his associates were able to move through elite royal spaces.Kellen's testimony is also significant because she occupies one of the most complicated positions in the Epstein story: she was named as a potential co-conspirator in Epstein's 2008 plea deal, yet she has told authorities she was also groomed, controlled, and repeatedly raped by Epstein. She described Epstein as a manipulative and dangerous figure who used his access to powerful people around the world as a tool of intimidation, and she said the abuse continued even after he was jailed, including an alleged Skype call from prison in which he ordered her to undress on camera. Her account places Andrew's palace access inside a broader pattern of Epstein using proximity to royalty, politicians, financiers, academics, and foreign leaders to project power and keep those around him trapped.to contact me:bobbycapucci@protonmail.comsource:Epstein's PA dined with Andrew in his Buckingham Palace rooms

Sarah Kellen, Jeffrey Epstein's former personal assistant, told the House Oversight Committee that she was brought into Prince Andrew's orbit, including private dinners in Andrew's Buckingham Palace apartment and Princess Beatrice's 18th birthday party at Windsor Castle. Kellen identified Andrew and Sarah Ferguson as notable figures within Epstein's network, saying Andrew had been at Epstein's New York home and that she had also been present at royal residences connected to him. Andrew has denied wrongdoing, but the testimony adds another layer to the long-running scrutiny over how deeply Epstein and his associates were able to move through elite royal spaces.Kellen's testimony is also significant because she occupies one of the most complicated positions in the Epstein story: she was named as a potential co-conspirator in Epstein's 2008 plea deal, yet she has told authorities she was also groomed, controlled, and repeatedly raped by Epstein. She described Epstein as a manipulative and dangerous figure who used his access to powerful people around the world as a tool of intimidation, and she said the abuse continued even after he was jailed, including an alleged Skype call from prison in which he ordered her to undress on camera. Her account places Andrew's palace access inside a broader pattern of Epstein using proximity to royalty, politicians, financiers, academics, and foreign leaders to project power and keep those around him trapped.to contact me:bobbycapucci@protonmail.comsource:Epstein's PA dined with Andrew in his Buckingham Palace roomsBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-epstein-chronicles--5003294/support.

Menú de la Semana: Noticias: Steph Curry, Anteto, Adam Silver, NBA Europe, Expansión, Divac, Mad Max, y mucho más A Fuego Lento - Playoffs 2026: Knicks-Spurs. Píldora NBA: El maestro Angel Castillo nos habla de Rip Hamilton Comentarios de los Oyentes y más: Pasamos por nuestra página de Facebook, los comentarios de Ivoox, Twitter, iTunes, Skype y comentarios@raciondenba.com Más información en raciondenba.com. Ración de NBA es un programa que trata el baloncesto NBA en español poniendo énfasis en los jugadores hispanos. Nuestra web: raciondenba.com . Mandar preguntas/comentarios: comentarios@raciondenba.com. Dejadnos un mensaje de voz en Skype: Racion de NBA. Publicamos avisos por Twitter al publicar los episodios para que sepáis cuando podéis ir a descargarlos: - Twitter - Chechu: @astrochechu - Twitter - Javier: @Racion_de_NBA_J Música: Playoffs - Ración de NBA - Limit de Folio en Blanco  Black Samba - Juanitos   http://freemusicarchive.org/music/Juanitos/Soul_Africa/03_-_Black_Samba  One Two Three - Ilona Akimova - Juice Big City  https://www.jamendo.com/track/1004899/one-two-three Slow Dance- Julie & Gent https://www.jamendo.com/track/1552411/slow-dance  Waitin´ - Betsy Olson  https://freemusicarchive.org/music/Betsy_Olson/Betsy_Olson_-_Live__KEXP_1142009/Waitin_1139/ 

Join Tyler Wells, Co-founder and CTO of BrainGrid, for a forward-looking discussion on how artificial intelligence is rewriting the rules of product development. Boasting over 25 years of distributed systems engineering—including a foundational tenure at Skype building Facebook's first video-calling engine and 7+ years directing Video and global SRE at Twilio—Tyler has built infra where structural failure was not an option. In this episode, we explore why the traditional constraints of software engineering—headcount, timelines, and budgets—are dissolving, leaving a brand-new bottleneck at the front of the innovation cycle: human imagination.

