Podcasts about Magnetar

It was a pleasure to welcome Alec Litowitz, the Founder of Magnetar Capital and QStar Capital, to the Alpha Exchange. Central to our discussion is an exploration of the ideas in Alec's new book, The Adaptability Quotient. Here, he draws on more than thirty years of investing across multiple market regimes. We begin with Alec's three decades in financial markets, from his early years at Citadel through the founding of Magnetar. Looking back across multiple market cycles, he argues that long-term investing success is driven by more than intelligence alone. Instead, he introduces the concept of Adaptability Quotient, or AQ, emphasizing the ability to revise views, respond to changing conditions, and distinguish between environments defined by risk, uncertainty, and black swans. A central theme throughout the discussion is decision-making under uncertainty. Alec explains why markets spend much of their time in environments where outcomes are possible, but probabilities remain difficult to estimate. He outlines a framework centered on metacognition, simulation, experimentation, and continuous feedback, encouraging investors to develop "strong opinions, weakly held" while remaining willing to revise conclusions as new information emerges. The conversation then turns to practical investing examples drawn from Alec's career. He reflects on building Citadel's risk arbitrage business by developing proprietary research processes around regulatory uncertainty, and later discusses Magnetar's emphasis on sourcing, structuring, and risk management in areas undergoing structural change. Examples include investments tied to energy infrastructure and AI-related computing capacity, illustrating how the firm approached evolving industries through the lens of uncertainty rather than prediction. I hope you enjoy this episode of the Alpha Exchange, my conversation with Alec Litowitz.

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

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What separates the investors and founders who thrive in moments of radical change from those who don't? According to Alec Litowitz, it isn't intelligence or emotional maturity - it's adaptability.Alec is the founder of Magnetar Capital, one of the most respected multi-strategy hedge funds in the world, and the founder and managing partner of QStar Capital, his single family office and investment platform. Over a 30-year career that began at J.P. Morgan, continued as a founding partner and global head of equities at Citadel, and culminated in building Magnetar from scratch, Alec developed a framework he calls the Adaptability Quotient - AQ - for making decisions under genuine uncertainty. His book, The Adaptability Quotient, publishes September 15th.Today, through QStar, Alec invests with no fund mandate and no LP constraints - thematically across both public and private markets, in everything from CoreWeave and SpaceX to top-tier VC and PE managers. That unconstrained vantage point, combined with three decades of pattern recognition across market regimes, gives him a distinctive lens on where venture capital sits inside the current moment of change.Nick and Beezer dig into the core distinction Alec draws between risk and uncertainty - a difference he argues most investors collapse at their peril - and how the AQ framework maps directly onto how founders build, how VCs back them, and how the venture ecosystem itself needs to adapt. They also get into what he calls the second cognitive revolution: why AI isn't just a new tool but a system-level regime change, what that means for the capital stack and liquidity timelines in venture, and why the answer for smaller players isn't resistance - it's remapping.Quotes"What entrepreneurs get paid for is not risk. They get paid for uncertainty, for resolving the uncertainty. People may stay at some stranger's house or they may not, but I don't know the probability. If it's high, I have a business. If it's zero, I don't have a business. Let's go resolve that probability. And when someone does a startup and tests it, raises money, probes around it, and gets feedback loops - the answer is yes. That's what they get paid for, for resolving that uncertainty."Time Stamps00:00 What Entrepreneurs Actually Get Paid For00:31 Introducing Alec Litowitz: Citadel, Magnetar, and QStar02:49 Three Career Chapters and the Through Line: A Systematic Approach to Uncertainty06:09 The Book: Why Alec Wrote The Adaptability Quotient07:29 AQ Defined: Why IQ and EQ Aren't Enough When the Frame Itself Changes9:40 Why QStar: No Constraints, No Mandates, Just Mapping the Moment12:22 QStar's Investment Framework: Thematic, Top-Down, Technocentric and Anthrocentric14:51 Why Venture Still Matters: The Venture 20, the Mag Seven, and Where Disruption Lives17:03 Risk vs. Uncertainty vs. Black Swan: The Framework Most Investors Get Wrong22:30 Applying AQ in Venture: MVPs as Probes, Pivots as Feedback Loops22:51 A Case Study in Failing Without Feedback Loops24:33 The Second Cognitive Revolution: Why AI Is a Regime Change, Not a Tool29:20 Is SaaS Uninvestable? What Becomes Abundant and What Becomes Scarce32:53 Mapping the Venture Ecosystem: Capital Intensity, New Entrants, and IRR Pressure37:08 The Liquidity Problem Reframed: DPI, TDPI, and Timeline Mismatch40:12 Secondary Markets as a Structural Response43:48 Final Advice: Upgrade Your Operating SystemLinksConnect with the guest and hosts on LinkedIn!Alec LitowitzBeezer ClarksonNick ChirlsLearn more about:The Adaptability Quotient (pre-order on Amazon)QStar CapitalMagnetar Capital⁠⁠Early Adapters Newsletter⁠⁠Asylum Ventures⁠⁠OpenLP

Neutron stars, the compact remains of a massive star following a supernova explosion, are the densest matter in the Universe. Some neutron stars, known as magnetars, also claim the record for the strongest magnetic fields of any object. How magnetars, which are a mere 15 kilometers across, form and produce such colossal magnetic fields remains a mystery.    New observations by a team of astronomers, including NSF's NOIRLab's Dr. André-Nicolas Chené, may shed important light on the origin of these magnetic powerhouses. Using various telescopes around the globe, including the Canada-France-Hawai'i Telescope (CFHT) on Maunakea, the researchers have identified a new type of astronomical object — a massive magnetic helium star (an unusual variant of a Wolf-Rayet star), which may be the precursor of a magnetar.  In this podcast, André-Nicolas Chené describes the process of finding the first known potential magnetar progenitor.   Bios:  - Rob Sparks is in the Communications, Education and Engagement group at NSF's NOIRLab in Tucson, Arizona. - Dr. André-Nicolas Chene is an associate astronomer at NOIRLab. He completed his PhD at the Université de Montréal in 2007 and learned everything about the fundamentals of astronomical observations at the Observatoire du Mont Mégantic. He was research fellow at the NRC Herzberg Astronomy and Astrophysics Research Centre and postdoc jointly at the Universidad de Concepción and the Universidad de Valparaíso before joining the Gemini Observatory (now a program of NOIRLab) in 2013. For almost 10 years, André-Nicolas took part in every phase of a Gemini observing program life cycle and has played a central role in Gemini's user support effort. André-Nicolas's research interests are massive stars, hot winds, star clusters, and stellar evolution.   Links:  NOIRLab Press Release: https://noirlab.edu/public/news/noirlab2323/ NOIRLab social media channels can be found at: https://www.facebook.com/NOIRLabAstro https://twitter.com/NOIRLabAstro https://www.instagram.com/noirlabastro/ https://www.youtube.com/noirlabastro   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

Crain's residential real estate reporter Dennis Rodkin and host Amy Guth discuss the latest local housing news, including the Gold Coast showing signs of a revival with recent high-end home sales. Plus: Pritzker opens door to new Bears talks, blames team for stadium stumbles; Baker Tilly moving headquarters out of Chicago with acquisition of New York's Anchin; Magnetar will replace humans with AI bots in new offering; and American Airlines teams up with Google on eco-friendly jet fuel purchase at O'Hare. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Join Dave, Adrian and Donner on Britain's biggest LIVE streaming metal detecting show discussing the hobby, the new machines, finds and many other fun and funny stories around the hobby of Metal Detecting...!Learn more at www.bigdetectingshow.com or find us on YouTube, Facebook, Twitter, Instagram or TikTokSponsored by Metal Detecting NewsBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-big-detecting-show--3690873/support.

