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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

What does it really mean to build bridges that last for decades? I want you to meet Bircan Unver, whose lifelong commitment to responsibility, creativity, and global citizenship has shaped more than 25 years of meaningful work through Light Millennium. From growing up as the eldest of seven children in Turkey to creating an internationally recognized nonprofit connected with the United Nations, Bircan shares how art, public media, and open dialogue can bring people together across cultures. We explore why responsibility begins with each of us, how local communities can help solve global challenges, and why issues like water security, environmental stewardship, and human connection matter more than ever. I believe you'll enjoy hearing how one person's unstoppable mindset continues to inspire collaboration, education, and hope for a better future. Highlights: 06:55 - How growing up with responsibility shaped Bircan's lifelong commitment to serving others. 18:30 - Why public access television became the foundation for sharing voices and building community. 26:15 - The story behind founding Light Millennium and creating a nonprofit built on dialogue and education. 36:55 - How local communities can play a meaningful role in advancing United Nations goals. 49:45 - Why water security, cybersecurity, and gender equality are becoming deeply connected. 55:40 - Bircan reflects on preserving ideas through books, creativity, and lifelong learning. About the Guest: Bircan Ünver is the Founder-President of The Light Millennium, a Charitable Global Human Advancement Organization (LMGlobal.Org, 2001, New York); Executive Producer of the LMTV Programs; Multi-Media and Event Producer; Host, Author, Publisher, Public Speaker; [Head] NGO Representative of The Light Millennium to the United Nations Department of Global Communications since 2005 (formerly, UN.DPI); and Chair, Outreach and Partnerships Subcommittee at Global NGO Executive Committee and director for the 2021-2023 term. She has been also a Content Provider at QPTV since 1992, and she is the editor and co-founder of the U.S. Turkish Library & Museum Project. She has undertaken the Organization's e-publications, public programs since its inception in August 1999, and #LightMillenniumTV Series and Specials in all aspects of it since January 2000; along with, the LMGlobal.Org's all UN-related activity and programs since 2005. Bircan is the author and director of the Compulsory Peace & ICTs Education from Kindergarten to K-12, and Beyond Campaign, which she launched it through the Light Millennium Global in support of the Summit of the Future in 2024. The Campaign has delivered its 8th session along with the Coalition for the Campaign, which contains 22 organizations and educational institutions including The Light Millennium Global as the leading organization. Further,  she is also author of the bi-annual international high school project, “J.U.C. Media, Research, Writing & Innovation Awards”. The third of its kind was dedicated to the “Water Action Decade” and was delivered  (virtually) on the World Water Day, March 22, 2025. Bircan served as a mentor to the GPODS for three consecutive terms in Winter'22, Summer'22, and Fall'22 Fellowship Programs; and delivered lectures on “How to associate with the UN-NGO”, Public Access TVs in the U.S., and the latest one was on the #WaterActionDecade and the #2023UNWater Conference. To attach that, on behalf of LMGlobal.Org, she organized and hosted an Outside-UN Side Event ID #W175, titled, “What Is Your Commitment To The #WaterActionDecade? ~ Seven Core Ideas Toward #WaterAwareness, #WaterConscience and #GameChangers” in NYC on March 21, 2023, and was the head delegate of the LMGlobal.Org at the #UN2023WaterConference. Based on LMGlobal.Org's Commitments to the #WaterActionDecade, she launched #OneMinuteWaterActionCommitment during the #W175 Side Event, along with continues to work toward #WaterAwareness toward building up #WaterConscience.  She served as the chair of the ATAA Committee on UN Relations, guided its association to the UNDGC in 2020, and also served as the New York Regional Vice-President of ATAA (Assembly of Turkish American Associations) from October 2019 to January 2022. Furthermore, she served on several Planning Committees of the UN-Civil Society Conferences, and the Planning Committee of the High-Level Forum on the Culture of Peace (HLF-CoP). She has been a strong advocate of the UN Vision, Programs & Sustainable Development Goals (2015-2030) along with the relevant UN Days, which are in line with the LMGlobal.Org's Mission/Purposes since its association with the UNDGC (formerly, UN.DPI/NGO) in December 2005. Currently, Bircan is on the Advisory Board of the Turkish Forum (based in Germany). She encourages and guides interested civil society organizations and members of academia toward their educational institutions' associations with the UN, along with engaging them with the UN Programs that are within her networks and beyond. Further, she is an Ambassador of Peace and Goodwill for Anuvrat (Anuvibha) Global Organization (Jaipur, India, since 2014); and a Member of the Global Movement for the Culture of Peace (GMCoP, since 2012). Bircan is also the founder and president of the IsikBinyili.Org Association based in Istanbul (2010), which is a sister organization and counterpart of the LMGlobal.Org. Author of the following four book titles in print (Turkish): “En Kutsalı Yaratmak” (The Most Sacred is to Create) (1995, 2022), “Sanatın Labirentlerinde” (The Labyrinths of Arts) (2016), and “Işık Yollarında (“The Ways of the Light”) (poetry, 2017), and “Bin Yıl Daha…” (2020, A Thousand Years More…). Also, she is the author of the concept and compilation of the “Hope Never Fades” (English) book (Istanbul, 2023). Bircan initially received her local and studio television production certificates at QPTV.Org in 1992. Based on this capacity, she produced and broadcasted near to 260 television programs through local channels in New York City, which are also available online through www.vimeo.com/channels/LightMillenniumTV including 36 #OneMinute #WaterAction PSAs. Bircan celebrated her 30th Anniversary at QPTV.Org through a video profile, titled, “A Turkish Experience in America” (Duration: ~9 min. Production Year: 2022). Her works through The Light Millennium Global is a recipient of various national (U.S.) and international awards and recognitions including two awards from the Foundation of Alliance Community 2023 Hometown Media Awards (F-ACM-HMA for her “GroundWater” Light Millennium TV program in Web-based Programming and Online Events / Independent Producer categories (received at Brick TV, New York), the Anuvrat Ahimsa Award For International Peace-2023 (received in Mumbai, India), and the 2023 International Volunteer President Award by Institution of Green Engineers (received in Chennai, India), and ashe is also the recipient of the 2024 Best Web-Based Program – Independent Producer for the #HopeNeverFades program by F-ACM-HMA, which was produced based on the 2023 J.U.C. Media, Research & Writing Awards (ACM HomeTown Media Awards, San Jose, CA). Bircan holds a Bachelor of Arts (B.A.) Degree from the Mimar Sinan Fine Arts University (Istanbul, 1988) and a Master's Degree (M.A.) from Media Studies of the New School University (New York City 1999). Ways to connect with Bircan**:** Websites: www.lmglobal.org  (active web site) | www.lightmillennium.org (serves as the organization's web archive)#LightMillenniumTV | www.vimeo.com/LMTV | www.vimeo.com/channels/LightMillenniumTV Social Media: Linkedin: http://www.linkedin.com/in/bircan-ünver-7353207 X@lightmillennium Instagram@lightmillennium About the Host: Michael Hingson is a New York Times best-selling author, international lecturer, and Chief Vision Officer for accessiBe. Michael, blind since birth, survived the 9/11 attacks with the help of his guide dog Roselle. This story is the subject of his best-selling book, Thunder Dog. Michael gives over 100 presentations around the world each year speaking to influential groups such as Exxon Mobile, AT&T, Federal Express, Scripps College, Rutgers University, Children's Hospital, and the American Red Cross just to name a few. He is Ambassador for the National Braille Literacy Campaign for the National Federation of the Blind and also serves as Ambassador for the American Humane Association's 2012 Hero Dog Awards. https://michaelhingson.com https://www.facebook.com/michael.hingson.author.speaker/ https://twitter.com/mhingson https://www.youtube.com/user/mhingson https://www.linkedin.com/in/michaelhingson/ Thanks for listening! Thanks so much for listening to our podcast! If you enjoyed this episode and think that others could benefit from listening, please share it using the social media buttons on this page. Do you have some feedback or questions about this episode? Leave a comment in the section below! Subscribe to the podcast If you would like to get automatic updates of new podcast episodes, you can subscribe to the podcast on Apple Podcasts or Stitcher. You can subscribe in your favorite podcast app. You can also support our podcast through our tip jar https://tips.pinecast.com/jar/unstoppable-mindset . Leave us an Apple Podcasts review Ratings and reviews from our listeners are extremely valuable to us and greatly appreciated. They help our podcast rank higher on Apple Podcasts, which exposes our show to more awesome listeners like you. If you have a minute, please leave an honest review on Apple Podcasts. Transcription Notes: Michael Hingson  00:04 What if the biggest thing holding you back isn't what's in front of you, but rather what you believe? Welcome to Unstoppable Mindset, where inclusion, diversity, and the unexpected meet. I'm your host, Michael Hingston, speaker, author, and advocate for inclusion and possibilities. This podcast explores how the beliefs we carry shape the way we live, lead, and connect with others. Each week, I talk with people who challenge assumptions, face adversity head-on, and show what's possible when we choose curiosity over fear. Together, we focus on mindset, resilience, and the small shifts that lead to meaningful change. Let's get started. Well, hello everyone, and I want to welcome you to another edition of Unstoppable Mindset, and today we get to talk to a very interesting person with a with a lot of roots in Turkey, but she is also doing a lot of work with the United Nations here in the U.S. and other places. Her name is Birchan Umfer, and I hope I got that right. And Bircan Unver  01:23 thank you. Michael Hingson  01:24 Oh, good. And Berjan is is with us, and is going to talk about a number of things that she is working on. She's working on, and for years has been involved with a program called Light Millennium, and we'll we'll talk about all that, but let's just start and and as usual we'll have a great conversation. So, Bir John, I want to welcome you to Unstoppable Mindset. We're glad you're here. Bircan Unver  01:50 Thank you so much, Michael Hingston. It's great honor and privilege to have your guests. Especially, I love the title Unstoppable Minds. It's really inspiring and triggering the mind. Thank you. Michael Hingson  02:08 Well, my pleasure. We're glad that you have the time to to spend with us, and we will we will we will do it and make it work. Well, let's start. I love to start this way because I really want people to learn about you. Tell us kind of about the early Birchan growing up, and then we'll go from there. So tell us about Birchan and growing up, and and and all of that. Bircan Unver  02:32 Thank you. This will be a little bit different than most people know about me, my work. Well, we'll get to Michael Hingson  02:38 your work. Bircan Unver  02:40 Yeah, I know. I mean, the people who already know my work. What I'm gonna say now is completely too different from Berja. So I am a elder of seven siblings, and I was born in an ancient village in Mid Anatolia, actually nearby the Black Sea region, and I'm proud with my age, 1959, and my family moved to town first from the village, and then Istanbul for our educations for their kids, and but I don't know really as a girl child growing up with seven kids, as especially with a traditional family, being older girl is not a good idea. But but maybe it was a kind of tough childhood as a girl child. I I don't feel that I never been a child. That's really what I feel. But on the other hand, and that was you know when you are growing up, living through like I was a kind of second mom, and also our home was kind of everyone our guest from the village town and stay over not only one night, like or a week, like sometimes months, even years. You won't believe that, and it was not because either. So, but those kind of build up maybe my feeling to be responsible, so I consider myself a responsible person because I was responsible for my sisters, brothers, and also washing dishes, making helping my mom to prepare the meal, lunch, breakfast, and clean up. Like you know, I was kind of second person, second mom at home. So those early things, all those stolen my childhood, but at the same time, provide me being able to do everything from very early age. Even though I remember several times at the time. The bread wasn't, you know, you you weren't buying the bread from the market, but from the bakery, real where they were baking the bread. So she was, and time to time also she was making the the bread at home, and then she she wasn't sending me to school because then I had to stay with my brothers and kids so she can make the you know breads a big amount for a couple days. So that said, also gave my idea. I want to suddenly want to work as soon as you know I'm 17, 1617, So and then I started early working at a bank, and it was my first eight years. I worked at a bank in Istanbul. It is called Istanbul Bank, and then my graphic is like life graphic is a little bit different, like curvy, not like straight line. Then after eight years working at a bank, I started to university and fine arts, and then I had kind of interest art history, especially TV production, art history, arts in in the concept is producing art documentaries. That was kind of my vision. Also, it was also my reason initially coming to the U.S. learning TV production. Of course, English as first. So that was very much my childhood until you know 18 until starting at the bank, and then the rest is another story. I have, I must say, I would have three different life. Michael Hingson  06:48 Well, you had certainly an interesting childhood, needless to say, and I, I can understand what you're saying about being the oldest and all the challenges. I was not. I was the youngest, but we only had two siblings, and neither of us we were we were both males, and so we we did not do a lot in terms of preparing meals and all that sort of stuff. Except my brother and I were both in the Boy Scouts, and so when we went camping and other things like that, we learned how to cook on an open or an open fire and and things like that. But still, I understand that you you had a a lot that you had to do. What lessons did you learn by being the oldest child? What kinds of things do you think that you you carry over to what to your life today, because you were the oldest. Bircan Unver  07:44 I think two different components of that. One is also because it wasn't only I was the as a kid at the home and as a girl, girl, but also at the time there wasn't that kind of awareness which we have today. Which I mean, my mom got married with my dad at the age of 15, and she had birth. She had given me birth at the age of 16, the reason I'm bringing up this, when we were going to shopping Kapolechar, Grand Market of Istanbul, when I was 1516, and literally we were halfway age difference between my mom and myself. And when I was saying mom, and the people were surprised, and she and isn't your sister, other sister? They were asking me, and she was my mom. So, but this is in the long later years. I always become the best friend with my mom, and even my brother was saying, "Oh, now I understood that she, you are the joy of my mom because she can kind of a joke, and the others, my kids, like they are kids, but I'm her friend because they kind of feel similar. That is one, you know, lifetime developed feeling and strong connection, different connection with my mom, and the other also, as I mentioned at the beginning, it really this whole, you know, not selected, but where you, which house you born, which condition you grow up, that build up for me early high level responsibility. I feel responsible to do everything, and I, you know, that's a little bit tiring because that's what I want to say here. You say something in our after our initial Zoom meeting. It's really very inspiring. You say something. I just took note. We can solve all the worst problem. We can. You say you don't say we cannot. You say we can. Solve all the world's problems. So, like, it wasn't possible. It's not possible, but you know, as if I could solve the problems or I could carry everything. So it's kind of you know build up my bricks in my life. Michael Hingson  10:18 One of the things that you certainly, it sounds like you learned as you were growing up, though, was was all about responsibility and and and learning to take charge, but learning about responsibility and and feeling that it was okay to be responsible for others in your family and so on. And I think a lot of people don't necessarily learn a lot of lessons about responsibility, like they should. But you were put in a position where you had to do that, which is is certainly a good thing because you clearly have have done that in your life. Bircan Unver  10:54 That's very true, and also it goes with everything. Let's say sometimes some people think maybe unnecessary action when people cut trees, even in their you know backyard or front yard, whatever trees or any type of trees or fruit trees. I really get mad and upset because it is irresponsible to nature, to their own environment, just feeling they're responsible to trees, loving the trees, loving the you know, for instance, if when I was it was two weeks ago, I was working on at my desk, a bees was you know flying around me, and I don't like you know trying to hit and chase the bees, and I just left the window open, moved the room two hours, and it's gone. So I don't want to hurt things, even though if it bites, it would hurt me, right? So to kind of find a way, being responsible, because also I have a subconscious thinking that if I try to chase a herd, if an insect, any of them, they you know they react sharp. So I'm not a threat to them. So they are not threat to me. So I kind of make a subconscious connection, communication. Michael Hingson  12:22 Well, and it's it's better to to learn to love than than not. So I I hear what you're saying. Now you didn't go to college or university, right? Or you did? Bircan Unver  12:35 Of course, I did. That's what I was just briefly brought this stair at that level. That after eight years working at at Istanbul Bank, from literally actually 17 to 24 or 23 late 23 early 24 then I started at the Fine Arts University in Istanbul, and I mainly study both as major Turkish tradition arts and also along with as a minor. We didn't have a minor, but we had we didn't have the minor degree, but we had a selected course. So I was following up several like four years in a row and also whole year around, not a quarter semester base artist troy and contemporary art. So I started writing on art and exhibit and interviewing artists while I started study at the Fine Arts University, which is top university in Fine Arts in Istanbul, Myanmar, Sinan Fine Arts University. Michael Hingson  13:44 So you learned a lot about fine arts, and Bircan Unver  13:47 yeah, Michael Hingson  13:47 and you had obviously learned about finance from the bank, which Bircan Unver  13:51 is didn't learn that Michael Hingson  13:53 much, huh? Bircan Unver  13:53 Not at all, and I didn't like it. That's why I, you know, I changed all my life line. Michael Hingson  14:00 Right, but but you enjoyed art. Bircan Unver  14:03 Yes, and because also art always for me, and it as it is today. Whatever the life problems we cannot be resolved, art is always for me a branch to hold on and to kind of feel strength, connect with the nature, with the art culture, whom I never might met, or maybe you know through art, through music, through books. So it's kind of really, I think the the best human conscious and what we leave behind is what we produce. Creative might be a little bit another discussion. What is the term defined? And now everyone using like creative for everything, but creativity is like really what stays, you know, central. And something as what is already before, and what you brought it something new, that new perspective, like cubism and faism and expressionism and all that different genres or pop arts and another one Dali's movement. So all the Dadaism, so each of them brought something what is already existed, and then you know moved to different level, and also technology and communication brought where we are today. So and music, I'm not any degree. Degree train or understand, but I love music as most people about music and arts and literature and writing is always for me. Also taking pictures. I don't trust my memory much, but this is not new, not because of my age now. But I somewhat I always like take the pictures, whether I have cell phone, the oldest, you know, digital or the manual photo photographs. So even though when I run into where where was it, where I was, who was with, who next to me? I can't remember. But why I love that? Because really, it brings, it captures your life, even you don't remember, and then reconnect. Also, I I come up with a new concept. I haven't done anything yet, but it's kind of developing in my mind, that we can, we are able to capture the past, but we cannot capture the future, because you know whether you know unexpected the one photo comes up, or an audio recording, or all those movies, musics, and documentaries and paintings throughout artistry, you can really capture the past depending on what you know your background or interest and education, but you cannot capture the future. But you can imagine, we can imagine the future. We can invest into the future. Michael Hingson  17:16 Well, and and the the reality is that that the future is based on so many things that happen in the past, and so you can learn to to use some of that information and and have a better idea of what the future might be like, and that's that's pretty fascinating too. So so, how long did you go to college Bircan Unver  17:42 in in Turkey the basic four university education is annual base, not semester base. It's a four years, and actually when I started it was five years. Then later on they the system education board changed it to like four years plus two years. So I graduated from a four years fine arts, and then I have a master's degree based in New York News School University. Michael Hingson  18:20 Ah, in New York. Bircan Unver  18:21 Yeah. Michael Hingson  18:22 Okay. So that was was that two years. Bircan Unver  18:27 Yes, but it was a little bit different again. Yeah. See, because first when I was here for the first time, 18 919, 89 to 1994, I started and I took to six credits and I love it. But I had to go back to Istanbul, so then I got the you know absence live of absence, and then but coming back three years later, I readmitted, and then I finish it. I got my graduate diploma in 1999 May 1999 Michael Hingson  19:12 Okay, so Bircan Unver  19:14 you've been dealing late. You've been Michael Hingson  19:17 dealing with school for a while, but but while all that was going on, you got interested in, and you started to to do things regarding public access television. And you started that, if I read your biography correctly, in in 1991, you started doing things with public public access TV. Tell us a little bit about that, and why did you do that? Bircan Unver  19:41 That's really the kind of key point in my life. In as for the initial one of the two reasons that I came to the U.S. as the first time, I was you know the first aim to learn English. The second also. To learn the TV production, and then my first year was in the U.S. was in L.A. and then I was looking for the schools and just I'm I I'd like to get the formal education, formal training, and then one of my friends there, whom I met there, she said my aunt is TV producer at Queens Public TV in New York, and she is looking for someone like you who really wants to get in and all that. And then I think a couple months later, I had a very brief trip to New York, and I met with her, and then I met with her at QPTV. Now she is unfortunately not with us, but she was also she was the first Turkish producer at QPTV, and I'm forever grateful in memory of her. She was the one who introduced me QPTV, so I met Victor at QPTV in 1990, and then when I went back to LA, I decided to move to New York. So as soon as I moved to New York and I registered to QPTV's both field production and studio, and at the time, there were seven, eight months waiting list. Eventually, I got it on the list, 1991, and first I got the field production certificate, and it was I think six months something, and we had a group project and solo project, and then studio and studio production. It's when the same group project after you are learning all the equipment and then shooting and everyone is crewing everyone is helping each other within the same group of the class and then also producing group project and then the individual project that it is the similar step for the field production and the studio, that also provides a certificate to enabling us to use QPTV equipment, studios taking out the equipment, inviting studios, studios. Unfortunately, after COVID 19, there has been a long dragging issue. They haven't opened the studios. This is I don't want to get in because it is beyond our concept, but because that was my initial reason also moving to New York. And then once I started producing program and airing, I really loved idea from one idea, just idea, and then put paper and develop it. Bring yes, bring the crew, especially with the manual three-inch tapes, and to present, put everything in a tape, and then submit it an ARC schedule. I really love that. So then, while I was there, and then I said, "Okay, I want to really have a master's degree on TV production. So my master's degree from New Zealand University is in media studies. So and I got my diploma May 1999 So, but New Zealand was amazing. I learned all the first my you know my life although it was 39 maybe 3839 years old when I was Photoshop and Premiere or Evid and all the first offline digital all the software program I learned at the new school and I edited my first program offline at the new circle as well, but when I got diploma, and then I had nowhere no access to continue. Also, you know, it's just I, I never been about the person. My mind and my heart wealthy, but not financial terms. And then, so QPT was always my second home because it provides me continuing the producing and provides me access, provides me equipment. It's like provides the community. That's what it is exists for, especially in support of the Article First Amendment and freedom of expression, and also QPTV format is really, really not well known, or not QPTV loaned. It is public access, pack programming, public education and government programming, non-commercial. So it's it is not much known widely, but it is really one of the best symbol of democracy. And now it's kind of a little bit diminishing because also widespreading out other web streaming platforms like this or other, so that there are so many. Discussion and issues, and also there is one petition to the New York Senate that is called Team New York that could also bring in all the web streaming to this community media access public access media. So then, Michael Hingson  25:19 oh, go ahead. Bircan Unver  25:20 No, I mean the question is so QPTV had has been the longest since 1991. I've been providing content, and as long as I am in the US, if I'm out of the country, even if I am out of the country, my previously submitted programs are rerunning in different, you know, whenever they schedule for my new programs, I determine which time slot because I have a time slot to be scheduled, and whether 28 minutes or one hour. But especially again after COVID 19, I couldn't produce anything at the studio at QPTV, but like this Zoom-based programs, and then for the late million organization, the programs I produce and then edit them, reformat them to schedule for QPTV. But it wasn't as it used to be produced at QPTV, at the studio with the crew, that's the excitement teaming up. It's really, I love it. But Zoom, this type also make more maybe manageable, but also bring more intonation speakers. You know, through online connection. If Speaker 1  26:54 you enjoy Unstoppable Mindset and would like to help us continue bringing these conversations to you each week, we've created a way for you to support the show. Your contribution helps us cover production costs and continue sharing stories, insights, and ideas that inspire people to live with purpose and possibility. If supporting the podcast feels right for you, you'll find the link in the show notes. Thank you for being part of the Unstoppable Mindset Community. Michael Hingson  27:28 So you you started in public access television and and and are still doing that essentially. But in 2000 you started Light Millennium TV. Tell us about Light Millennium Television. Bircan Unver  27:42 Thank you so much. Because Late Millennium, I still consider it's my third child because already 25th anniversary. The flyer on my back says 25th That also refers the 25th anniversary of the Light Millennium. I think a the experience and production, and also providing three platform QPTV, Queens Public TV, but actually the 1992 Communication Act and PAC programming, public government public education and government programming; those emerge with my new school education, like Fatrus, Plato, the concept of the two ways of messaging, and for the first time in my life, at the age of I think it was I was 39 I was able 3039, I was able to use. It was very new, also for the war. Internet. We had the internet at the new school as our media class, the open forum. One week media theory class. We have open discussion forum about Greek philosophy, further is communication, media theory. The other class we had the classic format. So all these together merged in my mind, and I really felt I found my medium. That medium is the internet, which me, especially someone is living in New York, who has you know 30 years first life in Turkey, Istanbul, that culture, and then I I didn't want to kind of disconnect from my root cultures, and internet provided me that connections, and also I always like to write and communicate, so so it becomes like natural medium. And then when I just graduated in May 1999 I send out by then like 40 people with my friends. Colleagues, and then I said, "Oh, I want to start a platform. This platform is going to be ours. And everyone wants to say, kind of to expand the QPTV's concept, because when I say QPTV's concept, this is the again First Amendment, and they don't control the content. They don't say anything you can do, you cannot unless it is a threat to someone's life. So it's all the content, or you are responsible as the producer. So I really love that freedom and responsibility again. And then then I was mentoring at the New York Food Moon Finance and Television, and I was kind of trying to find what I want to do, and I was already producing programs at QPTV. I never give up as as long as I could afford to produce the time and edit. Edit is always more time consuming, and then the executive director of the at the time 1999 Woman Film Man Television, she said you can form a nonprofit. I never taught it before, and I'm forever thankful to her. And then I said I don't know what to do where to start because she perhaps she saw the potential because I was producing non-commercial and publishing, trying to promote, you know, through group emails, and whatever the you know social media available at the time. Well, mostly email and group emails, and then she suggested me a volunteer lawyers of arts that she said they can help you, and I never knew and I never thought I if I never came to U.S. maybe I will never had a concept of forming a nonprofit or light minimum. So that U.S. and all combination brought me and news called Queens Public TV, First Amendment, United Nations Article of 19 Universal Declaration of Human Rights. Everything together kind of merged in my mind. Also, the projects I produce, I communicate, is contact with people, I learn from them. So then, when I contacted with them, they ask me, you know, what is my statement this type? And then I drafted all of them, and they put in medical, you know, terms and terminology, and I was lucky that we got a pro bono at the time one of the you know major U.S. law firms. I I think I can say now because they even though I think merged K Shuler, but they took our case and they formed my organization on on a pro bono basis and officially the first as a non profit organization done with the 500 1c T V together. So we officially formed 2001 July 17, and we and then right after that they filed 501c3 for us, and then we granted that in September 18, 2002. Bircan Unver  33:14 My memory is not good, but I I remember that date as of today because it was kind of imprinted in my mind, but also the beauty was that the granted granting 501 c3 was effective of the formation date, which is July 17 2001 So then, since I developed started the light minimum concept, introducing speaking people trying creating an open platform that everyone can you know contribute be part of it, so then I started in January 2000 on January 13 2000 started first monthly the like TV series at like at QP TV. That's how it came about. Michael Hingson  34:02 Yeah, you have certainly created quite a niche in in the whole light millennium and all the other things that you've done, which is pretty fascinating. How did you get connected with the United Nations? Because I know you're doing work with them. Bircan Unver  34:18 Thank you. Actually, the concept with the UN use, I don't do work for them. Michael Hingson  34:29 Right. Bircan Unver  34:29 We do work with them. Right. You're connected Michael Hingson  34:34 with them. You're not working for them. I understand that. Bircan Unver  34:36 Yeah, I just you know one word, but it's key key differences. But as as I mentioned, how the light millennium concept emerged, and also my master's degree program, media studies at the New School, and also there is another person I have to mention here, also in. Separate for her respectful and loving memory, Danielle Camille. She was my advisor for my thesis project advisor at the new school, and all that TV ideas and my thesis project. And she loved, and I got all A. And my first TV ideas project was peace, reality, or utopian dream, and it was 11 minutes or so. It was just before the first Clinton administration election, just October, I think 2002 and then I interviewed many academicians, civil society, non-profits who whose focus area on peace and war and you know against nuclear or all that and and then that actually the concept peace reality or utopian dream that took me to the United Nations I don't know now as a as an outsider, at the time it was so welcoming, and I went to photo department. They share me with photos, and I got all the books I was looking for. Of course, public library. I got all the books about the peace and peace organization. So that 12 minutes program, peace rate or utopian dream, and physically led me to step in the UN for the first time, and then producing this project, searching kind of open to me new horizon, and also my advisor was persistent. Birjan, we know you are great. You produce fundamental programs on arts, but I want you to do something else. So that actually also forced me to move beyond producing something beyond arts. And so then then peace, reality, or utopian dream concept came in, but also it wasn't far topic in my life because my son was born 20 when I was 2022 Now he is 45 His name is Borush, and mean is in Turkish peace. Peace also at the time, you know, Africa hunger, and I was hoping when I get 4050, this will be over. It's not the case, but as a vision, I always look for humanistic positive for the humanity, not you know for myself well-being. Michael Hingson  37:38 Well, you you kind of act as a bridge between UN programs and local communities. Tell me more about that because you view that you view yourself as being a bridge between UN programs and the local community. Why do you why do you view yourself as more of a bridge? Bircan Unver  38:00 That's very nice metaphor, and I think it's very true for me too. Maybe I could take it two three step backs. One is the village I was born. There is a river, and and then there was a bridge, and that bridge was kind of fundamental. Really, going to where you know people are established, living in all the agriculture and large fields, and it was kind of nostalgic to me. It's a bridge, but it's a stone, short bridge. That's the the first part. The second Istanbul. You know, my life main path when I was in Istanbul, Turkey in Istanbul, and Istanbul the Bosphor and then the Istanbul Bridge. Now the name changed, and now three bridges, and not just the bridge itself, but also Turkish geographic Turkey's geographical space place on Earth and especially on in between Europe and Asia and Black Sea and then the Mediterranean Sea and then when you go down North Africa, and then you go up like Russia and North Europe. So you see that that connection physically, geographically, and troy physical bridges. We live, we grow up with this kind of as part of our DNA and vision. When I was working on this, the first the initial need you know in the non-profit world to describe the need need was to connect with my culture, people, friends, family, the internet medium, and then village is also metaphor, and when we associated with the. I let I'm proud to say it. Let the association process. That was also my vision from that small video for the class project. And then, then I realize this is also. I think today is real fact that local communities, even Queens here, people are paying 1000s of dollars to come to New York to get visas. All that trying to get the sponsor, you know, to attend a conference, and I I didn't see much interest within the local nonprofits there, and that little bit was shocking realization for me. So I brought to my programs UN visions programs, MDGs, UN days, sustainable development goals. For instance, when it was announced at the UN for the 2023 agenda, we created dreams for humanity. So we brought people from lots of life, poets, authors, and youths to really share their ideas for humanity, for peace. Michael Hingson  41:12 Well, you-it sounds like, from all that I've read and and studied, you're you're, or at some point, you kind of have moved more into dealing with a lot of things with the environment. So I know that your program has been able to get a credit accredited as an observer observer status with the UN Environment Program. How is how has that affected what you do and all of your work going forward. Bircan Unver  41:43 Thank you. This is also another important, I think, milestone for us. The first in connection with the UN, associated with the in the formerly United Nations Department of Public Information, and then it has changed to Department of Global Communications. Officially, we've been associated since 2005, active and good standing, and we are able to be presented at the UN with six representatives, and therefore we are able also to reflect back to our committee's networks from the UN and it's two ways. I really like the concept two ways messaging, and then always environment in somewhat it involves in our programs. But let's say through water, through energy, through, for instance, when the forest fires really burns my heart. So being sensible and trying to, as much as I could, to attend the meetings, especially MDGs for the first phase of the Millennium Declaration, really, it's. I consider UN is the best school I ever had, and it's for me lifetimes to call, and I learn about so many countries I will never otherwise know them much. Some of the names you hardly hear in media and anywhere else, so it's kind of opens and to see the global spectrum. Also, one of the things I really love at the UN, whether you you are a ambassador or president or prime minister of a country that has maybe a population 1 million or 2 million or 1 billion or two 1.6 billion, the largest one, so you have the same minutes of speaking time, same voting, especially of course, I'm talking about the general assembly. So those things I follow up, and we try to incorporate time to time. And water issue always, I wanted to involve. I want to do something about, but it's just not like because I love the tourists, and I'm not, you know, scientist, so I'm not officially trained. So also, I'm not able to protect the, you know, fires in the forest. And every year it's getting worse and worse. And then I'm sensible, but you know, you can't do much about it. And eventually, although our last name is same, but we are not blood connected. Professor Oljoymer, at the time he was vice president for the UN Water at the UN, and we connected. And I was following up, and I was aware his work before we met. We connected through the UN. So then, especially during the COVID era, then when I've seen his speeches, natural-based solutions, and then his piece on smart water, so I propose him if we can do something. About water, and then he was very supportive, very kind, very modest. We had our first water program on the World Water Day in March 2001 and then it has turned out an annual program, and we kind of made the global connections and collaborations, and especially first three, four of them, Professor Olja led and guide, and also we worked together in a sense that I brought one part of the speakers and he brought other parts. So it was also great, diverse speakers and also the level of the speakers from whether civil society, academia, UN, and youth, all that. So we already presented six of them, and 2023 we were part of the UN Water Conference. We presented a program, and then last year we participated and presented a program site event in Nice on the Ocean Conference, and we kind of we also had previously on the ocean again. So plastic also become whether ocean, whether water, whether land life, like plastic, is becoming a huge in every level. Not plastic, but also chemical, all all sorts of polluters on in environment. So then we become promoting, producing, and there are really great level of academicians and experts, and they also like to share and connect their research and their findings. So it's a platform to break down together, and then when it is a video, when it is posted as both a summary along with the full presentation or speech, so it becomes a really source where you know you can find diverse voices, diverse solutions, diverse ideas, and also we align them in support of the UN days, UN conferences, SDGs, sustainable development goals like Water Days. Also, we collaborate with an organization in India, IGEN Green Institution, Green Engineers Institute of Green Engineers. So they are focusing on energy SDG seven. So we contribute them, bringing speakers. Bircan Unver  47:40 So then they join us to our program. So really, it's an organic volunteer base. So that that actually, I was so proud that we got a call, invitation to submit the application for this accredited status, and it was June 2025 after the ocean conference. I think ocean conference kind of brought them cumulatively and maybe brought their attention. And then October they asked our official documents, the bylaws and and certificate of organization, which is the regular require any formal application at the UN, and then I I submitted them in I think it was October 1525 but I haven't heard anything, and then I thought maybe you know they didn't consider, and then literally a year later, in June, last June, we got approval, and with a, it's not just accredited for a special one particular conference. It is an observer status assembly to contribute draft ideas. I mean, to contribute drafts, draft resolution to observe, to participate, and then also to be responsible to provide every four-year quadrennial report. Let's say if we fail to produce contribution at any level to this cause, then you know we may lose that observed status, but of course we'll do our best to contribute and to continue. Michael Hingson  49:27 With all of the work that you've done in all of this, I want to come back to World Water Day in a moment. But with all the work that you've done and all the the the things that you've done in public television and so on, when you host these meetings or you participate in these meetings, do they get televised? Also, are you able to bring those to to the public through public television? Bircan Unver  49:53 When it is Zoom, then I edit within my time slot within the limits of. Time slots and QPTV provides two different time slots. One is 20-eight minutes, the other one is 50-eight minutes. So then, let's say if it's two-hour session, I you know a little bit edit and then title, etc. and part one, part two, and also depending on the length of the session or 20-eight minutes. So then I schedule them through the local channels, as we spoke at QPTV. Also, I make them available through our vmail.com/lmtv channel on web on the web. Also, when we have a summary or outcomes, so we also provide those links and share through social media. Try our best to make those information available, visible, and reachable. Michael Hingson  50:51 Well, that I'm glad you do that. I'm glad that it gets to be something that is a lot more publicized than you just attending a meeting-that's important, I would think, to do when when you talk about World Water Day and so on. One of the things that that I I understand you do is you compare water security directly with cybersecurity and gender equality. Those are three different sorts of things when you include water security. Tell us more about that. Bircan Unver  51:24 This was our latest program in last March, and we had in our prior program on AI a discussion on AI a public discussion on AI policy or AI arc. So he's a professor and at the SUNY, and he's a mathematician, and he's also teaching cybersecurity. And when I was talking about when we met about the water conference upcoming, actually in this December, in the United Emirates on I think December six to eight. Even I was thinking to attend, but I wanted part a bit this time virtually instead of going there. So when we were talking about and and I really like this idea and I say as I asked exactly what you asked for, and then his really statement explanation made great sense because everything is nowadays control troy some form of electronic or AI or a combination of those systems, right? Competing systems. So if or if there is a technical issues, that's one case. But if there is a hack, especially water is the life source for everyone, whether you are billion or trillion, or you are you know ordinary people on the you know everyday life. So water is the source for everything, including producing the energy. AI. So water is the life itself. So then, when he said, if any hacking, whether you know small you know some you know people with a bad intention or some maybe big corporation because some profit purpose they want to sell more you know bottled water whatever or some international security issues you you may name this many other things. So if a computer system, if the control mechanism, if the AI system, whatever that combination in that system, is hack, then everything is a huge risk, and that's number one. How it connects with the water. Also now you know we smart how everything is electronic. Everything and then when that somewhat the life collapses there. So including water, including energy. So everything is so much you know interconnected. And then how that the gender equality came. Also, we follow Michael the UN annual-based dedication team for each UN Day or conferences. For instance, last year, water and gender equality, because water and gender required is also so inseparable concept and conditions in many parts of the world, and then also our professor Emre Tokus, he also. Make beautiful connection that if woman or when woman, if and when women are more in the security, cyber security, IT technology sectors, they are more sensitive. They are more like maybe natural in their dealings, like mother protection. So more protective, so there is less risk than where it is now. So to increase the gender equality, both not burden on the woman, you know, where it's the water scarcity that woman has to carry the burden. Girls, even five, seven years old, when developing countries, when it comes to developing countries, and then if the girls are, and also that was staggering. Stats he gave sort of shocked me today because he is a professor at SUNY. So what I mean by that, he said one of his class has only four girls out of 31. of his class has only one girl. One of his class doesn't have no girl. So even though if if girls are girls are not getting in this field, how we gonna bring the gender balance in all cross-cutting sectors of society. So that's how both from the UN perspective, the dedication team, also cybersecurity, the profile from a university that girls are not showing attention, interest studying cybersecurity. That was the fact based on his class last semester. One Michael Hingson  56:46 of the things that you have done, as I understand, is you've written several books. Bircan Unver  56:53 Yes, there are more, but hopefully I could get work on them, publish them as well. The my first book was Michael, based on my interviews with artists and my reviews on art exhibits, and also when I had questionnaire surveys like with artists, galleries, academicians, professors, students. When I before my first life prior prior the U.S. So when I went back after four years and I was really eager to get in a job for as a TV producer and somewhat that didn't work despite all my efforts even I my project load and everyone was after me, but they want to take the project, but not give me the role what I deserve for. So then I kind of close myself, and then I said I want to compile my books, my articles, my interviews. So that was how my first book came about in Turkish. To create is the most sacred. So, like the value to give the highest value for the creativity, intellectual production. That is through all those interviews, reviews, and questions. So that's that was the first book. The second book is in a way a bridge again between U.S. and Istanbul, New York and Istanbul, or maybe L.A. New York and Istanbul. Because when I came in, also I've done freelance journalism, so I wrote some you know reviews and the magazine. I used to, you know, send and appear my interviews and reviews. So and it was include not too frequent, but it was include some of major exhibits, both from LA and also from the Metropolitan Museum in MoMA in New York. Also Turkish artists here. So I met them, you know, when I was living. I started living in here. So then I a combination of those, and also that was another metaphor developed in my mind when I was Istanbul. I was feeling like the labyrinths of arts. So that's my second book title in Turkish labyrinths in arts. So like you are like this kind of narrow and small world in Istanbul art worlds and very high competition, and then nobody expanding to New York and LA expansion means bringing some, for instance, Franz Bacon, Thomas Hart Benton, and Max Ernst and Marina Magritte. Those also the last part connecting with the New York Istanbul from my art based on my art background. Michael Hingson  59:51 So, were any of the books published in English? The Bircan Unver  59:54 last 120, 23 is English, but. It is a compilation concept is written me. I directed the project, and of course I have some pieces also beside the concept. But it is a compilation by 24 contributors. It is based on the JUC Media Research Writing Awards 2021 and 2023 High School Awards Project, and also it is also engaging the youth to involve the UN programs at an early age and encourage them award them. So that also the next one is going to be on the Water Action Decatur in the same line, same concept, and also I have a book project since 2022 I couldn't still 2022 2020 actually. I dream, therefore I am. I have 80% of the book manuscript is ready, but I couldn't finalize it, and so the other one also I was hoping but couldn't to have a book for the 25th anniversary of the latentium. Unfortunately, it didn't happen. It's a little bit seemingly not going to happen this year to come up 25th anniversary of late millennium, but I had 20 anniversary of the late millennium in Turkish, and so this is completely different issue because I introduced like Millennium both in Turkish and in English, so it's like Tiffany Sister is the Turkish one, and then I formed Turkish sister in 2010, and then so on the 20th anniversary of the Turkish organization, as it is association, official association. So I compile the first part, the concept vision, very much like Millennium, because it's the system. But the second part based on the programs, activities, and challenges in Istanbul. So this book also in English will be first the the manifesto and the first all the history will be very much in English, the original one. But the second part is going to be completely, you know, selected ideas and projects and programs from the website and public programs from the last 20-five years, and it's hard hard things to do. It it's very challenging. I couldn't do it. Michael Hingson  1:02:47 It's it's a lot of work. Well, Berjan, I I want to tell you that we have now been talking for more than an hour. Time flies when you're having fun. Man, I'm going to go. I'm going to have to go ahead and and end our time, but we'll have to do this again and and continue the discussion. But I really appreciate you taking the time to be here, and I hope that people will monitor your programs and and and read your books if they can read Turkish or English, whichever works. Troy Bircan Unver  1:03:18 has so many English pieces, and now everyone welcome to look out our website. And what's the Michael Hingson  1:03:26 website again? Bircan Unver  1:03:28 lmglobal.org. Michael Hingson  1:03:32 lmglobal.org. Okay. Bircan Unver  1:03:34 Yes, but the whole archive from 2019 to 2000 no, 2019 no. of course not 2019 1999 to 2018 The archive. Our initial website is light millennium.org Millennium is with double N double N. So the everything we are talking about, all the process, our UN programs, and the reports from the UN NGO briefings, some conferences we participated. If anyone has any question, I'll be happy to provide the link and you know to contact them. Michael Hingson  1:04:17 And we will put that information in the show notes, the notes for the podcast. So I want to thank you for being here. This has been absolutely informative. I really appreciate your time, and I hope the programs continue to go well. So I want to thank you again for being here, and this has just been a lot of fun. Bircan Unver  1:04:39 Thank you so much, Michael Kingston, it is great honor and privilege. Especially, you are a hero, and your your story is amazing, and you are also taking really one of the challenges works like producing these programs, and we never met in real life. And then this is also like Millennium and your work. How we connected through? Otherwise, there was no like regular environment or database environment that we could meet. Your work and your dedication and your inspiring contribution really connected us and bring us together in this session. Thank you, and I'm really grateful for that. Michael Hingson  1:05:29 Thank you for being here with me on Unstoppable Mindset. I hope today's conversation left you with a fresh perspective, a new insight, or at least something worth thinking about. If you're ready to go deeper into the ideas that shape how we see ourselves and others, I have a free gift for you. Head over to michaelhingson.com and download my free ebook, Blinded by Fear. It explores the invisible beliefs that hold us back and shows you how to reframe them so you can move forward with clarity and confidence. Be sure to subscribe to our podcast, leave a review, and share this show with someone who can use a reminder that growth starts with mindset. When people think differently, we all move forward together. Thanks again for listening. Keep learning, keep questioning, and keep choosing to live with an unstoppable mindset