Today's guest is the name behind a decade of names you already know by heart. Eight weeks at #1 with Drake. Over a billion streams off a single Travis Scott beat. A top-six Hot 100 record with Migos, Nicki Minaj, and Cardi B — all on one song. Nipsey Hussle. French Montana. A whole generation of trap and rap that doesn't sound the way it sounds without him. And here's the part that should annoy every producer alive: he made most of it in under 20 minutes, by himself.And The Writer Is... Murda Beatz!In this episode of And The Writer Is, we go deep on:- Why doubt is important- The principal who told him being a producer was "unrealistic" — and what he'd say to him now- Making "Nice For What" in 20 minutes — and why it was never actually mixed- Selling beats over Western Union for $50–$200 — until working with the Migos got him flagged for fraud- DMing his way from a Canadian bedroom to Chief Keef, the Migos, and Nipsey Hussle- The Migos teaching him to cook beats in 10 minutes: "you gotta be faster"Losing his dad at 21 — and how he handles grief while the machine keeps runningand his new mixtape, 'Bando'And much more...Hit subscribe and turn on notifications. Every week, we go deep with the most interesting creatives in music.Follow us on socials: @andthewriterisA special thank you to our sponsors for making these conversations possible.Our lead sponsor, NMPA — the National Music Publishers' Association. Your support means the world to us.And @splice — the best sample library on the market. Period.Chapters:0:00 Intro1:45 The best producer tag that isn't yours2:32 The songs: "Nice For What," "Butterfly Effect," "MotorSport"3:03 The plaque wall — and the one with "some crazy number"4:29 When the label wouldn't put a producer's name on the plaque7:00 Born in Niagara Falls, a town of 3,000 on the Buffalo border8:33 A dad who played guitar, a left-handed kid on the drums11:36 Why so many great musicians come from Canada13:08 Trading the drum kit for trap beats14:29 Digging for Lex Luger drum kits in Skype groups16:25 "Murda Beatz on the track" — building a fanbase on Facebook and YouTube19:38 The principal who said being a producer was "unrealistic"21:51 "The doubt is important" — Michael Jordan and manufacturing motivation24:50 How you go from YouTube to a $20,000 check26:33 Learning his value — refusing to be a "sound producer"27:26 Selling beats on Western Union, and getting flagged for fraud33:00 Being a white kid making rap on Chicago's South Side35:02 How he met the Migos on the internet40:36 Making "Pipe It Up" — and learning to cook beats in 10 minutes42:32 World #1s in 15–20 minutes: "Butterfly Effect" and "Nice For What"45:35 Curating a beat pack — and remembering every beat by name51:24 The crazy fact about "Butterfly Effect": it was never mixed52:13 "MotorSport" hits #6 — sitting on it for four months53:24 Making Nipsey Hussle's beat his first day in LA56:55 "Nice For What" — made in Canada, #1 for eight weeks62:10 Adjusting as hip-hop changes: "I made rap because I wanted to make rap"63:42 Producer vs. featured artist — why go solo68:17 Simplicity: 8–10 stems and nothing wasted69:27 Losing his dad at 21, and how he deals with grief70:14 The alone time that built everything71:15 What's next: the "Bando" project, ten years after his first mixtape73:02 Rapid fire: signature beat, Mount Rushmore of producers77:25 Murda Melodies — the plugin that landed on a Bad Bunny record80:28 Advice for upcoming producers80:31 A message to his mom — and what he'd tell his dadCredits:Hosted by Ross GolanProduced by Joe London & Jad SaadEdited by Jad SaadPost-Production VFX by Pratik Karki Hosted on Acast. See acast.com/privacy for more information.

Menú de la Semana: Noticias: Shai desde Cancun, Lebron, Sean Sweeney, Mazulla, Reofmar del draft 29-1, y mucho más A Fuego Lento - Playoffs 2026: Cerramos las semis y nos centramos en las finales Knicks-Spurs. Píldora NBA: El maestro Angel Castillo nos habla de Derek Harper Comentarios de los Oyentes y más: Pasamos por nuestra página de Facebook, los comentarios de Ivoox, Twitter, iTunes, Skype y comentarios@raciondenba.com Más información en raciondenba.com. Ración de NBA es un programa que trata el baloncesto NBA en español poniendo énfasis en los jugadores hispanos. Nuestra web: raciondenba.com . Mandar preguntas/comentarios: comentarios@raciondenba.com. Dejadnos un mensaje de voz en Skype: Racion de NBA. Publicamos avisos por Twitter al publicar los episodios para que sepáis cuando podéis ir a descargarlos: - Twitter - Chechu: @astrochechu - Twitter - Javier: @Racion_de_NBA_J Música: Playoffs - Ración de NBA - Limit de Folio en Blanco  Black Samba - Juanitos   http://freemusicarchive.org/music/Juanitos/Soul_Africa/03_-_Black_Samba  One Two Three - Ilona Akimova - Juice Big City  https://www.jamendo.com/track/1004899/one-two-three Slow Dance- Julie & Gent https://www.jamendo.com/track/1552411/slow-dance  Waitin´ - Betsy Olson  https://freemusicarchive.org/music/Betsy_Olson/Betsy_Olson_-_Live__KEXP_1142009/Waitin_1139/ 

Skype of Cthulhu presents a Call of Cthulhu scenario. This is Our Home by Jim Phillips. November 25, 1976 Staten Island, New York City, New York Having learned more of the sinister forces arrayed against them, the residents discover that they are not the only targets for murder. Dramatis Persone: Jim as the Keeper of Arcane Lore Randall as Frank Romero, Electrical Engineer Meredith as Marsha Janelle, Waitress Steve as Trae Grier, Gas Station Attendant Edwin as Kevin Mazer, Chemistry Teacher Gary as Peter Michale, Ex Pro Quarterback Sean as Kirk Griffin, Actor Download Subcription Options Podcast statistics

Send us Fan MailGet vidIQ Boost for an exclusive price! https://vidiq.com/podcastWant a 1 on 1 coach? https://vidiq.ink/theboost1on1Join our Discord! https://www.vidiq.com/discordWatch the video: https://youtu.be/_kUOrWhNynwWe sit down with Jordi “Kwebbelkop” to trace how a teen posting Call Of Duty clips turned into a creator who studies CTR and retention like a sport and now runs YouTube as one department of a larger business. We also unpack his AI detour, the backlash that followed, and why he believes the real winners use AI to unlock new audiences rather than replace the human on camera. •the origin of “Kwebbelkop” and how an old gamertag became a career identity •how early Call Of Duty uploads evolved into tutorials, myth-busting, and viral growth •what his first viral video taught him about demand, packaging, and repeatable patterns •why watch time and click-through rate still drive YouTube distribution •how Skype and Discord creator groups accelerate learning when the feedback is real •the hidden cost of grinding daily uploads and why chaos eventually catches up •building systems that remove you as the bottleneck and make long-term planning possible •why he regrets skipping business strategy and risk assessment early on •his reboot plan for bigger, sponsor-funded videos and a slower upload cadence •the real timeline behind quitting, experimenting with AI, and launching Blue •how he evaluated AI backlash using subscriber loss, brand interest, and audience signals •why AI dubbing is his favourite “adds value” workflow for global channel growth •how health, therapy, and higher standards changed the way he leads and creates •his blunt advice for new creators: post first, then refine the system If you're listening to this and you're interested in having a little group of content creators, we have a free Discord. You can join. There'll be a link in the description and in the show notes. 

