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

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

The rapid rise of prediction markets is prompting questions about their influence on politics, military operations and the way we consume news. We discuss with guests including PredictIt founder John Aristotle Phillips. See omnystudio.com/listener for privacy information.

Part 1: UK-based political bettors William Kedjanyi, Paul Krishnamurty, and Pip Moss debate how long  Starmer can last as prime minister. Part 2: Chougule announces Polymarket sponsorship of DC Forecasting and Prediction Markets meetup. Timestamps 0:00: Chougule introduces UK segment 0:39: Polymarket markets on UK politics 1:35: Intro ends 3:36: UK segment begins 6:02: What has gone wrong for Starmer? 11:10: Starmer's communication problem 11:31: Perceptions of Starmer as a liar 11:41: Starmer is hated 17:40: Free speech 24:36: Market odds on Starmer's departure 25:23: Policy challenges 26:46: Housing 28:10: Local elections 32:50: Policy decisions 37:12: Immigration 39:30: Process After May Elections 42:14: May election odds 52:27: How Labour would remove Starmer 54:29: Blair-Brown rivalry 56:06: Trade unions 56:26: Soft left faction 1:05:52: Starmer U-turns 1:07:51: Challengers to Starmers 1:09:15: Reaction to Labour losses in May elections 1:12:30: UK segment ends 1:12:45: DC Forecasting and Prediction Markets Meetup Follow Star Spangled Gamblers on Twitter/X @ssgamblers  Bet on UK politics at Polymarket.com, the world's largest prediction market. The next DC Forecasting & Prediction Markets Meetup will be on Thursday, January 29 from 6:00 PM - 9:00 PM at The Flying Mexican in Washington DC. ​ All 2026 DC meetups are sponsored by Polymarket! ​ Polymarket is the world's largest prediction market, allowing you to stay informed on future events across various topics. Polymarket's markets reflect accurate, unbiased, and real-time probabilities for the events that matter most to you. Markets seek truth. Learn more at Polymarket.com. ​ Thanks to Polymarket, food and drinks will be provided to all attendees of this month's meetup. Open to all ages. ​ Last-minute/onsite walk-in RSVPs here on this Luma event page are welcomed! https://luma.com/dld19288?tk=XIracE ​ Who are we? We are prediction market traders on prediction markets like Polymarket, Manifold, PredictIt, and Kalshi, forecasters (e.g. on Metaculus and Good Judgment Open), sports bettors (e.g. on FanDuel, DraftKings, and other sportsbooks), consumers of forecasting (or related) content (e.g. Star Spangled Gamblers, Nate Silver's Silver Bulletin, Scott Alexander's Astral Codex Ten), effective altruists, rationalists, futurists, and data scientists. ​ This meetup is hosted by the Forecasting Meetup Network. Get notified whenever a new meetup is scheduled and learn more about the Forecasting Meetup Network here: https://bit.ly/forecastingmeetupnetwork ​ Join our Discord to connect with others in the community between monthly meetups: https://discord.com/invite/hFn3yukSwv

Part I: Akhi Pillalamarri (@AkhiPill) and Pratik Chougule (@pjchougule) explains why India and Pakistan won't resort to nuclear weapons.  Part II: David Glidden (@dglid) interviews Amb. Thomas Miller, former chair of the board of the U.S. subsidiary of Intralot, a corporation that runs lotteries in 11 states and the District of Columbia.  Timestamps 0:00: Chougule introduces segment with Pillalamarri 1:07: Chougule introduces Glidden interview with Miller 1:46: Parallels between lotteries and prediction markets 2:47: Intro ends 4:48: Pillalamarri segment begins 5:12: Kylasa (@aenews) side bet with Mehndiratta (@tenad0me)  5:51: Odds on nuclear war 6:01: Anti-nuclear norms 6:55: Why India and Pakistan haven't used nukes 7:34: Terrorism vs. nukes 8:46: India's no first use doctrine   10:05: Variance 10:18: Anthropic effects 10:52: Nuclear taboos 10:58: Why Pakistan won't transfer nukes 11:36: Polymarket market on nuclear detonation 11:45: Segment ends 12:00: Interview with Miller begins 12:17: Miller's background 12:46: Rumsfeld 14:27: Chougule 14:44: Washingtonian profile of Chougule 15:06: Intralot 15:26: Lotteries offering sports betting 15:58: Prediction markets 16:46: Business of prediction markets 17:02: Amazon 18:36: How Miller got into lottery business 19:43: Lottery expansion into sports betting 20:46: Women 21:19: Lottery regulation 21:44: How lotteries gained acceptance 24:10: Demographic of prediction market users 25:02: Forecasting as an ambassador   26:07: Black swan events 26:20: History 26:30: Intelligence before Russian invasion of Ukraine 28:59: Data in diplomacy 29:47: Iraq War 31:23: AI 32:04: Prediction markets for diplomacy 37:09: Using prediction markets to anticipate bad events 37:36: Prediction markets for resource allocation decisions 37:52: Medical research 39:43: Segment ends 39:57: DC Forecasting and Prediction Markets Meetups   Trade on markets related to nuclear weapons and war at Polymarket.com, the world's largest prediction market.     Join us for the final DC Forecasting and Prediction Markets meetup on Wednesday, December 17 from 6-9pm at the Flying Mexican on Capitol Hill, close to the Eastern Market metro station (blue/orange lines), NOT our usual Rocklands BBQ location in Arlington. Be sure to show up on the correct side of the river this month!     Meet and socialize with others interested in forecasting, prediction markets, political gambling, sports betting, or anything else relating to predicting the future. Thanks to our sponsor, food and drinks will be provided to all attendees of this month's meetup. Open to all ages. Last-minute/onsite walk-in RSVPs here on this Partiful event page are welcomed!   Who are we? We are prediction market traders on prediction markets like Kalshi, Manifold, PredictIt, and Polymarket, forecasters (e.g. on Metaculus and Good Judgment Open), sports bettors (e.g. on FanDuel, DraftKings, and other sportsbooks), consumers of forecasting (or related) content (e.g. Star Spangled Gamblers, Nate Silver's Silver Bulletin, Scott Alexander's Astral Codex Ten), effective altruists, rationalists, futurists, and data scientists.   This meetup is hosted by the Forecasting Meetup Network. Get notified whenever a new meetup is scheduled and learn more about the Forecasting Meetup Network here: https://bit.ly/forecastingmeetupnetwork   Join our Discord to connect with others in the community between monthly meetups: https://discord.com/invite/hFn3yukSwv  

Akhi Pillalamarri (@akhipill) assesses whether and when India will attack Pakistan. Timestamps 0:00: Chougule introduces episode 0:58: DC Forecasting and Prediction Markets Meetup 2:25: Intro ends 4:25: Interview begins 4:46: Pillalamarri's background 5:53: Pillalamarri's experience with prediction markets 6:33: Polymarket lines on Indian strike on Pakistan 7:10: Background on India-Pakistan conflict 8:11: Why do India and Pakistan go to war? 9:34: Pakistan's strategy 11:47: Spike in Polymarket market 12:13: Nuclear weapons 12:57: Terrorist attack in India 18:10: Indian military options 19:50: Indian hardliners 20:51: Pakistan's hand 25:18: China 26:38: Saudi Arabia 27:50: Munir 28:14: Pakistani military 29:06: India's point of view 30:01: Hardline Indian policy 32:50: Timing of an Indian strike 35:21: Market rules on "strike" 36:33: No Indian ground forces 37:06: Indian strike in 2027 Star Spangled Gamblers is a podcast on betting and winning real money on politics.  Follow SSG on Twitter @ssgamblers Bet on the India-Pakistan conflict at Polymarket.com, the world's largest prediction market. https://polymarket.com/event/india-strike-on-pakistan-by?tid=1764041734299 The next DC Forecasting and Prediction Markets Meetup is on Tuesday, Nov 25 from 6-9pm at Rocklands Barbeque and Grilling Company. This month's speaker is John Bennett. John was the co-organizer of the recent Manifest x DC conference that took place earlier this month and will be leading a discussion about what worked well, what didn't, and what we could do to scale next time.  A BBQ buffet and fountain drinks will be provided free of charge to this month's attendees. Alcoholic beverages will be available for purchase. Last-minute/onsite walk-in RSVPs here on this Partiful event page are welcomed! https://partiful.com/e/VoLn8aAh4pabxrxczwiz Who are we? We are prediction market traders on prediction markets like Kalshi, Manifold, PredictIt, and Polymarket, forecasters (e.g. on Metaculus and Good Judgment Open), sports bettors (e.g. on FanDuel, DraftKings, and other sportsbooks), consumers of forecasting (or related) content (e.g. Star Spangled Gamblers, Nate Silver's Silver Bulletin, Scott Alexander's Astral Codex Ten), effective altruists, rationalists, futurists, and data scientists. Forecast on Manifold how many people will attend meetups this year: https://manifold.markets/dglid/how-many-attendees-will-there-be-at?play=true  Help us grow the forecasting community to positively influence the future by supporting us with an upvote, comment, or pledge on Manifund: https://manifund.org/projects/forecasting-meetup-network---washington-dc-pilot-4-meetups Get notified whenever a new meetup is scheduled and learn more about the Forecasting Meetup Network here: https://bit.ly/forecastingmeetupnetwork Join our Discord to connect with others in the community between monthly meetups: https://discord.com/invite/hFn3yukSwv  

Jon enjoys his cough syrup. David checks PredictIt about the government shutdown. Support us on Patreon https://www.patreon.com/electionprofitmakers Send questions and comments to contact@electionprofitmakers.com Watch David's show DICKTOWN on Hulu http://bit.ly/dicktown Follow Jon on Bluesky http://bit.ly/bIuesky

Prediction markets once lived on the academic fringe. Now they're trading billions on politics, sports, and celebrity gossip — under rules never designed for retail gamblers. Originally published on September 16, 2025.