Episode: S05E112 — Tuesday, 26 May 2026 Hosts: Anna & Avery Network: Bitesz.com Podcast Network Website: astronomydaily.io  |  Social: @AstroDailyPod   Story Summaries 1. NASA Unveils Ambitious Moon Base Plan As this episode was recorded, NASA Administrator Jared Isaacman was preparing to announce a landmark plan for a permanent human outpost at the lunar south pole by 2036. The programme carries a price tag of approximately $30 billion across a seven-year foundational phase, relies on nuclear power systems, leverages lunar water ice for fuel and life support, and effectively retires the Gateway orbital station concept. Commercial partners will supply rovers and habitat modules. Phase one targets around two dozen lunar launches, including Artemis IV, by 2028. Full details will be covered in tomorrow's episode. 2. Starship V3 Flight 12 — Engine Drama, Historic Debut SpaceX launched the first Starship V3 rocket on Friday, 22 May 2026, from brand-new Pad 2 at Starbase, Texas. Ship 39 reached space and completed a controlled splashdown in the Indian Ocean despite losing one of its six vacuum Raptor engines during ascent. The flight computer compensated by extending burns on the remaining five. The Super Heavy booster was lost in the Gulf of Mexico after a failed boostback burn. The FAA has opened a review. SpaceX declared most pre-planned test objectives met. 3. JWST Maps First Daily Weather Cycle on a Distant World Published in Science on 21 May 2026. Researchers from Johns Hopkins and Arizona State Universities used Webb's NIRISS instrument to observe WASP-94Ab — a hot Jupiter 690 light-years away — and detected the first daily cloud cycle ever recorded on another planet. Thick magnesium silicate clouds form each morning, then completely clear by evening. The finding also corrected a decade of skewed atmospheric composition data. 4. NASA's Fermi Telescope Solves 20-Year Supernova Mystery An international team led by Fabio Acero used NASA's Fermi Gamma-ray Space Telescope to confirm the first definitive gamma-ray detection from a superluminous supernova — SN 2017egm. The data confirms a newly formed magnetar as the power source behind these extraordinarily bright explosions. Published in Astronomy & Astrophysics, 2026. 5. Most Rocky Exoplanets May Lack Earth-Like Metallic Cores A new paper submitted to the Astrophysical Journal challenges the long-held assumption that dense metallic cores are standard features of rocky planets. Researchers argue that most rocky exoplanets may have formed without Earth-style metallic cores — meaning no global magnetic field, with significant implications for atmospheric retention and habitability. 6. The Soviet Rover That Went Silent — and Came Back Lunokhod 1 was the world's first remote-controlled rover on another world (1970). After traversing 10.5 km of Mare Imbrium, contact was lost in 1971. For nearly 40 years its exact position was unknown — until NASA's Lunar Reconnaissance Orbiter identified it in 2010. The APOLLO project then fired laser pulses and received ~2,000 photons back from its French-built retroreflector — four times stronger than expected. It remains an active contributor to lunar science today.   Sources & Further Reading •       NASA Moon Base announcement: nasa.gov/2026-news-releases •       Starship Flight 12 updates: space.com •       WASP-94Ab paper: Science, 21 May 2026 — DOI via Johns Hopkins Hub •       Fermi supernova paper: Astronomy & Astrophysics, 2026 — DOI: 10.1051/0004-6361/202558547 •       Exoplanet cores paper: submitted to Astrophysical Journal, May 2026Become a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-space-news-updates--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

Talking about the new releases of the Nokta Magnetar 9000 Pulse Induction machine and XP Metal Detectors new Icon / IconX metal detectors....RELICS RADIO is live via video broadcast on the 5280 Adventures YouTube channel and Adventures In Dirt YouTube channel every Wednesday night at 8:00 pm (Eastern) and is available for download wherever you get your podcasts.  See links below to catch us live.DK's LINKS:All Ken's Links Here: https://linktr.ee/adventuresindirtAdventures in Dirt on YouTube: https://www.youtube.com/adventuresindirtAdventures in Dirt Facebook Group page: https://www.facebook.com/groups/AdventuresInDirtTONY's LINKS:5280 Adventures on YouTube: https://www.youtube.com/c/5280adventures5280 Adventures on Facebook: https://www.facebook.com/5280adventures5280 Adventures on Instagram: https://www.instagram.com/5280.adventures/Thanks yall for spending your night with us. Appreciate you all!#metaldetecting#relichunting#treasurehunting #metaldetectingpodcast

La puntata si apre con fresche notizie da Ginevra. Giorgio ci racconta di un trasporto davvero eccezionale. AL CERN, per la prima volta nella storia, sono riusciti a trasportare dell'antimateria su strada. Una sfida non da poco visto che è stato necessario compattare il complesso sistema di intrappolamento dell'antimateria in un camion. Ciò ha permesso di trasportare 92 antiprotoni in giro per le strade del laboratorio senza perderne neanche uno.Passiamo poi la linea a Gioele che con Walter Riva, Direttore dell'Osservatorio Astronomico del Righi e organizzatore di eventi per la promozione dell'Università degli Studi di Genova, ha parlato di FameLab, un contest di divulgazione scientifica aperto a giovani ricercatori e ricercatrici la cui selezione locale genovese si terrà il 6 e il 7 maggio.Tornati in studio, dopo l'immancabile barza, Andrea ci parla di un corpo celeste veramente curioso: le Magnetar. Queste sono particolari stelle di neutroni che hanno un campo magnetico enorme. Un recente studio farebbe nuova luce sulla nascita di questi mostri galattici.Per approfondire:Trasporto di antimateriaNascita delle magnetarDiventa un supporter di questo podcast: https://www.spreaker.com/podcast/scientificast-la-scienza-come-non-l-hai-mai-sentita--1762253/support.

SpaceTime Series 29 Episode 32 *The birth of a magnetar seen for the first time Astronomers have for the first time seen the birth of a magnetar — a highly magnetized, spinning neutron star. *Rewriting the textbooks on the history of the Moon A new study claims the lunar near and far sides experienced similar levels of asteroid and meteor bombardment, despite the very different appearances of the two. *Spectacular fireball light up the skies of Europe The European Space Agency is analysing a spectacular fireball which lit up the skies over Europe last week dropping debris all along its trajectory. *The Science Report Study shows little science showing cannabis can help people with mental health conditions. Australia's digital ID scheme moves to phase II forcing some adults to adopt it. The weird ancient crocodile that walked on two legs. Skeptics guide to predicting the end of the world.Become a supporter of this podcast: https://www.spreaker.com/podcast/spacetime-with-stuart-gary--2458531/support.

Back to Basics: Fundamental Attribution Error; News Items: Improved Photosynthesis, Birth of a Magnetar, US Bioweapons Research, False Health Information from Chatbots; Your Questions and E-mails: Consistency; Name That Logical Fallacy; Science or Fiction

Back to Basics: Fundamental Attribution Error; News Items: Improved Photosynthesis, Birth of a Magnetar, US Bioweapons Research, False Health Information from Chatbots; Your Questions and E-mails: Consistency; Name That Logical Fallacy; Science or Fiction