What does it take to write the very first check into a company that has almost nothing to show yet, sometimes not even a finished idea?Afore Capital helped invent the pre-seed category. When Gaurav Jain and Anamitra Banerji started the firm ten years ago, "pre-seed" was almost a slight, a label for founders who couldn't raise a proper seed round. They set out to build the world's largest pre-seed fund anyway, closing $47 million on a $40 million target, and every fund since has closed above plan. Afore now runs more than $500 million across four funds, with top-quartile DPI on the first three. The idea has become so mainstream that when Sequoia launched its latest fund, it said, "I guess we're pre-seed investors too."The real substance of the conversation is how Gaurav thinks. He is clear about what matters most in venture, and the order tends to surprise people. Being in the very best companies matters more than anything else, ownership comes after that, and the entry price that so many investors fixate on matters least, because fifty per cent of zero is still zero. He is also convinced that the genuine bottleneck is talent. There is a great deal of money in the world and very few people who can build something truly large, which is why at the earliest stage founders tend to choose their investors as much as investors choose them. You give a founder a million dollars with no collateral, and then you still have to convince them to take it. A pre-seed pitch, he says, is almost entirely storytelling with very little data behind it.If you want to understand how the earliest checks actually get written, and what it really costs to say no, this episode is worth your time.00:00 - Trailer01:00 - From Dehradun to Google to starting Afore02:08 - The Waterloo co-op that talked him out of every job03:18 - Back when "pre-seed" was an insult05:44 - When Sequoia said "I guess we're pre-seed investors too"07:26 - Afore's three products, and the experiments that failed09:01 - Hightouch was a travel company when they invested11:02 - Goldcast: no visa, no money, funded anyway12:07 - The through line is always the team14:44 - The Ramp miss17:24 - "Founders pick us more than we pick them"18:45 - The constraint isn't capital, it's talent22:24 - The Solana miss, when it was still Loom Protocol24:46 - Ramp's Super Bowl ad, the buses, his wife's business25:32 - What he looks for in founders28:50 - Coachability, happy ears, and the Mom Test31:28 - The biggest mistake: falling in love with the idea35:04 - The three things that matter, and "50% of zero is still zero"39:25 - "100% storytelling, 0% data"41:25 - Investing in India, and the fear of being dumb capital44:41 - "Sign the deal before Monday"47:26 - One engineer now does the job of 2051:43 - Raising from LPs, the undiscussed part of VC-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

In this special episode of Tank Talks, recorded live during Toronto Tech Week at Moomoo Canada's flagship store in Yorkville, Matt Cohen sits down with two of venture's sharpest data and investment minds for an unfiltered conversation on the state of private markets.Peter Walker, Senior Director of Insights at Carta, brings the hard numbers from 60,000+ companies and 3,000+ US venture funds, revealing the stark reality behind valuation markups, unicorn deterioration, and the widening dispersion between top-tier and median deals.John Rikhtegar, Vice President at Northleaf Capital Partners and former RBC investor, offers the LP perspective on why trust matters more than ever, why emerging managers are bearing the brunt of capital allocation challenges, and how disciplined pacing and vintage diversification separate winning funds from the rest.Together, they tackle the 4.3 trillion-dollar NAV overhang, the brutal graduation rates for 2021 vintage funds, whether valuations have permanently shifted, and why the ATM analogy might be the best way to understand AI's impact on venture careers.If you're a GP raising capital, an LP sorting through manager pitches, or just trying to make sense of where venture is headed, this episode is a must-listen.The Great LP Reset: Trust Over Performance (08:05)* Why LPs are letting go of newer relationships while sticking with 15-year partners.* The COVID furlough analogy: why junior and newer team members are the first to go.* How trust became the ultimate table stakes in today's fundraising environment.The 4.3 Trillion Dollar NAV Problem (12:33)* Why SpaceX's IPO would return only 10% of capital deployed over the last decade.* The staggering number of unicorns still sitting on stale marks from 2021.* What happens when 50% of unicorn down rounds become the new normal.Valuation Dispersion Is Breaking the Model (17:38)* Seed valuations jumped from $15M post-money (2022) to $24M (2025).* Series A went from $46M to nearly $80M in the same period.* Why the gap between the top decile and the median has never been wider.* How GPs must adapt ownership expectations or get priced out of deals.The Unicorn Graveyard: Stale Marks and Deteriorating Assets (19:28)* December 2021: 640 unicorns on Carta; 85% of current US unicorns.* 30% have raised new up-rounds; of the rest, half raised down rounds of 50% or more.* How GPs are forced to tell LPs that their “trophy assets” are no longer real.Pacing, Reserves, and Portfolio Construction (22:53)* Why disciplined 3-4 year deployment beats 18-month “firehose” strategies.* The 80/20 reserve debate: why leading rounds can become a net negative.* How “deal 13” is just as likely to succeed as “deal 12”, and why slightly larger portfolios make sense.LP Diligence: It's Not About the Marks (31:55)* Why TDPI and DPI are just 2 of 100 mosaic factors in LP decision-making.* How LPs now go company-by-company, not fund-by-fund.* The importance of founder references, especially from failed companies.Canada vs. The US: A Fractal Problem (40:44)* Why every market (Toronto, Sydney, London, Seattle) faces the same “Silicon Valley problem.”* The importance of domestic liquidity and secondary markets over chasing US LPs.* Why returns, not international capital, will ultimately scale Canadian firms.AI and the Future of Venture Careers (44:44)* The ATM analogy: AI will eliminate tasks, not jobs.* Why the role of the investor becomes more important as noise and froth increase.* How family offices are shifting their mix between fund investing and direct deals.Retail Access to Private Markets: Feature or Bug? (53:27)* Why illiquidity in private markets is a feature, not a bug.* The absurdity of allowing crypto “shitcoins” but blocking friends from investing in startups.* Why “401k-entrance” to private equity is a bigger story than retail venture access.About the GuestsPeter Walker is the Senior Director of Insights at Carta, where he leads the team responsible for analyzing data from over 60,000 companies and 3,000+ venture funds. His work on valuations, liquidity, and fundraising trends is widely cited across the venture ecosystem. He is a regular speaker at industry events and writes extensively on LinkedIn about the intersection of data and venture capital.Connect with Peter Walker on LinkedIn: linkedin.com/in/peterjameswalkerLearn more about Carta: carta.comJohn Rikhtegar is a Vice President at Northleaf Capital Partners, joining in early 2026 after a distinguished career at RBC and as an operator at Shopify and in the UK. He brings a unique blend of LP and operational perspectives, with deep expertise in due diligence, portfolio construction, and the dynamics of emerging manager investing.Connect with John Rikhtegar on LinkedIn: https://www.linkedin.com/in/johnrikhtegar/Learn more about Northleaf Capital Partners: https://www.northleafcapital.com/Connect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

In the July 2026 episode of 12:01: The Death Penalty in Context, DPI's Executive Director Robin M. Maher speaks with Samuel Spital, Associate Director Counsel of the NAACP Legal Defense Fund (LDF). To mark the 50th anniversary of the Supreme Court's decision in Gregg v. Georgia, they explore the history of the landmark case, the pivotal role played by the NAACP Legal Defense Fund in the litigation, and the enduring legacy of legendary LDF attorney Anthony Amsterdam. They conclude their conversation by looking back on the results of that 1976 decision.

Ausgerechnet die Daten eines pro-westlichen Thinktanks zeigen: Washingtons Kriege, seine Unterstützung Israels und seine Abkehr von einer regelbasierten Ordnung haben die Weltöffentlichkeit gegen den Westen aufgebracht. Ein Artikel von Michael Holmes.Dieser Beitrag ist auch als Audio-Podcast verfügbar. Der Democracy Perception Index 2026 ist eine globale Erhebung, die mehr als 90 Prozent der WeltbevölkerungWeiterlesen

Welcome to this episode of The Business of Dairy. In this live recording from the 2026 Raising the Roof conference in Shepparton, Victoria, Claire Waterman from Agriculture Victoria and Sheena Carter share the latest update from the Total Mixed Ration Feeding Systems – Dairy Farm Monitor report. Covering the 2024–25 financial year, the report now brings together nine years of analysis, with insights into farm profit and the key cost drivers shaping TMR systems, including feed, overhead and finance costs. Raising the Roof was a sold-out event hosted by Dairy Australia and its supporting partners, bringing together international speakers, Australian dairy service providers and farmers. A link to resources related to intensive farm systems can be found in the show notes.Resources:DFMP – Total Mixed Ration Feeding System reportsDairy Australia – Farm Systems resource page   This podcast is an initiative of the NSW DPI Dairy Business Advisory Unit – further information and resources are available here - Dairy | Department of Primary IndustriesIt is brought to you in partnership the Hunter Local Land ServicesPlease share this podcast with your fellow farmers and colleagues and feel free to contact us with suggestions or comments via this email address thebusinessofdairy@gmail.comFurther NSW DPI Dairy channels to follow and subscribe to include:NSW DPI Dairy Facebook pageNSW DPI Dairy Newsletter - Connect with us | Department of Primary Industries Transcript hereProduced by Liam DriverThe information discussed in this podcast are for informative and educational purposes only and do not constitute advice. 

Defensive Pass Interference plays and we break down a tricky high school football penalty enforcement play involving a completed pass, a foul near the line of scrimmage, and a run that ends beyond the line to gain.The key question: is this a loose-ball play or a running play? We walk through when the status changes, why the foul is enforced from the previous spot, and how the clock and play clock should be handled.We also discuss: Previous spot vs. spot-of-the-foul enforcement  Fouls behind the line with runs ending beyond  Clock status after penalty enforcement  25-second vs. 40-second play clock rules  Why timing of the foul matters  Multiple play discussions on Defensive Pass Interference, DPI judgment, and high school football rules philosophy A strong rules-study segment for officials who want to sharpen penalty enforcement, clock awareness, pass-play judgment, crew communication… and much more.

In this episode of The Private Equity Podcast, Alex Rawlings speaks with Daniel Pianko, Co-Founder of Achieve Partners, about Achieve's talent-led investment strategy, its $465 million exit of Optimum, and how the firm reached top 5% performance for DPI in Cambridge Associates' US buyout benchmark.Daniel shares how Achieve Partners invests in businesses where the biggest growth constraint is access to trained talent. Rather than simply competing for experienced hires, Achieve builds apprenticeship-style programmes inside portfolio companies, creating new talent pipelines that drive revenue, margin expansion, retention, and differentiated value creation.The conversation explores the relationship between private equity firms and operators, why data-driven decision-making matters, how Achieve partners with universities and underrepresented talent pools, and why doing good and generating alpha do not need to be in conflict.Key Takeaways:Private equity firms should empower operators to challenge assumptions with data.Achieve invests where talent shortages can be solved through focused training.Apprenticeships can increase capacity, margins, retention and scalability.Optimum shows how training pathways can unlock healthcare IT growth.Strong impact and strong returns can reinforce each other.Timestamps:00:03 – Introduction to Daniel Pianko and Achieve Partners00:29 – Daniel's career path and linking social impact with financial return01:52 – The mistake PE firms and portfolio companies make in the boardroom03:44 – How to avoid PE investors driving strategy without enough data05:10 – Achieve's unique strategy: investing where talent shortages constrain growth06:38 – Building apprenticeship programmes to solve supply-demand talent gaps07:08 – Daniel's Goldman Sachs training experience and how it shaped Achieve's model08:25 – Rebuilding the talent pyramid in lower middle market companies09:49 – Why Achieve focuses on business services, tech services, and healthcare services11:12 – Building talent programmes at the portfolio company level12:10 – Solving the gap between university education and first jobs13:04 – Why companies should stop searching for “purple squirrels”14:58 – Partnering with universities and building access to talent16:44 – The Optimum exit: $465 million sale to Infosys17:12 – Optimum's healthcare IT thesis and value creation plan19:00 – Building healthcare IT training pathways with universities and industry bodies20:56 – Challenges in expanding Optimum beyond its historic core22:24 – How Achieve reached top 5% DPI performance22:50 – Why Achieve sells when the underwriting target is achieved23:42 – How training programmes create a natural exit point25:07 – Aligning impact with alpha creation27:31 – Talent arbitrage, underrepresented communities, and overlooked graduates29:40 – Why solving major social problems can create superior returns30:08 – Daniel's recommended podcasts, books, and shows31:57 – How to contact Daniel Pianko32:23 – Closing remarksRaw Selection partners with Private Equity firms and their portfolio companies to secure exceptional executive talent. We focus on de-risking executive recruitment through meticulous search and selection processes, ensuring top-tier performance and long-term success.

What does it actually take to build AI for 70 crore users?Vikram sits down with Rahul Chowdhury -  co-founder and CTO of PhonePeto talk about how India's most scaled fintech is approaching AI. Not with hype or a top-down mandate, but with a quiet, deliberate, engineering-first philosophy that started four years ago with a small team focused on making developers happier.Rahul shares the inside story of PhonePe's AI journey from building their own LLM gateway and Agent Hub, to launching AI search with Microsoft, to betting on on-device models for privacy and cost. And it ends with the biggest idea of all: India's DPI stack has spent a decade making data AI-ready. The opportunity now is to use it to build the bank branch of one — truly personalized financial products for every Indian.If you're a founder, engineer, or product leader trying to understand where India's AI story is really headed, don't miss this.What you'll learn

Le DPI-A ou diagnostic préimplantatoire pour aneuploïdie n'est pas encore autorisé en France, alors qu'il l'est dans de nombreux pays voisins comme l'Espagne, la Belgique, la Suisse ou le Portugal. De plus en plus de patientes françaises, de couples, se déplacent à l'étranger pour pouvoir y accéder dans le cadre d'une FIV.Dans cet épisode, je vous explique concrètement ce qu'est le DPI-A, comment il se déroule, pour qui il est indiqué, et ce qu'il peut et ne peut pas vous apporter dans votre parcours PMA.

Dr. Pramod Varma, chief architect of Aadhaar, UPI, and the India Stack, and co-founder of Networks for Humanity, joins host Tushar Shetty to discuss the design philosophy and the next stages of India's Digital Public Infrastructure.We discuss the core principles that distinguish the India Stack from platform models like Alipay and PayPal, the Account Aggregator framework's consent architecture and its relationship to data protection regimes like the GDPR, the extension of DPI logic to business identity and flow-based lending through the Unified Lending Interface, the role of AI as both a driver of demand for DPI foundations and an opportunity for, the case for DPI as a replicable global model for the democratization of technology.For more in-depth analysis on South Asia, subscribe to the Beyond the Indus podcast on Spotify or Apple Podcasts, or follow us on YouTube for video episodes.

Math doesn't have to be intimidating, especially when it's the kind that helps fund companies and move science forward. In this episode, host Elaine Hamm, PhD, is joined by Isaiah Reeves, PhD, Biomedical Analyst at Solas BioVentures, for a practical and approachable deep dive into venture math. Drawing on his background as a scientist turned investor, Isaiah breaks down the core financial concepts every biotech founder should understand: from valuations and dilution to IRR, cap tables, and deal terms. The conversation offers real-world guidance for navigating fundraising, choosing the right partners, and avoiding common pitfalls that can derail long-term value creation. In this episode, you'll learn: How venture capitalists think about valuations, dilution, and returns, and why fully diluted post-money matters. Key metrics like IRR and DPI, and how they influence investment decisions and fund performance. Common deal terms and cap table “red flags” founders should watch out for as they raise capital. Tune in to learn how understanding venture math can help founders make smarter funding decisions, protect long-term value, and build biotech companies positioned for sustainable growth and impact. Links: Connect with Isaiah Reeves, PhD, and check out Solas BioVentures. Connect with Elaine Hamm, PhD, and learn about Tulane Medicine Business Development and the School of Medicine, as well as Cadenza Bio. Connect with Josh Eckelberry, MBA, and Mark Corrigan, MD. Check out the books The Go-Giver and Venture Deals. Check out the podcasts STAT, Biotech Hangout, and 20VC. Check out our previous episodes on Networking as an Introvert and Solas BioVentures with Travis Manasco. Connect with Ian McLachlan, BIO from the BAYOU producer. Learn more about BIO from the BAYOU - the podcast. Bio from the Bayou is a podcast that explores biotech innovation, business development, and healthcare outcomes in New Orleans & The Gulf South, connecting biotech companies, investors, and key opinion leaders to advance medicine, technology, and startup opportunities in the region.

rsync's founder came back, patched real security bugs with AI help, and triggered an open source meltdown. Plus, two more projects reject AI-generated code as the community's newest fault line cracks wide open.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:ConnecTen Internet — Get $35 off your order total with Jupiter35

Send us Fan MailLEARN THE CAPITAL RAISING STRATEGIES AND FRAMEWORKS used by alternative asset professionals: go.fundraisecapital.coThis episode of Making Billions with Ryan Miller & Aman Verjee delivers the secondary market playbook that gives managers a structural advantage over every fund ignoring this shift.How do venture secondaries solve LP liquidity problems in 2026? Former PayPal and eBay CFO Aman Verjee reveals the exact system for buying into elite VC deals at 70% below market value. Fund managers face a quiet crisis: DPI timelines stretching 10-12 years while LPs demand exits far sooner. What separates fund managers who retain LP trust from those who lose it? Verjee breaks down how to audit your fund structure today, identify liquidity gaps before they become emergencies, and build relationships with secondary buyers years before you need them. He shares the due diligence framework used to evaluate SpaceX, Anthropic, and Canva positions when information is limited and markets are opaque.[THE HOST]: Ryan Miller is a fund manager, capital strategist, and former CFO turned angel investor in technology and energy. He is the founder of Fund Raise Capital and Aequor Capital Partners, and has mentored over 1,000 fund managers across private equity, private credit, venture capital, real estate, and alternative assets globally.[THE GUEST]: Aman Verjee has more than 20 years of financial and operational experience from both private and public technology companies. He has been a member of the management teams at some of the most successful companies in the world, including PayPal, eBay, 500 Startups and Sonos. His new book, A BRIEF HISTORY OF FINANCIAL BUBBLES, comes out in December.Subscribe on YouTube:https://www.youtube.com/channel/UCTOe79EXLDsROQ0z3YLnu1QQConnect with Ryan Miller:Linkedin: https://www.linkedin.com/in/rcmiller1/Instagram: https://www.instagram.com/ryanmilleroffical/X: https://x.com/_MakingBillionsWebsite: https://making-billions.com/Support the showSupport the showDISCLAIMER: This podcast is for entertainment and general informational purposes only — not legal, financial, tax, or investment advice. Nothing herein constitutes a solicitation or offer to buy or sell any security or investment product. Past performance does not indicate future results. Always consult qualified legal, financial, and tax professionals before making any investment decision. NAME NOTICE: "Making Billions with Ryan Miller" reflects the profile and aspirations of guests featured — it is not a promise, projection, guarantee, or representation of any financial result, income, or outcome for any listener, viewer, or reader. Most individuals who consume this content do not raise any particular amount of capital, and many achieve no financial result whatsoever. "Fund Raise Capital" is a brand identifier only — it is not a promise, guarantee, or representation that any member, subscriber, or listener will raise capital, attract investors, or achieve any financial or professional outcome. This show does not constitute a business opportunity, franchise, investment program, or offer of any product or service of any kind. No part of this show should be construed as a solicitation for investment in any way. Guest views are their own and do not necessarily reflect those of the show or host. Host and/or guests may hold positions in assets discussed. This episode may contain paid sponsorships, advertisements, or endorsements. Sponsored content is identified where...