【主播的话】（本期节目涉及性暴力与性犯罪的讨论，可能引起部分听众不适，请你斟酌收听。）上周，我们走进了柏林的法院，在现场见证了近来引发高度关注的华人迷奸案庭审。被审理的是核心八人群组里的邵姓嫌疑人，这个网络里的成员散布在德国、美国、荷兰等地，通过一个 Telegram 群组彼此相连——他们用药物迷昏女性后实施性侵，并把过程拍下来，在群里分享。受害者大多是他们身边的华人女性：留学生、房东、邻居、甚至当时的伴侣。而目前进入审判的，只是这张网络浮出水面的一角；在这八人之外，还连着一个最大约四千多人的群组。这起案件已经有了不少报道，但我们仍然觉得，这背后有太多值得停下来追问的东西：同一张犯罪网络，为什么在德国、美国刑期能差出几十年？一个见不得人的犯罪幻想，是怎么在群体里被翻转为可执行、被点赞、甚至被鼓励的行为？它又和几年前震动整个西方的佩利科特（Gisèle Pelicot）案，构成了怎样的对照？这一期，我们和常驻德国的记者、也是这组报道的作者之一的孙谦，以及中国政法大学刑事司法学院副教授、刑法学者陈碧，一起聊了聊法庭里的不同刑罚考量、群体如何被磨掉了羞耻，以及在巨大的无力感和集体愤怒面前，我们还能做什么、还能相信什么。如果你或你认识的人正遭受或曾经遭遇任何形式的性别暴力，请寻求协助和支持，可参考：https://lila.help如果你人在德国，正在经历或曾经遭遇数字暴力，可联系：https://hateaid.org/【本期主播】若含：小红书@若含王磬：微博@王磬【本期嘉宾】孙谦：媒体人，常驻德国，案件深度报道的记者之一陈碧：中国政法大学刑事司法学院副教授【本期剧透】03:01 柏林庭审现场：众多华人女性前来，社群自发为不懂德语的同胞翻译08:55 令人不安的转变：犯罪的制造与传播，由单一的恶行，变为群体交流、炫耀12:01 Telegram 如何降低犯罪门槛？17:21 孤独感、性偏好障碍... 有关犯罪动机的解释，是在为罪恶开脱吗？24:44 量刑争议：德国与中美的司法理念有何不同33:21 该案在德国本土关注度低，难以“破圈”进入公众视野39:21 关于被告邵某曾于北京多次性侵的指控，跨国追诉可行吗？49:04 不畏罪销毁证据，反而要留下录像01:02:40 厌女景观与“非人化”的性别战争01:13:37 让羞耻感“换边站”01:15:46 媒体在报道该案时的伦理问题：一定要保护受害者【相关阅读】德国华人跨国迷奸案：流动的药，狂欢的Telegram群组，被直播的性暴力作者：孙谦、张漫盈纪录片｜STRG_F：Telegram 上的性侵犯网络发布时间：2024 年该纪录片发布在德国公共广播联盟（ARD）旗下的知名青年调查报道节目 《STRG_F》。女性调查记者 Isabell Beer 与同事进行了长达两年的暗访与卧底，成功渗透进隐藏在 Telegram 加密群组背后的暗黑网络，发现其中最大的群组规模竟高达 7万人，成员横跨欧洲、美洲及亚洲。群内男性（包含大量德语及英语语境用户）会公开分享如何在家中、邻里间对女性（包括伴侣、妻子甚至亲人）实施下药、迷奸的“技术指南”。群成员不仅会上传受害者失去意识后遭受侵害的照片和录像，甚至有人在群内直播施暴过程。纪录片播出时引发了全德对“数字性暴力”的强烈谴责。由于当时德国法律存在漏洞（单是“持有/观看违背违背当事人意愿拍摄的性侵视频”在当时可能不直接构成犯罪），该片直接推动了德国司法部的反思，德国随后开始紧急推动收紧针对“迷奸药”、数字暴力以及性侵视频传播的相关法律法案。法国佩利科案件Pélicot Case法国佩利科案件（Pélicot Case）是近年来全球性质最恶劣、引发国际社会巨大震动的跨国“家庭成员下药+大规模合伙迷奸”案件。从2011年至2020年的近十年间，主犯多米尼克在妻子完全不知情的情况下，长期在她的晚餐或饮料中暗中掺入大剂量的镇静剂和安眠药。在妻子失去意识后，他通过法国本土的网络聊天室（如 Skype 等社交渠道）招募并引导多达72名男性陌生人来到家中，对毫无知觉的妻子实施残酷的轮奸与性虐待，而主犯则在旁负责拍摄、录像并指挥施暴。2024年12月19日，法国法院对该案作出了历史性判决：主犯多米尼克·佩利科被判处加重强奸罪等多项罪名成立，处以法国法律该项罪名的最高刑期——20年监禁。其余50名被成功锁定的共同被告中，47人被判强奸罪成立，另有数人因未遂或性侵罪名成立，分别被判处3至15年不等的有期徒刑。预防性羁押（Sicherungsverwahrung）德国刑法（StGB）第66条规定的一种双轨制（Zweigleisigkeit）刑罚保安处分。它不属于惩罚性的“刑罚”（Strafe），而是一种以“保护公众”为目的的“矫正与保安处分”。这意味着，即便罪犯已经执行完了全部的刑期，如果司法评估判定其对社会仍具有极高的重复犯罪危险性（Gefährlichkeit），法院可以决定将其继续无限期关押在特定的替代性场所。性偏好障碍（Neophilic Disorder / Paraphilic Disorder）庭审中使用的临床心理学与精神病学学术词汇，在《国际疾病分类第十一版》（ICD-11）中被称为“性偏好障碍”。它指代通过非自愿、非人类或涉及痛苦、羞辱的特定对象/行为来获得性兴奋的持续性心理扭曲。域外管辖（Extraterritorial Jurisdiction）指一个国家对发生在领土之外的犯罪行为行使法律管辖权。在刑法中，通常需要基于属人原则（犯罪者或受害者是本国公民）或普遍管辖原则（针对战争罪、反人类罪等国际核心罪行）来行使。厌女情节加重量刑德国联邦议院于 2023 年 6 月通过了《制裁法修改法案》，并于当年正式生效。法条在原本的“种族主义、仇外、反犹”等法定量刑加重动机的基础上，明确首次将“基于性别的（geschlechtsspezifische）”以及“针对性取向的”犯罪动机，作为法定从重处罚的情节。【本期音乐】Bleu-Komiku【节目制作】方改则【Logo设计】刘刘（ins: imjanuary)【互动方式】小红书@不合时宜微博@不合时宜TheWeirdo商务合作可发邮件至 hibuheshiyi@126.com 或微博私信会员计划咨询可添加微信：hibuheshiyi3 或发送邮件至 hibuhehsiyi@gmail.com