It's a bumper Friday edition of Astronomy Daily. NASA gives Artemis II the official green light to launch on April 1st, marking the first crewed lunar mission in over 53 years. Astronomers witness the birth of a magnetar for the very first time, confirming a decade-old theory and demonstrating Einstein's general relativity in a supernova. A star 11,000 light-years away shows evidence of two planets catastrophically colliding in real time. A bus-sized asteroid buzzed past Earth last night closer than the Moon, discovered just five days ago. A fast solar wind stream from a coronal hole could bring auroras to higher latitudes tonight. And scientists may have identified the source of the most energetic neutrino ever recorded. Story 1: Artemis II — Green Light for April 1 Launch NASA completed its Flight Readiness Review on 12 March 2026, with all mission teams voting unanimously ‘go' for launch. The Space Launch System and Orion capsule will roll out to Launch Complex 39B on 19 March, with the primary launch window opening on 1 April at 6:24pm ET. Backup windows exist on 2–6 April and 30 April. The crew of four — Reid Wiseman, Victor Glover, Christina Koch, and Jeremy Hansen — will fly a 10-day figure-eight loop around the Moon. It will be the first crewed mission beyond low Earth orbit since Apollo 17 in December 1972. The previously planned Moon landing on Artemis III has been moved to Artemis IV, though NASA's 2028 goal for a lunar landing remains unchanged. •       NASA Artemis II Mission Page: https://www.nasa.gov/mission/artemis-ii/ •       CNN coverage of FRR outcome: https://www.cnn.com/2026/03/12/science/nasa-artemis-2-launch-date-risk-assessment Story 2: First-Ever Observed Birth of a Magnetar Astronomers have for the first time directly observed the birth of a magnetar — a highly magnetized, rapidly spinning neutron star — confirming it as the power source behind some of the universe's brightest stellar explosions. The discovery, published in Nature on 11 March 2026, centres on superluminous supernova SN 2024afav, located approximately one billion light-years from Earth. Graduate student Joseph Farah at UC Santa Barbara, working with Las Cumbres Observatory's global telescope network, detected a distinctive ‘chirp' pattern in the supernova's fading light — four oscillations with shortening intervals. This pattern is explained by a wobbling accretion disc around the newborn magnetar, driven by Lense-Thirring precession — a general relativistic effect. The finding confirms a 2010 theory by UC Berkeley physicist Dan Kasen, and marks the first time general relativity has been required to explain supernova mechanics. •       Berkeley News: https://news.berkeley.edu/2026/03/11/astronomers-capture-birth-of-a-magnetar-confirming-link-to-some-of-universes-brightest-exploding-stars/ •       Space.com: https://www.space.com/astronomy/stars/astronomers-witness-colossal-supernova-explosion-create-one-of-the-most-magnetic-stars-in-the-universe-for-the-first-time Story 3: Two Planets Caught Colliding 11,000 Light-Years Away Researchers at the University of Washington have published evidence of a catastrophic planetary collision observed in real time around star Gaia20ehk, located approximately 11,000 light-years from Earth near the constellation Puppis. The star began flickering erratically from 2016, before its light output went ‘completely bonkers' around 2021 — the signature of a massive debris cloud from two colliding worlds passing in front of the star. The debris orbits at roughly one astronomical unit from the star — the same as Earth's distance from the Sun — and may eventually coalesce into new planetary bodies resembling an Earth-Moon system. The paper was published 11 March in The Astrophysical Journal Letters. •       University of Washington: https://www.washington.edu/news/2026/03/11/uw-astronomers-spot-planet-collision-evidence/ •       ScienceDaily: https://www.sciencedaily.com/releases/2026/03/260311213429.htm Story 4: Asteroid 2026 EG1 Flies Past Earth A bus-sized asteroid designated 2026 EG1 made its closest approach to Earth at 11:27pm EDT on 12 March 2026, passing just 197,466 miles away — closer than the Moon. Estimated at 32–72 feet (10–22 metres) across and travelling at over 21,500 mph, it posed no threat. Notably, the asteroid was only discovered on 8 March — five days before its flyby — highlighting the ongoing challenge of detecting small near-Earth objects with short warning times. NASA's Vera Rubin Observatory has already catalogued over 2,000 previously unknown solar system bodies since beginning operations. •       Space.com: https://www.space.com/stargazing/bus-sized-asteroid-will-fly-past-earth-tonight-mere-days-after-being-discovered-heres-what-to-expect-march-12-2026 Story 5: Solar Wind & Aurora Alert A fast-moving stream of solar wind from a large coronal hole on the Sun is expected to reach Earth on 13 March 2026, potentially triggering G1 (minor) geomagnetic storm conditions. Auroras may be visible from higher latitudes including Edinburgh and the Scottish Highlands, Reykjavik, northern Scandinavia, Seattle, Minneapolis, and Hobart (Tasmania) during local nighttime hours. The Moon is a waning crescent at approximately 34% illumination, making for reasonably dark skies. Observers can check real-time aurora forecasting at spaceweather.com or SpaceWeatherLive. •       EarthSky solar wind update: https://earthsky.org/sun/sun-news-activity-solar-flare-cme-aurora-updates/ •       Real-time aurora forecasts: https://spaceweatherlive.com/ Story 6: KM3NeT & the Record-Breaking Neutrino Scientists working with the KM3NeT neutrino detector on the floor of the Mediterranean Sea off Sicily believe they may have identified the source of the most energetic neutrino ever recorded. Detected three years ago, the particle had energy levels exceeding anything previously observed of its kind. Researchers now believe a population of blazars — galaxies with supermassive black holes firing particle jets directly towards Earth — is the most likely source. Blazars are among the most violent and energetic phenomena in the observable universe. The finding represents a significant step in multi-messenger astronomy. •       Universe Today: https://www.universetoday.com/Become a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-space-news-updates--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

In this episode, we speak with Michael Henriques, Partner and Senior Portfolio Manager at Magnetar, a multi-strategy alternative investment manager with more than $22 billion in AUM. Founded in 2005, the firm invests across public and private markets in the U.S. and Europe, with a focus on alternative credit, fixed income, and venture strategies. Its Alternative Credit & Fixed Income business targets Specialty Finance, Structured Solutions, and Opportunistic Markets, seeking to generate attractive risk-adjusted returns, particularly during periods of market dislocation. Michael brings three decades of experience in fixed income, structured securities, and real estate. He joined the firm in 2007 and previously served as a Managing Director at Deutsche Bank. Before that, he spent more than ten years at Goldman Sachs, where he began as a structured finance analyst and ultimately co-headed the CDO and Synthetic ABS group. Michael received his MBA from Wharton and his BA from Princeton. Magnetar was recently recognized as a Top Private Credit Firm of 2025 by GrowthCap. Michael supports HE3AT. To learn more about this organization click here. I am your host, RJ Lumba. We hope you enjoy the show. If you like the episode, click to follow.

The Magnetar AnthologyBecome a supporter of this podcast: https://www.spreaker.com/podcast/darkest-mysteries-online-the-strange-and-unusual-podcast-2026--5684156/support.Darkest Mysteries Online

By the end of 2026, AI capital expenditure is projected to hit nearly $700 billion. The question isn't who has the best model, but who has the most creative financing to build out AI infrastructure and beyond. Sarah Guo is joined by Neil Tiwari, Managing Director at Magnetar Capital, a financial innovator helping the AI industry scale from billions to trillions of dollars in CapEx. Neil explains some of the debt structures used to finance massive GPU clusters, who is taking the risk, and how the industry is maturing. Sarah and Neil also discuss how power distribution, energy storage, and physical materials like steel are the bottlenecks of the AI industry. Plus, Neil gives his take on the future of inference-optimized clouds, and why the market shift away from software and into infrastructure might be an overreaction. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil  Chapters: 00:00 – Cold Open 00:05 – Neil Tiwari Introduction 00:26 – Magnetar's Story 01:28 – Why CoreWeave Helped Magnetar Win 06:15 – Scaling CapEx Efficiently 09:02 – Debunking GPU Collateral Risk 11:42 – How Deal Structures Evolve 13:01 – What Bottlenecks Buildout 15:28 – Circular Financing Critiques 17:35 – The Shift from Training to Inference Workloads 23:10 – AI Factories 24:12 – Constraints of the Current Power Grid 28:27 – Sovereign Compute Buildouts 29:54 – Physical AI Capital Needs 32:48 – The Capital Rotation Away from SaaS 36:04 – Conclusion