The post-WW2 world order is dead. The UN doesn't work. The WTO can't function. Multilateralism has collapsed. And the world is now in a dangerous "interregnum" — a period of fragmentation, conflict, and competing alliances where every country is fighting to shape what comes next. So what does this mean for India? In this conversation with Roshan Cariappa, Ambassador Dr. Mohan Kumar — Former Indian Ambassador to France and Bahrain, India's lead negotiator at the WTO/GATT for nearly a decade, Professor of Diplomatic Practice at OP Jindal Global University, and Chairman of RIS — takes us inside the rooms where India's biggest global negotiations actually happen. This is not theory. This is a 40-year practitioner explaining how it really works. We cover: - Why the liberal world order has "certainly ended" - The non-polar world and India's multi-alignment strategy - "No light at the end of the tunnel" — his honest diagnosis - Can India be a Vishwa Guru? The truth about DPI and AI - The Poverty Veto — why 800M on dole holds India back - What really happens behind closed doors in negotiations - His toughest negotiations: TRIPS Doha and Paris climate - The Nvidia comparison — India's economy = one company - Why India can't have a confrontation with China - Trump-XI "bilateral strategic stability" and India - Jaishankar's "three mutuals" approach with China ⏱️ TIMESTAMPS 00:00 Cold open: The world order is dead 00:54 Are we witnessing the collapse of the post-Cold War order? 02:13 "The liberal international order has certainly ended" 03:42 What changed about globalization 05:05 Was it Trump — or structural factors? 07:00 The "non-polar" world explained 08:13 India's multi-alignment strategy 11:04 Fragmentation of the world order 12:08 "I've never seen this deficit of cooperation in 40 years" 13:25 "There is no light at the end of the tunnel" 14:39 Can India step up as Vishwa Guru? 16:27 "800 million on dole is dragging India down" 17:52 India's 1991 redux moment — bite the bullet 20:26 Multilateralism has collapsed — UN and WTO 21:11 The huge gap between US, China and the rest 23:36 What actually happens behind closed doors 25:35 The brief, the non-negotiables, the tradeables 27:21 The Poverty Veto — Mohan's original concept 31:37 The toughest negotiation: TRIPS in Doha (2001) 33:25 The Paris climate accords — India's red lines 36:20 Is there bipartisan consensus on foreign policy? 38:14 Pranab Mukherjee's all-party meeting idea 40:08 What makes an effective negotiator? 44:33 Why "anyone can become Ambassador overnight" is wrong 45:07 Should India look beyond the IFS cadre? 49:00 Why India can't have a Jared Kushner 49:26 40 years of negotiation — how India's leverage has grown 51:32 India = the size of Nvidia ($4 trillion comparison) 53:00 "9-10% growth for 10 years — the world will be at your feet" 58:43 The final question — US-China dynamics 1:00:00 Trump-XI "bilateral strategic stability" 1:01:44 Why India can't have a confrontation with China 1:02:13 Jaishankar's "three mutuals" with China 1:03:13 Closing thoughts

In the May 2026 episode of 12:01 The Death Penalty in Context, DPI Managing Director Anne Holsinger speaks with Dr. Naomi Yavneh Klos, Dean of the Honors College at the University of New Mexico, and a prominent scholar of the Holocaust. Dr. Yavneh Klos is a founding member of the Jews Against Gassing Coalition, a New-Orleans area group formed to oppose the use of nitrogen gas as a method of execution in Louisiana. She joins DPI's podcast during Jewish American Heritage Month to discuss the historical ties between lethal gas executions and the use of gas as a tool of genocide during the Holocaust.

What happens when a classically trained musician from Juilliard ends up managing a $6 billion endowment? Today's episode of Origins explores exactly that journey - and what it reveals about how the best institutional investors really think.Nicholas Csicsko, Managing Director of Investments at Trinity Church NYC, brings a rare perspective to venture capital - one shaped by years of classical music training, a doctorate from Juilliard, and a decade building out one of America's most unique institutional investors. Trinity Church, founded in 1697 and endowed with 215 acres of Manhattan by Queen Anne in 1705, has grown its diversified investment pool to over $4 billion under Nicholas and CIO Meredith Jenkins.Together with hosts Nick & Beezer, the group digs into what institutional LPs really look for in venture managers, and what puts them off. From the tension between patient capital and the need for liquidity, to skepticism around sky-high private market valuations and the growing disconnect between private and public markets, Nicholas delivers the kind of frank, independent thinking that makes for a truly exceptional investor.Along the way, they explore the virtues of "cynical optimism" in early-stage investing, the institutional pressures that push LPs toward brand-name funds, and why Nicholas believes the best venture managers are those who know themselves deeply. From the challenges of scaling a venture firm to whether today's AI-driven capital surge is sustainable, this conversation offers a grounded, data-aware take on what it takes to build lasting returns in private markets.—Quotes"If you could put a bunch of investments into a line item that wasn't going to receive scrutiny, that left tail risk of something going to zero would probably be less. But if it's visible, it's discussable. You probably don't get fired for doing the next a16z fund, but you might be questioned if you take a flyer on someone who's up and coming. And so there's this institutional pressure towards, dare I say, conformity. But what's safe? What's perceived as safe?”"It's all fine and good that folks think they can raise and put more money to work, but I'm a little worried about where it's taking us because when open AI raises around 4x larger than any IPO in history, I kind of worry that we're creating a market that is not sustainable because ultimately there's not enough liquidity. There is a massive disconnect there.”"Knowing thyself is probably the number one thing I would attribute to all of the best investors I've met. And as they get older and more experienced, they know what they think they need more and more without stopping challenging their bias, without adding that new person to make them better.”—Time Stamps00:55 Meet Nicholas Csicsko, Managing Director, Investments at Trinity Wall Street02:24 Musician Mindset to Investing03:40 From Juilliard to Finance05:56 Trinity Church Endowment Story09:56 Building the Portfolio and Venture12:31 Institutional Risk and Conformity14:47 Private Public Market Disconnect18:57 DPI, TVPI and Secondaries20:41 Backing Off Radar Managers23:47 Cynical Optimism in Venture28:04 Building a VC Firm Team34:34 Where Venture Fits Today39:02 Too Much Capital and Liquidity?42:20 Closing and Next Episode—LinksConnect with the guest and hosts on LinkedIn!Nicholas CsicskoBeezer ClarksonNick ChirlsLearn more about:Trinity Church NYCAlfred P. Sloan FoundationOpenLPAsylum Ventures

What's possible in the first few weeks of true transformation work? This episode answers that question with clarity—and proof.In this episode of the Coleman Associates Innovation Podcast, Amanda sits down with a powerhouse frontline team from a rural clinic in North Carolina that is already seeing remarkable results through the DPI™ (Dramatic Performance Improvement) Collaborative. In just two months, this small but mighty team has: Reduced No-Show rates consistently for 7 straight weeks  Increased productivity by 27% at the time this was recorded. Since then, the average increase is 33% from baseline.  Decreased cycle times overall by over 25%And they're just getting started.You'll hear directly from the people doing the work every day—provider Ashley, nurse Maddie (Madison), PSR Vanessa, and Peer Support Specialist Hannah, alongside Coleman coach Gabriel DelMuro. Together, they share what it really takes to turn intention into action, and action into measurable results.This episode pulls back the curtain on: What the early days of DPI™ actually look like on the ground  The specific challenges this team faced—and how they pushed through them  How aligning roles across the care team unlocks rapid improvement  The unexpected wins that come from doing this work the right way Most importantly, this conversation highlights a powerful truth: you don't have to wait months—or years—to see meaningful change.Whether you're a frontline team member actively in DPI™ or a leader wondering what's possible in your own organization—especially in rural settings—this episode offers both inspiration and practical takeaways you can act on immediately.Host: Amanda LaramieGuests: Ashley, Maddie, Vanessa, Hannah, and Coleman Coach, Gabriel DelMuro Thanks for listening! If you or someone you know should be interviewed for this show, send us an email. Check us out on: FacebookInstagramLinkedInOur WebsiteTikTokTwitterYouTube 

This week on Riding Unicorns, we're joined by Charlotte Palmer, Vice President of Venture Capital at Integra Global Advisors.Charlotte sits on the other side of the table as an LP, backing emerging venture funds globally. In this episode, she lifts the lid on how LPs actually evaluate VCs, what really matters beyond headline performance, and why many GPs still get fundraising wrong.We cover:• How LPs really underwrite venture funds and why early DPI is often misunderstood • What matters more than performance in the early years of a fund • Why access and ownership drive returns more than anything else • The reality of backing emerging managers and why smaller funds win • Team dynamics, attribution, and how LPs assess partners under the hood • Why fewer funds are getting backed and what's changed in the market • The shift in venture towards early-stage and how late-stage AI impacts LP strategy • Portfolio construction from an LP perspective and how diversification actually works • The role of co-investments and why LPs increasingly lean into them • How GPs can create urgency in fundraising and what actually cuts throughCharlotte also shares practical advice for GPs, including how to re-engage LPs, how to position a fund without strong DPI, and why most outreach fails to land.A clear, honest view from the LP side on what it takes to get backed and build a fund that lasts.

Most GPs walk into LP meetings ready to prove they have access to the best deals. Iren Reznikov, Partner at Vintage Investment Partners, barely cares. In this episode, Iren breaks down what sophisticated LPs actually underwrite, how Vintage's three-strategy flywheel creates an information edge across fund of funds, direct, and secondaries, and what the Anthropic cybersecurity move really means for investors in that sector.Vintage manages $4.5B across 23 years of venture investing across the US, Europe, and Israel. This is a masterclass in how the best capital allocators think.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comWe talk about -- Why access is table stakes — sophisticated LPs underwrite picking discipline, not just deal flow- The three-strategy flywheel — how fund of funds, direct, and secondaries compound into one information edge- AI-native teams, not just products — founders who don't rebuild their orgs for AI won't compete- Anthropic and the cybersecurity supercycle — cyber wins in up markets and down markets- Size is the enemy of returns — why a $4.5B platform still refuses to raise large vehiclesTimestamps:(00:00) - Preview(00:45) - Introduction to Iren Reznikov and Vintage Investment Partners(02:18) - What makes a fund stand out in the first 10 minutes?(03:01) - The importance of a consistent, durable strategy and a manager's "right to win"(05:38) - Biggest misconceptions GPs have about what LPs actually care about(06:21) - Why disciplined decision-making matters more than just access(08:26) - Access is table stakes; picking and winning capabilities are the real differentiators(09:48) - The evolution of VC value creation and its strategic importance(12:23) - How Vintage's three-strategy flywheel (Fund of Funds, Directs, Secondaries) creates an information edge(14:50) - The power of data and "business karma" in long-term investing(16:48) - How the investment committee handles disagreements and makes decisions(17:42) - The role of partner conviction and fundamentally proof-testing assumptions(19:30) - Balancing allocations between existing and new fund managers(22:26) - Differentiating a "double-down" manager from a solid performer(23:18) - Key indicators for doubling down: consistency, grit, and genuine founder relationships(26:30) - Where is the biggest edge today: fund investing or direct deals?(27:45) - The edge in direct investing: AI-native teams and founders willing to completely rebuild(30:45) - Leveraging an information edge in the burgeoning secondary market(31:41) - How founders and VCs should approach liquidity and secondaries today(34:45) - The impact of Anthropic's move into cybersecurity on the market(36:45) - Why cybersecurity budgets remain robust in all market conditions(38:38) - The convergence of the CIO and CISO roles driven by AI(40:35) - The market bifurcation between large multi-stage platforms and smaller specialized funds(42:05) - A founder's perspective: The importance of people over brand on a cap table(44:58) - How a Fund of Funds allocates capital when established funds raise mega-funds(46:20) - Vintage's disciplined approach to fund size and manager re-ups(49:25) - Managing the extended lifecycle and DPI in a Fund of Funds model(50:45) - Strategies for accelerating DPI: smaller fund vehicles and backing top-performing managers(54:00) - The ideal fund size for VCs that Vintage backs(55:14) - Start of the Rapid Fire Round(55:53) - Where to follow Vintage and Iren ReznikovLinks:Vintage Investment Partners - https://vintage-ip.com/Connect with Iren Reznikov - https://www.linkedin.com/in/iren-reznikov/Connect with Prashant: https://linkedin.com/in/choubeysahabSubscribe to VC10X newsletter - ⁠https://vc10x.beehiiv.com⁠Subscribe on YouTube - ⁠https://youtube.com/@VC10X ⁠VC10X website - ⁠https://vc10x.com#VentureCapital #FundOfFunds

Marlon Nichols is Co-Founder and Managing General Partner at Mac Venture Capital — a seed-stage firm that closed its first fund at $110M with institutional backing from day one and has grown to over $600M in AUM across three funds.In this episode, Marlon breaks down the fundraising arc that built Mac VC, the four-part founder framework he never compromises on, and the inside story of two portfolio companies — Pipe, which went from a $13M valuation to $2B in 18 months, and Gimlet Media, his early bet on the HBO of podcasting.Whether you're an emerging manager trying to crack institutional LP relationships, a founder wondering what top seed investors actually look for, or an LP benchmarking how the best funds are built — this conversation is essential listening.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comWe talk about -- Raising fund one with institutional LPs — no proof of concept fund required.- The four founder qualities Marlon never compromises on- Seed discipline at scale — how Mac VC stays true to stage at $600M+ AUM- What actually wins competitive deals at seed- Pipe: $13M valuation to $2B in 18 months — conviction, pivot, and recovery- Gimlet Media: betting on the HBO of podcasting before the category existed---Links:Mac Venture Capital - https://macventurecapital.com/Connect with Marlon Nichols - https://www.linkedin.com/in/marloncnicholsConnect with Prashant: https://linkedin.com/in/choubeysahabSubscribe to VC10X newsletter - ⁠https://vc10x.beehiiv.com⁠Subscribe on YouTube - ⁠https://youtube.com/@VC10X ⁠Subscribe on Apple Podcasts - ⁠https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986⁠Subscribe on Spotify - ⁠https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQ⁠VC10X website - ⁠https://vc10x.comTimestamps:(00:00) - Preview(01:24) - Introduction to Marlon Nichols and MaC Venture Capital.(02:58) - MaC VC's journey from Fund 1 to Fund 3.(04:53) - How MaC VC attracted institutional LPs from its first fund.(06:48) - The fundraising experience for their recent $150M fund.(07:40) - Comparing the fundraising timelines for Fund 1, 2, and 3.(10:34) - The strategy behind fund sizing and when to stop raising.(12:59) - How LP expectations change from Fund 2 (TVPI) to Fund 3 (DPI).(14:46) - A deep dive into MaC VC's portfolio construction model.(17:17) - How Marlon's investment mindset has evolved with experience.(19:01) - The four essential qualities Marlon looks for in a founding team.(21:33) - How portfolio construction strategy changed from 50 companies to 36-40.(22:47) - Defining "winning" at a fund level: Why DPI is the ultimate goal.(24:31) - What wins allocations in competitive deals.(27:13) - PIPE's journey: From initial investment to a major pivot.(31:07) - The Gimlet Media story: The bet, the growth, and the Spotify acquisition.(33:48) - Rapid Fire: Sectors and regions MaC VC invests in.