Skype of Cthulhu presents a Call of Cthulhu scenario. Curse of Nineveh by Mike Mason, Mark Latham, Scott Dorward, Paul Fricker, and Andrew Kenrick. November, 1925 London The team tries to stop whatever foul plans the mastermind behind all these events has for the King's garden party. Dramatis Persone: Sean as the Keeper Edwin as Dame Agatha, Authoress Jonathan as Katherine "Kitty" Hall, Dilettante Steve as Connor Shaw, Archivist Max as Oswald Nickels, Big Game Hunter Gary as Anthony Kelly, Consulting Detective Randall as Dean Banks, Big Game Hunter Jim as Roger Schindler, Alienist Rachael as Maude Throckmorton, Adventuress Download Subcription Options Podcast statistics

Guest: Fling Officer John de Hoop (RAF 1810752, 191161) Wireless Operator-Air Gunner Host: Dave Homewood Recorded: ‎19th of November 2013 Released: 29th of May 2026 Duration: 1 hour 6 minutes 13 seconds John de Hoop was a Londoner who joined the Royal Air Force in 1943, and trained as a Wireless Operator-Air Gunner. The following year he was posted to No. 75 (NZ) Squadron at RAF Mepal, as a Wireless Operator on Avro Lancasters, in the crew captained by New Zealand pilot F/Lt Wylie Wakelin DFC. His final few ops were flown with S/Ldr Bob Rodgers as his captain. This recording was made at the time using Skype, as John lived in the UK. It was recorded as part of a wider WONZ Show project I was working on at the time that ended up not proceeding to completion. So it seems like it’s about time to share it with listeners. John passed away on the 7th of August 2016. John de Hoop (Photo via Dee Boneham) Two more photos of John, from Dee Boneham. Quick Links: •  John de Hoop obituary on the 75 (NZ) Squadron Blog •  The Wylie Wakelin Crew on the 75 (NZ) Squadron Blog •  The Bob Rodgers Crew on the 75 (NZ) Squadron Blog The music at the end of this episode is Wild Flower by Joachim Karud.

Skype of Cthulhu presents a Call of Cthulhu scenario. This is Our Home by Jim Phillips. November 23, 1976 Staten Island, New York City, New York The residents learn more about their landlord and receive an unusual gift. Dramatis Persone: Jim as the Keeper of Arcane Lore Randall as Frank Romero, Electrical Engineer Meredith as Marsha Janelle, Waitress Steve as Trae Grier, Gas Station Attendant Edwin as Kevin Mazer, Chemistry Teacher Gary as Peter Michale, Ex Pro Quarterback Sean as Kirk Griffin, Actor Download Subcription Options Podcast statistics

Menú de la Semana: Noticias: Kidd, Mosley, Jeff Van Gundy, Anti-Shais, All NBAs y mucho más A Fuego Lento - Playoffs 2026: Repasamos todas las eliminatorias para el título.  Comentarios de los Oyentes y más: Pasamos por nuestra página de Facebook, los comentarios de Ivoox, Twitter, iTunes, Skype y comentarios@raciondenba.com Más información en raciondenba.com. Ración de NBA es un programa que trata el baloncesto NBA en español poniendo énfasis en los jugadores hispanos. Nuestra web: raciondenba.com . Mandar preguntas/comentarios: comentarios@raciondenba.com. Dejadnos un mensaje de voz en Skype: Racion de NBA. Publicamos avisos por Twitter al publicar los episodios para que sepáis cuando podéis ir a descargarlos: - Twitter - Chechu: @astrochechu - Twitter - Javier: @Racion_de_NBA_J Música: Playoffs - Ración de NBA - Limit de Folio en Blanco  Black Samba - Juanitos   http://freemusicarchive.org/music/Juanitos/Soul_Africa/03_-_Black_Samba  Owl Time - Kellee Maize http://freemusicarchive.org/music/Kellee_Maize/Owl_Time/03__Kellee_Maize__Owl_Time__Owl_Time__FROSTWIRECOM_FROSTCLICKCOM__CREATIVE_COMMONS_1846 Go West Young Man - Extracto de The Man Who Shot Liberty Valance Requiem for a Fish - The Freak Fandango Orchestra http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Tales_Of_A_Dead_Fish/Requiem_for_a_Fish_1403 Waitin´ - Betsy Olson  https://freemusicarchive.org/music/Betsy_Olson/Betsy_Olson_-_Live__KEXP_1142009/Waitin_1139/ 