Credit Card Churners are people who repeatedly open and close credit card accounts to primarily earn sign-up bonuses like cash back, points, or travel miles. Is this type of hack strategy illegal? On this episode, Jason and Peter ask financial expert and senior advisor at Magnetar, Roger Hochschild, to explain how this process works. We'll also have Roger clear up a few myths and answer some questions that you might also have when it comes to finance and credit cards. For example, does it actually hurt your credit score every time you check it? Is it actually a good thing or bad thing to pay off your credit card every month? Why do lenders look more at your Fico Score instead of your credit score? Roger also takes a moment to explain the process of closing our your loved ones credit cards after they have passed away. He'll also offer his advice on finding a good company for long term investments instead of trading. Learn more about your ad choices. Visit megaphone.fm/adchoices

Darrick Horton is the CEO and co-founder of TensorWave, the company making waves in AI infrastructure by building high-performance compute on AMD chips. In 2023, he and his team took the unconventional path of bypassing Nvidia, a bold bet that has since paid off with nearly $150 million raised from Magnetar, AMD Ventures, Prosperity7, and others. TensorWave is now operating a dedicated training cluster of around 8,000 AMD Instinct MI325X GPUs and has already hit a $100 million revenue run rate. Darrick is a serial entrepreneur with a track record of building infrastructure companies. Before TensorWave, he co-founded VMAccel, sold Lets Rolo to LifeKey, and co-founded the crypto mining company VaultMiner. He began his career as a mechanical engineer and plasma physicist at Lockheed Martin's Skunk Works, where he worked on nuclear fusion energy. While he studied physics and mechanical engineering at Andrews University, he left early to pursue entrepreneurship and hasn't looked back since.In this conversation we discussed:Why Darrick chose AMD over Nvidia to build TensorWave's AI infrastructure, and how that decision created a competitive advantage in a GPU-constrained marketWhat makes training clusters more versatile than inference clusters, and why TensorWave focused on the former to meet broader customer needsHow Neocloud providers like TensorWave can move faster and innovate more effectively than legacy hyperscalers in deploying next-generation AI infrastructureWhy power, not GPUs, is becoming the biggest constraint in scaling AI workloads, and how data center architecture must evolve to address itWhy Darrick predicts AI architectures will continue to evolve beyond transformers, creating constant shifts in compute demandHow massive increases in model complexity are accelerating the need for green energy, tighter feedback loops, and seamless integration of compute into AI workflowsResources:Subscribe to the AI & The Future of Work NewsletterConnect with Darrick on LinkedInAI fun fact articleOn How the new definition of work

「アバック、MAGNETAR「UDP900MK2」「UDP800MK2」の9店鋪合同試聴会」　アバックは、MAGNETARの最新UHDブルーレイプレーヤー「UDP900MK2」「UDP800MK2」の発売に合わせ、全国9店舗で視聴イベントを開催する。期間は2025年12月7日（日）から2026年1月18日（日）までで、参加は事前予約制。会場は新宿／横浜／名古屋／梅田／福岡／琉球／仙台／金沢／静岡の各店となる。

Illinois says it's already facing a $267 million budget shortfall. Crain's reporter John Pletz discusses with host Amy Guth.Plus: Chicago auditor BDO cuts jobs under pressure from Apollo debt and client's collapse, Metra to increase fares to plug budget gap, cash-strapped CPS taps $200 million from credit line, Magnetar sold $1.9 billion in CoreWeave shares as insiders and investors started cashing in. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

MIA Universo Hostil, El Resplandor de una Estrella, El Pulmón de un Magnetar. MIA | Universo Hostil: el cosmos no es decorado, es fuerza y vértigo. En El Resplandor de una Estrella seguimos vidas que nacen y mueren en segundos. El Pulmón de un Magnetar deja un latido que dobla la noche. De cuásares voraces a rocas ocultas: mirar sin parpadear.