The Great private Capital Reset is upon us. Markets are volatile and driving new economic imperatives. Are VC funds still VC funds, even if they raise billions per fund? What happened to the rest of the market? What is driving VC investments? What do Limited Partners think? What is on their minds? This and more, in episode 76 of Tech Deciphered. Navigation: Intro The State of the Reset: The Hangover from the Party? LP Fatigue and VC Differentiation What Really Matters: Performance.. Returns The Mega Fund Question The Case for Smaller… Rightsized Funds What Comes Next? Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Bertrand Introduction Welcome to episode 76 of Tech Deciphered. This episode will be about the great private capital reset. As you know, or you have probably heard, there is significant structural transformation in the world of venture capital, and we are probably witnessing a fundamental reset of the private capital stack. We got a huge bubble in 2020, 2021. Fueled by near-zero interest rates. We got inflated fund size, compressed due diligence, and now a generation of zombie funds and zombie startups. Now that rates have normalized, exits have not been as much as expected. LP patience is a warning sign, and I guess the industry is being forced to confront an uncomfortable truth: most VC funds raised since 2017 might not return what their LPs expected. You know, how do we start?   Nuno This is going to be a relatively nuanced episode. Obviously, there is going to be a lot of haves and have-nots, both in terms of VC funds, also in terms of startups. And so I want to start with that. This is going to be more nuanced than all transformational and disruptive.   Bertrand It’s not the end. It’s not the end.   Nuno State of the Reset: The Hangover from the Party? It’s not the end. There’s still huge mega funds that are raising more and more. It’s clear that the music has stopped, right? So if we’re playing the game of chairs, the music has stopped. Around ’22, ’23, we started seeing the first signals that funds had raised way too much money. Firms collectively raised around $669 billion globally in 2021 alone. If we fast forward now to last year, 2025, depending on the sources, we did some internal analysis at Chameleon. We came up with $75.6 billion was raised last year by 493 funds, right? So That’s a significant drop, right, in terms of fundraising. Other sources would say a little bit more. There’s a little bit of a discussion around how much did the top 30 funds capture. If you believe some of the stats out there, they would say that actually top 30 funds captured 75% of all capital raised last year. We did again some internal analysis at Chameleon, and the conclusion we came to, it was closer to 50 to 55%. So not as dramatic as some of the sources out there, but still pretty dramatic. There’s a lot of capital concentration on the top funds. Again, the top 30 funds would’ve raised 50 to 55% of capital or up to 75% according to other sources. So definitely a tremendous amount of concentration. There was a lot more fragmentation in terms of capital raised if we’re looking at the years from 2010, 2011, all the way through 2021. So 2021 would’ve been sort of the peak of non-concentration if you look at that. And that again, now we are getting more and more concentration. There’s more and more of this arbitrage around, I’ll give money to the top funds, I will not give money to the smaller funds, or I’ll give less money to the smaller funds. There’s a little bit of a movement around concentration. We’ll talk about it later and what that means. Are mega funds really better? Are the small funds still the way to go? We’ll talk a lot about that later in today’s episode. There seems to be a little bit of a bifurcation. We could say it’s either bifurcation around top-tier VCs or larger VC funds versus smaller VC funds. My perspective is the bifurcation that we’re seeing right now is more of a bifurcation between funds that are no longer just stepped into the VC space, but they’re actually becoming more and more private equity firms with full asset management range from early stage all the way to late stage. Think of it almost like a private equity hedge fund, quasi, versus classic VC funds. And I think what we’re seeing is the Andreessen Horowitzes, the a16zs of the world, the NEAs, the Sequoia Capitals, just to name a few, becoming more and more broad asset class managers across private equity, whereas you have more classic VC happening in earlier stages. And so that’s the real bifurcation that I think is actually happening.   Bertrand And maybe not really hedge fund, because they are always still long-only funds. So there is no hedging happening, at least as far as I know.   Nuno Well, some of these guys have become RIAs, like A16z has become an RIA, so they can do secondaries.   Bertrand That’s true. Yeah.   Nuno And they can also sell stuff, etc. So I don’t know how aggressive they’re going to be in terms of secondaries and selling and actually doing other kinds of services you can do if you’re an RIA. But it’s not, I think, out of the realm of possibility that they would sort of acquire and sell stock more rapidly. In that way, to your point, Bertrand, maybe they actually become beyond just long guys, right?   Bertrand Yes. Another trend I have seen is some of the larger VC funds seems to have no problem investing in multiple competitors. This was not possible before. I mean, if you’re a VC fund, you had some sort of duty not to invest in the competitors, but now some invest OpenAI, Anthropic at the same time. Do you see that as part of this evolution?   Nuno For sure. And I think there’s a lot of people like the ostrich putting their heads below the ground and it’s like, “Eh, no, no, nothing to see here.” But that does constitute a conflict of interest. And if I’m a startup raising, this assumption that you will not invest in one of my competitors is no longer there, certainly for the mega funds, because of that notion of deployment of capital. Now, some funds will still hide under the notion, actually formally from a fund perspective, we’re not investing in competitors. It just happens that different types of our funds are investing in competitors. Like maybe my growth fund is investing in a competitor to my early stage fund, right? But our funds are relatively independent. So I think there’s a little bit of hide and seek that will go on if you talk to some of the fund managers. Well, they say, well, we’re not investing out of the same fund into these competitors. But between you and I, as we know, a lot of these partnerships actually do a lot of stuff together at the general partnership level. So are there really actual Chinese walls between the funds? Well, it really depends on the partnership. And to be honest, most of the partnerships don’t have very significant Chinese walls between the funds, right? The managing general partners sometimes actually occupy investment committee roles across different funds. So I think the conflict of interest is there. So that’s why I say there’s a little bit of ostrich behavior. Put your head behind the ground or below the ground and just pretend nothing is happening. Just sharing maybe a couple of interesting stats. Global fund closings for 2025, according to our numbers at Chameleon, 1,098 closed. In 2025. Closed is when you start deploying capital, right? Whereas— so it’s not closed down, it’s closed like we start deploying capital. And that number, 1,098, is dramatically down from 1,600 in 2024. And it’s actually the lowest number of closings that we saw since 2014. So again, this is bad, right? It means there’s less funds doing fund closings and deploying capital in the market than since 2014 and dramatically below the 2024 numbers, right? Where we already saw some market readjustments. The number of active VC firms in the US that did 2+ deals, which is not a huge bar, has dropped 38% back to numbers in 2023. So we don’t have numbers that are a little bit more up to date, but basically in 2023, those numbers are already dramatically dropped. So there’s less and less active funds. So there’s funds that might be in the market, but they’re not actually deploying that much capital, not doing that many investment. They’re sort of either zombie funds or relatively passive funds that have passed their investment period. For those listening to us, the investment period for a VC fund is normally between the first 3 to 5 years of the fund, which is when you build your portfolio, when you can invest in new companies. After that time period, everything that you do up to normally what would be year 10 is follow-ons. You put more money into the companies that you’re already invested in, that you already constructed portfolio with during those 3 to 5 years.   Bertrand Yeah, that’s a pretty scary change. And obviously, I guess we’ll come to it, but the time it takes to fully liquidate investments is getting longer and longer. In the old days, we used to talk about VC funds having a 10-year life, maybe a +1/+1 in terms of extension of the fund life. But it looks like it’s taking 16 to 18 years actually to get full liquidity from a fund investment.   Nuno LP Fatigue and VC Differentiation And I think that’s the scariest piece. I mean, just to share some numbers, we in venture capital talk about vintages, right? Which year did your fund start in? Normally when you did your first close onto the fund, as we were saying before, close is when you get all your investors at that moment in time to come in and you do your first close so the next fund starts running. 2018 vintage funds, right? This is now almost 7 years ago. So you should start having— actually 8 years ago almost at this point in time. You should start already getting distributions or you start getting cash back if you’re a limited partner and investor in those funds, you should start getting cash back. Half of all 2018 vintage funds have returned $0 to their LPs. So they’ve had no distributions to their LPs. 2020 vintage, which was a very hot vintage, only 42% have begun any distribution. So 58% have distributed $0, right? 2021, only 25% have done any distributions. Now, I happen to have a 2018 vintage fund and a 2021 fund. My 2018 fund has already distributed over 3x net of fees in distributions, and my 2021 fund’s already over 10% distributed back in distribution. So we’re very proud of that. But in general, the numbers are awful. There’s no liquidity back to LPs. And to your point, that’s kind of a big deal because some of these funds have been going on for 7, 8 years, and where’s the liquidity going to come from? On the other hand, if you look at TVPI, so DPI is distributions to paid-ins cash on cash. But if you look at TVPI, which is total value to paid-in, which also includes the book value or the value that you’re marking it on your books, basically the paper value as we call it for the company, even on that, the median 2017 fund, so 2017 vintage fund has a TVPI, total value to paid-in, of only around 1.76x, which is well below what should be, which is sort of the 2 to 3x benchmark of a really good performing fund. So the median funds are doing very, very poorly overall. So if you add that to the fact of what’s happening and distributions are taking a long time, back to your point, Bertrand, it’s taking like— this should be a 10-year asset class, maybe 11, 12 years, and now it’s looking a little bit like a 15, to 18-year asset class, which is not what most limited partners sign up for. Part of this dynamic, I think, is that we’ve had tremendously overvalued private companies over the last few years, right? Secondly, these companies have just stayed private longer. And I was having a discussion recently with a friend of mine, it’s like, hey, what’s this thing about companies are staying private much longer? Is there some dynamic around secondaries? And the reality is there is a dynamic around secondaries, right? Because if I’m a very large fund and I can get away with doing secondaries on my portfolio, I will get liquidity at some point, right? But someone else is stuck with private stock, which hopefully will IPO, but who knows, right? And so there’s this funny dynamic right now of because of secondaries, because of a couple of other things that are happening in the market, actually a lot of these startups are staying private for tremendous amounts of times, and some of them will IPO and they’ll be huge deals. Some of them might not and might not warrant the latest private valuations that they’ve exercised. And so there’s this tremendous noise that we’re seeing in the mid to late funnel of privately held companies where some are just waiting to be public. Some of them might not be able to go public at anything that is an up round versus private valuations that they’ve had in previous moments and in previous rounds.   Bertrand And obviously the 2 to 3x returns that funds are targeting, and obviously more 3x than 2x, I mean, that was good and nice if it’s a 10-year fund, but if it’s the same 3x for 15 to 18 years, it’s not at all the same rate of return annualized. So it’s a really, really, really big issue if you keep the return the same, but you extend the duration of the fund. Concerning going IPO, there is a lot of complexity going public, the IPO process itself, but also after that when you’re a public company. It changed how you can run the business. Some would argue that we have had an issue with more companies delisting than companies listing on the public market. So I think there might be also separate issues about the efficiency of the public market and maybe a need for change. We went very strongly in one direction for the public market, have post and run, but was it really ultimately the right thing to do? I’m actually not so sure.   Nuno Yeah, I mean, just to be clear, this is anecdotal, but when we tell prospective LPs at Chameleon about our returns, the last few funds, 2018, 2021, the first reaction is, “You must be lying, right? Surely you can’t have distributions already for 2021,” et cetera, et cetera. So clearly there’s almost a state of disbelief right now from limited partners. And liquidity does matter. So clearly you have to move forward. So how did we get to this point where we had this bubble 2021 all around that time space and now things don’t look so good. Well, the macro conditions have changed dramatically. I mean, rates when they were near zero, safer assets yield nothing or yield nothing. So basically you had to push capital into longer duration risk assets like venture capital. And so you had to push it. So the opportunity cost of capital also has fundamentally shifted. Obviously a 3x VC return in 15 years over 10 actually competes very poorly against 5% annual credit returns over several years. So there’s been a readjustment of stuff. And then the public equities in particular, the tech public equities have had a lot of volatility, but some of them have done extremely well, right? Chipsets, things like NVIDIA, the Amazons of the world, Alphabets, et cetera, et cetera. They’ve done very, very well. So why would I invest in a long-term illiquid asset that takes now longer to give me money back, and in some case doesn’t give me back, if I can invest just in public equities, and a variety of other things. The venture debt costs have increased dramatically. The burn rates that were sustainable back in the day with sort of the addition of venture debt, private credit, et cetera, now are overblown at this moment in time. At the end of the day, there’s been a lot of movements also overall in the pipeline in terms of valuations, et cetera, et cetera. Now, I would put a grain of salt into all the numbers I just told you. There still is a little bit of the haves and have-nots in startup land. Certainly in early stage where if you’re a hot AI company, you can get away with raising a Series C or $480 million. This is actually a true story. Series C, right? Not Series C, a $480 million at $4 billion pre-money valuation. Whereas if you are maybe in a space that’s less hot, you’ll have more difficulty in raising money at this point in time, might not be able to even raise a Series C, right? So there’s a little bit of the haves and have-nots happening on the VC side in early stage that has been really amplified by the macro regime and where we’re at, which is actively zero-rate era is done and now the new regime is quite different. And so I can get better returns by doing something else.   Bertrand Kind of makes sense. I mean, if you have some ways the SaaSpocalypse in the public market because there is that fear that AI is going to completely change the game for especially for the more typical software companies. Good luck raising private money to quote unquote just build traditional software companies. You cannot expect a warm embrace from the private market if the public markets are completely destroying that category. I’m not saying that this is there forever, uh, things might change over time, but for sure what’s happening on the public markets always have a very strong impact on the private market.   Nuno Indeed. So what’s happening in this relationship between limited partners and VCs, the general partners? Again, limited partners are the people that give venture capital firms and venture capital funds their capital to actually deploy. And they are a variety of different players, right? Could be endowments, like university endowments, pension funds, family offices, very high net worth individuals, fund of funds, et cetera, et cetera. I mean, in particular, if you look at the institutional investors, the endowments, the pension funds, the fund of funds, they have allocations that they do to different asset classes typically. And the feedback that we’ve received from the market is they are increasingly frustrated with what’s happening in terms of distributions. They’re not getting capital back. It’s like, I gave you capital 8 years ago, 9 years ago, 2017, 2018 vintages, and I’m not getting any capital back. So what the hell’s happening? On paper, it looks maybe the fund’s doing okay or it’s doing great in some cases, but where’s my money? And so that creates a little bit of wait-and-see kind of game on portfolio allocation. As we’re thinking through their re-ups, putting more capital into funds that they’re already actually put capital or putting in capital into new slots, into new fund managers that they want to put money into. They’re like, well, let’s wait and see. I want to get my money back or get some money back first before I redeploy it. Again, this is a little bit the haves and have-nots because we’ve seen, for example, a couple of top-end LPs in terms of returns that have a little bit the opposite problem, right? Because they are into funds that are performing extremely well. They actually are over that period and they want to actually redeploy. But to be honest, the average in the industry right now is a wait-and-see game. It’s like, I want to wait and see, which leads to what can only be characterized— I was hearing someone the other day, one of the top advisors in the LP community, saying this is the worst fundraising environment ever for venture capital. Not the last 20 years, 30 years, like ever, right? Since this became an asset class more institutionally in the late ’60s, early ’70s, Pulse Robo 2 as it was created, this is the worst fundraising environment ever. Oh, wow.   Bertrand And concerning TVPI, let’s not forget that typically it’s not mark-to-market. So the metrics in terms of TVPI, correct me if I’m wrong, you know, but the metrics in TVPI are based on typically the last fundraise. So if the valuation went down but there was no additional fundraise, we wouldn’t know by looking at the TVPI metrics. It will only be updated if there is a new Financing, equity financing, or an exit.   Nuno Yeah, normally most funds act like that. Some funds are a little bit more aggressive and do do mark-to-market, but normally funds would be conservative and say, hey, I’m being conservative, it’s whatever is the last known valuation of the company. And if there wasn’t a priced round, it’s a little bit more obscure than that, right, Bertrand? Because it might actually be the company has raised money on a note, or either convertible note or a SAFE note, and that wouldn’t count as a priced round. So I would say actually, even if it was a cap that’s below with a significant discount, I won’t recognize the assets as a down round. I won’t recognize the asset with a lower valuation because formally it wasn’t a price round. So it’s on the one hand conservative, on the other hand, it’s only relating to price rounds or exits to your point. So it’s sort of, you can be like, hmm, well, we opt to do that because we think it’s actually the most conservative route. Mark-to-market is extremely difficult to do. And who would do the mark-to-market for you, right? It’s like it’s some valuation firm, et cetera.   Bertrand I’m not saying a mark-to-market is easy, but I’m not sure I would call using the last valuation something conservative in the context that most startups will fail. So it’s not clear.   Nuno Well, in some cases it is, some cases it’s not, right? Depends on the startup situation, to be honest. Yeah, yeah.   Bertrand But yeah, at least that’s how it’s done. So for instance, to evaluate the impact of the SaaS apocalypse, it’s tough to know. We will have on the private market. I mean, we will see that in a few quarters. Because if companies still exist in that environment, if they still do additional truly price rounds after that, that’s when I will start to know.   Nuno I mean, just to share a little bit more data, like VC fund close time stretched to 15 months. Basically, it’s just taking a long time to raise money. It’s taking a long time to do your first close, get your fund running. When entrepreneurs complain to me that their fundraising is difficult, I always say, you have no clue how difficult it is compared to ours. First-time funds have collapsed. We had some numbers that only 77 first-time funds actually closed. I assume this is in 2025 versus 215 in 2023. So that’s a huge number. We did some internal analysis on our side and we did some analysis that emerging fund managers, emerging fund managers are normally people that are in their first one or two funds. Basically emerging fund managers gained some ground until 2017. Reaching by then a slice that was 63.7% of all capital raised in 2017. But since then, the capital deployed to emerging managers has been largely reduced to actually 24.2%, right? So it’s gone from 63.7% in 2017 to 24.2%. So this has been a culling of sorts on emerging managers and almost like a slaughterhouse of emerging managers. Compared to previous situations, which is obviously incredibly concerning if you’re an emerging manager starting your VC firm, et cetera, et cetera. So really tremendously problematic for those. We think capital’s not leaving VC. I think we see a lot of the institutionals saying— there’s some numbers as high as 33% of institutional investors plan to invest more in venture in the next 12 months. So I don’t think capital’s leaving VC. I think it’s really concentrating. We’ll come back to the concentration issue later in the episode. And part of that concentration comes from a topic that has been widely spoken in venture capital recently, which is differentiation. How do you differentiate in venture capital if you’re talking to a limited partner, right? How does my firm differentiate versus the firm next to mine? And that’s incredibly, incredibly challenging. Bertrand, what are your thoughts on that?   Bertrand Differentiation is always a question. I mean, if you’re an entrepreneur, Typically, you think fully about the best possible partner for your stage and for your type of business model. You want a VC who understands fully your business model, because if they don’t, then it’s going to be troubled down the line. But that’s true that another piece of the puzzle is that the best VCs help you get more visibility in terms of achieving potential customer deals, in terms of attracting the best talent. And that’s where VCs’ brand names can help. If you can say you have backing by some of the top, most visible names in the industry, and usually these are the mega funds because others have trouble to be as visible, then they have some sort of unfair advantage compared to others. So I can see that there is some level of concentration happening naturally, especially in the later stage from Series B onwards.   Nuno What Really Matters: Performance… Returns Yeah, I mean, we did some analysis internally about What are the top funds that invested in the top performing companies in early stage, Series C, Series A? And we looked at it by size of fund and the top performing normally are funds below $100 million, but in some cases very closely followed by funds between $100 and $500 million. And actually funds above $500 million, so $500 million to $1 billion and then $1 billion and above are actually tremendously underperforming. So this notion of the industry that says, well, the mega funds still see The top investments early on, because they still deploy in Series C and Series A opportunistically, in some cases even spray and pray if they have their own incubation and acceleration programs, is not true. Actually, we verified that over the last 12 to 13 years. It is not 12 to 13 years in vintage, right? So up to a 2021 vintage fund. So we went basically 12, 13 years back from there. And it’s not true. Actually, the most performing are 0 to 100 and then 100 to 500. And as I said, there’s 100 to 500 in a couple of years actually are a little bit better. Than the $0 to $100 million ones. So that’s the first thing that’s a conclusion. And actually, that’s not shocking. If we remember back in the day, Kleiner Perkins used to raise funds up to $600 million, Benchmark raised their $425 million funds. It seems like the sweet spot for a VC fund would be around $500 million at the top end, like maximum. And now somehow people are saying, well, I’m raising a $3 billion VC fund. It’s like, well, it can’t be a VC fund. The return profile is totally different, right? You can’t deploy that capital just based on early stage investing. And by the way, you’re not seeing the guys at early stage, all that you’re seeing, you’re going to make your returns in mid to late stage, right? Back to what we said at the beginning of the episode. So there’s a little bit of the haves and have-nots there. The big guys are raising more and more money, but they’re no longer venture capital. And I think limited partners that are a little bit more evolved, that are a little bit more conscious of this, that have been in the market longer, are realizing that shift. So it’s like if they want to have the alpha of venture capital, they need to deploy to the sub-$100 million funds or the sub-$500 million funds, right? That’s where they need to actually focus their VC capital. They can still deploy to mega funds, but they’re deploying to a different asset class. They’re deploying to a private equity, mid to late stage asset class, which looks maybe a little bit more like a growth fund or something like that. The second part of differentiation is the honest truth is most VC funds are like, I have proprietary network access, right? I’m ex-Stripe or I’m ex-Google or I’m ex-Facebook or whatever, and I have access to that. I mean, we know proprietary networks from that standpoint are no longer true. The whole thing that created Silicon Valley back in the ’70s of what I used to call the country club deals where there were a few people coming out of the big companies, the Fairchilds of the world, later on the Intels of the world, et cetera, et cetera, that made some money along the way that sort of bootstrapped their next companies, were well-known quantity to the existing VCs and raised money relatively easy on ideas, that doesn’t work anymore. Someone was telling me the other day one interesting thing that I wasn’t quite aware of, a lot of it had to do with the NDAs. I don’t know if you knew this, Bertrand, but like the fact that in California, it was sort of the Silicon Valley community sort of imposed this, we don’t sign NDAs thing and Boston continued signing it. And this whole NDA enforcement issue and non-compete, actually not the NDA thing, but more strongly that California did not enforce non-competes. I could leave Fairchild and start a company that magically was doing something that could be considered competitive to Fairchild. And that was sort of part of the acceleration actually of venture capital in California versus, for example, Boston, which was sort of hand in hand at the beginning.   Bertrand Yeah, I mean, I’m a big, big believer in California success coming from not enforcing or banning non-compete agreements. I think it’s a key part of the game. If you lock people into not doing something similar in the next 6 months to 24 months. And the industry has always been moving fast. So this is a significant time where you are blocked to do something very similar. I think it was really an issue. So I think it’s a key part of the game and it has been there. I don’t know how it started, but I think that non-enforcement of non-compete has been a key part of the success of California. I’m actually pleased to say that Washington State is going in the same direction. They are just signing a non-compete ban. And you might remember that at the federal level, I think in 2024, there was also a ban that was put in place to ban non-compete, but this has been reversed by the courts. So this is not there anymore. So that’s why we see a state like Washington State putting their own ban, and we might see more state by state moving in that direction. I think it was not helping at all, this non-compete. I mean, there is obviously stuff that needs to be done, like you cannot steal secrets, you cannot steal IP.   Nuno Yeah.   Bertrand Even stealing employees, there should be some restraints. We need to find the right balance, but you have to be careful there. That was key for the success of California, and I’m glad to see that this is a trend that’s going to go beyond California. And I hope most states will have a ban on non-compete.   