Skype of Cthulhu presents a Call of Cthulhu scenario. Curse of Nineveh by Mike Mason, Mark Latham, Scott Dorward, Paul Fricker, and Andrew Kenrick. November, 1925 London While some prepare for another incursion into the subway, the police engage others to look into a brutal set of murders. Dramatis Persone: Sean as the Keeper Edwin as Dame Agatha, Authoress Jonathan as Katherine "Kitty" Hall, Dilettante Steve as Connor Shaw, Archivist Max as Oswald Nickels, Big Game Hunter Gary as Anthony Kelly, Consulting Detective Randall as Dean Banks, Big Game Hunter Jim as Roger Schindler, Alienist Rachael as Maude Throckmorton, Adventuress Download Subcription Options Podcast statistics

So many people need remote recording for co-hosts and guests. Yet in the 20+ years of podcasting once we get a solid solution, they upgrade the software and we're back to always having a backup "Just in case." So I reached out to my audience to see what they used and they chimed in.The HistoryBlog Talk Radio (now gone) was an EASY choice but sounded like the phone. There was Skype (also gone), but everyone needed an account, and for the technically challenged, it was intimidating. Squadcast came on with a winning strategy with a firm understanding of what podcasters needed. Make it simple. Make it reliable.Then Video Entered the PictureThen tools like Squadcast added video, and while I never had an issue I know people who spoke of "Drift" where the audio didn't line up with the video (making it look like a bad Godzilla movie). There are tools like Evmux (browser based), Ecamm (Mac Only), Descript (browser based), and Streamyard (brwoser based).Text Based EditingWhen Descript entered the picture with text based editing (you edit the transcript, and it edits the audio) it became impressive after a few years. They purchased Squadcast, but haven't implemented all the tech from Squadcast (like being able to schedule a future episode in their "Rooms.").All in One SolutionsThis is one of the symptoms of a "All in one" solution. They do most things about 75%, but the details in that last 25 is what makes the difference. Riverisde started as remote recording, added text based editing, clip generation, and recently podcast hosting (the podcast hosting is very basic see video as of May 2026).It May Not Be All Riverside's FaultI wrote a blog post about all the things podcasters could do to be ready to make great recordings with Riverside.If you want Riverside to work, don't overcomplicate it:Solid internetUpdated browserDecent computerEnough disk spaceDon't rush the uploadThat's it.Do those things, and suddenly Riverside becomes “magically reliable.”What I Use For Live Streaming and RecordingBefore moving to a Mac computer, I use Streamyard, and loved it. When I got a Mac Mini, I switched to Ecamm. It's amazing and much you have more control over how things look. If you have a Streamdeck, you can do some pretty magical things. Worth that said, I'm considering going back to Streamyard even though it's $5 more a month (I used Ecamm for making recording for the School of Podcasting, but I now do those in Tella).What is The Most Reliable?For me, after talking with the School of Podcasting members and now hearing from the audience I would say Ecamm (mac only) and Streamyard (browser based).That doesn't mean Riverside, Evmux, Squadcast are not reliable, but I feel Ecamm and Streamyard are more reliable. They also are primarily focused on one thing RECORDING (although streamyard just added clip generation).So What If I Don't Want an All In One?Then you record with something like Ecamm or Streamyard, if you need clips, there is Opus Clip. There is free video software like Davinci Resolve, and free audio editing like Audacity.Thanks to The ContributorsFrank Bravo From Your Tech MakeoverTodd the Gator from Gaurdian DowncastChris From Cool Cars with ChrisEd from the Days Dumpster FireTim from My Solo MS JourneyMentioned In This EpisodeStreamyardEcammRiversideDescriptEvMuxCleanfeedZencastrOBS ProjectVDO NinjaPodtrack P4NextZoom H6Samson Q2U MicrophoneOpus ClipBoomer BunkerWar Room Online JournalTakeaways:Remote recording can be a total pain if you don't have solid internet; trust me, I know.Zoom works great for audio-only shows but struggles with video quality when the internet hiccups.Streamyard's simplicity makes remote recording a breeze; just send a link and boom, done!Clean Feed is solid for high-quality audio, especially for those who want to keep it simple.For video, Riverside sounds fancy but can be hit or miss; make sure it meets your needs first.Discord is free and surprisingly powerful for remote recordings, even if you're not a gamer.Mentioned in this episode:Live AppearancesI will be at the Empower Podcasting Conference (Year 3!) in Charlotte North Carolina. This is my favorite type of conference with a cap at 250 people, it's a great crowd without being overwhelming. Great speakers, great networking, and a great location.Where Will I Be?Question of the MonthThis might be harder question to answer because when I ask people, the sometimes freeze. The question? How do you measure success for your podcast beyond download numbers? I need your answer by June 26th, 2026. Don't forget to tell us a little bit about your show and your website address so I can link to it in the show notes.Question of the MonthPodcasting in Six Weeks Starts SoonIf you've tried to start a podcast before and got lost in the jargon, and felt overwhelmed, this is the course for you. We will meet LIVE for six weeks and go step by step in launching your successful podcast. The best part, we are only charging $1 Check it out at www.schoolofpodcasting.com/sixweeksPodcasting in Six WeeksPodpage is Now Included with Blubrry HostingBlubrry Podcasting — one of the longest-running podcast hosting platforms in the industry — has chosen Podpage to replace their built-in website tool entirely. That means every Blubrry hosting customer gets a professional, automatically updated podcast website powered by Podpage, included with their hosting plan. For Podpage, this is more than a partnership announcement. It's validation that podcast websites deserve dedicated website tools built specifically for podcasters.Podpage