On this week's show we look at the CDEDIA Best of show winners for this years event. We also read your emails and take a look at the week's news. News: Google's Home app just got a huge upgrade that makes automations even smarter Paramount to launch new Sports Entertainment division The first Roku-powered smart projector is here Other: The Streaming War Is Over. Piracy Won. TWICE Best of Show Awards Winners for CEDIA 2025 AiSPIRE/WAC Group VENTRIX Lighting, Power and Control System - is an innovative, modular linear lighting solution designed for high-end architectural applications in both commercial and residential spaces. VENTRIX provides a scalable, customizable framework for illumination challenges, such as recessed grid ceiling installations or linear layouts in retail, restaurants, offices, or upscale homes. No pricing available. BZBGEAR BG-AIR4KAST-MKX | 4K@60Hz Wireless HDMI Extender with Multi-Receiver Support - Is a professional-grade wireless HDMI extender kit designed for transmitting uncompressed 4K video signals over the air without the need for long cables. The system uses BZBGEAR's proprietary ipcolor STREAM technology to ensure high-definition video with low latency, operating on the 5GHz wireless frequency band for stable, interference-resistant transmission. Up to 164 feet (50 meters) line-of-sight for 4K@60Hz; extends to about 230 feet for 1080p@60Hz. Available for Pre-order $450 Crestron Home OS - Since 1972, Crestron has been the leader in creating innovative technologies that remove barriers to connection, collaboration, communication, comfort, and control in just about every meaningful aspect of our professional and personal lives. Engineered to be simple, reliable, secure, and easy to use, Crestron sets the standard for intelligent video conferencing, digital content distribution, smart home systems, as well as control and management technology. Solutions that empower people around the world to do more, learn more, enjoy more, and achieve more. Furrion Aurora Partial-Sun 2 4K LED Outdoor Smart TV - is a weatherproof outdoor television designed specifically for partially sunny environments, such as patios, decks, or yards where sunlight is present but not direct or prolonged on the screen. It is part of Furrion's Aurora series, engineered for backyard entertainment with rugged construction to withstand rain, humidity, dust, and temperature fluctuations while delivering high-quality 4K viewing. 55” is going for $1700 HangSmart TV DIY TV wall mount system -  designed for easy installation without the need for wall studs, making it ideal for renters, homeowners, or anyone avoiding complex drilling or hiring professionals. It supports TVs from 19 to 100 inches and holds up to 150 pounds, compatible with most flat-screen LED, LCD, or curved models (including brands like Samsung, LG, and Sony) via standard VESA patterns. Kaleidescape Strato M Movie Player - an entry-level movie player it serves as a standalone device or part of a larger Kaleidescape ecosystem, designed for residential, marine, and commercial theater setups. Priced at around $1,995–$2,000, it offers about half the cost of Kaleidescape's previous lowest-entry system (the Strato V at $4,000) while delivering premium audio and video quality without relying on streaming services. madVR Envy Core MK2 - is a high-end video processor developed by madVR Labs, designed specifically for premium home theaters and media rooms. It represents an upgraded iteration in the company's Envy lineup, building on the original Envy Core (introduced in 2024) by incorporating 48 Gbps HDMI 2.1 support for 8K input and output, enhanced gaming capabilities, and improved overall performance. Announced on September 2, 2025, alongside the Envy Extreme MK3 and Pro MK3 models, it aims to deliver advanced video processing at a more accessible price point compared to flagship models like the Extreme series, while maintaining near-identical image quality for many core functions. $5995 Nice ELAN OS 9.0 - is the latest software platform for the Nice Home Management system, a customizable smart home automation solution developed by Nice North America (formerly Core Brands). Released around mid-2025, OS 9.0 focuses on enhanced personalization, seamless integration with Nice's broader ecosystem (including shading, audio, gate motors, access control, and security), and intuitive user experiences for whole-home control. Samsung OLED TV (S95F) - The Samsung S95F is Samsung's flagship 4K OLED TV series for 2025, succeeding the popular S95D model and positioning itself as a premium smart TV with advanced QD-OLED panel technology. It combines vibrant quantum dot colors with OLED's self-emissive pixels for superior contrast, deep blacks, and lifelike visuals, making it ideal for home theater enthusiasts, gamers, and streaming users. Available in sizes including 55-inch, 65-inch, 77-inch, and 83-inch, it runs on Samsung's Tizen OS with integrated Vision AI for enhanced personalization and upscaling. 55” $2200 - 83” $5800 Samsung HW-QS700F Soundbar - is a premium Q-series 3.1.2-channel soundbar system featuring a dedicated wireless subwoofer. It supports Wireless Dolby Atmos and True 3.1.2ch sound, with Q-Symphony technology that synchronizes seamlessly with compatible Samsung TVs for amplified audio output. The innovative Convertible Fit design allows flexible placement—either as a standalone bar or mounted with rear speakers for expanded surround sound. Priced at $599.99. SimpliSafe Outdoor Security Camera Series 2 with Active Guard Outdoor Protection - is a wireless, AI-powered outdoor camera designed for integration with the SimpliSafe home security system. Released in late 2024, it's an upgrade over the original model, focusing on proactive threat detection and deterrence. It requires a SimpliSafe base station to operate and is available for $199.99 directly from SimpliSafe or major retailers like Amazon and Best Buy. Battery-powered for flexibility, it can also be wired for continuous operation, which is essential for unlocking advanced features like Active Guard Outdoor Protection. Skyworth Canvas Elite Art TV - is a premium lifestyle television series launched by Skyworth USA in August 2025, designed to blend high-performance entertainment with gallery-quality art display. It features the world's largest art TV at 100 inches, alongside an 86-inch model, making it ideal for custom home integration where aesthetics meet advanced technology.  Starting at $4000 Sony BRAVIA Projector 7 4K HDR Laser Home Theater Projector with Native 4K SXRD Panel - is a premium native 4K HDR laser home theater projector that features Sony's advanced SXRD (Silicon X-tal Reflective Display) technology with a compact 0.61-inch native 4K panel (3,840 x 2,160 pixels), delivering over 8 million pixels for sharp, detailed images with inky blacks, vibrant colors, and rich textures. Powered by a long-lasting laser light source providing up to 2,200 lumens of brightness, it excels in rendering high dynamic range (HDR) content like Dolby Vision and IMAX Enhanced, ensuring vivid highlights and deep shadows even on screens up to 120 inches in moderately lit environments. $10,000 Sony BRAVIA Theater System 6 - is a 5.1-channel home theater system featuring a soundbar, wireless rear speakers, and a dedicated subwoofer for immersive surround sound. Delivering 1,000W of total output, it supports Dolby Atmos and DTS:X for dynamic, three-dimensional audio with precise dialogue via Voice Zoom 3. Easy to set up and compatible with select BRAVIA TVs for seamless control, it includes HDMI eARC, Bluetooth, and the BRAVIA Connect App for enhanced connectivity and customization. $800 What Hi-Fi? Best of Show Awards Winners Bluesound PULSE CINEMA - is a premium wireless streaming soundbar that is an all-in-one solution that delivers immersive Dolby Atmos audio without requiring a separate AV receiver, making it ideal for users who want cinematic sound for movies, music, gaming, and TV in a clutter-free design. Positioned as a competitor to brands like Sonos, it emphasizes high-resolution multi-room streaming via Bluesound's BluOS platform, high-fidelity performance, and easy expandability to a full surround system. The PULSE CINEMA is designed for larger spaces, pairing best with 55-inch TVs and above, and measures 47 inches wide for a low-profile fit under or mounted below your screen. $1500 Coastal Source 1000 Series Bollards - are a premium line of modular outdoor speakers. Designed for high-end landscape audio installations, they build on the success of the earlier 10.0 Bollard Series, offering enhanced performance while maintaining a sleek, weather-resistant design that blends into outdoor environments like patios, pools, or gardens. These bollards are engineered to "Defy the Elements," with sealed enclosures that provide superior durability against rain, sun, salt air, and extreme temperatures, making them ideal for coastal or harsh climates. No Pricing Kaleidescape Strato M Movie Player - See above L-Acoustics HYRISS - (Hyperreal Immersive Sound Space) is an audio solution launched by the French audio company L-Acoustics in September 2024. HYRISS transforms everyday environments into dynamic, immersive auditory experiences. It's particularly aimed at high-end residential, hospitality, corporate, retail, and even yacht settings, where it integrates seamlessly with architecture to create customizable soundscapes. Unlike traditional home audio systems, HYRISS isn't just about speakers—it's a complete ecosystem combining hardware, software, advanced processing, and professional installation to deliver concert-quality sound while preserving visual aesthetics. Ara's note on pricing - I didn't bother looking it up. It's French and it's designed for high end. I think that sums it up! Magnetar UDP900MKII - is a high-end universal disc player designed for audiophiles and cinephiles who prioritize reference-grade playback of physical media. It serves as an upgraded successor to the original UDP900 model, incorporating enhancements based on user and dealer feedback to deliver superior audio fidelity, video processing, and build quality. It's positioned as a flagship device in Magnetar's lineup, emphasizing support for a wide array of formats while addressing the growing scarcity of premium Blu-ray players (following exits by brands like Oppo, Reavon, LG, and Samsung). Shipping in Q4 2025 with suggested retail prices of $3300 Sony BRAVIA 8 II 65-inch Class QD-OLED 4K HDR Google TV - The Sony BRAVIA 8 is Sony's flagship OLED television for 2025. It leverages advanced QD-OLED panel technology from Samsung Display—the latest generation, shared with models like the Samsung S95F—for superior brightness, color vibrancy, and contrast. This TV is designed for cinematic immersion, blending high-end picture processing with immersive audio, making it ideal for movie enthusiasts, gamers (especially PS5 owners), and those seeking a premium home theater experience. It's available in 55-inch and 65-inch sizes only, with no larger options to avoid overlapping Sony's Mini-LED flagship, the BRAVIA 9. $3100 Sony BRAVIA Projector 7 4K HDR Laser Home Theater Projector with Native 4K SXRD Panel - See above  

Tom is away this week, so Lee Overstreet joins Rob H. And keep your eyes peeled for a 2nd AV Rant episode this week, as we will be posting a CEDIA 2025 Special with Joe Klusnick! Dolby Vision 2 and Dolby Vision 2 Max were announced. Magnetar announced new MKII versions of their universal disc […] The post AV Rant #984: Sofabaton X2 Coming Soon appeared first on AV Rant.

Tom is away this week, so Lee Overstreet joins Rob H. And keep your eyes peeled for a 2nd AV Rant episode this week, as we will be posting a CEDIA 2025 Special with Joe Klusnick! Dolby Vision 2 and Dolby Vision 2 Max were announced. Magnetar announced new MKII versions of their universal disc […] The post AV Rant #984: Sofabaton X2 Coming Soon appeared first on AV Rant.

In this episode of SpaceTime, we dive into the depths of cosmic mysteries and groundbreaking discoveries, including the enigmatic Long Period Transient and the stellar companion of Betelgeuse, alongside exciting findings from Mars.Unraveling the Long Period TransientAstronomers have uncovered a new cosmic phenomenon, ASCAP J1832-0911, which emits both radio and X-ray pulses every 44 minutes for just two minutes at a time. This first-ever detection of a Long Period Transient has left scientists puzzled about its nature and origins. Lead author Dr Andy Wang from Curtin University discusses the potential theories, including the possibility of a magnetar or a binary star system, while emphasizing the need for further observations to unravel this cosmic mystery.Betelgeuse's Stellar CompanionIn a remarkable discovery, astronomers have identified a stellar companion orbiting the red supergiant Betelgeuse. This pre-main sequence star, approximately one and a half times the mass of the Sun, resides within Betelgeuse's outer atmosphere. As Betelgeuse approaches the end of its life, this companion is also on a collision course with destiny, likely spiraling into Betelgeuse within the next 10,000 years. This finding could shed light on the periodic brightness changes observed in similar red supergiant stars.Mars Perseverance Rover's New DiscoveriesNASA's Perseverance Rover continues its exploration of Jezero Crater, uncovering unusual rock formations that may reveal significant geological history. The rover is investigating an intriguing contact area where clay-bearing units meet olivine-rich rocks, potentially preserving evidence of ancient intrusive processes. Despite challenges in studying these formations, mission managers remain determined to unlock the secrets they hold about Mars' past.www.spacetimewithstuartgary.com✍️ Episode ReferencesAstrophysical Journal Lettershttps://iopscience.iop.org/journal/1538-4357NASA's Perseverance Rover Missionhttps://mars.nasa.gov/mars2020/Become a supporter of this podcast: https://www.spreaker.com/podcast/spacetime-space-astronomy--2458531/support.00:00 Space Time series 28 episode 90 for broadcast on 28 July 202500:47 Long Period Transient emitting radio and X ray pulses every 44 minutes07:48 Astronomers have discovered what appears to be a companion star in binary orbit12:30 NASA's Mars Perseverance Rover is continuing its exploration of Jetro Crater15:00 New study links early smartphone use to poorer mental health later in life17:39 There are new reports of Bigfoot activity in the Pacific Northwest state of Washington