Nuno Maybe just to close on the differentiation process, two things. One, I think there’s this notion When you talk to some LPs, that seems to be a little bit ingrained, some LPs that prefer specialized funds. We’ve also done some significant analysis internally and have talked to a couple of datasets other than our own, or people that own datasets other than our own, and the feedback has actually been not so fast. Actually, generalist funds over time cannot perform specialist funds. There seems to be a little bit of a sweet spot around generalist funds. We like to call ourselves multi-specialized at Chameleon, but ultimately from the perspective of specialized versus Generalist funds, the picture’s not as clear as specialized funds outperform generalists or generalists outperform specialized. We’ve seen there are pockets where actually generalists outperform specialized, in other pockets where specialized of a certain size can outperform generalists. So that’s one topic on differentiation that is a little bit broader. And then the final topic on differentiation, it’s really an industry that hasn’t innovated dramatically on where it creates the most value, which is really the picking stage, right? So it’s having great deal flow, very optimal, productive, efficient due diligence with very few resources and the ability to then get into those deals. That’s where most of the value is created. And then hopefully liquidating the asset if there’s an opportunity to do so at the right time, either through secondary trade sales or an IPO or something else. And what we’ve seen is the industry has innovated very little. I mean, the only thing I could point out in terms of core innovation at the top of the funnel has been the creation of the mega funds, the well-known funds, right? Like a16z, Union Square Ventures, et cetera, et cetera. But there needs to be more innovation on that cycle. And that’s why we certainly at Chameleon believe that the future is to have quant and AI-native VC firms that develop their own tooling, their own platforms. We have Mantis in our case that allow you to have this unfair advantage in how you source deals and how you do due diligence, how you get into the deals, et cetera, and how you take it to the next level. And we think that’s the beginning of the next stage is that the industry becomes more tech-enabled, shockingly enough, an industry that has made all its returns on tech or almost all of its returns on tech. That we need to be more tech-enabled ourselves. But I think the writing is on the wall there, and that will be a source of differentiation certainly over the next 3 to 5 years.   Bertrand One thing the industry has innovated somewhat and maybe could innovate even more is providing liquidity beyond trade sale and an IPO, because it’s clear that if VCs want more liquidity without waiting 18 years, you need that liquidity at different stage, not just when it’s time to do an exit, a full exit for the business. And for employees as well. I mean, it’s one thing to stay for a company for 4 years, which is your typical vesting. Maybe you extend that to 6 years, to 8 years, you have a great time at the company. But to think that maybe you have to stick around for 15 to 20 years in order to get liquidity on your stock options. I mean, that’s too much to ask for most people. I mean, people have a life, they have other things to do, other plans, they might want to move, they come at a different stage of life. So you need to provide them liquidity. The new game is we are not going to exit until 15 to 20 years, else it’s truly unfair. It’s not just unfair, but people will say, you know what, I’m going to go across the street, go work for Amazon or Google. I will have RSUs at best regularly that are liquid, and why bother? I mean, we need to find pathways to liquidity for both investors but also employees. There has been a change in that direction, but I think we need more of this change, and maybe not just reserved for the absolute biggest, most successful companies like OpenAI or SpaceX, but also us as well. Hopefully we can find a way.   Nuno Well, now we have these AI companies that actually grow so fast that they will IPO in one year. Now, isn’t that what’s going to happen? They raise They raised $500 million in Series C or $1.4 billion in Series C, and they’re going to IPO in 2 years. No? Is that not the new reality? I’m being facetious.   Bertrand At the same time, I mean, there are rumors that some of them are going to IPO this year. I mean, we talk about OpenAI, about Anthropic. I mean, OpenAI is quite old, but Anthropic is a relatively new business, quote unquote. So I think it’s a good time.   Nuno The Mega Fund Question So maybe it will be true after all. Moving to the next section, are mega funds still venture capital, Bertrand? Are they still venture capital funds?   Bertrand Yeah, I guess venture capital is a term that can encompass from small to very big funds. I truly don’t know. I mean, once you reach a growth stage, are you truly a VC fund? I don’t know. I think some of these definitions are kind of arbitrary from my perspective. What is clear is that you as a business need different providers of capital. And as we just discussed, you as a business, probably need to keep going and stay private for longer. One reason being, again, there is a tremendous cost to being a public company. There are some true strategic disadvantages. And at the same time, just practically, I mean, you need to get bigger and bigger in order to have a chance of a successful IPO. So you cannot just go IPO at a $500 million valuation. I mean, that’s like committing suicide, at least in the US market on NASDAQ. So my point is, you truly have no choice. You need to extend and If you need to extend, then you need to have capital providers that are there at later stage and therefore have more money. Is it still true venture capital? Is it true venture? I don’t know. At some point, it makes sense that from the startups to the capital providers, everyone adjusts to a reality where the life cycle is getting longer.   Nuno We don’t think it is. We don’t think mega funds are venture capital. We have actually some data that shows that they’re not in terms of actual returns. The alphas you can generate, the IRR that you can generate is actually not comparable. We did some analysis again with some of our datasets and from 2012 to 2022, so that’s the datasets that we used so that we had actual distributions and stuff we could take into account and so on and so forth. And looking at IRR, just to share some numbers in terms of IRR over those 10 years on sub-$100 million funds versus above $1 billion funds, the differences are incredibly stark. And this is true for global and US IRR, right? So just to quote some numbers in terms of average, sub-$100 million funds, global IRR of 22.9%, US IRR of 21.6% versus above $1 billion, 9.1% and 9.0%. Median IRR, if we just looked at median, 7.3% and 16.6% for sub-$100 million funds, 7.5% and 8.1% above $1 billion. Top quartile IRR, sub-$100 million, 31% versus 30.4% US IRR. And then above $1 billion funds, 14.7%, 15.5%. So it’s very clear if you sort of cut this in different ways, averages, medians, top quartiles, et cetera, over all these years that sub-$100 million funds are in a very different asset class than above $1 billion funds. They’re in different alpha that you can generate and so on and so forth. Now to the point you made, Bertrand, I don’t fully disagree with the point you made of the bigger funds should become bigger. I just think they’re becoming different things. Now, again, some of these funds will hide under the facts like, well, wait a second, we have all these assets under management, but they’re over different funds. Sequoia, we’re still raising small early-stage funds, $500, $600 million funds. And then we have larger funds for growth, et cetera, et cetera. Andreessen Horowitz, a little bit less clear what they’re actually doing. We heard that they’ve raised $15 billion across funds. I’m not sure if that’s the exact number at the end of the day. But the point is, if I’m a multi-asset class manager, like early growth, et cetera, et cetera, then it still applies what Nunu is saying. I’m still going after the $500 million, $600 million early-stage funds. Well, not so fast, right? Because you still have all this capital with managing general partners that are maybe across funds for which their incentives in particular, both carry and management fees are coming from the larger funds. Et cetera, et cetera. So there’s necessarily conflicts of interest. In many cases, the funds are just straight up big, right? And so they are above a billion. And so I don’t think a lot of these guys are in early-stage investing anymore, right? It may appear that they are, but I don’t think that’s where the returns necessarily are going to come from. And so if you are a limited partner, if you’re looking at your asset class allocation, again, you’re absolutely free to put money into mega funds because that’s the kind of asset class you want to play in. In terms of a blended private equity asset class that has a little bit of growth, a little bit of whatever, or actually a lot of growth, a lot of late stage, and maybe a little bit of early stage. And I want something that’s a little bit more blended, right? But if I still want the alpha venture capital, I need to deploy to funds that are early stage, right? And that’s like up to $100 million, up to $500 million. I think that’s my two cents on that topic. We see crossover things coming around, like guys who do both public and private markets. Again, that starts feeling a bit like a hedge fund. A lot of these funds have also become RAs, as we discussed earlier. So I feel the writing’s on the wall. The mega funds are going more and more after either some mechanism of edging or a mechanism that’s a little bit more blended in terms of private equity than classic venture capital.   Bertrand Yes, I think a few things. One, if you’re an LP, I can imagine that dealing with multiple $100 million funds might be more difficult. You, you need to know the partners, you need to have some background, uh, visibility. You need potentially to change regularly of VC investments. So I can see some level of simplicity if you just focus on the bigger ones, especially if you have a lot of assets you have to put to work. Another piece of the puzzle, I would guess that the bigger funds are able to return money faster because they are at later stage of the cycle. So instead of that 15 to 18 years, maybe they are more in a 5 to 10 year range, while the smaller funds being there more early might be the one who are taking longer to deliver. So I can see that Yes, there is an IRR picture, but there is also time to liquidity that is not the same. So that can probably also influence. And in terms of crossover PE hybrid model, I mean, for sure we have seen some of the public equity investors doing crossover, meaning going into private equity firms like Coatue, like Tiger Global and others. And for companies that are preparing for IPO, there is a lot of value to work with these firms because they have very good visibility and understanding of the public markets. And their presence in the cap table is also a sign of quality, typically for public market investors. So there is a lot of value and logic for them to be there on both sides of the puzzle. But again, the fact that firms keep delaying IPOs, that the market is not so much startup-friendly, makes this model a bit more difficult. But personally, I think there is value there.   Nuno Yeah, I think on the mega fund, just so that I’m not boo-booing everything, I mean, but there’s definitely angles in terms of the asset class that make a lot of sense. And there’s the scalability of the model. The ability to go after Series B, Series C, as well as mid-stage, as well as late-stage, even secondaries over time, to your point, in some cases even public equities. And that level of skill I think matters. We’ve also seen, as we’ve known, we won’t mention any brands, but people will know who they are, that late-stage hedge funds and investors, even if they’ve done okay-ish in growth in private equity, don’t necessarily do well in venture. So it’s clearly a very different asset class, right? So once you start getting venture teams together, The returns are not quite the same. Actually, sometimes they’re not even quite the same as the growth investments. So clearly they’re very good at the growth side, but not so good in early stage. But definitely there is a case for it. The Case for Smaller…Rightsized Funds But if we switch gears maybe to the small, or I would call right-sized funds, maybe just to quote a couple of numbers and then open up the discussion. Small funds do seem to outperform larger funds. There’s a lot of data in the market that shows some of that dynamic outperformance frequency. All the Very historical numbers from Cambridge Associates from 1981 to 2010. 19 out of 30 vintages were won by sub-$150 million funds. We did our own analysis as I was sharing before. Funds between $0 and $100 won most years between around 2010 and 2021. And the years that they didn’t outperform in terms of investing in the top-performing companies in early-stage Series C, Series A, they were outperformed by the $100 to $500 million funds. The $500 to $1 billion funds and $1 billion or above were never even in the same league in terms of performance, of having identified those top performers in terms of quantity over those early-stage investments. Top 10 funds by vintage, 2004 to 2006, 2016 numbers. Top 10 funds, 73% were sub-$100 million. 2004 to 2016, top 10 funds by vintage, 73% of those were sub-$100 million. So there seems to be a little bit of a case that actually smaller funds, sub-$100 million, sub-$500 million in some cases, are outperforming the larger funds over time. Now, these funds are complex in and of itself. The positive of it is small fund GPs like myself, we are deeply invested in our own funds. We’re not there to just make management fee monies. I mean, we’re not making $1 million, $2 million a year in management fees of salary ourselves, like some of the larger funds. So we are there to really get the carry and be less focused on management fees. And so I think there’s a little bit of alignment around that and really taking that kind of perspective on portfolio construction and liquidation, being also more aggressive on the individual time that we spend with our startups. On the negative side, obviously a lot of these smaller funds, not the case of Chameleon, but others out there are single GPs, very little teams or very small teams. And so it’s sometimes difficult to actually do a lot for portfolio companies as well. And this is where the mega funds, for example, a16z notably would say, hey, we have 600+ people that can support you, right? On market development, business development, communications, talent recruiting, all this stuff. Question mark whether that’s the right way to do it in terms of operating model, if technology is not a better way of supplying that value back to your portfolio companies, or if there’s no better way of doing it. But still, that’s one of the appeals of actually dealing with a larger mega fund if you’re a startup, right? That they will have the resources, also the financial resources to put more capital in you. But also, again, if there’s entrepreneurs listening to this right now, and hopefully there are, it’s a two-edged sword, right? Because if you have Andreessen Horowitz putting money in you, or NEA, or General Catalyst, or whatever, putting money in you on a Series C and then not doubling down on the Series A or the Series B, there will be questions, right? Because like they have the capital, they have other funds, so why the hell are they not putting more money in? Um, so, so it’s a little bit of a two-edged sword.   Bertrand Yeah, I think that one is a pretty big one. And on top of it, as we discussed, some of these big firms have multiple funds managed technically by different teams. So you might have convinced the early-stage teams, they have investors, they’re happy, but you don’t convince the growth-stage firm. As you say, it might raise questions because people might think that there is some communication between the early-stage team and the growth-stage team. So why the heck are they not deciding to invest? And as we also discussed, even worse possible situation, what happens if the growth-stage team has invested in your competitor? It’s even more trouble. So I think trying to understand how firms behave, what’s the reputation of the firm, what’s the reputation of the partner you are working with, I mean, can have tremendous importance and impact. When it’s time for you to work with a firm.   Nuno Indeed. I mean, at the end of the day, we still believe that the smaller fund— we at Chameleon discuss the notion that our limit should be $500 million per fund, right? And that’s the logic of it. We think that model is the model that works well in venture capital. We do recognize, as I said before, why mega funds keep raising more and more money, right? It becomes a harm’s race at that end of the market. As I said, probably a slightly different asset class, or if not a significantly different asset class as well. So seeing a little bit both sides of the market, I mean, we often compete with the mega funds, but honestly, a lot of the mega funds are kind to us and they let us in. And this whole notion of elbows out, we haven’t felt it that much in the market. And people see our value at the table. And in many cases, I, I do see the larger funds more and more seeing the value of smaller funds coming in on the same rounds and even in some cases co-leading early stage rounds like Series C. So it’s not like elbows are out everywhere across the board. So I don’t mean to say this is like an all-out war between small funds and big funds and the small funds need to win or the big funds need to win. I think actually there’s a lot of potential for coexistence. My point is more that the asset classes and the returns are quite different over time, and that’s how I would think through it. And if you’re an entrepreneur, you should think about that as well, right? What are the implications of taking money from certain funds versus others in terms of the expected returns, expected time allocated to you? For example, if you’re not doing very well as a as a company, right? Will the big funds spend the same amount of energy on you if you’re not doing great and all of that? So it’s a little bit sort of a beware, open your eyes, both for limited partners and for startups. What do you actually want, right? What do you want from your VC firm if you’re a startup? And what do you want from your VC firm if you’re an LP?   Bertrand I must say, as an entrepreneur, uh, a board member, I have seen some situations where the bigger funds are actually trying sometimes to elbow out the existing investors. Like, uh, we have that much money to put to work, we cannot do less. And you’re like, yeah, but I don’t need that much money. And then they’re like, okay, just don’t let your existing investors do their pro rata. I don’t think it’s great because an entrepreneur, if your investors, your VCs, trusted you earlier stage when it’s more risky, and when it’s becoming less risky, you don’t give them the right to their pro rata because you have to let this big guy come in. That’s not great. Or even if there is not this pro rata issue, when an investor tries to put more money to work than it’s really necessary, it’s also not a good idea as an entrepreneur to take more capital than you could use. It will dilute you more, it will set higher expectations in terms of valuation, it will push you to use that capital faster than maybe would be reasonable. So I think that’s something you want to be careful with the bigger funds. So don’t talk to funds that are in some ways beyond your stage and try to make it work in that context. Or don’t accept to have your strategy change dramatically for no good reason by funds that just want to put too much money to work in your business. And that for me is surprising because it should also be in their best interest not to invest in businesses that are not ready to accept that much capital. But as we have seen, there were in the past some funds that believe that capital is a moat. Was a good idea. So hopefully, I guess we’re a bit behind that. But yeah, I would say entrepreneurs, be careful, find partners that are the right partners for you at your current stage. Sometimes some big names look great, but at the same time, if it comes with a lot of issues, from too much capital to also taking the risk that these partners don’t understand the stage of the business you are in or your industry, Just be careful. There is a lot of value to have firms that are very focused on your stage, on your industry, are finely attuned to that situation.   Nuno What Comes Next? Maybe to end in terms of sections, what comes next? And maybe we can come up with some predictions that are a little bit provocative on what’s going to happen to the market. You, if you’re listening to us, feel free to interact with us on LinkedIn, on X. If you have our email address, shoot us an email as well. We’d love to hear from you if you think these are the right predictions or if we’re totally off. Maybe I’ll throw in the first one, Bertrand, and we’ll go one by one. So we’ll each put one at the table and see where we head. My first one is that we’ll have a huge culling of VC investors. We had this rapid expansion of the VC asset class with arguably at least tens of thousands of firms globally, maybe even over 10,000 in the US. I think we’ll have a culling and the culling will continue and we’ll have several firms sort of getting eliminated over the next couple of years that will have either because they’re having tremendous difficulty doing their first close in their next fund, or the returns are not there, or it’s a firm that has done 3, 4 funds, but for some reason the returns have just gone out of whack in the last few years during the bull years. And so therefore, actually they can’t justify to raise more funds out there. So I predict there will be a significant elimination of active firms in the next at least 2 to 3 years. So maybe by 2028, and we’ll be below, I don’t know, 30% of number of active firms that we are today. The other side of it is I do think if we look beyond that, 2029, 2030, and so on, we’ll have the reemergence of not micro funds, but nano funds where people will start deploying capital very, very early and writing small angel checks, but doing it in a way that it’s sort of not this cottage industry that we’ve had of angel investors. So I think angel investment will be disrupted by people that will use more and more of the AI toolification out there to actually manage their portfolios of 10, 15, 5K investments in a way that is a lot more professional, creating sort of an advent of nano funds.   Bertrand Yeah, makes sense. On my side, in terms of prediction, I think there is a possibility that the mega fund model keeps expanding and looks more similar over time to some PE models. So do we have the top 10 VC firms that look more like a Blackstone than a Kleiner Perkins or Sequoia used to be? That for me will be an interesting question and development. I think that there is some possibility that it keeps going in that direction. A lot of incentives are pushing things that way.   Nuno My next prediction is that DPI, distributions to paid-in cash on cash, just cash back, will become essential for limited partners. I think TVPI, total value to paid-in, that also has in there, as we just said, paper valuations. There’s a lot of disbelief now around the TVPI metric if there isn’t distributions going alongside it. For those who, again, don’t know what TVPI is, it’s total value paid in, but it also includes DPI. So it’s cash on cash component plus a remaining valuation to paid in, an RVPI. And the problem is the RVPI really, in reality, it’s that kind of on-paper valuation that never gets attributed. I think LPs, they’ve seen the writing on the wall and they’re like, dude, just show me your DPI numbers. I don’t care about TVPI. Some LPs will still ask about TVPI just to make sure that the rest is sort of looking in order. Like, show me the money, show me the cash. Actually, it’s not money, show me the cash, right? I want money back.   Bertrand But that’s an issue. I mean, if you’re supposed to raise financing every 3 or 4 years, good luck getting DPI to show for that. So you need to be at least on your third fund in order to be able to show DPI, I guess.   Nuno I mean, my corollary to that, Bertrand, is if you allow me just to have a corollary kind of prediction, is that we’ll see certainly for funds like $50 million and above, $100 million, $200 million, et cetera, even increased concentration, right? I really need to have anchors that believe in me over time. And we might start having, again, the advent— we had it some decades ago, the advent of cap table kind of VCs, right? Like Sutter Hill Ventures, right? Where they’re not really raising funds anymore. And so we might have the advent of that, that we’ll have structures that are created that have more permanent capital allocated to them, or at the very least more concentrated capital by very few players.   Bertrand Interesting. Me on my side, as I shared before, I believe secondaries are, are important and here to stay. Um, in the past, some could argue, is it a distress signal or something? I, I don’t think it’s true anymore. In a world where your average startup might take 15 to 18 years to exit through M&A or IPO, we need to have other options. For funds, for employees, they cannot be expected to stick around for so long and have no liquidity. I mean, it’s just pure madness. It’s just bad alignment at some point to do that. So I think secondaries are becoming the third liquidity pathway for VCs, for employees, and it should be more and more a key part of the game, a key infrastructure in the VC/startups tech industry.   Nuno I mean, on specialized versus generalist funds, I believe we’ll continue seeing the coexistence of those two models where the specialized funds will in many pockets actually outperform generalist funds, but where we’ll continue seeing that the large franchises, the tier one franchises will likely be generalist funds. I mean, we just saw it in the cycle. The AI cycle went upon us. We had a 2021 fund. We could easily adapt and go into AI and figure out that AI was growing very fast. I mean, if you have an ultra-specialized fund and that’s your remit and that’s the only thing you can invest on, very difficult to change even during our investment period. I will put a caveat on that. We don’t call, for example, ourselves at Chameleon generalist. We call ourselves multi-specialized because our scoring models for the verticals that we track are specialized within Mantis. Because the partnership is specialized, we all focus on different areas. And because we have the Kin network that allows us to tap into that level of expertise, Again, I think the world will be specialized coexistence. Some pockets specialized will do very well, certainly on the smaller fund size, but the big franchises will likely look a little bit more generalist. And as I said, multi-specialized from our perspective is the future. We’ll start seeing more and more funds that are multi-specialized like ourselves. Do you want to talk about AI and how it’ll distort the metrics? No.   Bertrand Yes. I think AI is an exciting moment in the tech industry. It feels in some ways that the same way we had a big distortion coming with COVID and work from home in 2020, 2021. 2021, where suddenly everyone and their mother will build a SaaS company or invest in a SaaS company. AI feels a bit of the same. I mean, to be clear, I truly believe it’s deserved. I mean, we are facing a dramatic shift in how computing is being done in terms of value you can get from software. So at the same time, AI will probably distort this matrix for a long time. We clearly see a split where investments are going, in what startups are being created. So I think, yeah, we will see some distortion. And we know that maybe 50% of all deal value is going to AI in 2025. We have seen single rounds reaching 40 billion, like to OpenAI. We have seen, as you discussed, some seed stage investment of 400 million. So AI investing and AI startups are definitely a beast on their own. And will distort VC metrics for a long time. And we might need two sets of metrics in parallel, you know, AI versus everything else. So that would be an interesting bifurcation in the industry in some ways. I would say it’s fair to separate AI versus non-AI. We reach a point where it’s two different beasts.   Nuno Conclusion So in conclusion, AI has changed the world and it’s changing VC as well, as we discussed earlier in the episode. We have a tremendous momentous occasion for the asset class where venture capital is really bifurcating into very large funds, which no longer are in venture capital or seemingly may be distributed between different asset classes, and the smaller funds, sub-$500 million and sub-$100 million, that keep having the better returns, but also with much smaller scale. We’re seeing a culling of the industry where the industry is definitely getting smaller and smaller and more concentrated at both ends, number of VC firms, as well as a number of limited partners per fund and the interest that some of these limited partners have of being more and more concentrated in their own portfolio allocations. And last but not the least, the discussion around specialized versus generalist, where it seems like there’s some clear winners on some asset classes, on some sizes, in some industries, but on others, there’s other kinds of winners. And so maybe the future is multi-specialized, as I framed at the end. Thank you so much for listening. If you want to check us out and if you want to comment, feel free to send us messages on X, LinkedIn, to both myself and Bertrand, as well as send us an email. Thank you so much, Bertrand.   Bertrand Thank you, Nuno.