Skype of Cthulhu presents a Call of Cthulhu scenario. This is Our Home by Jim Phillips. November 21, 1976 Staten Island, New York City, New York Amidst the tragedy of the previous night, the residents gain new information on their role in all of these strange happenings. Dramatis Persone: Jim as the Keeper of Arcane Lore Randall as Frank Romero, Electrical Engineer Meredith as Marsha Janelle, Waitress Steve as Trae Grier, Gas Station Attendant Edwin as Kevin Mazer, Chemistry Teacher Gary as Peter Michale, Ex Pro Quarterback Sean as Kirk Griffin, Actor Download Subcription Options Podcast statistics

Hosts Fran Chismar and Tom Knezick connect with Sarah McAnulty (Biologist and Executive Director of Skype a Scientist) to discuss Squid and Native Plants in urban setting.  Topics include making science accessible to schools, getting the idea of native plants to an unsuspecting patron, marketing in unconventional ways, and the connection between education and art. Music by Egocentric Plastic Men, Outro music by Dave Bennett. Follow Skype a Scientist Here. Follow Sarah McAnulty Here. Have a question or a comment?  Call (215) 346-6189. Follow Native Plants Healthy Planet – Website / Instagram / Facebook / YouTube Follow Fran Chismar Here. Buy a T-shirt, spread the message, and do some good. Visit our store Here! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Skype of Cthulhu presents a Call of Cthulhu scenario. Curse of Nineveh by Mike Mason, Mark Latham, Scott Dorward, Paul Fricker, and Andrew Kenrick. November, 1925 London Following Mr. Kelly's strange visions into the subway system proves to be deadly for one of the team. Dramatis Persone: Sean as the Keeper Edwin as Dame Agatha, Authoress Jonathan as Katherine "Kitty" Hall, Dilettante Steve as Connor Shaw, Archivist Max as Oswald Nickels, Big Game Hunter Gary as Anthony Kelly, Consulting Detective Randall as Bertie Weiss, Graduate Student Jim as Roger Schindler, Alienist Rachael as Maude Throckmorton, Adventuress Download Subcription Options Podcast statistics

In this interview, Denis Yurchak shares how he turned Skype's shutdown into Yadaphone, grew it to more than 20,000 users, and reached $17,500 in monthly revenue in a little over a year. He also explains how he launched eSIMPal, a second travel-focused venture that is already making about $2,000 per month. Denis explains the marketing tactics, pricing choices, customer outreach, and automation systems behind both products. He shows how he validated demand, won users through listicle outreach, kept pricing low-friction, and used automation to manage support as a solo founder. It's a detailed look at how to build quickly, find demand, and turn simple ideas into revenue.   Sponsor: Quiet LightGet a free, confidential valuation at https://quietlight.com/! Links & ResourcesLearn more about Yadaphone: https://www.yadaphone.com/ Check how you can use eSIMPal: https://www.getesimpal.com/ Follow Denis: https://x.com/denisyurchak    Be sure to get more content like this in the Niche Pursuits Newsletter Right Here: https://www.nichepursuits.com/newsletter  Want a Faster and Easier Way to Build Internal Links?  Get $15 off Link Whisper with Discount Code "Podcast" on the Checkout Screen: https://www.nichepursuits.com/linkwhisper  Get SEO Consulting from the Niche Pursuits Podcast Host, Jared Bauman: https://www.nichepursuits.com/201creative 

Skype of Cthulhu presents a Call of Cthulhu scenario. This is Our Home by Jim Phillips. November 20, 1976 Staten Island, New York City, New York A late night battle against a monstrous entity brings death to an innocent. Dramatis Persone: Jim as the Keeper of Arcane Lore Randall as Frank Romero, Electrical Engineer Meredith as Marsha Janelle, Waitress Steve as Trae Grier, Gas Station Attendant Edwin as Kevin Mazer, Chemistry Teacher Gary as Peter Michale, Ex Pro Quarterback Sean as Kirk Griffin, Actor Download Subcription Options Podcast statistics

Skype of Cthulhu presents a Call of Cthulhu scenario. Curse of Nineveh by Mike Mason, Mark Latham, Scott Dorward, Paul Fricker, and Andrew Kenrick. October, 1925 London The investigators are called to assist the Museum with locating more missing objects. Dramatis Persone: Sean as the Keeper Edwin as Dame Agatha, Authoress Jonathan as Katherine "Kitty" Hall, Dilettante Steve as Connor Shaw, Archivist Max as Oswald Nickels, Big Game Hunter Gary as Anthony Kelly, Consulting Detective Randall as Bertie Weiss, Graduate Student Jim as Roger Schindler, Alienist Rachael as Maude Throckmorton, Adventuress Download Subcription Options Podcast statistics

Menú de la Semana: Noticias: Shai MVP, Morey fuera, Anteto, Cancún, slogans, DEP BC y JC y mucho más A Fuego Lento - Playoffs 2026: Repasamos todas las eliminatorias para el título. Contamos con Fran EG (El Repartidor) para el repaso. Comentarios de los Oyentes y más: Pasamos por nuestra página de Facebook, los comentarios de Ivoox, Twitter, iTunes, Skype y comentarios@raciondenba.com Más información en raciondenba.com. Ración de NBA es un programa que trata el baloncesto NBA en español poniendo énfasis en los jugadores hispanos. Nuestra web: raciondenba.com . Mandar preguntas/comentarios: comentarios@raciondenba.com. Dejadnos un mensaje de voz en Skype: Racion de NBA. Publicamos avisos por Twitter al publicar los episodios para que sepáis cuando podéis ir a descargarlos: - Twitter - Chechu: @astrochechu - Twitter - Javier: @Racion_de_NBA_J Música: Playoffs - Ración de NBA - Limit de Folio en Blanco  Black Samba - Juanitos   http://freemusicarchive.org/music/Juanitos/Soul_Africa/03_-_Black_Samba  Owl Time - Kellee Maize http://freemusicarchive.org/music/Kellee_Maize/Owl_Time/03__Kellee_Maize__Owl_Time__Owl_Time__FROSTWIRECOM_FROSTCLICKCOM__CREATIVE_COMMONS_1846 Go West Young Man - Extracto de The Man Who Shot Liberty Valance Requiem for a Fish - The Freak Fandango Orchestra http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Tales_Of_A_Dead_Fish/Requiem_for_a_Fish_1403 Waitin´ - Betsy Olson  https://freemusicarchive.org/music/Betsy_Olson/Betsy_Olson_-_Live__KEXP_1142009/Waitin_1139/ 