Leaning on his engineering expertise and audio industry savviness is nothing new to Rob Jones—nor is launching brands into the CEDIA channel. This go, Jones is helping lead the charge for Magnetar Audio as the brand enters the North American market. In this conversation, we chat with him about Magnetar, their niche-yet-necessary product, and how it serves the evolving audio consumer's needs. https://www.youtube.com/watch?v=-QxOOa-53Hs

In this episode, we speak with Piotr Tomasik, Co-Founder and President at TensorWave, a cutting-edge cloud platform purpose-built for AI workloads that delivers high-performance computing with AMD MI300X accelerators and a best-in-class inference engine. TensorWave recently raised $100 million from Magnetar, AMD Ventures, Maverick Silicon, Nexus Venture Partners, and Prosperity7. The capital infusion strengthens the company's ability to scale its infrastructure for memory-intensive AI workloads and expands its reach as a next-gen alternative in the AI and HPC cloud space. Piotr supports Startup Vegas. To learn more about this organization click here. I am your host RJ Lumba. We hope you enjoy the show. If you like the episode click to follow.

From February 3, 2021. A radio-loud magnetar first observed in March 2020 suffered an apparent identity crisis, behaving like a pulsar until gradually settling into magnetar-like emissions in July. Plus, Mars' moon Phobos, Jupiter's moon Ganymede, and an interview with SETI Institute scientist Veselin Kostov about last week's sextuple star system.   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

CoreWeave set to go public on the NASDAQ today – in the street's biggest U.S. tech offering since 2021… David Faber, Carl Quintanilla, and Courtney Reagan broke down the latest indications this hour - and talked expectations ahead of the first trade with a Senior Managing Partner from Magnetar (the largest institutional investor in CoreWeave; they're set to own ~29% of Class A shares after the offering). Plus, the latest from the ground at the NASDAQ with their Head of Capital Markets.   Also in focus: Lululemon's consumer warning – the team had a bull/bear debate about what to do with shares after tough guidance last night; and the latest out of Washington, as President Trump's battle with ‘Big Law' grows… and Evercore's Senior Chairman & Founder Roger Altman says the move is bad for business.   Squawk on the Street Disclaimer

Crain's health care reporter Katherine Davis talks with host Amy Guth about the latest news from the local health scene, including innovations from a Northwestern wearable sensor tech spinout.Plus: Stellantis offers buyouts, retirement incentives to workers in Illinois, Michigan and Ohio; Evanston hedge fund Magnetar has a huge stake in one of Wall Street's most anticipated IPOs;  a big downtown office tenant that isn't cutting back on space; and Crain's newest list ranks Chicago's largest wealth management firms.

We were very fortunate to have Dan Tucker from Crown Magnetar on the podcast to talk about their new EP, "Punishment". Enjoy!Crown Magnetar Socials: Twitter: https://x.com/crownmagnetarInstagram: https://www.instagram.com/crownmagnetar/Facebook: https://www.facebook.com/crownmagnetar/TikTok: https://www.tiktok.com/@crownmagnetar_YouTube: https://www.youtube.com/@crownmagnetarApple Music: https://music.apple.com/us/artist/crown-magnetar/1324870952Spotify: https://open.spotify.com/artist/0DlST2L7efoM5Lb0uxG3TxGrab some GNP Merch!: https://goodnoisepodcast.creator-spring.com/Check out the recording gear we use: https://www.amazon.com/shop/goodnoisepodcastSupport the show on Patreon: https://www.patreon.com/goodnoisepodcastGood Noise Podcast Socials:Twitter: https://twitter.com/good_noise_castInstagram: https://www.instagram.com/goodnoisepodcast/Facebook: https://www.facebook.com/goodnoisepodDiscord: https://discord.gg/nDAQKwTYouTube: https://www.youtube.com/channel/UCFHKPdUxxe1MaGNWoFtjoJASpotify: https://open.spotify.com/show/04IMtdIrCIvbIr7g6ttZHiAll other streaming platforms: https://linktr.ee/goodnoisepodcastBandcamp: https://goodnoiserecords.bandcamp.com/

SpaceTime Series 28 Episode 08Origins of Fast Radio Bursts UnveiledAstronomers have pinpointed the source of fast radio bursts, specifically linking them to magnetars, a type of highly magnetic neutron star. This breakthrough, reported in Nature, was achieved by analysing the scintillation of FRB2022 1022A, indicating its proximity to a magnetar within 10,000 kilometres. This discovery sheds light on the mysterious phenomenon, suggesting that all fast radio bursts might originate from similarly extreme environments.Lunar Water's Terrestrial OriginsA groundbreaking study reveals that much of the Moon's water may have originated from early Earth. By examining Apollo-era lunar samples with a high precision triple oxygen isotope technique, scientists discovered a dual heritage of lunar water, tracing back to both proto-Earth and cometary impacts. This finding offers new insights into the Earth-Moon system's formation 4.5 billion years ago.Juno's Insights into IO's VolcanismNASA's Juno mission has uncovered that Jupiter's moon IO's volcanoes are powered by individual magma chambers rather than a global magma ocean. This revelation, stemming from Juno's close flybys and gravitational measurements, highlights the unique geological dynamics of the solar system's most volcanically active body. The findings provide a deeper understanding of tidal flexing and its effects on celestial bodies.00:00 Astronomers have finally narrowed down the source of those mysterious fast radio bursts08:01 New study shows much of moon's water originates on early proto Earth10:32 Scientists with NASA's Juno mission to Jupiter have discovered volcanoes on IO16:33 There now seems to be more carbon stored in human made stuff than natural world18:41 Study shows each of the Disney princesses could have exposed themselves to illnesses19:40 Alaska Triangle has highest recorded numbers of paranormal incidents in the worldwww.spacetimewithstuartgary.comwww.bitesz.com

Do discs matter anymore? This week, host Jorden Guth is joined by Rob Jones, president of Magnetar Audio North America, to discuss the brand's high-performance universal disc players, the fuzzy lines between hi-fi and home theater, and the future of physical media. Source: “Magnetar UDP900 4K Ultra HD Universal Blu-ray Disc Player–DAC” by Roger Kanno: https://www.soundstagehifi.com/index.php/equipment-reviews/1854-magnetar-udp900-4k-ultra-hd-universal-blu-ray-disc-player-dac Chapters: 00:00:00 Announcement 00:00:30 The road to Magnetar 00:23:23 Music Break: “IDK” by Modern Aquatic 00:24:15 The intersection of home theater and hi-fi 00:37:47 Outro: “Let's Not Rush Out and Tell Everyone” by Glories

It’s our final episode of 2024! And unexpectedly, Lee Overstreet joins Rob H as Tom was forced to be away with the flu. It wasn’t the plan. But we answer lots of questions, since we’re taking next week off for Christmas! LG has discontinued all of their Blu-ray and Ultra HD Blu-ray players. Apple will […] The post AV Rant #947: You’re Gonna Be a Magnetar appeared first on AV Rant.