Jeffrey Pritchard, Legal Director of the Coalition for Political Forecasting, analyzes lawsuits about Kalshi's sports contracts and their implications for prediction markets. Rule3O3 discusses Indian-American gender divides and the impact of childhood grievances on politics. Timestamps 0:11: Chougule introduces segment with Pritchard 1:07: Chougule introduces Rule3O3 segment 1:28: Mamdani victory 2:10: Intro ends 4:10: Pritchard segment begins 4:13: Why Kalshi wants to be regulated under federal law 4:41: State regulation 6:34: CFTC 7:24: State compliance costs 7:43: Kalshi's goal 9:09: Liquidity 10:59: Criticisms of Kalshi 11:08: Zubkoff tweet 12:40: Pritchard agreement with Zubkoff 12:54: Contradictions in Kalshi's position 13:41 : Mansour response to Zubkoff 14:37: Pritchard response to Mansour 16:28: Chougule's view of Kalshi sports contracts 18:28: Chougule defends Kalshi 19:46: Market demand for sports betting 20:24: The need to attract sports bettors  21:22: Regulatory environment 22:53: Retail traders 24:01: Gaming industry 29:48: Lawsuits 29:58: Nevada 30:37: New Jersey 31:15: Maryland 31:23: Illinois 31:46: Third Circuit 32:11: Timing 32:24 : Pritchard segment ends 32:39: Rule 3O3 segment begins 32:41: Gender divides among Indian-Americans 32:54: Saira Rao 33:22: White women 35:51: Finding an edge through elite thinking 36:06: Childhood trauma 36:57: Outsider psychology 37:34: Political biographies 38:20: UVA rape accusation 40:31: Crime demographics in mainstream media 42:41: Rule3O3 segment ends 42:57: DC August Forecasting and Prediction Markets meetup Star Spangled Gamblers is a podcast on betting and winning real money on politics. SUPPORT US: Patreon: www.patreon.com/starspangledgamblers FOLLOW US ON TWITTER/X: @ssgamblers VISIT OUR WEBPAGE: www.starspangledgamblers.com  Trade at Polymarket.com, the world's largest prediction market. Join us for our monthly DC Forecasting & Prediction Markets meetup on Thursday, August 14 from 6-9pm. We're returning to Rocklands BBQ in Arlington a few blocks from the Virginia Sq-GMU metrorail stop on the Orange/Silver line. Free parking also available. We'll be in the private space upstairs; head to the back of the restaurant, and up the stairs on your left. Our guest speaker this month is Ambassador Tom Miller. A 29-year career diplomat, Ambassador Miller's experience in the Foreign Service spanned many continents, including posts in Greece, Bosnia-Herzegovina, Cyprus, Thailand as well as the State Department in Washington, where he worked on North Africa, the Middle East, and counter-terrorism issues. From 2019 to 2022, Tom was Chair of the Board of the US subsidiary of Intralot, Inc., a US corporation that runs lotteries in 11 states. Last-minute/onsite walk-in RSVPs here on this Partiful event page are welcomed! https://partiful.com/e/2VIW9cQaw6pexbaQSmUh?f=1&photo=all Who are we? We are prediction market traders on prediction markets like Kalshi, Manifold, PredictIt, and Polymarket, forecasters (e.g. on Metaculus and Good Judgment Open), sports bettors (e.g. on FanDuel, DraftKings, and other sportsbooks), consumers of forecasting (or related) content (e.g. Star Spangled Gamblers, Nate Silver's Silver Bulletin, Scott Alexander's Astral Codex Ten), effective altruists, rationalists, futurists, and data scientists. Forecast on Manifold how many people will attend meetups this year: https://manifold.markets/dglid/how-many-attendees-will-there-be-at?play=true This meetup is hosted by the Forecasting Meetup Network. Help us grow the forecasting community to positively influence the future by supporting us with an upvote, comment, or pledge on Manifund: https://manifund.org/projects/forecasting-meetup-network---washington-dc-pilot-4-meetups Get notified whenever a new meetup is scheduled and learn more about the Forecasting Meetup Network here: https://bit.ly/forecastingmeetupnetwork Join our Discord to connect with others in the community between monthly meetups: https://discord.com/invite/hFn3yukSwv

Jeffrey Pritchard, attorney and writer at Comped.com, returns to discuss developments in prediction market regulation under the Trump administration. Timestamps 0:00: Intro begins 0:37: CFTC prediction market roundtable 1:40: Polymarket investigation 3:02: Regulatory entrepreneurship 5:43: Intro ends 7:43: Interview begins 8:16: Comped.com 9:51: Trump administration 11:41: Quintenz 14:34: Pham 15:15: Kalshi's strategy 18:27: Prediction market roundtable 20:26: Gaming law 26:35: Reaction to Kalshi sports markets 31:53: Kalshi lawsuits 32:51: CEA 34:45: Federalism 37:03: Injunctions 38:21: Maryland case Follow Star Spangled Gamblers Twitter: @ssgamblers YouTube: https://www.youtube.com/@starspangledgamblers1029 TikTok: https://www.tiktok.com/@starspangledgambl7 Trade on Polymarket.com, the world's largest prediction market. Join us for our monthly DC Forecasting & Prediction Markets meetup on Thursday, July 31. https://partiful.com/e/NIWa277GHtddC5sxSTU6 Our guest speaker this month will be former U.S. diplomat Thomas Miller, who previously served as the non-executive chairman of the U.S. subsidiary of Intralot, one of world's largest lottery/sports betting operators. Meet and socialize with others interested in forecasting, prediction markets, political gambling, sports betting, or anything else relating to predicting the future. We're returning to Rocklands BBQ in Arlington a few blocks from the Virginia Sq-GMU metrorail stop on the Orange/Silver line. Free parking also available. We'll be in the private space upstairs; head to the back of the restaurant, and up the stairs on your left. Food and drink available for purchase. Open to all ages. Last-minute/onsite walk-in RSVPs here on this Partiful event page are welcomed! Who are we? We are prediction market traders on Manifold (and other prediction markets like PredictIt, Kalshi, and Polymarket), forecasters (e.g. on Metaculus and Good Judgment Open), sports bettors (e.g. on FanDuel, DraftKings, and other sportsbooks), consumers of forecasting (or related) content (e.g. Star Spangled Gamblers, Nate Silver's Silver Bulletin, Scott Alexander's Astral Codex Ten), effective altruists, rationalists, and data scientists. Forecast on Manifold how many people will attend meetups this year: https://manifold.markets/dglid/how-many-attendees-will-there-be-at?play=true This meetup is hosted by the Forecasting Meetup Network. Help us grow the forecasting community to positively influence the future by supporting us with an upvote, comment, or pledge on Manifund: https://manifund.org/projects/forecasting-meetup-network---washington-dc-pilot-4-meetups Get notified whenever a new meetup is scheduled and learn more about the Forecasting Meetup Network here: https://bit.ly/forecastingmeetupnetwork Join our Discord to connect with others in the community between monthly meetups: https://discord.com/invite/hFn3yukSwv