What is a rock? How big is a boulder? Why are they pretty and heavy? It's rock talk with a true enthusiast, the charming and beloved Geologist Schmitty Thompson. Schmitty walks us through different types of rocks, minerals, crystals, geodes, roadside wonders, the best rock puns, and why you should take a closer look at your countertops. So pull up a petrified stump, take a seat, and enjoy Schmitty's Geology Corner.  Schmitty's bio Donations went to Skype a Scientist & MinDat.org Full-length (*not* G-rated) Geology episode + tons of science links More kid-friendly Smologies episodes! Become a patron of Ologies for as little as a buck a month OlogiesMerch.com has hats, shirts, hoodies, totes! Follow Ologies on Instagram and Bluesky Follow Alie Ward on Instagram and TikTok Sound editing by Mercedes Maitland of Maitland Audio Productions, Jake Chaffee, and Jarrett Sleeper of MindJam Media Made possible by work from Noel Dilworth, Susan Hale, Kelly R. Dwyer, Aveline Malek and Erin Talbert Smologies theme song by Harold Malcolm Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Tim Draper didn't become a legendary investor with billions in his account overnight; he started by taking a $6 million loan to launch his first venture fund, knowing failure was a real possibility. That decision didn't just shape his career; it laid the foundation for backing some of the biggest companies in the world, including Hotmail, Skype, Tesla, and more. In this episode, Tim joins Ilana to break down how to spot world-changing ideas before they make sense, why most people miss their biggest opportunities, and how listening to your gut can be more powerful than logic alone. He shares the lessons behind massive wins like Hotmail, painful misses like Netflix, and the mindset required to navigate uncertainty, fear, and reinvention. Tim Draper is a third-generation venture capitalist, founder of Draper University, and one of Silicon Valley's most legendary investors. He is a lifelong advocate for entrepreneurship, innovation, and the next generation of world-changing founders. In this episode, Ilana and Tim will discuss: (00:00) Introduction  (04:11) Early Lessons on Money, Risk, and Capitalism (08:43) Leaving Home at 13 and Learning to Figure It Out (11:48) Venture Capital Roots and Family Background (14:17) Taking a $6 Million Loan and Betting on Himself (17:14) Spotting Billion-Dollar Companies Before Anyone Else (20:11) The Viral Growth Strategy Behind Hotmail (23:26) How Skype Changed Global Communication Forever (29:18) Navigating the Dot-Com Crash (34:13) Will AI and Crypto Follow the Same Boom Cycle? (35:55) The Power of Listening to Your Gut as an Entrepreneur (46:59) Q&A: How Do I Know What to Invest In?  Tim Draper is a third-generation venture capitalist and founder of Draper University. He backed world-changing companies, including Hotmail, Skype, Tesla, SpaceX, Coinbase, and Robinhood. Known for spotting transformative trends before the mainstream, he's also a passionate advocate for entrepreneurship education, cryptocurrency, and celebrating innovation. His show Meet the Drapers reaches roughly 300 million people globally. Connect with Tim Tim's Website: https://timothydraper.com/  Tim's LinkedIn: https://www.linkedin.com/in/timothydraper/  Tim's Instagram: https://www.instagram.com/timdraper/  Resources Mentioned: Tim's Book, How to be the Startup Hero - A Guide and Textbook for Entrepreneurs and Aspiring Entrepreneurs: https://www.amazon.com/How-Startup-Hero-Textbook-Entrepreneurs/dp/1973585340/ref=cm_cr_arp_d_product_top?ie=UTF8 Leap Academy: Ready to make the LEAP in your career? There is a NEW WAY for professionals to fast-track their careers and leap to bigger opportunities.  Check out our free training today at https://bit.ly/leap--free-training

In this remastered classic, Roy and Kamila get into the spirit of Valentine's Day — teaching you all the romantic Polish phrases and compliments you need! From "Kocham Cię" to "Jesteś cudowna", this episode is packed with practical, fun vocabulary perfect for the 14th of February — or any day you want to impress someone special.

Skype of Cthulhu presents a Call of Cthulhu scenario. This is Our Home by Jim Phillips. November 16, 1976 Staten Island, New York City, New York The team gains valuable information by intimidating a contact and visit an old neighbor in jail. Dramatis Persone: Jim as the Keeper of Arcane Lore Randall as Frank Romero, Electrical Engineer Meredith as Marsha Janelle, Waitress Steve as Trae Grier, Gas Station Attendant Edwin as Kevin Mazer, Chemistry Teacher Gary as Peter Michale, Ex Pro Quarterback Sean as Kirk Griffin, Actor Download Subcription Options Podcast statistics

Skype of Cthulhu presents a Call of Cthulhu scenario. Curse of Nineveh by Mike Mason, Mark Latham, Scott Dorward, Paul Fricker, and Andrew Kenrick. October, 1925 London The investigators strike a strange bargain to stop Mrs. Lewis' ascent. Dramatis Persone: Sean as the Keeper Edwin as Dame Agatha, Authoress Jonathan as Katherine "Kitty" Hall, Dilettante Steve as Connor Shaw, Archivist Max as Oswald Nickels, Big Game Hunter Gary as Anthony Kelly, Consulting Detective Randall as Bertie Weiss, Graduate Student Jim as Roger Schindler, Alienist Rachael as Maude Throckmorton, Adventuress Download Subcription Options Podcast statistics