Track Listing: 1. Pandora's Box 2. Demonize Them 3. Leviathan 4. Division 5. Dawn 6. Reborn 7. We're Wolves 8. Fading Embers 9. The Source Learn more about your ad choices. Visit megaphone.fm/adchoices

Astronomy Daily - The Podcast: 10th October 2024Welcome to Astronomy Daily, your Daily dose of space and Astronomy news. I'm your host, Anna. Today we have an exciting lineup of stories that I can't wait to share with you. First, we'll delve into NASA's Europa Clipper mission, which is ready to embark on an epic journey to Jupiter and its intriguing moon Europa. Then we'll talk about Elon Musk and SpaceX's ambitious plans to launch uncrewed starships to Mars in just two years, paving the way for future human colonization. We'll also uncover a groundbreaking study on fast radio bursts that might finally solve the mystery behind these cosmic phenomena. And if that isn't enough, we'll explore new findings suggesting the moon might still be volcanically active today. Lastly, we'll highlight NASA's innovative solar sail that you can actually spot from Earth. Buckle up, space enthusiasts. Let's dive in.Highlights:- NASA's Europa Clipper Mission: NASA's Europa Clipper spacecraft has reached a significant milestone by passing its final technical review. This means it's now all set for its journey towards Jupiter. With a launch window slated between October 10 and 30th, the mission aims to delve into the mysteries of Jupiter's moon Europa, potentially harboring an ocean beneath its icy crust.- SpaceX's Mars Ambitions: Elon Musk recently announced that SpaceX plans to launch its first uncrewed starships to Mars within the next two years. These missions are crucial for testing the reliability of landing these advanced spacecraft intact on the Martian surface. If successful, crewed flights to Mars could follow just two years later, paving the way for human colonization.- Fast Radio Bursts Mystery Possibly Solved: A groundbreaking new study by the Italian National Institute for Astrophysics has advanced our understanding of fast radio bursts (FRBs). Using the Very Large Array telescope, researchers recorded the weakest persistent radio emission for an FRB, shedding light on the mysterious origins of these powerful cosmic events.- Volcanic Activity on the Moon: Recent findings from the Chinese Chang'e 5 mission suggest that the moon might still be volcanically active. Tiny glass beads found in lunar samples indicate that volcanic activity might have occurred as recently as 123 million years ago, challenging the traditional belief that lunar volcanism ceased 3 to 3.8 billion years ago.- NASA's Solar Sail: NASA's advanced composite solar sail system is now visible from many locations around the world. This groundbreaking solar sail, which harnesses sunlight for propulsion, represents an exciting step towards more sustainable and accessible deep space missions. Engage with NASA's "Spot the Sail" campaign and track the solar sail using the free NASA app.For more space news, be sure to visit our website at astronomydaily.io. There you can sign up for our free Daily newsletter, catch up on all the latest space and Astronomy news with our constantly updating news feed, and listen to all our back episodes.Don't forget to follow us on social media. Just search for #AstroDailyPod on Facebook, X, YouTubeMusic, and TikTok to stay connected with our community and never miss an update.Thank you for tuning in, and remember to keep your eyes on the skies. Until next time, may you be blessed with clear skies.Sponsor Links:NordVPNNordPassMalwarebytesProton MailBecome a supporter of this podcast for commercial-free editions not very much moeny: https://www.spreaker.com/podcast/astronomy-daily-the-podcast--5648921/support

In this episode of SpaceTime, the mystery of the famous "Wow!" signal may finally be solved, the European Space Agency's Cluster mission is set to end in a fiery re-entry over the South Pacific, and Earth gets hit by another powerful solar storm. Join us for these fascinating updates and more!00:00:00 - This is SpaceTime series 27, episode 104, for broadcast on the 28 August 202400:00:45 - New study may have identified the source of the famous "Wow!" signal00:12:30 - European Space Agency's Cluster mission to end with a controlled re-entry00:23:45 - Earth hit by another powerful solar storm00:32:15 - The science report: Higher levels of plant fats linked to lower risk of heart disease00:45:00 - Neuralink's brain implant shows promising results in second trial patientFor more SpaceTime, visit our website at www.spacetimewithstuartgary.comwww.bitesz.comBecome a supporter of this podcast: https://www.spreaker.com/podcast/spacetime-with-stuart-gary--2458531/supportSponsor Link:NordPassProtonMail & SecurityMalwarebytesNordVPN

V tomto podcaste budeme hovoriť o pseudovede v nemocničných letákoch, o zaujímavom magnetare a dáme si fakt a fikciu. Zdroje Magnetic Star Awakens After Sleeping For 10 Years And It's Acting Super Weird A remote digital memory composite to detect cognitive impairment in memory clinic samples in unsupervised settings using mobile devices New window film drops temperature, slashes energy consumption Solar-powered technology converts saltwater into drinking water emission-free Image by ESO/L. Calçada, CC BY 4.0, via Wikimedia Commons

Bloomberg Radio host Barry Ritholtz speaks to David Snyderman, global head of Magnetar Capital LLC's alternative credit and fixed income business. He also serves as chairman of Magnetar's investment committee and as a member of its management committee. Snyderman, who joined Magnetar in 2005 shortly after its launch, was previously the head of global credit and a senior managing director at Citadel Investment Group, and he served as a member of the management, portfolio management and investment/risk committees. Prior to joining Citadel, David focused on convertible securities, merger arbitrage and special situations portfolios at Koch Industries Inc. Snyderman is a founding board member of the Magnetar Capital Foundation,See omnystudio.com/listener for privacy information.

Welcome to Stream In Terror! A live stream for talking about horror stories. Tonight I have the star exploring Magnetar. I hope you will join us for our chilling chat. We go Live on YT every Friday at 7pm EST #narration #livestream #horrorstories @MagnetarYT Support the stream: https://streamlabs.com/to42reads #chat #LiveStream #Podcast #Roundtabletalk #Chill #coffee Magnetar YouTube: https://www.youtube.com/@MagnetarYT Magnetar Twitter: https://twitter.com/MagnetarYT Oddios Stories: https://www.youtube.com/@oddiostories To_42 Slumbers: https://www.youtube.com/@To42Slumbers Check out my website: https://www.to42reads.com Got a story? E-mail me at To42reads@Gmail.com Some Photos, Texts and other elements of the video are found at: https://elements.envato.com/ Others are from: https://unsplash.com/ I upload every day with a short video. Two Sentence horrors. I upload a full Story on Sunday's, one week is Day (True) and one week is Night (Fictional stories). I have a live coffee stream every Saturday morning with a different guest. This Channel is about stories of day and night. Day Stories are your true stories. Let's not meet, glitch in the matrix and all stories said to be true by the author. All brought to me by To. Night Stories are those fictional stories. Like No sleep, creepypasta and others like that. All brought to me by 42.

Neutron stars, the compact remains of a massive star following a supernova explosion, are the densest matter in the Universe. Some neutron stars, known as magnetars, also claim the record for the strongest magnetic fields of any object. How magnetars, which are a mere 15 kilometers across, form and produce such colossal magnetic fields remains a mystery.    New observations by a team of astronomers, including NSF's NOIRLab's Dr. André-Nicolas Chené, may shed important light on the origin of these magnetic powerhouses. Using various telescopes around the globe, including the Canada-France-Hawai‘i Telescope (CFHT) on Maunakea, the researchers have identified a new type of astronomical object — a massive magnetic helium star (an unusual variant of a Wolf-Rayet star), which may be the precursor of a magnetar.  In this podcast, André-Nicolas Chené describes the process of finding the first known potential magnetar progenitor.   Bios:  Rob Sparks is in the Communications, Education and Engagement group at NSF's NOIRLab in Tucson, Arizona.   Dr. André-Nicolas Chene is an associate astronomer at NOIRLab. He completed his PhD at the Université de Montréal in 2007 and learned everything about the fundamentals of astronomical observations at the Observatoire du Mont Mégantic. He was research fellow at the NRC Herzberg Astronomy and Astrophysics Research Centre and postdoc jointly at the Universidad de Concepción and the Universidad de Valparaíso before joining the Gemini Observatory (now a program of NOIRLab) in 2013. For almost 10 years, André-Nicolas took part in every phase of a Gemini observing program life cycle and has played a central role in Gemini's user support effort. André-Nicolas's research interests are massive stars, hot winds, star clusters, and stellar evolution.   Links:  NOIRLab Press Release: https://noirlab.edu/public/news/noirlab2323/ NOIRLab social media channels can be found at https://www.facebook.com/NOIRLabAstro https://twitter.com/NOIRLabAstro https://www.instagram.com/noirlabastro/ https://www.youtube.com/noirlabastro   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

Wie Schnelle Radioblitze vor gut 15 Jahren entdeckt wurden und was bislang über ihren Ursprung bekannt ist, berichtet Michael Kramer vom Max-Planck-Institut für Radioastronomie in dieser Folge.