Jacky Pritchard, political bettor and former NY state lawyer, explains how Andrew Cuomo emerged as the frontrunning for New York City mayor. Timestamps 0:17: Jacky Pritchard 0:55: Pratik's impressions from Queens 2:44: Follow @iabvek and @politicalkiwi 3:17: Intro ends 5:17: Interview begins 6:00: How Jacky became interested in political betting 8:31: Jacky's legal background 9:52: How Cuomo made a comeback 22:46: Cuomo's scandals 26:10: Cuomo's legal problems 27:01: #metoo movement 30:34: Eric Adams 33:46: Cuomo as frontrunner 34:32: DoJ investigation against Cuomo 36:10: Zohran Mamdani 37:07: Ranked choice voting 38:41: Margin of victory 40:39: Number of RCV rounds 41:10: End of interview with Jacky 41:24: DC Forecasting and Prediction Markets Meetup 42:43: Forecasting meetups Follow Star Spangled Gamblers on Twitter/X: @ssgamblers Trade on the NYC Mayor's race at Polymarket.com, the world's largest prediction market. The next Washington DC Forecasting and Prediction Markets meetup is on Wednesday, June 25 from 6-9pm. Guest speaker: Robin Hanson. Details here: https://partiful.com/e/50RbhKj6jsiww1A8QKYO  

Domer is a prediction market trader, and is one of the biggest bettors on Polymarket. He talks about his journey from Intrade, to Predictit, to Polymarket. He also talks about the 2024 election, which was a rollercoaster. It featured highs - like backing Kamala Harris before President Joe Biden had even dropped out of the race - and also lows, like being short Trump on election night.Domer also talks about unraveling the mystery of the so-called French Whale.Domer on Polymarket: https://polymarket.com/profile/0x9d84ce0306f8551e02efef1680475fc0f1dc1344Domer on Twitter: https://x.com/DomahhhhSupport the show by subscribing on Substack: https://riskofruinpod.substack.comFollow the show on Twitter: https://x.com/halfkelly

In 2016, Blitz (@blizzythegoat24) bet on Donald Trump to win the general election. In 2020, Blitz not only bet on Biden to win the election, he guessed the outcome of every state correctly. In 2024, he managed to do the same. He bet on Trump to win the election and guessed every state correctly. In this episode, Blitz explains for the first time how he did it. Timestamps 0:40: Blitz's achievement 3:25: Intro ends 5:25: Interview begins 6:00: Blitz's background 7:16: Blitz's bad start on PredictIt 7:59: Tweet markets 11:00: John Phillips's defense of tweet markets 13:43: Andrew Yang death threats 18:01: Trump VP pick in 2016 19:57: Blitz's methodology in 2024 26:26: Blue wall 29:52: Florida early vote 33:34: Nevada early turnout 37:50: Georgia 38:08: 2020 41:18: Women/abortion 42:01: Black voters 44:26: Polls 52:21: Concerns about democracy 1:01:30: Political bias Follow Star Spangled Gamblers on Twitter/x @pjchougule Trade on tweet markets and many more at Polymarket.com, the world's largest prediction market. Forecasting Meetup Network. Help us grow the forecasting community to positively influence the future by supporting us with an upvote, comment, or pledge on Manifund: https://manifund.org/projects/forecasting-meetup-network---washington-dc-pilot-4-meetups Get notified whenever a new meetup is scheduled and learn more about the Forecasting Meetup Network here: https://bit.ly/forecastingmeetupnetwork Join our Discord to connect with others in the community between monthly meetups: https://discord.com/invite/hFn3yukSwv

Rutgers statistician Harry Crane critiques Nate Silver's model. Timestamps 1:49: Intro ends 3:55: Interview begins 4:08: Crane's bio 8:50: Nate Silver 10:42: Critique of Silver's model 12:42: Silver's criticism of prediction markets 23:57: Silver as tout 25:04: Silver's model 27:48: Simulations in Silver's model 30:51: 50-50 election forecasts Follow Star Spangled Gamblers on Twitter/X @ssgamblers Bet on elections at Polymarket.com, the world's largest prediction market. Attend our forecasting and prediction markets meetup in Washington DC on 23 January 2025. RSVP here: https://partiful.com/e/tgYHaoevQ78X7hpnDUHA

Dr. Cruse (@predoctit) argues that Pete Hegseth, Trump's nominee for Secretary of Defense, is much less likely to get confirmed than the current markets prices indicate. Dr. Cruse and Pratik Chougule also discuss the universe of Republican senators who are willing to vote against Trump nominees. Timestamps 0:00: Pratik introduces episode 0:11: Thune whip count on Hegseth 8:05: Intro ends 10:06: Interview with Cruse begins 10:42: Trump nominees' confirmation prospects 11:24: Democratic Senators 12:11: Rubio 15:44: Most controversial nominees 16:27: Hegseth scandals 31:14: Factors in likelihood of confirmation 33:46: Republican Senators 46:41: Influence of Hegseth markets 46:56: Sexual harrassment allegations Follow Star Spangled Gamblers on Twitter @ssgamblers  Trade on Hegseth's nomination at Polymarket.com, the world's largest prediction market. https://polymarket.com/event/of-senate-votes-to-confirm-hegseth-as-secretary-of-defense?tid=1736804670342 https://polymarket.com/event/which-trump-picks-will-be-confirmed/will-pete-hegseth-be-confirmed-as-secretary-of-defense?tid=1736804692254 https://polymarket.com/event/who-will-be-trumps-defense-secretary/will-pete-hegseth-be-trumps-defense-secretary?tid=1736804733018 Join us for our first DC Forecasting & Prediction Markets meetup of the year! This will be a very casual meetup to meet and socialize with others interested in forecasting, prediction markets, political gambling, sports betting, or anything else relating to predicting the future. Location is TBD but you'll be notified when we've finalized a venue. Last-minute/onsite walk-in RSVPs here on this Partiful event page are welcomed! Who are we? We are prediction market traders on Manifold (and other prediction markets like PredictIt, Kalshi, and Polymarket), forecasters (e.g. on Metaculus and Good Judgment Open), sports bettors (e.g. on FanDuel, DraftKings, and other sportsbooks), consumers of forecasting (or related) content (e.g. Star Spangled Gamblers, Nate Silver's Silver Bulletin, Scott Alexander's Astral Codex Ten), effective altruists, rationalists, and data scientists. Forecast on Manifold how many people will attend this month: https://manifold.markets/dglid/how-many-people-will-attend-a-forec-OzPZILyc5C?play=true Forecast on Manifold how many people will attend meetups this year: https://manifold.markets/dglid/how-many-attendees-will-there-be-at?play=true This meetup is hosted by the Forecasting Meetup Network. Help us grow the forecasting community to positively influence the future by supporting us with an upvote, comment, or pledge on Manifund: https://manifund.org/projects/forecasting-meetup-network---washington-dc-pilot-4-meetups Get notified whenever a new meetup is scheduled and learn more about the Forecasting Meetup Network here: https://bit.ly/forecastingmeetupnetwork Join our Discord to connect with others in the community between monthly meetups: https://discord.com/invite/hFn3yukSwv

Dr. Cruse (@predoctit) returns to discuss the strategy behind Trump's nominations and administration appointments. Timestamps 0:08: Pratik introduces segment on Trump appointments 1:18: Call to support SSG 2:16: Golden Modelos 2:58: Intro ends 4:59: Trump segment begins 7:12: Trump's strategy 7:35: 4-D Chess? 25:45: Sarah Palin 33:02: Golden Modelos Worst Trade 39:03: Golden Modelos Best Fight Bet on Trump administration appointments at Polymarket.com, the world's largest prediction market. Forecasting Meetup Network. Help us grow the forecasting community to positively influence the future by supporting us with an upvote, comment, or pledge on Manifund: https://manifund.org/projects/forecasting-meetup-network---washington-dc-pilot-4-meetups Get notified whenever a new meetup is scheduled and learn more about the Forecasting Meetup Network here: https://bit.ly/forecastingmeetupnetwork Join our Discord to connect with others in the community between monthly meetups: https://discord.com/invite/hFn3yukSwv

Learn how prediction markets work, the legal gray areas in which they operate, and how they could be regulated in the future. What are prediction markets like PredictIt, Polymarket and Kalshi, and how do they work? Is it legal to bet on elections in the United States? Hosts Tess Vigeland and Anna Helhoski welcome Sam Taube, the writer of the Nerdy Investor email newsletter, to break down how event contracts operate, explore the legal gray areas of election betting, and discuss whether prediction markets are a smart financial move—or just gambling in disguise. Then, Tess and Anna break down this week's money headlines, including the latest inflation figures and what they mean for interest rates, the CFPB's plan to enforce new click-to-cancel subscription rules, and Spirit Airlines' Chapter 11 bankruptcy filing. In this episode, the Nerds discuss: how prediction markets work, betting on elections, event contracts explained, investing vs gambling, election betting legality, gambling vs investing, Commodity Futures Trading Commission, event contracts legality, prediction market regulation, prediction markets news, event contracts explained simply, and Consumer Price Index. To send the Nerds your money questions, call or text the Nerd hotline at 901-730-6373 or email podcast@nerdwallet.com. Like what you hear? Please leave us a review and tell a friend.