Ever wonder how venture capitalists actually judge your startup behind closed doors? In this episode of the BRAVE Southeast Asia Tech Podcast, Jeremy Au breaks down the brutal reality of VC economics and the math that drives the tech industry. We explore why just 5% of startups generate almost all of a fund's returns (The Power Law) and why struggling founders often get left behind. We also dive into real-world case studies, analyzing the dramatic valuation shifts during Instacart's IPO, and unpacking the legendary $1 Billion dilemma faced by the founders of Instagram (Kevin Systrom) and Snapchat (Evan Spiegel). Whether you're building an AI-native startup in Singapore or scaling a deep-tech company in the Philippines, understanding MOIC, DPI, and how the smart money moves is crucial. 00:00 - The VC's Dual Role: Value-Add vs. Portfolio Judge 01:02 - The Power Law: Why 5% of Startups Carry the Fund 01:38 - The Help Paradox: Prioritizing Winners Over Strugglers 02:23 - Case Study: Valuation Shifts in the Instacart IPO 04:21 - Exit Outcomes: Liquidations, Acqui-hires, and Cash-outs 04:58 - The $1 Billion Dilemma: Instagram vs. Snapchat 05:59 - Going Public & Raising VC Funds (LP/GP Dynamics) 06:51 - The Brutal Math of VC Returns: MOIC & DPI 09:03 - Outro & Community Resources Watch, listen or read the full insight at https://www.bravesea.com/blog/vc-economics-exit-strategies Get transcripts, startup resources & community discussions at https://www.bravesea.com WhatsApp: https://whatsapp.com/channel/0029VakR55X6BIElUEvkN02e TikTok: https://www.tiktok.com/@jeremyau Instagram: https://www.instagram.com/jeremyauz Twitter X : https://x.com/jeremyau LinkedIn: https://www.linkedin.com/company/bravesea English: Spotify | YouTube | Apple Podcasts Bahasa Indonesia: Spotify | YouTube | Apple Podcasts Chinese: Spotify | YouTube | Apple Podcasts #VentureCapital #Business #Startup #Podcast #southeastasia #techpodcast