Welcome to Astronomy Daily for Friday, July 21st, 2023. I'm your host, Tim Gibbs, and joining me in the studio is Hallie, my AI assistant. Let's dive into today's headlines. Headline 1: India's Chandrayan 3 spacecraft is on track for its moon landing attempt on August 23rd or 24th. The spacecraft has been raising its orbit around Earth with a series of burns, and a Translunar injection burn is scheduled for July 31st. This mission is India's second attempt to land on the moon after the failed Chandrayan 2-lander in 2019. Headline 2: Comet Ponds-Brooks has brightened by five magnitudes and can now be seen in a six-inch telescope. Despite its distance of over 530 million kilometers from Earth, its recent outburst has made it visible with smaller telescopes for now. Headline 3: The master recording for NASA's Voyager Golden Record, created by astronomer Carl Sagan and Andrew Yann, is up for auction. These reels, estimated to be worth more than ten times their weight in gold, were used to produce the iconic golden records on the Voyager spacecraft. Headline 4: Astronomers have discovered a mysterious magnetar, GPMJ1839-10, located about 15,000 light years away in the direction of the constellation Scutum. It emits energy bursts every 22 minutes, making it the longest period magnetar ever found. And now, Hallie's terrible dad joke for the week: Why don't scientists trust atoms? Because they make up everything! That's all for today's episode of Astronomy Daily. Remember, you can catch Steve on Monday and me on Friday, with occasional one-story episodes on Wednesdays. For more episodes, visit spacenuts.io and bitesz.com.

Welcome to To_42's Café. Magnetar and The Panic Within (The Queen of Panic) this morning for my Stream. Please grab yourself a tea or a coffee as we sit and talk today! Support the stream: https://streamlabs.com/to42reads #Chat #LiveStream #Podcast #Roundtabletalk #Chill #coffee @MagnetarYT @thepncwithin4949 Magnetar YouTube: https://www.youtube.com/@MagnetarYTMagnetar Twitter: https://twitter.com/MagnetarYTThe Panic Within YouTube: https://www.youtube.com/@thepncwithin4949The Panic Within Twitter: https://twitter.com/TheQUEENofPncTo_42 Slumbers: https://www.youtube.com/channel/UCWbW2PkHoFo1hv6C81z50CACheck out my website: https://www.to42reads.comGot a story? E-mail me at To42reads@Gmail.comSome Photos, Texts and other elements of the video are found at: https://elements.envato.com/Others are from: https://unsplash.com/I upload every day with a short video. Two Sentence horrors. I upload a full Story on Sunday's, one week is Day (True) and one week is Night (Fictional stories). I have a live coffee stream every Saturday morning with a different guest.This Channel is about stories of day and night. Day Stories are your true stories. Let's not meet, glitch in the matrix and all stories said to be true by the author. All brought to me by To.Night Stories are those fictional stories. Like No sleep, creepypasta and others like that. All brought to me by 42.This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

Garza sits down with Colorado tech deathcore band Crown Magnetar. Check out their new album EVERYTHING BLEEDS out July 14th! https://www.linktr.ee/Crownmagnetar SPONSORS: Click this link to purchase from Sweetwater & help support the podcast: imp.i114863.net/rnrmVB CROWN MAGETAR is: Dan Tucker - Vocals Byron London - Drums Nick Burnett - Guitar Grant Robinson - Bass TIME STAMPS: 00:00 - Chaos & Carnage, Transportation Problems 05:40 - Keeping Up With Other Musicians On Tour 08:25 - New Album, Everything Bleeds/Art by Caelan Stokkermans 13:49 - Dan Inspired by Jack Kerouac, Starting Crown Megnetar 18:10 - Dan & Nick Meeting, Recording First Songs 20:44 - Developing Vocal Skills From Jazz Choir 23:22 - Byron & Grant Joining the Band 25:48 - Playing Shows Before Recording Music 26:20 - Origin of Band Name  27:44 - Moshing, Avoiding Injuries  29:55 - Garza & Crown Magnetar Meeting on Carnifex Tour 30:58 - Awkward Craigslist Auditions 34:29 - Early DIY Shows, Writing With the Live Performance in Mind 36:33 - Moving to Colorado From Nashville, TN 42:35 - Ken Bedene (Aborted Drummer) is Insane 45:19 - Bands That Influenced Crown Magnetar 48:55 - The Growth of Heavy Music & Deathcore 50:53 - Discovering Music at Hot Topic 55:00 - Relationship Problems, Inspiration for the Music 56:59 - Inspiration for Music & Lyrics 59:01 - Staying in Shape on Tour 01:05:30 - Everything Bleeds, out July 14th/Writing New Material

In this episode, fall into our Magnetar pit trap as the Fellowship discusses Magnetars? What are they? How are they? Is Magneto involved? Grab your favorite drink and join us! *Always Drink Responsibly* Listen and Subscribe to us on: Anchor.fm Spotify YouTube Apple Podcasts Google Podcasts Cosmoswithcosmos.com Follow Us! Twitter: @drinkingcosmos Instagram: @cosmoswithcosmos Credits: Eric Skiff - Resistor Anthems http://EricSkiff.com/music Theme Music Remixed by: Ron Proctor https://www.youtube.com/channel/UC__fjzKFm0X0BQWHjYX8Z_w Wildixia https://www.etsy.com/shop/Wildixia?ref=profile_header Rolling Bluff Planetarium https://www.rollingbluffsplanetarium.com/ --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

Doug Pagitt and Dan Deitrich sit down with Astrophysicist Paul Wallace to talk about the mysteries of the universe like a dead star with something like a volcano (but also not actually at all like a volcano) emitting "alien" radio pulses.   Paul Wallace is an astrophysicist, professor, pastor, and avid birder. He writes and speaks at the intersection of faith and science and holds a PhD in physics from Duke University and an MDiv from Emory University's Candler School of Theology. facebook.com/Paul.Matthew.Wallace   /   twitter.com/paulmwall  / pwallace.net   Doug Pagitt is the Executive Director and one of the founders of Vote Common Good. He is also a pastor, author, and social activist.  @pagitt   Daniel Deitrich is a singer-songwriter, former-pastor-turned-activist, and producer of The Common Good Podcast. @danieldeitrich Our theme music is composed by Ben Grace. @bengracemusic   votecommongood.com votecommongood.com/podcast facebook.com/votecommongood twitter.com/votecommon

"Fast Radio Bursts" sind superschnelle Radioausbrüche die wir erst 2007 entdeckt haben. Zuerst dachte man sie kämen aus der Küche der Sternwarte. Tatsächlich sind sie aber viel mysteriöser. Mehr dazu erfahrt ihr in der neuen Folge der Sternengeschichten. Wer den Podcast finanziell unterstützen möchte, kann das hier tun: Mit PayPal (https://www.paypal.me/florianfreistetter), Patreon (https://www.patreon.com/sternengeschichten) oder Steady (https://steadyhq.com/sternengeschichten)