Pratik Chougule and Mick Bransfield discuss why the courts are allowing Kalshi to offer election markets for now, and provide an update on the litigation between Kalshi and the CFTC. Timestamps  0:00: Introduction begins 0:20: 2024 elections and prediction markets 0:31: Victory for Polymarket 1:53: Kalshi legal case 7:09: Interview with Bransfield begins 7:28: Bransfield joins Coalition for Political Forecasting 8:06: District court case 10:37: Gaming 15:45: Jia Cobb's ruling 18:17: DC Circuit appeal 18:30: DC Circuit court judges 30:43: Perception of harm from election contracts 33:07: Foreign prediction markets 35:27: Behnam comments on UK betting scandal 36:33: Kid Rock manipulation anecdote 38:33: Manipulation in prediction markets Follow Star Spangled Gamblers on Twitter @ssgamblers Trade on Polymarket.com, the world's largest prediction market. Sign up for the Forecasting Meetup Network: https://forecastingmeetupnetwork.kit.com/eb6374e5e8

On the latest episode of Two Think Minimum, TPI hosts Tom Lenard, Sarah Oh Lam, and Scott Wallsten explore the world of polls and prediction markets with Aristotle CEO John Phillips and General Counsel David Mason. Aristotle helps run PredictIt, a platform which enables research into how markets can forecast events in real-time. The conversation covers how PredictIt is navigating CFTC regulation, the broad value of small-dollar prediction markets to understanding public opinion and risk forecasting, and how PredictIt determines which questions to create contracts for. This episode offers valuable insights for anyone interested in the intersection of market dynamics, public opinion, and data-driven insights.

In the week leading up to election day, presidential election contracts on Polymarket, Kalshi, and PredictIt all indicated Donald Trump had a wider lead ahead of Kamala Harris than traditional polls. In his first-ever TV interview, Polymarket CEO Shayne Coplan discusses his company's role in elections and the rising popularity of prediction markets, which allow bettors to wager on election outcomes. The Wall Street Journal's Tim Higgins highlights Elon Musk's influence among young men, saying Musk gave them “purpose” in voting for Trump. Plus, the House race remains uncalled, smart ring maker Oura is out with a new report on election day stress levels, and Wall Street awaits the Federal Reserve's next rate cut decision. Shayne Coplan - 14:51Tim Higgins - 32:15 In this episode: Shayne Coplan, @shayne_coplanTim Higgins, @timkhigginsBecky Quick, @BeckyQuickJoe Kernen, @JoeSquawkAndrew Ross Sorkin, @andrewrsorkinCameron Costa, @CameronCostaNY

APAC stocks began the week mostly positive but with the gains capped ahead of this week's major risk events including the US Presidential Election.PredictIt odds shifted over the weekend in favour of a Harris election victory; NYT/Siena final polls showed the race was deadlocked in 6/7 battleground states.European equity futures are indicative of a steady cash open with the Euro Stoxx 50 future +0.1% after the cash market closed higher by 1.0% on Friday.DXY is softer vs. peers in a scaling back of the USD supportive "Trump trade"; JPY and antipodeans have been the main beneficiaries.OPEC+ agreed to delay the December oil output increase by one month, according to a Reuters source.Looking ahead, highlights include EZ Sentix Index, Manufacturing PMIs, US Employment Trends, US Durable Goods, Australian PMIs (Final), Comments from ECB's Elderson, Supply from EU & US.Earnings from Volvo Car AB, Ryanair, Kingspan, Fidelity National Information Services, NXP Semiconductors, Vertex Pharmaceuticals, Diamondback Energy, Palantir Technologies, Marriott International & Fox.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

European bourses generally trade very modestly in positive territory, alongside slight gains in US futures.PredictIt odds shifted over the weekend in favour of a Harris election victory; NYT/Siena final polls showed the race was deadlocked in 6/7 battleground states.USD is on the backfoot as a shift in polling in the US election towards Harris has seen a scaling back of "Trump trades" across the board.USTs benefit from a scaling back of the Trump trade, whilst Bunds lag; reports suggesting that China's NPC is reviewing local government debt swaps weighed on the complex.Crude benefits amid reports that OPEC+ agreed to delay the December oil output increase by one month; base metals move higher in anticipation of the ongoing China NPC meeting.Looking ahead, US Employment Trends, Durable Goods, Australian PMIs (Final), Comments from ECB's Elderson, Holzmann, Supply from the US. Earnings from Fidelity National Information Services, NXP Semiconductors, Vertex Pharmaceuticals, Diamondback Energy, Palantir Technologies, Marriott International & Fox.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

Jon canvasses with his mom. David makes his final PredictIt trade. Support us on Patreon at http://patreon.com/electionprofitmakers Send your election prediction questions to contact@electionprofitmakers.com Watch David's show DICKTOWN on Hulu http://bit.ly/dicktown