What does a genuinely great VC fund look like today, from an LP's perspective?In this episode, James and Hector are joined by Dave Neumann, Investment Manager at Schroders Capital, one of the most experienced institutional investors in venture. With a career spanning decades and exposure to top-tier global funds, Dave shares how leading LPs actually evaluate venture firms, and where many GPs get it wrong.The conversation covers what separates top quartile funds from the rest, why venture is increasingly about building a firm rather than just making investments, and how the best managers create a long-term flywheel across talent, track record and capital.They also go deep on often overlooked topics including DPI, liquidity, fund size, and portfolio construction. Dave explains why access is everything in venture, why consistency matters more than one-off performance, and how LPs think about returns in a world where companies stay private for longer.A sharp, practical look at venture through the LP lens and what it takes to build a durable, high-performing fund.Topics Covered What defines a top VC fund today  The LP perspective on venture performance  Why venture is about building a firm, not just investing  Top quartile vs lower quartile returns and the compounding effect  Talent, incentives and the VC flywheel  Portfolio construction myths vs reality  Fund size and where returns are really made  DPI, liquidity and secondaries in Europe  How LPs think about risk, time horizons and outcomes  Why access is the biggest advantage in venture

Vishal Verma's family office has been operating out of Silicon Valley for over thirty years. His father arrived from India in 1977 with eight dollars in his pocket, worked as a rocket scientist, and eventually became an entrepreneur and venture capitalist. The family formalized their office in the late nineties with early LP positions in Sequoia Fund IX and Kleiner Perkins. Today Vishal manages a portfolio split across twenty-one venture capital firms and twenty-eight direct co-investments in generational companies including Anthropic, Wiz, Stripe, and xAI.In this episode, Prashant and Vishal go deep on how a thirty-year family office actually thinks about venture capital — the vintage strategy, the concentration framework, the Anthropic bet, and why most of what you hear about the first mover advantage is wrong.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comWe talk about -– The family origin story: $8 at the border to Silicon Valley– Portfolio construction: 70/30 public to private– The vintage strategy: why you have to be at every party– Three concentrations reshaping the VC ecosystem– The Anthropic investment at $18B valuation– AI vs crypto: behavioral change is everything– Bigger funds not returning DPI is hogwash– Emerging managers: what actually earns a check– DPI reality and the IPO bottleneck– Why family offices exist and what banks can't doTimestamps:(00:00) -Preview(01:40) - Introduction to Vishal Verma and His Family's VC Legacy(03:39) - The Family Office Origin Story: From India to Silicon Valley(06:57) - Challenges and Triumphs of Early Indian-American Entrepreneurs(08:56) - Why the Indian-American Community Thrives: Hard Work, Education, and Family(10:22) - Portfolio Construction and the First Investment in Sequoia(14:15) - The Rationale Behind a 30% Allocation to Venture Capital(17:22) - How Shorter Fundraising Cycles Have Changed LP Strategy(22:25) - The Differentiator for Top-Tier VC Funds(24:34) - Understanding the "Concentration" of Returns, Capital, and Founders in VC(28:08) - Do Bigger Funds Actually Lead to Shrinking Returns?(30:17) - The "Mafias" of Silicon Valley and Their Role in Deal Flow(32:32) - The Investment Thesis for Anthropic at an $18B Valuation(36:55) - AI vs. Crypto: The Critical Difference of Behavioral Change(39:15) - First-Mover vs. Best-to-Market: Lessons from Tech History(40:32) - The Reality of Stretched DPI and Liquidity Challenges(41:35) - The Rise of "Megacorns" and the Upcoming IPO Wave(44:34) - AI Investing: When Does Conviction Become Overexposure?(48:38) - Public Market Strategy: A Tech-Heavy Portfolio(52:50) - ConclusionLinks:Edgewood Ventures - https://www.edgewoodvp.com/Connect with Vishal Verma - https://www.linkedin.com/in/vishal-verma-551327Connect with Prashant: https://linkedin.com/in/choubeysahabSubscribe to VC10X newsletter - ⁠https://vc10x.beehiiv.com⁠Subscribe on YouTube - ⁠https://youtube.com/@VC10X ⁠Subscribe on Apple Podcasts - ⁠https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986⁠Subscribe on Spotify - ⁠https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQ⁠VC10X website - ⁠https://vc10x.com

Subscribe to the newsletter:New Wave | Hugo Rauch | Substack****Thanks you, Morning for hosting the podcast this week. Get 20% off your next meeting room (in Paris), just say you're coming from Hugo at New Wave.****

W marcu i kwietniu 2026 roku Rosja zrobiła coś, czego długo unikano: rozpoczęła frontalny test kontroli odłączenia własnego internetu. Blokady Telegrama i VPN, problemy z aplikacjami bankowymi, a nawet zakłócenia w funkcjonowaniu infrastruktury miejskiej pokazały skalę eksperymentu. W pewnym momencie to już nie była tylko cenzura — to był test odporności całego systemu.W tym wydaniu podcastu Bartosz Gołąbek analizuje największy kryzys cyfrowy w najnowszej historii Rosji. Kluczowe pytanie brzmi: kto realnie kontroluje proces? Coraz więcej wskazuje na rolę Federalna Służba Bezpieczeństwa, a nie tylko formalnych regulatorów.W odcinku m.in.:— jak przez lata budowano infrastrukturę kontroli (ustawy, suwerenny Runet, DPI)— dlaczego model rosyjski odbiega od chińskiego i w jakim kierunku ewoluuje— skutki techniczne blokad (VPN, usługi, infrastruktura miejska)— napięcia w elitach i brak spójnej linii politycznej— rola mediów społecznościowych i paradoksy władzy korzystającej z blokowanych narzędzi— cyfrowy rubel jako potencjalne domknięcie systemu kontroli. Link od Nikodema Cudzicha: Небайдужі Люди https://www.instagram.com/nebaiduzhi.lyudy

What does patience look like today when the best companies take 10–15 years to exit, and is it still worth it to wait that long? Today's episode of Origins dives into one of the most pressing questions in today's market: how to balance long-term conviction with the need for liquidity.David Clark, CIO of VenCap and a three-decade veteran of institutional venture investing, returns to the show to bring his rare LP perspective shaped by backing some of the most established venture franchises in the industry. Known for a data-first approach, David offers insight into how top-tier firms consistently generate returns, and how those dynamics evolve as fund sizes scale into the billions.Together with hosts Nick & Beezer, the group explores the implications of venture capital consolidation, the persistence of power law outcomes, and the shifting role of liquidity in private markets. From the rise and returns of mega-funds like a16z, to the growing importance of secondaries and delayed IPO timelines, the conversation surfaces the core tension of capturing extreme right-tail outcomes while still delivering tangible distributions to LPs. Along the way, they debate whether “patient capital” is truly a viable strategy, or if today's venture structure inherently rewards more active portfolio management. Ultimately, today's discussion offers a data-driven look at how venture is changing and how fund managers can look to stay ahead.—Quotes“If our managers have one of those top 1% companies, we want to encourage them to let it ride. Because we don't see the very best managers selling their best companies prematurely. That's not how we've seen the best fund level performance. And if you are able to hold those companies through to their full potential, that's where the real value is created.” – David Clark“I actually think the late stage private markets have become the public markets for early stage venture fund managers. And I think if you consider [that possibility], you can find much more predictability and consistency in performance, returns, and DPI. As much, or maybe even more than large later stage managers.” – Nick Chirls“What I've seen in the last few years is people taking exits into consideration, which makes my heart very happy. Because for years people were not, and they weren't thinking about returning capital along the way. You can take 10%, 20% and return 1x or 2x your fund. That is a very credible conversation to have with your LPAC.” – Beezer Clarkson—Time Stamps01:13 Meet David Clark, CIO of VenCap03:29 a16z Fundraising Surge05:31 First Principles Venture Model07:18 Public Markets And IPO Scale09:32 Do Big Funds Want Private13:17 Power Law Still Rules14:28 Fund Size And DPI Timing16:36 Early Stage Fund Math20:59 Small Funds Versus Platforms24:02 Portfolio Construction Tradeoffs25:40 Late Stage As New Public28:44 Liquidity As A New Skill30:55 When To Take Chips Off33:25 Founder Secondaries And Alignment35:13 Venture Capital Consolidation Risks40:47 Big Firms Funding Emerging Managers44:30 Patient Capital Debate48:00 Going Public Incentive Concerns—Links Connect with the guest and hosts on LinkedIn!David ClarkBeezer ClarksonNick ChirlsLearn more about:Read Packy McCormick's blog post on a16z: The Power BrokerVenCapOpenLPAsylum Ventures

On Monday's "Dan O'Donnell Show," Dan breaks news of a suit filed against DPI over its infamous waterpark retreat and urges the Wisconsin Legislature not to strike a deal with Governor Evers and let him off the hook for how high his 400-year veto has spiked property tax rates.See omnystudio.com/listener for privacy information.

On Monday's "Dan O'Donnell Show," Dan breaks news of a suit filed against DPI over its infamous waterpark retreat and urges the Wisconsin Legislature not to strike a deal with Governor Evers and let him off the hook for how high his 400-year veto has spiked property tax rates.

Send us Fan MailBuckle up, because this week we're sitting down with Neha Champaneria Markle, who runs the Private Equity Solutions group at Morgan Stanley Investment Management.Neha walks us through the entire private equity landscape and answers the questions you've been dying to ask an insider including: - Is "AI is going to destroy software and therefore private equity"? - Why are fundraising cycles getting longer?- What does vintage year really tell you about a fund's performance? - What's actually a "good" DPI, IRR, and TVPI- Why does every fund somehow claim to be top quartile? She also pulls back the curtain on subscription credit lines and how GPs use them to juice early IRRs, gives us a definition of "fund of funds" and "co-investment" that actually makes sense, and settles the score on whether PE investing is really just "volatility laundering".As the walls around private equity are coming down, it's important to understand which sectors are secretly crushing it, how managers actually get selected, the fee structures, and what the "democratization of private markets" really means for returns going forward.For a 14 day FREE Trial of Macabacus, click HEREShop our Self Paced Courses:Investment Banking & Private Equity Fundamentals HEREFixed Income Sales & Trading HEREWealthfront.com/wss. This is a paid endorsement for Wealthfront. May not reflect others' experiences. Similar outcomes not guaranteed. Wealthfront Brokerage is not a bank. Rate subject to change. Promo terms apply. If eligible for the boosted rate of 4.15% offered in connection with this promo, the boosted rate is also subject to change if base rate decreases during the 3 month promo period.The Cash Account, which is not a deposit account, is offered by Wealthfront Brokerage LLC ("Wealthfront Brokerage"), Member FINRA/SIPC. Wealthfront Brokerage is not a bank. The Annual Percentage Yield ("APY") on cash deposits as of 11/7/25, is representative, requires no minimum, and may change at any time. The APY reflects the weighted average of deposit balances at participating Program Banks, which are not allocated equally. Wealthfront Brokerage sweeps cash balances to Program Banks, where they earn the variable APY. Sources HERE. 

Dans cet épisode, je reçois Emmanuel Delaveau, General Partner chez Partech en charge des relations investisseurs, Geoffroy De Cooman, entrepreneur et cofondateur de Clariteer, et Ludovic Phalippou, professeur d'économie financière à l'université d'Oxford, pour une discussion autour de la performance en private equity — ses indicateurs, ses limites et ses angles morts.Nous avons parlé :du triptyque de base du reporting LP : TRI, TVPI et DPI, et de ce que chacun mesure réellement par rapport à ce qu'on croit qu'il mesuredu TRI comme taux fictif impossible à calculer à la main, et de pourquoi un TRI de 50% peut avoir rapporté moins d'argent qu'un TRI de 15%des trois techniques concrètes utilisées pour gonfler artificiellement un TRI : subscription lines de crédit, sortie rapide des meilleurs actifs, et cherry-picking de track recordde la raison structurelle pour laquelle les fonds américains affichent systématiquement de meilleurs TRI que les fonds européens ou africains — non pas en raison de leur performance réelle, mais de leur accès aux outils de manipulation des cashflowsdu catch-up clause et du hurdle rate : pourquoi la quasi-totalité des professionnels, y compris très seniors, pensent comprendre la mécanique des frais alors qu'ils se trompent sur l'essentielde l'hypothèse héroïque cachée dans la formule du TRI — celle qui pose que le capital non appelé et les distributions sont réinvestis au même taux que le TRI lui-même — et de ce que ça implique concrètement pour un LP individuelde ce que pourrait être un reporting idéal : des positions à jour, nettes de frais, comparables, avec des hypothèses de cashflows futurs partagées par le GP pour permettre une vraie planification côté LPUn épisode dense, technique et sans concession, qui pose une question centrale : si même les professionnels ne comprennent pas vraiment comment sont calculés les rendements qu'on leur vend, que dire des investisseurs particuliers à qui on démocratise aujourd'hui le private equity ?Recommandations des invités:“Pitch Anything” de Oren Klaff “Measuring Private Equity Funds performance” INSEAD de 2019 https://www.insead.edu/sites/default/files/assets/dept/centres/gpei/docs/Measuring_PE_Fund-Performance-2019.pdf Podcast et livres toutes les infos de Ludovic Phalippou https://pelaidbare.com/ Liens utiles:Emmanuel Delaveau: https://www.linkedin.com/in/emmanueldelaveau/ Geoffroy De Cooman: https://www.linkedin.com/in/gdecooman/ Ludovic Phalippou: https://www.linkedin.com/in/ludovic-phalippou-5488b147/ Your IRR is not my IRR: https://www.clariteer.com/blog/your-irr-is-not-my-irr***************************Finscale, c'est bien plus qu'un podcast. C'est un écosystème qui connecte les acteurs clés du secteur financier à travers du Networking, du coaching et des partenariats.

This week on Swimming with Allocators, Earnest and Alexa chat with Anthony Giambrone, Partner at StepStone Group. Anthony shares his unconventional path from gas station manager and nightclub worker to leading a major global venture allocation platform. The conversation covers his break into investment banking, the scaling of GreenSpring into StepStone, and why relationships, EQ, and consistency across vintages matter more than market timing in venture. Key takeaways include the power-law nature of VC returns, how emerging managers and spinouts can stand out with a real edge and long-term relationship-building, why asset quality matters more than discounts in secondaries, and how AI, liquidity pressures, and longer private company lifecycles are reshaping the next decade of venture capital. Also, Rebecca Stuart, an employment-focused partner at Sidley, explains how she helps venture-backed companies navigate complex employment and co‑founder separations, equity and vesting pitfalls, evolving worker classification and pay transparency laws, and the fast-changing regulatory landscape around AI in hiring and employment decisions. Highlights from this week's conversation include: Anthony's Background and Humble Beginnings (0:42) Importance of Empathy and Relationships in Venture (4:16) Applying Greenspring/StepStone Experience to Today's Market (6:15) StepStone Venture Team, AUM, and Global Footprint (8:07) Why You Can't Time Early Stage Venture (9:38) Vintage Volatility and Power Law in Venture Outcomes (11:23) How Founder Ambition Affects GP and Fund Diligence (14:28) Insider Segment: Co‑Founder Divorce (18:04) Using New Investments to Clean Up Equity and IP (21:43) Employees Demanding Human Review in AI‑Driven Processes (25:43) Fund Slot Constraints and LP Down‑Selection (28:33) Advice for New LPs on Capturing Upper Quartile Returns (31:36) Is Top Quartile Performance Still Good Enough? (33:08) Secondaries Strategy and Asset Quality Over Discounts (34:31) Liquidity Pressures, DPI, and GP‑Led Solutions (38:37) StepStone's 10‑Year Lifecycle Partner Vision (40:35) StepStone Group is a global private markets firm focused on providing customized investment solutions and advisory and data services to its clients worldwide. The firm's venture capital and growth equity platform, built on the foundation of Greenspring Associates, manages $25B+ in AUM across primary fund investments, secondaries, and co-investments, as of June 30, 2025. Learn more at www.stepstonegroup.com. Sidley Austin LLP is a premier global law firm with a dedicated Venture Funds practice, advising top venture capital firms, institutional investors, and private equity sponsors on fund formation, investment structuring, and regulatory compliance. With deep expertise across private markets, Sidley provides strategic legal counsel to help funds scale effectively. Learn more at sidley.com. Swimming with Allocators is a podcast that dives into the intriguing world of Venture Capital from an LP (Limited Partner) perspective. Hosts Alexa Binns and Earnest Sweat are seasoned professionals who have donned various hats in the VC ecosystem. Each episode, we explore where the future opportunities lie in the VC landscape with insights from top LPs on their investment strategies and industry experts shedding light on emerging trends and technologies.  The information provided on this podcast does not, and is not intended to, constitute legal advice; instead, all information, content, and materials available on this podcast are for general informational purposes only. Learn more about your ad choices. Visit megaphone.fm/adchoices

Governments are racing to adopt AI in public services. EU-funded projects show the pace is only accelerating. But this push raises a deeper question: what lies beneath? Too often, the answer is the same – weak or uneven digital foundations.Digital public infrastructure (DPI) can help integrate and connect siloed systems into a coherent digital government platform. So, where does it stand globally, as an enabler of AI development? What makes it work? And why does it matter more than ever for AI adoption? We explored these questions on this episode of the Digital Government Podcast with Krisstina Rao, now at Co-Develop, and outgoing Research Fellow at the Institute for Innovation and Public Purpose at University College London (UCL), where she led the work on the Digital Public Infrastructure Map.Listen now and explore what's really powering AI-enabled public services behind the scenes!This podcast and podcast blog were produced within the project “EU Commission Project (24ES06/24DE33): Supporting regional entrepreneurship through the adoption of innovative technologies, including AI, in public services” with the financial assistance of the European Union via the Technical Support Instrument. The views expressed by the speakers in the project video are their own and do not necessarily reflect the official opinion of the European Union.