For this episode, let's revisit a Case Interview & Management Consulting classic where we speak about learning cases through listening and watching. When learning cases, it is far more effective to watch a person on Skype or in person. The problem with merely practicing over the phone or another verbal format is that you cannot observe crucial mannerisms or allow your practice partner to observe you. The only time practicing verbally makes sense is when you have a very experienced person working with you and they can infer things about your performance based on their experience. We advice most clients to practice in person should the opportunity present itself. Here are some free gifts for you: Overall Approach Used in Well-Managed Strategy Studies free download: www.firmsconsulting.com/OverallApproach McKinsey & BCG winning resume free download: www.firmsconsulting.com/resumepdf Enjoying this episode? Get access to sample advanced training episodes here: www.firmsconsulting.com/promo

Skype of Cthulhu presents a Call of Cthulhu scenario. This is Our Home by Jim Phillips. November 15, 1976 Staten Island, New York City, New York Mr. Mazer speaks with a detective who might be an ally while others try a little breaking-and-entering. Dramatis Persone: Jim as the Keeper of Arcane Lore Randall as Frank Romero, Electrical Engineer Meredith as Marsha Janelle, Waitress Steve as Trae Grier, Gas Station Attendant Edwin as Kevin Mazer, Chemistry Teacher Gary as Peter Michale, Ex Pro Quarterback Sean as Kirk Griffin, Actor Download Subcription Options Podcast statistics

Skype of Cthulhu presents a Call of Cthulhu scenario. Curse of Nineveh by Mike Mason, Mark Latham, Scott Dorward, Paul Fricker, and Andrew Kenrick. October, 1925 London After learning the fate of one of their companions, the investigators attend an unusual party. Dramatis Persone: Sean as the Keeper Edwin as Dame Agatha, Authoress Jonathan as Katherine "Kitty" Hall, Dilettante Steve as Connor Shaw, Archivist Max as Oswald Nickels, Big Game Hunter Gary as Anthony Kelly, Consulting Detective Randall as Montgomery Helmsworth, Librarian Jim as Roger Schindler, Alienist Rachael as Maude Throckmorton, Adventuress Download Subcription Options Podcast statistics

00:00:00 – Monster May plans and Spirit Airlines chatter 00:04:07 – Area 51 earthquake swarm sparks testing rumors 00:22:38 – Trump's YMCA dance annoys Melania 00:27:25 – Onion-era InfoWars parody falls flat 00:37:20 – Alex Jones responds to the InfoWars shutdown 00:42:12 – UFO disclosure talk hits Indian news 00:47:12 – Underwater UFO civilizations resurface 00:51:59 – Gallaudet confronts Kirkpatrick over UFO disinformation 00:56:54 – Richard Dawkins considers AI consciousness 01:05:58 – Local AI tools and agent workflows 01:10:28 – Ohio gas prices hit five dollars 01:14:06 – Steven Greer claims future humans visited Rendlesham 01:19:06 – Caller revisits the Cash-Landrum UFO radiation case 01:23:59 – Nuclear moon reactors and helium-3 energy 01:28:54 – Caller brings up Salla, Saturn, and secret space claims 01:33:38 – OpenAI explains the goblin language glitch 01:43:39 – Oscars crack down on AI performers and scripts 01:53:36 – Taco Bell worker shoots over soda in a water cup 02:03:15 – Outro clips and end-show chatter Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research ▀▄▀▄▀ CONTACT LINKS ▀▄▀▄▀ ► Phone: 614-388-9109 ► Skype: ourbigdumbmouth ► Website: http://obdmpod.com ► Twitch: https://www.twitch.tv/obdmpod ► Full Videos at Odysee: https://odysee.com/@obdm:0 ► Twitter: https://twitter.com/obdmpod ► Instagram: obdmpod ► Email: ourbigdumbmouth at gmail ► RSS: http://ourbigdumbmouth.libsyn.com/rss ► iTunes: https://itunes.apple.com/us/podcast/our-big-dumb-mouth/id261189509?mt=2  

00:00:00 – AI rollout chaos 00:04:43 – Alex Jones throwback clips 00:17:28 – Missing scientists panic 00:27:12 – Trump-as-Jesus meme war 00:35:01 – Looksmaxxing meltdown 00:44:39 – Ohio data center backlash 01:08:01 – Holy-river snakebite cure kills boy 01:12:52 – ChatGPT health advice costs teen a testicle 01:22:37 – Iran war hits prefab toilet supply 01:27:31 – DUI driver flashes Barnes & Noble gift card 01:36:42 – Million-dollar Lego heist 01:41:19 – Universe death clock moves up 01:46:09 – Iran war troops live on caffeine 01:53:36 – AI art recap and Bigfoot box plugs 01:56:26 – Double rainbow sign-off Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research ▀▄▀▄▀ CONTACT LINKS ▀▄▀▄▀ ► Phone: 614-388-9109 ► Skype: ourbigdumbmouth ► Website: http://obdmpod.com ► Twitch: https://www.twitch.tv/obdmpod ► Full Videos at Odysee: https://odysee.com/@obdm:0 ► Twitter: https://twitter.com/obdmpod ► Instagram: obdmpod ► Email: ourbigdumbmouth at gmail ► RSS: http://ourbigdumbmouth.libsyn.com/rss ► iTunes: https://itunes.apple.com/us/podcast/our-big-dumb-mouth/id261189509?mt=2