This week we talk about DJT, Polymarket, and Kalshi.We also discuss sports betting, gambling, and PredictIt.Recommended Book: Build, Baby, Build by Bryan CaplanTranscriptTrump Media & Technology Group, which trades under the stock ticker DJT, has seen some wild swings since it became a publicly tradable business entity in late-March of 2024.The Florida-based holding company for Truth Social, a Twitter-clone that was released in early 2022 following former President Donald Trump's ousting from Twitter—that ousting the result of his denial of his loss in the 2020 presidential election—is a bit of an odd-bird in the technology and media space, as while it's ostensibly an umbrella corporation for many possible Trump-themed business entities, Truth Social is the only one that's gotten off the ground so far, and that platform hasn't done well in traditional business or even aspirational tech-business terms: a financial disclosure in November of 2023 indicated that the network had tallied a cumulative loss of at least $31.5 million since it was launched, and the holding company's numbers were even worse: when they filed their regulatory paperwork in March of 2024, they noted that Trump Media & Technology Group had lost $327.6 million, while making a mere $770,000 in revenue.Those kinds of numbers, the company hemorrhaging money, would be a huge problem if DJT was a typical media business, or business of any kind, really. But for most people who invest in the company's stock, this entity seems to be less a traditional stock holding, like you might buy shares of NVIDIA or Coca-Cola, hoping to earn dividends or see the value of the stock increase over time based on the performance and assumed future performance of the company in question, but instead it seems to operate as a means of betting on Trump and his political aspirations: many people who have been asked why they're buying the stock of a clearly fumbling company say that they do it because they like Trump and what he stands for, and some have suggested they assume the stock will do much better if and when he's back in office.Other entities, especially those who oppose Trump and his politics, have pointed out that this publicly traded business provides foreign and US entities an easy, and easily deniable means of basically bribing Trump—or getting on his good side, if you want to use less charged language—as they could simply, and legally pick up a large number of shares, raising the price of the stock, which in turn increases the size of Trump's fortune, which he could then, if he so chooses, cash out of at some point, but in the mean time this allows him to do the more typical rich person thing and just borrow money against the non-money, stock assets he owns.All of which would be difficult to prove, which is part of why this would, in theory, be an excellent means of funneling money to someone who might hold the reins of power in the near-future, if one were so inclined to do so.But at the moment that's all speculation, and with ongoing investigations into other purported bribery schemes on the part of Trump and his campaign, it's not clear that Trump would need DJT in order to get money into his coffers, as more direct approaches—like simply depositing ten million dollars into his campaign account from Egypt's state-run bank, seem more straightforward, and just as unlikely to result in any kind of pushback from the US's oversight panels, based on how they've addressed that particular accusation so far, at least.Of course, some people are simply looking for points of leverage anywhere they can find it, not for political or regulatory manipulation purposes, but to earn money by gambling on assets that change value in dramatic and seemingly predictable ways.For day traders and other arbitrage-seekers, then, a stock that goes up and down based on the perceived successes and failures of a public figure who's constantly saying and doing things that can be construed in different ways by different people is an appealing target, even lacking a political motivation for tracking (and perhaps even influencing, to a limited degree) those numbers.What I'd like to talk about today is another type of political betting, and how a recent court case may make politics in the US a lot more tumultuous, maybe more measurable, and possibly more profitable, for some.—In mid-2021, a New York-based online prediction market called Kalshi launched in the US, and this service was meant to serve as a platform through which users could place bets—in the form of trades—on all sorts of things, ranging from when the Fed would next cut interest rates, and by how much, to who would win various global awards, like the Nobel in chemistry.Bets can only be placed on yes or no questions, which shapes the nature of said questions, and delineates the sorts of questions that can be asked, and in general the platform pays out a dollar for each winning contract—so if you buy one contract saying the Republican party will control the House after November's election, and they do, you would win a dollar, but if they don't, you would lose whatever money you spent to buy that contract—and these contracts can be purchased for sums that are based on how likely the event is currently expected to be: so if there's a low chance, based on all available variables, that the Republicans will take the House, that contract might cost substantially less than a dollar to purchase, whereas if it's likely they'll take it, it would cost close to a dollar—so the payout is larger for events considered to be unlikely.The original idea behind Kalshi, and similar platforms, of which there are many, operating in many different places around the world, was to provide investors with a hedge against events that are otherwise difficult to work into one's asset portfolio.It's relatively simple to have a bunch of bets that will pay out big time if the US economy does well, for instance, and simple enough to buy counter-bets that will pay out decently well if it does badly—many investors buying some of each, so they're not wiped out, no matter what happens—but there are all sorts of things that can mess with one's otherwise well-balanced investment strategies, like the emergence of global pandemics and the surprise decision of the UK to leave the European Union.If you can place bets that will pay out big-time when unlikely things happen, though, that can help re-balance a financial loss that arises from the occurrence of said unlikely events; if you lose a bunch of money from your stock portfolio because the UK voted for Brexit, but you also bought a bunch of contracts on this kind of market that would pay out substantially if Brexit was successful, you'll reach a kind of equilibrium that isn't as simple to achieve using other markets, because of how difficult it can be to directly link a stock or bond with that kind of not-directly-financial event.So Kalshi pitched itself as that kind of alternative asset market, predicated on bets, but while they had a license from the US Commodities Futures Trading Commission, or CFTC, to function as a contract market in the States, acquired the year before they launched, their proposal to start a political prediction market, which would allow folks to bet on which party would control the US congress, was denied by the CFTC in September of 2023, the agency claiming that allowing such bets would create bad incentives in the electoral process, and that offering these sorts of contracts would violate US market regulations for derivatives.A judge ruled in Kalshi's favor a year later, in September of 2024, saying that the agency had exceeded its authority in banning this type of contract-issuance by Kalshi, and while the CFTC attempted to stall that component of their market's implementation, on October 2 of this year, a federal appeals court ruled in Kalshi's favor, and the platform was thus formally allowed to offer contracts that served as a betting market for US politics on which actual money could be lost and earned.That last point is important, as throughout this process, and even before Kalshi was launched, other betting markets have been common, including those that have allowed bets on US political happenings.It's just that the majority of them, and the ones that have persisted and grown in the US in particular, haven't allowed folks to bet actual money on these things: they've allowed, in some cases, the betting of on-platform tokens, which represent credibility, not money, though a few money-trading entities, like PredictIt, have been on the agency's radar, but in PredictiIt's case, it was granted what amounts to a “we won't take action against you, despite what you're doing being questionable” letter from the CFTC, which until Kalshi's case turned out in their favor, meant PredictIt was one of the few, large-scale, reputable real-money political prediction markets available in the US.Not all such markets have been so lucky, but that luck has been highly correlated with their approach to handling money, the structure of the company, and the degree to which they've been willing to play ball with the CFTC and other interested agencies.All that said, we've reached an interesting point in which these markets have conceivably become more serious and useful, because rather than relying on not-real tokens that have no actual value to anyone—so you could create an account on one of these sites, bet all your tokens on a silly position that makes no sense, and suffer no consequences for that bet—we now have platforms that allow folks to put their money where their beliefs are, which in turn should theoretically make these markets more reliable in terms of showing what a certain segment of the population actually believes; how likely different candidates are to win, different parties are to hold Congress, and how likely various bills are to be passed into law.Interestingly, though, that theory may already be destined for the dustbin, as one of the larger betting platforms, Polymarket—which allows folks to place bets on all sorts of things using a crypto asset called USDC, and which isn't regulated by the CFTC because its operations are not based in the US—is experiencing what looks like market manipulation, possibly meant to sway poll forecasts that take these sorts of markets into account.What that means in practice is that of the nearly $2 billion in bets that have been placed on the outcome of the upcoming US presidential election on Polymarket, as of the day I'm recording this, about $30 million seems to have been recently bet by just four accounts, all of which have behaved so similarly that a report from the Wall Street Journal posits that they might be the same person, or a collection of people operating alongside each other.In any case, the net-impact of this investment, which landed in late-October, was to bump Trump's odds of winning to 60% from where it was previously, at 53.3%.There's a chance, of course, that this is just the result of a person or some people with money wanting to earn what they consider to be an easy buck, betting on the candidate they think is most likely to win, and there's also a chance that they're plowing that money into this bet in order to show support for their favored candidate.But there's also a chance that this is the first example, at this scale at least, of betting market manipulation that's sizable enough to shift the balance of polls that take betting market numbers into consideration.Some of the poll predictions you in see in the news work these numbers from these betting markets into their formulae alongside the findings of more conventional polling entities, basically, so if you have tens of millions of dollars to throw into this kind of market, you can bump your favored candidate's seeming chances significantly higher, which then in turn can make it seem like that candidate has achieved a surge in support more broadly—despite that seeming support actually just having been bought and paid for by one or a few enthused supporters on this kind of market.So if it does turn out that this is a conscious effort on someone's part to shift perceptions of the election—maybe big-time Trump fans, maybe someone affiliated with him or one of the PACs trying to get him elected—that could be a big deal, especially considering that Trump and his people have said that they won't accept the outcome of the election if they don't win, and if they can show strong expectations, or seeming expectations in the shape of favorable poll numbers that their candidate was meant to win, that could be a point of seeming evidence in favor of their argument that there was voter manipulation by their opponent; this of course wouldn't be the case, but because of how the news, and even more so social media platforms, sometimes present superficial versions of what's actually happening, seeing the candidate who had 60% support lose could seem like a valid argument at a highly charged post-election moment, despite all the other evidence to the contrary.One more important point to make here is that election markets don't actually represent probabilities—they represent a relatively small population of people's expectations or hopes about what will happen.It's in the interest of these markets to imply that there's substantial meaning and real-deal data in their numbers, but that's mostly marketing copy to try to get more people involved; at the end of the day, these markets are often wrong, are populated by outliers who don't represent the voting public, and in many cases they're heavily biased in all sorts of directions—some of them more popular with folks on the left, some more popular with folks on the right, and some more popular with folks who just love making big bets that feel like gambling, and in some cases creating chaos or funny outcomes just for laughs.On that final point, it's worth mentioning that sports gambling has recently become legal, to some degree at least, across much of the United States, and this has already become a huge industry, representing an expected $14.3 billion in 2024, alone, with an anticipated annual growth of something like 10%, which is astonishing for something that was mostly illegal until just recently—the Supreme Court decision that paved the way for it as a nation-spanning market was only made in 2018.So there's a chance that these prediction markets will boom, as there's clearly an appetite for betting on stuff in the US, as a form of entertainment, as a means to try to get ahead, and potentially as a way to put one's money where one's mouth is.Though all of these incentives and purposes could potentially make these markets less valuable for political researchers hoping to better understand odds, as the incentives may or may not align with those that lead to more accurate predictions, and there's no way to really know how those post-money-injection numbers will align with actual voting tallies, or fail to do so, until we have more data about this and other near-future elections' outcomes.Show Noteshttps://www.wsj.com/finance/investing/how-investors-are-betting-on-the-election-from-utility-stocks-to-djt-c2b9e838https://www.yahoo.com/news/hes-sale-trump-djt-stock-001901595.htmlhttps://www.cnbc.com/2024/09/03/trump-egypt-democrats-letter.htmlhttps://en.wikipedia.org/wiki/Truth_Socialhttps://www.axios.com/2024/09/10/prediction-markets-electionhttps://stanfordreview.org/kalshis-court-victory-a-turning-point-for-prediction-markets-2/https://www.politico.com/news/2024/10/04/harris-trump-election-betting-00182432https://en.wikipedia.org/wiki/Prediction_markethttps://www.investopedia.com/terms/p/prediction-market.asphttps://www.axios.com/2024/09/16/prediction-markets-electionhttps://asteriskmag.com/issues/05/prediction-markets-have-an-elections-problem-jeremiah-johnsonhttps://www.chapman.edu/esi/wp/porter_affectingpolicymanipulatingpredictionmarkets.pdfhttps://www.ft.com/content/82199ea0-9707-4d37-b4c4-b65a65d17ecbhttps://worksinprogress.co/issue/why-prediction-markets-arent-popular/https://www.wsj.com/finance/betting-election-pro-trump-ad74aa71https://www.washingtonpost.com/technology/2024/10/19/election-betting-trump-harris-odds-polymarket-predictit/https://www.wsj.com/finance/investing/how-investors-are-betting-on-the-election-from-utility-stocks-to-djt-c2b9e838https://www.wsj.com/livecoverage/stock-market-today-dow-sp500-nasdaq-live-10-03-2024/card/betting-markets-on-the-presidential-race-set-to-go-live-NnRne85QCyVAnc9nZy8zhttps://www.wsj.com/finance/regulation/are-you-ready-to-bet-on-u-s-elections-a-judges-ruling-opens-the-door-556abc73https://en.wikipedia.org/wiki/Kalshihttps://www.coindesk.com/policy/2024/09/13/kalshis-new-political-prediction-markets-halted-as-cftc-appeals-loss/https://www.brookings.edu/articles/how-betting-platform-predictits-legal-struggle-could-hamper-regulators-and-hurt-regulated-firms/https://www.wsj.com/finance/betting-election-pro-trump-ad74aa71https://en.wikipedia.org/wiki/Polymarkethttps://www.statista.com/outlook/amo/online-gambling/online-sports-betting/united-states This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe

The Heartland Institute's Donald Kendal, Jim Lakely, and Chris Talgo present episode 469 of the In The Tank Podcast. Over this past week, we witnessed an engineering breakthrough from Elon Musk's SpaceX. The successful launch and recapture of the SpaceX Starship booster marks a pivotal moment in the future of space travel, showcasing advancements in reusability and cost efficiency. SpaceX is paving the way for a new era where space access becomes more routine and affordable, pushing the boundaries of what is possible beyond Earth's orbit. But remember, Elon Musk is frowned upon by the political elite. Instead of commemorating this occasion and encouraging this innovation, government bureaucrats have instead chosen to stifle Musk by denying his request for additional rocket launches. Also, we are another week closer to the election and it appears Kamala Harris is getting increasingly desperate.Elon Musk's Starship BreakthoughSpace - SpaceX plans to catch Starship upper stage with 'chopsticks' in early 2025, Elon Musk sayshttps://www.space.com/spacex-starship-upper-stage-chopstick-catch-elon-muskEconomist - Starship will change what is possible beyond the Earthhttps://www.economist.com/leaders/2024/10/16/starship-will-change-what-is-possible-beyond-the-earthProgressives vs. ProgressNYT - California Rejects Bid for More Frequent SpaceX Launcheshttps://www.nytimes.com/2024/10/12/us/spacex-launch-california.htmlLA Times - SpaceX sues California regulators, alleging anti-Musk bias in rocket rejectionhttps://www.latimes.com/california/story/2024-10-16/spacex-political-bias-coastal-commission-lawsuitKamala's Bret Baier InterviewFox News - VP Harris to sit down hours from now with Bret Baier for first Fox News interviewhttps://www.foxnews.com/politics/vp-harris-sit-down-hours-from-now-bret-baier-first-fox-news-interviewKamala Is Getting DesperateReuters - Harris could join Joe Rogan podcast in hunt for male votes, sources sayhttps://www.reuters.com/world/us/kamala-harris-could-join-podcaster-joe-rogan-an-interview-sources-2024-10-15/Yahoo - Harris-Trump polls tighten, but PredictIt and Polymarket tell a different storyhttps://finance.yahoo.com/news/harris-trump-polls-tighten-predictit-173916471.html

David Glidden (@dglid) draws lessons from the sports betting world for building the forecasting community. Timestamps 0:00: Intro begins 0:09: Saul Munn 1:26: David Glidden 1:52: Washington DC Forecasting and Prediction Markets Meetup 6:54: Interview with Glidden begins 13:32: Importance of building forecasting community 14:39: Effective altruism 22:02: DC Forecasting and Prediction Markets Meetup 24:49: Sports betting 40:53: Repeatable lines in prediction markets Show Notes September 26 DC Forecasting and Prediction Markets Meetup RSVP: https://partiful.com/e/zpObY6EmiQEkgpcJB6Aw DC Forecasting and Prediction Markets Meetup Manifund: https://manifund.org/projects/forecasting-meetup-network---washington-dc-pilot-4-meetups For more information on the meetup, DM David Glidden @dglid. Trade on Polymarket.com, the world's largest prediction market. Follow Star Spangled Gamblers on Twitter @ssgamblers.

Pratik Chougule summarizes the latest developments in the legal battle between Kalshi and the CFTC on election betting. Pratik Chougule and Mick Bransfield do a deep dive into the CFTC's arguments in front of DC federal district court Judge Jia Cobb.  Timestamps 0:00: Pratik introduces segment with Bransfield 1:43: Update on latest in Kalshi-CFTC legal battle 9:34: Bransfield segment begins 11:18: CFTC counsel's poor presentation 20:33: Definition of a contest 21:47: Market manipulation 27:02: Cobb's concerns 27:29: CFTC's arguments about gaming 29:34: Importance of public comments to CFTC 25:38: DC Forecasting and Prediction Markets Meetup Show Notes September 26 DC Forecasting and Prediction Markets Meetup RSVP: https://partiful.com/e/zpObY6EmiQEkgpcJB6Aw DC Forecasting and Prediction Markets Meetup Manifund: https://manifund.org/projects/forecasting-meetup-network---washington-dc-pilot-4-meetups For more information on the meetup, DM David Glidden @dglid. Trade on Polymarket.com, the world's largest prediction market. Follow Star Spangled Gamblers on Twitter @ssgamblers.

Welcome to RBC's Markets in Motion podcast, recorded September 9th, 2024. I'm Lori Calvasina, Head of US Equity Strategy at RBC Capital Markets. Please listen to the end of this podcast for important disclaimers. Three big things you need to know: First, Friday's jobs report added to investors' uncertainty regarding the labor market, but the data point that concerned us from last week was the spike in Tech layoffs in the Challenger report. Second, election uncertainty has persisted with policy getting greater attention from both sides. We run through our US equity market read throughs from Trump's economic speech last week. Third, in our discussion of other updates from our high frequency indicators, we review what we're watching in terms of potential near-term downside levels for the S&P 500, sentiment, and the Semis trade. If you'd like to hear more, here's another 6 minutes. Now, let's jump into the details. Starting with Takeaway #1: Employment Uncertainty Has Grown After Friday's Jobs Report, But The Spike In Tech Layoffs In The Challenger Report Spooked Us The Most Regarding StocksRBC's economics team noted that while the report “doesn't point to a sharp contraction in the labor market, it also gave no indications that the broader cooling trend – which is not welcomed by the Federal Reserve – has in any way run its course.” From our seat in US equity strategy, we generally agree with the idea that the jobs report is still consistent with cooling and normalization as opposed to an economy on the cusp of recession. That being said, we were a little spooked by some of the details in the Challenger layoff report that came out earlier in the week. The overall level of layoffs moved up in August, but remained well below the spikes associated with past recessions, and was even a bit below the moves higher seen in 2023-2024 and 2015. What caught our attention was the spike in layoffs for Technology companies which wasn't as bad as those seen in late 2022 and early 2023, but otherwise rivals some of the worst spikes this industry has seen over time. This primarily worries us in regards to the Tech sector itself and the broader market by way of the rotation trade. Though layoff announcements moved up slightly in a few other industries, those were generally mild relative to history. Moving on to Takeaway #2: Election Uncertainty Persists, With Policy Getting Greater AttentionWe continue to see the US election as a key challenge that the US equity market will need to work through in coming months, due to the uncertainty that the event has injected into the outlook. We do usually see a pullback in the S&P 500 in September and October of Presidential election years, with a rebound afterwards. Thinking about today specifically, a number of companies referred to this idea that the election has injected some uncertainty into the outlook in their recent earnings calls. Meanwhile, Harris has pulled ahead of Trump in the PredictIt betting market and RCP polling average, but the race still looks quite close on these data sets, as well as in the polling for the swing states. We do believe the stock market has been paying attention to the event given the alignment we've continued to see between S&P 500 performance and expectations that Trump will win in betting markets. One of the primary things the stock market cares about regarding the election is domestic policy, and investors have been getting new information on the policy leanings of both Harris and Trump over the past few weeks. In our latest report, we've recapped our early thoughts on the stock market read throughs of Trump's domestic policy agenda as described in his speech to the Economic Club of New York last week. We think it's premature to put on any significant sector or industry trades...

Jon explains negative risk, the safest way to make money on PredictIt. To hear the entire episode, join our Patreon. Thanks! Support us on Patreon at http://patreon.com/electionprofitmakers Send your election prediction questions to contact@electionprofitmakers.com Watch David's show DICKTOWN on Hulu http://bit.ly/dicktown

Who will win the election? What will the vote margin be? Will Donald Trump post on X before November? People can place bets on all these real-world questions — and more — on prediction markets. And these online platforms like PredictIt and Polymarket are increasingly being looked to as crystal balls in this chaotic election, promising real-time political insights and the chance to make a few bucks. Marketplace's Meghan McCarty Carino spoke to Chris Cohen, the deputy site editor of GQ, who recently wrote about his experience getting in on the action of what appears to be a prediction market “gold rush.”

Who will win the election? What will the vote margin be? Will Donald Trump post on X before November? People can place bets on all these real-world questions — and more — on prediction markets. And these online platforms like PredictIt and Polymarket are increasingly being looked to as crystal balls in this chaotic election, promising real-time political insights and the chance to make a few bucks. Marketplace's Meghan McCarty Carino spoke to Chris Cohen, the deputy site editor of GQ, who recently wrote about his experience getting in on the action of what appears to be a prediction market “gold rush.”

Who will win the election? What will the vote margin be? Will Donald Trump post on X before November? People can place bets on all these real-world questions — and more — on prediction markets. And these online platforms like PredictIt and Polymarket are increasingly being looked to as crystal balls in this chaotic election, promising real-time political insights and the chance to make a few bucks. Marketplace's Meghan McCarty Carino spoke to Chris Cohen, the deputy site editor of GQ, who recently wrote about his experience getting in on the action of what appears to be a prediction market “gold rush.”

Part I: Pratik Chougule offers his thoughts on what to say in comments to the CFTC on its event contracts proposal. Part II: Mick Bransfield discusses the federal judge who will decide the lawsuit between Kalshi and the CFTC Timestamps 1:40: How to submit a comment to the CFTC 2:57: How to watch the CFTC Open Meeting on YouTube 3:43: What to write to the CFTC 5:38: The CFTC's goal with its event contracts proposal 7:39: What kind of comments will move the needle 12:36: Questions about enforcement in the proposal 13:44: Political betting moving offshore 15:17: Delaying the rule 17:23: Need for a balancing test 22:36: Interview with Bransfield begins 28:31: Jia Cobb 34:32: Ignorance about prediction markets 38:36: Gambling references on Kalshi's website CFTC Proposal on Event Contracts: https://www.cftc.gov/PressRoom/PressReleases/8907-24 SUPPORT US: Patreon: www.patreon.com/starspangledgamblers FOLLOW US ON SOCIAL: Twitter: www.twitter.com/ssgamblers VISIT OUR WEBPAGE: www.starspangledgamblers.com Trade on Polymarket, the world's largest prediction market, at polymarket.com

Jon talks about the kids today. David refuses to put more money into PredictIt. Support us on Patreon at http://patreon.com/electionprofitmakers Send your election prediction questions to contact@electionprofitmakers.com Watch David's show DICKTOWN on Hulu http://bit.ly/dicktown

David learns about the presidential debate. Jon describes Medieval Manhattan. Support us on Patreon at http://patreon.com/electionprofitmakers Send your election prediction questions to contact@electionprofitmakers.com Watch David's show DICKTOWN on Hulu http://bit.ly/dicktown

Today's PCE report could provide some good news for the Fed. Former Fed Vice Chairman Roger Ferguson previews the data. Plus, shares of Nike are plunging after the company slashed current quarter and full year sales guidance. Oppenheimer's Brian Nagel explains. And, PredictIt polling shows a 53% probability of Donald Trump re-taking the White House in November following last night's first presidential debate. Fordham Global's Tina Fordham discusses.

Part I: Professor Anthony Pickles (@polgambling), an expert on political gambling, does a deep dive into Keir Starmer and precisely how many seats Labour and the Conservatives are likely to win in UK's upcoming elections. Part II: Vegas bookmaker Alex Chan (@ianlazaran) explains how professional bettors are driving irrational odds in the VP markets. Part III: Pratik Chougule (@pjchougule) discusses why opponents of election betting have the upper hand in Washington. Timestamps 0:09: Pratik introduces segment on UK elections 2:01: Pratik introduces segment with Alex Chan on VP odds 3:17: Pratik introduces segment on event contract regulation 5:55: Segment on UK elections begins 6:12: Anthony's background 8:17: Keir Starmer's background 10:16: Starmer's odds of becoming Prime Minister 11:01: How many seats Conservatives and Labour will win 22:04: Why Sunak and the Conservatives are unlikely to make a comeback 28:37: Why Sunak called an early election 30:54: Labour seat totals 34:03: Right-wing bias in the markets? 36:59: Discussion of class politics 39:27: Segment with Chan begins 39:46: VP odds on Tulsi Gabbard and Vivek Ramaswamy 40:48: "Juice" in sportsbooks 41:46: Regulation segment begins 42:56: Incentives in the current CFTC rulemaking 45:00: Need for a counter-lobby on event contracts Follow Star Spangled Gamblers on Twitter: @ssgamblers Trade on Polymarket, the world's largest prediction market, at Polymarket.com

Pratik Chougule previews Manifest 2024. Ben Freeman (@benwfreeman1) and Alex Chan (@ianlazaran) negotiate a side bet on whether Trump will select Tim Scott, Doug Burgum, or Marco Rubio as his running mate. They discuss how Trump will make the decision and when he'll announce it.  Timestamps 0:09: Pratik introduces segment on Trump VP selection 1:44: Manifest 2024 7:18: Episode on VP nomination begins 7:43: What are SSG Title Belt Championships? 9:37: How Ben Freeman won the SSG Title Belt 11:56: Ben's proposed side bet on Trump's VP selection 13:36: Alex Chan's background 15:38: OpenBet/DonBest 19:26: How Alex got into political gambling 20:49: Political nerds vs. professional gamblers 22:36: Ben and Alex negotiate theie side bet 27:09: How Trump will select his VP 30:10: Loyalty considerations 32:21: Trump's strength in GOP 34:48: Will Trump penalize those who ran against him? 35:32: When will Trump announce the pick? 36:38: How deep is Trump's VP bench? 40:09: Has Trump already decided? Follow SSG on Twitter: @ssgamblers Trade on Polymarket, the world's largest prediction market, at polymarket.com Attend Manifest, a festival celebrating predictions, markets, and mechanisms, hosted by Manifold Markets. June 7-9 at Lighthaven Campus, Berkeley, CA. Tickets are available at https://www.manifest.is/#tickets. Use the discount code SSG10 to get 10% of the ticket price.

Jon reaches out to PredictIt. David wakes up defeated. Support us on Patreon at http://patreon.com/electionprofitmakers Send your election prediction questions to contact@electionprofitmakers.com Watch David's show DICKTOWN on Hulu http://bit.ly/dicktown

Every year, Star Spangled Gamblers hosts the Golden Modelos—an awards show for the best and worst of political gambling in the previous year. Abhi Kylasa (AENews) and Vanilla Vice return to the show to discuss which nominees should make the ballot. Timestamps 0:00: Pratik introduces the Golden Modelos and why they matter 5:56: Vice introduces the Golden Modelos 6:53: Pratik explains the Golden Modelos process 8:33: Best Market 9:27: Room temperature superconductor 10:15: Will 2023 be the hottest year market 11:23: Best Trade 11:27: Bonding the Bitcoin ETF market 12:33: Domer buying Ramaswamy at 500-1 12:49: Ian Bezek recommending buying Javier Milei 13:05: Gaeten Dugas buying Taylor Swift to be number one song 13:12: Domer debt limit profits 13:17: Worst Trade 14:19: Mr. Beast subscriber count 14:44: MagaVacuum side betting that DeSantis won't run for president 15:05: Polymarket user losing $100k on Trump reinstatement 15:21: Abe Kurland side bets on Ramaswamy 17:21: Best Shitposter 19:06: Domer's shitposting 19:56: RelayThief's shitposting 20:44: Rookie of the Year 21:12: Naman Mehndiratta 22:39: Manifold Markets 23:20: Betting platforms 23:48: TheWinner 25:29: Trader of the Year 25:46: ANoland 26:07: Gaeten Dugas 27:59: Jonathan Zubkoff (ZubbyBadger) 28:33: Doug Campbell 29:05: Worst Pump 31:04: Kalshi election contracts 31:15: Hamas control of Gaza 32:00: Trump third indictment  32:44: Semiconductor yes holders 33:40: RFK Democratic nominee 33:46: AI to win Time Person of the Year 34:22: Best News Source 34:41: Politico Punchbowl 35:00: PredictIt comments 35:28: The Information's coverage of OpenAI 35:50: RacetotheWH by Logan Phillips 37:29: CSP Discord 38:36: Service to Political Gambling 38:36: PredictIt 39:50: Biggest Rules Cuck 39:56: Government Shutdown 40:33: Lower case "trump" versus upper case "Trump" 41:23: "widespread flooding" in Los Angeles 42:19: submarine debris 43:04: Trump indictment on March 31 43:48: U.S. rescue of Hamas hostages 44:19: Biggest Rules Dispute 44:50: Did Israel have advanced knowledge of Hamas attack 45:59: Postscript 46:17: Abe Kurland's response to Worst Bet nomination 47:53: CSP vs. CatClan Discords Trade on Polymarket, the world's largest prediction market at polymarket.com  Follow SSG on Twitter @ssgamblers

A PredictIt market finally reacts to a piece of news. Support us on Patreon at http://patreon.com/electionprofitmakers Send your election prediction questions to contact@electionprofitmakers.com Watch David's show DICKTOWN on Hulu http://bit.ly/dicktown

In July of 2023, the Fifth Circuit reversed the district court's decision in Clarke v. CFTC, and remanded with instructions to enter a preliminary injunction against the Commodity Futures Trading Commission. The case is one concerning the CFTC's revocation of its "no-action letter" concerning PredictIt Market. PredictIt Market is an online marketplace for people to trade contracts predicting important political events, started as a research tool by Victoria University of Wellington in New Zealand. Before going into operation, PredictIt sought a "no-action letter" from the CFTC to operate in the US without registering under the Commodity Exchange Act as a designated contract market, which the CFTC issued in 2014.However, in August 2022, the CFTC withdrew the letter and issued notice to PredictIt to cease operations within 6 months, which led to suit being filed by supporters of PredictIt. Questions included whether the revocation was arbitrary and capricious, whether the letter constituted "final action" on the part of the agency, and whether the plaintiffs had standing to sue.Join us as a panel of experts discuss this interesting case.Featuring:Michael Edney, Partner, Hunton Andrews Kurth LLPHon. David Mason, General Counsel and Chief Compliance Officer, Aristotle InternationalConnor Raso, Deputy General Counsel, Public Company Accounting Oversight Board(Moderator) Russ Ryan, Senior Litigation Counsel, New Civil Liberties Alliance

Part 1: Washington-based lobbyist and former Rand Paul counsel Brian Darling returns to the show to discuss Trump's VP selection. Part 2: Mick Bransfield, an expert on prediction market regulation, returns to discuss reports that PredictIt is pursuing a settlement with the CFTC. 0:57: Current market odds on Republican VP nominee 3:44: Pratik introduces Bransfield segment 4:37: Trump interview on his VP choice with Maria Bartiromo 8:28: Darling interview begins 10:00: Why Noem is the front-runner to be Trump's VP 11:49: Noem's history with Trump 15:15: Noem's alleged affair with Corey Lewandowski 16:26: Odds Trump will pick a woman running mate 17:26: The Republican Party's role in the VP selection 18:32: Trump's perspective on loyalty 21:45: Would Noem accept the VP nomination? 22:06: History of people rejecting offers to be VP 22:50: What price to pay for Noem yes shares 24:29: Why Haley is trading so high 31:42: Elise Stefanik's odds 33:57: Bransfield segment begins 39:03: Signs PredictIt will not pursue a constitutional challenge Follow SSG on Twitter @ssgamblers

Dr. Lucas (@Talophex) returns for a deep dive into Donald Trump's health and how it should inform a bet on whether he'll be elected president. Follow Star Spangled Gamblers on Twitter @ssgamblers 0:00: Pratik introduces the episode 1:17: Discoloration of Trump's hand and Trump's alleged body odor 4:17: Interview begins 5:46: Trump's coronary artery disease 6:32: Trump's physique 10:12: Trump's psychology 11:05: Note from Trump's doctor 13:38: Why hasn't Trump had a serious heart condition? 15:06: Trump's genetics 17:46: Is weight protective in old age? 21:00: Trump's cognitive decline 25:29: Trump's OCD 27:00: Trump's purpose in life 29:48: How Trump's legal issues could impact his health 35:00: How to trade on Trump's health 39:52: What to look for to assess Trump's health Follow Star Spangled Gamblers on Twitter @ssgamblers Donate to Star Spangled Gamblers via PayPal https://www.paypal.com/donate/?hosted_button_id=9Q6KS5JPLNRRY

Ever thought of cashing in on your knack for predictions? Today we explore the world of prediction markets like Augur, Kalshi, and PredictIt.  Side Hustle School features a new episode EVERY DAY, featuring detailed case studies of people who earn extra money without quitting their job. This year, the show includes free guided lessons and listener Q&A several days each week. Show notes: SideHustleSchool.com Email: team@sidehustleschool.com Be on the show: SideHustleSchool.com/questions Connect on Twitter: @chrisguillebeau Connect on Instagram: @193countries Visit Chris's main site: ChrisGuillebeau.com If you're enjoying the show, please pass it along! It's free and has been published every single day since January 1, 2017. We're also very grateful for your five-star ratings—it shows that people are listening and looking forward to new episodes.

On today's show, cowards killing people, Netflix is done sending DVDs, Hurricane Idalia, Only Fans' top earners, kids getting slapped in schools, protestors fuck around and find out & hot boy summer continues for the Taliban… (00:03:15) Get yourself some merch: https://store.hardfactor.com/  (00:04:06) Boxed Water is BETTER! ☕ Cup of Coffee in the Big Time ☕ (00:06:13) NHL Puck Talk (00:09:25) UNC faculty member killed in shooting on campus, suspect in custody (00:10:34) Controversial Canadian teacher with Z-cup prosthetic breasts returns to the classroom at new school (00:16:30) Netflix ending its DVD mail service after 25 years, plan to let subscribers keep DVD's (00:22:59) Florida braces for Hurricane Idalia (00:23:57) 'You're not welcome here!' DeSantis booed at vigil for Jacksonville shooting victims (00:26:52) Trump's federal election subversion trial to begin one day before Super Tuesday primary

On Episode 1282 they boys break down all the latest funny crime stories including a gasoline drinker in Seattle, literal porch pirate in Georgia, land pirate in Virginia, the “terrorist challenge” and MUCH more… Timestamps: (00:00:00) Happy Birthday Pat!

On today's show....Wagner boss is dead, more bad teachers, robots taking jobs, Fyre Fest II, crowd surfing babies, bear spraying kids slides & rat's feet in Olive Garden soup & more! (00:03:11) Rep. Nancy Mace Interview: Joins us tomorrow at 7 PM EST: youtube.com/hardfactornews ☕ Cup of Coffee in the Big Time ☕ (00:05:17) Fun fact: 90% survive all plane accidents (00:06:21) Wagner boss Yevgeny Prigozhin ‘on board' jet that crashed in Russia, Russian authorities list no survivors (00:10:24) Happy Birthday Kobe Bryant  (00:12:39) NYC teacher who once posted about ‘helping kids understand consent' charged with raping 14-year-old student (00:16:23) 3 dead after drinking milkshakes tied to a listeria outbreak in Washington state (00:21:15) India becomes 4th country to land on the moon, first on the south pole, with Chandrayaan-3 spacecraft landing (00:22:55) Trump's legal team's mugshots just dropped (00:26:44) 'He held the baby up like Simba': Baby crowd-surfs at Flo Rida concert in wild video

On today's episode….. Happy Hanukkah, from everyone except the NY Times! (00:09:08), Cop pranks homeless people with shit sandwiches, hot cat ladies in action, Elon loses a twitter poll, House of Representatives member George Santos caught lying about past (00:32:04), deep dive in to being “Transabled”, Pup Ravage is rearing pups in the military (01:00:41) & Space Force promotes a sex toy guy Watch Full Podcasts on Spotify and YouTube + Get Bonus Podcasts via Anchor and Patreon NEW “CREAM OF THE CROP” & “CUP OF COFFEE IN THE BIG TIME” MERCH IS OUT AT STORE.HARDFACTOR.COM (00:03:52) - The boys discuss the effect of uniforms and sexual tension, plus discuss an experiment with scrubs and Whole Foods ☕ Cup of Coffee in the Big Time ☕ (00:06:32) - Hanukkah Fun Fact & Jewish Gambling (00:09:08) - NY Times crossword looks like Swastika (00:12:56) - Cop pranks homeless man with shit sandwich (00:17:30) - Only fans cat lover using funds to start cat rescue (00:19:04) - Old ladies refuse to stop feeding cats (hilarious bodycam footage) (00:29:32) - Elon Musk loses twitter Poll for CEO job