On March 6, 2026, WisconsinEye's Rewind Co-Host and WisPolitics.com Editor JR Ross and Milwaukee Journal Sentinel State Politics Reporter Jessie Opoien reviewed this week in state politics. On this week’s episode: Evers pushes ban on partisan gerrymandering DPI waterpark conference “routine” Congressional delegation responds to the war in Iran Baldwin opposes extending Schimel as U.S. attorney Evers signs […]

After a brief discussion of Trump and Netanyahu's war with Iran, we turn to defeating authoritarianism by fighting for policies that help people with their most pressing priorities, like good paying jobs, well funded public schools, healthcare and childcare. We discuss the introduction of new legislation for a BadgerCare Public Option, which represents the most comprehensive healthcare affordability proposal introduced in Wisconsin this session. The legislation would open Wisconsin's trusted BadgerCare program to anyone who lacks adequate employer-sponsored coverage. Citizen Action announces a statewide virtual town hall with all the Democratic Governor candidates, Tuesday, April 14th 6pm. We bring attention to Legislative Republicans taking a chunk out of Department of Public Instruction's (DPI's) already approved funding over debunked allegations that they paid for a junket in the Dells. What kind of budget deal allows the Republicans to unilaterally veto agreed funding levels after ignoring the results of their own investigation? We lament the expiration of the Warren Knowles-Gaylord Nelson Stewardship Program due to GOP opposition following a large land purchase to extend the Ice Age Trail in Devils Lake area. And, as the Legislature may do nothing to regulate data centers – as Big Tech and utilities want – local people fight back, as a Judge allows a Port Washington referendum to continue. Finally, what is the division between Governor Evers and Legislative Democrats on gerrymandering? Will Vos reach another damaging deal with Evers before they both head off into the sunset?

What happens when an organization decides to be bold — not just in vision, but in execution?In this episode, we sit down with a Chicago-based healthcare organization that completed the DPI™ journey and came out stronger, sharper, and more aligned than ever. They share candid lessons learned, the challenges they faced, and the mindset shifts that made the biggest difference.Most importantly, we explore how they stayed relentlessly focused on quality while upskilling their team — proving that operational excellence and people development aren't competing priorities, but powerful partners. From building capability at every level to creating sustainable improvements in access, flow, and outcomes, their story is both practical and inspiring.If you've ever wondered what it really looks like to commit to transformation — and sustain it — this conversation will leave you ready to Be Bold: DPI™ Now and Forever in Chicago.Guests: Nicole Kazee & Robin VarnadoHost: Amanda LaramieThis episode is sponsored by Stat. With Stat, you can finally move from guessing to knowing, ensuring your operations are as precise as your clinical care. Schedule a demo at stat.io/coleman Thanks for listening! If you or someone you know should be interviewed for this show, send us an email. Check us out on: FacebookInstagramLinkedInOur WebsiteTikTokTwitterYouTube

Math doesn't have to be intimidating, especially when it's the kind that helps fund companies and move science forward. In this episode, host Elaine Hamm, PhD, is joined by Isaiah Reeves, PhD, Biomedical Analyst at Solas BioVentures, for a practical and approachable deep dive into venture math. Drawing on his background as a scientist turned investor, Isaiah breaks down the core financial concepts every biotech founder should understand: from valuations and dilution to IRR, cap tables, and deal terms. The conversation offers real-world guidance for navigating fundraising, choosing the right partners, and avoiding common pitfalls that can derail long-term value creation. In this episode, you'll learn: How venture capitalists think about valuations, dilution, and returns, and why fully diluted post-money matters. Key metrics like IRR and DPI, and how they influence investment decisions and fund performance. Common deal terms and cap table “red flags” founders should watch out for as they raise capital. Tune in to learn how understanding venture math can help founders make smarter funding decisions, protect long-term value, and build biotech companies positioned for sustainable growth and impact. Links: Connect with Isaiah Reeves, PhD, and check out Solas BioVentures. Connect with Elaine Hamm, PhD, and learn about Tulane Medicine Business Development and the School of Medicine, as well as Cadenza Bio. Connect with Josh Eckelberry, MBA, and Mark Corrigan, MD. Check out the books The Go-Giver and Venture Deals. Check out the podcasts STAT, Biotech Hangout, and 20VC. Check out our previous episodes on Networking as an Introvert and Solas BioVentures with Travis Manasco. Connect with Ian McLachlan, BIO from the BAYOU producer. Learn more about BIO from the BAYOU - the podcast. Bio from the Bayou is a podcast that explores biotech innovation, business development, and healthcare outcomes in New Orleans & The Gulf South, connecting biotech companies, investors, and key opinion leaders to advance medicine, technology, and startup opportunities in the region.

Send a text"RAISE CAPITAL LIKE A LEGEND: https://go.fundraisecapital.co/apply"DOWNLOAD The DPI Liquidity Execution Pack: https://go.fundraisecapital.co/dpi-execution-packThe Private Equity market in 2026 is facing a massive DPI liquidity trap with a $3.2 trillion backlog of unsold companies. Are you a fund manager sitting on unrealized gains but zero cash to distribute to your LPs? In this masterclass, Ryan Miller breaks down the architect's blueprint for survival, exploring how to manufacture liquidity when the IPO window is shut. We dive deep into NAV facilities, continuation vehicles, strip sales, dividend recaps, and preferred equity to help you move from a "paper tiger" to a capital solution architect.This isn't just a podcast; it's a strategic briefing on the advanced financial engineering and secondary market maneuvers used by the world's elite firms. From mastering LPAC negotiations to surviving forensic audits, we're showing you how to satisfy the liquidity demands of pension funds and family offices without sacrificing your internal growth engine. Stop managing paper dreams and start distributing real-world alpha.Subscribe on YouTube:https://www.youtube.com/channel/UCTOe79EXLDsROQ0z3YLnu1QQConnect with Ryan Miller:Linkedin: https://www.linkedin.com/in/rcmiller1/Instagram: The Fresh Patch Podcast - Where Good Pets Get It. Welcome to the Fresh Patch Podcast where we talk about everything, from dog...Listen on: Apple Podcasts Support the showDISCLAIMER: The information in every podcast episode “episode” is provided for general informational purposes only and may not reflect the current law in your jurisdiction. By listening or viewing our episodes, you understand that no information contained in the episodes should be construed as legal or financial advice from the individual author, hosts, or guests, nor is it intended to be a substitute for legal, financial, or tax counsel on any subject matter. No listener of the episodes should act or refrain from acting on the basis of any information included in, or accessible through, the episodes without seeking the appropriate legal or other professional advice on the particular facts and circumstances at issue from a lawyer, finance, tax, or other licensed person in the recipient's state, country, or other appropriate licensing jurisdiction. No part of the show, its guests, host, content, or otherwise should be considered a solicitation for investment in any way. All views expressed in any way by guests are their own opinions and do not necessarily reflect the opinions of the show or its host(s). The host and/or its guests may own some of the assets discussed in this or other episodes, including compensation for advertisements, sponsorships, and/or endorsements. This show is for entertainment purposes only and should not be used as financial, tax, legal, or any advice whatsoever.

In this episode, Duane Mancini sits down with Sarah to unpack her path from healthcare operator to investor and what founders should know when raising capital today. Sarah shares how her experience as the 10th employee at a digital health startup shaped her empathy for founders and the practical lens she brings to diligence, from ICP and pricing to building durable foundations early. The conversation pulls back the curtain on venture mechanics—how syndication and relationships really work, why fundraising is difficult when LPs demand DPI, and how fund structure, lifecycle, and co-investments can shape outcomes for startups. Sarah also explains Angelini Ventures' global strategy and thesis-driven focus in areas like cardiology and neurology, and why “exitability” requires forward-looking insight into strategic buyers, technology shifts, and long-term fit.Sarah Fox LinkedInAngelini Ventures WebsiteDuane Mancini LinkedInProject Medtech WebsiteProject Medtech LinkedInThank you to our sponsors: Ward Law and JumpStart Inc.

Durante décadas, instituciones como Yale y Harvard transformaron la forma de invertir adoptando el llamado modelo endowment, reduciendo su exposición a mercados públicos y asignando más del 60% a activos alternativos En este episodio explico qué son realmente los activos alternativos, private equity, venture capital, private credit, infraestructura y real estate institucional, y por qué capturan primas de iliquidez y complejidad que no están disponibles en la bolsa tradicional. Analizamos la dispersión extrema entre el top quartile y el promedio en private equity, por qué el IRR neto, MOIC, DPI y la estructura de fees importan más de lo que la mayoría entiende, y cómo la selección del gestor es la verdadera habilidad del inversionista sofisticado. También comparto el framework A.L.T.E.R.N.A.T.I.V.O. para evaluar fondos con criterio estructural: asignación estratégica, liquidez, track record real, riesgo estructural, alineación de incentivos y timing de ciclo No se trata de perseguir retornos, se trata de entender la estructura. Mira el episodio completo y aprende a pensar como un inversionista de verdad y si quieres llevar esta conversación a ejecución real, únete a Wealth Club, una comunidad diseñada para inversionistas que buscan elevar su criterio, analizar oportunidades con profundidad y construir una estrategia patrimonial sólida en mercados públicos y privados. 

Gary Tan is the President and CEO of Y Combinator.YC is the startup accelerator behind companies like Airbnb, Stripe, Coinbase, Reddit, Twitch, and thousands more. According to Garry, they've invested in 20% of all startups worth $5B or more started since 2012.Gary has lived every side of the YC ecosystem. He went through YC as a founder, later became a partner, started Initialized Capital where he backed companies like Coinbase and Instacart, and then returned to lead YC.We walk through the different “eras” of YC, from the early Paul Graham and Jessica Livingston days in Cambridge, to scaling in San Francisco, to today's push back toward in person community and what Gary calls “founder mode” for the organization itself.We also talk about why the Bay Area still matters so much for startups, what's happening with California taxes and policy, and why Gary has gotten more involved in local politics to keep it the best place for founders to build companies.Then we go deep on the parts of startups people don't talk about enough. Co-founder conflict, rage quitting, therapy and coaching, and why companies inevitably take on the personality and emotional patterns of their founders.We also cover what YC looks for in applications, how the 13 week batch is structured, how Demo Day really works, how to choose the right investors, and what Gary thinks the next phase of YC looks like, including helping founders even after Series A.At the end, Gary shares his personal AI workflow, including meta prompting, comparing outputs across models, and the tools he uses every day to think and build faster.Try Numeral, the end-to-end platform for sales tax and compliance: https://www.numeral.com⁠Sign-up for Flex Elite with code TURNER, get $1,000: https://form.typeform.com/to/Rx9rTjFzTimestamps:(0:05) Moving from Winnipeg to California as a kid(1:35) How YC interviews work(2:55) The first batch in 2005(6:46) Why YC moved from Boston to SF(8:17) California's Billionaire Tax(11:00) Tech should care about public policies(17:01) Going direct to your audience(20:28) The 2nd Era of YC(24:01) Rage quitting Palantir, learning to understand himself(32:41) Co-founder conflict kills most startups(35:15) Joining YC as a group partner(37:22) Initialized Fund 1 (55x DPI)(39:44) Why Garry went back to lead YC(42:44) YC funds 20% of all $5B+ companies(44:30) Lessons from Brian Chesky(48:01) Garry's thoughts on YC rejection(51:41) How to get into YC(58:03) What it's like inside a 13-week YC batch(1:02:23) 20% of YC is hard tech(1:05:55) YC's 3rd era: founder mode, re-batching(1:07:56) Escaping the matrix(1:11:26) Garry's personal AI stack(1:20:25) Tech optimismReferencedY Combinator: https://www.ycombinator.com/Initialized Capital: https://initialized.com/Torch: https://torch.io/Perplexity: https://www.perplexity.ai/Anthropic: https://www.anthropic.com/OpenAI: https://openai.com/Airbnb: https://www.airbnb.com/Kyle Vogt on his new startup: https://www.youtube.com/watch?v=XQoFbvyWEy8Follow Aaron Levie on X: https://x.com/levieFollow GaryTwitter: https://x.com/garytanLinkedIn: https://www.linkedin.com/in/garytan/Follow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

Welcome back to the EUVC Podcast, where we bring you the people and perspectives shaping European venture.In this pitch episode, Andreas Munk Holm sits down with Pedro Ribeiro Santos, Partner at Armilar, to walk LPs through the story, strategy, and succession plan behind Armilar Fund IV — the firm's new pan-European early-stage fund.Armilar is one of Europe's longest-standing independent tech VCs and Portugal's original venture firm. Born inside a bank 25 years ago, spun out almost a decade ago, and now a multi-generational partnership, the firm has backed some of Portugal's most important tech companies and quietly built a track record of dragons (fund-returners), not just unicorns.Fund IV doubles down on what the team knows best: early-stage, tech-intensive companies across data, digitalization, and connectivity, with a strong focus on Portugal & Spain and selective investments across the rest of Europe.ShareHere's what's covered:01:17 | What is “Armilar”?02:30 | Origins & Spinout 03:40 | Why being based in Portugal with almost no local ecosystem 04:50 | From US to Europe, Then Back Home 07:22 | Fund IV in a Nutshell 09:44 | Geography & LP Backbone11:41 | Track Record, DPI & Dragons 13:51 | Selected Portfolio & Staying Power 16:19 | Team & Generational Design 21:38 | Iberia's State of Play (Portugal & Spain) 27:45 | Golden Visa & LP Angle 29:29 | Closing & What LPs Should Care About

Japan is often seen as a “mature” financial market.But the real story is in what's quietly shifting underneath.In this episode of Couchonomics with Arjun, Arjun is joined from Tokyo by Pieter Franken (Co-Founder & CEO at GFTN Japan) to unpack what's actually changing in Japan's fintech and digital assets landscape and what it means for founders, investors, and global players looking at the Japan opportunity.They go deep on stablecoins and why Japan may be ahead on regulation, the upcoming move to classify crypto and digital assets as financial instruments, and why payments remain fragmented despite Japan's innovation legacy. The conversation also explores what could unlock faster adoption (from interoperability to cost structures), how Japan's national digital ID rollout could become a foundational layer, and why Japan–MENA collaboration is still early but strategically important.

(00:00-28:10) People texting in at 4:56 A.M. Spelunking with Doug. Drama with the Ottawa Senators. Thrilling game last night and the Miami Hurricanes are onto the CFP National Championship. Dumb penalties and dropped interceptions. Audio of Mario Cristobal in the postgame. Soy sauce stains. Mr. Lix denies his Ole Miss attendance. Audio of Pete Golding talking about the uncalled DPI at the end of the game. Mutual combat. Third straight year with no SEC representation in the championship. But also, who cares?(28:18-44:50) Was it 1987? Audio of Jim Montgomery on The Fast Lane yesterday and discussed what went down with Jordan Binnington and Joel Hofer in Chicago. Doug remains skeptical. Drop audit.(45:00-59:45) Is it even possible for Tim to have a better show than yesterday? Martin's EMOTD ballot. Jimbo Fisher audio that Jackson forgot to cut. Wait, that's not Jimbo. Doug's mad at Lane Kiffin. Love you guys. What happened to the committee the president put Saban on?See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

(0:00) Falcons upset Rams(20:00) Bijan Robinson's huge night(42:00) Sean McVay on loss to Falcons(48:00) Did officials miss a late DPI? Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Daniel Schwalbe, DomainTools Head of Investigations and CISO, is sharing their work on "Inside the Great Firewall." This two-part research project analyzes an extraordinary 500–600GB leak that exposes the internal architecture, tooling, and human ecosystem behind China's Great Firewall. Across both parts, you break down thousands of leaked documents, source code repositories, diagrams, packet captures, and telemetry that reveal how systems like the Traffic Secure Gateway, MAAT, Redis-based analytics, and modular DPI engines work together to censor, surveil, and fingerprint users at scale. Taken together, the research shows how the Great Firewall functions not just as a technical system, but as a living censorship-industrial complex that adapts, learns, and coordinates across government, telecoms, and security vendors. The research can be found here: Inside the Great Firewall Part 1: The Dump Inside the Great Firewall Part 2: Technical Infrastructure Learn more about your ad choices. Visit megaphone.fm/adchoices

This Week In Startups is made possible by:Uber -  http://uber.com/twistPilot - https://pilot.com/twistNorthwest Registered Agent - https://www.northwestregisteredagent.com/twistToday's show: Boom is still making supersonic jets but ALSO plans to start selling their turbines as power sources for AI data centers. It's a perfect example of problem-solving on the go and how “the best founders… MAKE IT HAPPEN.”Join us for another insightful VC roundtable episode, featuring special guests Bryan Kim (a16z) and David Clark (Ven Cap).They're discussing why Boom's turbine announcement is about necessity AND opportunity PLUS…- Why we might NOT be in an AI bubble after all- Promoting your startup without spending your entire runway on marketing- Why founders need to be RELENTLESS- Bill Gurley's classic response about Uber's Total Addressable Market- AND LOTS MOREBill Gurley's iconic “Miss By a Mile” post: https://abovethecrowd.com/2014/07/11/how-to-miss-by-a-mile-an-alternative-look-at-ubers-potential-market-size/Link to David's LinkedIn (including the AI Bubble chart): https://www.linkedin.com/feed/update/urn:li:activity:7404139606398443520/Timestamps:(02:11) It's a VC Roundtable with special guests Bryan Kim (a16z) and David Clark (Ven Cap)(03:07) Why Bryan is leading a Series A into learning app Oboe(06:20) Calculating a startup's value to make everyone “somewhat unhappy”(09:19) How Oboe hits a lot of the same metrics that LAUNCH looks for in startups(11:58) Uber AI Solutions - Your trusted partner to get AI to work in the real world. Book a demo with them TODAY at http://uber.com/ai-solutions(12:57) How funds decide when to cash out and lock in some DPI(18:12) When some LPs want to sell and others want to buy…(19:57) Pilot - Visit https://www.pilot.com/twist and get $1,200 off your first year. (24:07) Is the threat of AI job displacement boosting self-improvement apps?(27:37) Why Jason says we're all standing on the shoulders of Bill Gurley(29:46) Northwest Registered Agent - Form your entire business identity in just 10 clicks and 10 minutes. Get more privacy, more options, and more done—visit https://www.northwestregisteredagent.com/twist today!(31:28) Boom's turbine pivot, and why it's about necessity AND opportunity (in that order)(34:14) THE BEST FOUNDERS find a way to make it happen!(39:10) So… are we in an AI bubble? David says NOT NECESSARILY! Checking out the actual metrics.(44:36) We're still SO EARLY in AI… We're still seeing mostly skeuomorphic uses! (It's a real word!)(48:06) William Gibson was right: “The Street finds its own uses for things”(51:11) How AI startups should think about margins(55:58) Why LAUNCH tells founders to “start at the high end”(57:33) Should founders spend a lot of $$$ on marketing in 2025? The panel disagrees!(1:00:05) Momentum vs. Product Release Velocity(1:03:26) It all comes back to the “relentlessness of the founder”(1:05:17) Our panel's hopes and dreams for the coming year*Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcp*Follow Lon:X: https://x.com/lons*Follow Alex:X: https://x.com/alexLinkedIn: https://www.linkedin.com/in/alexwilhelm/*Follow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanis/*Thank you to our partners:(11:58) Uber AI Solutions - Your trusted partner to get AI to work in the real world. Book a demo with them TODAY at http://uber.com/ai-solutions(19:57) Pilot - Visit https://www.pilot.com/twist and get $1,200 off your first year. (29:46) Northwest Registered Agent - Form your entire business identity in just 10 clicks and 10 minutes. Get more privacy, more options, and more done—visit https://www.northwestregisteredagent.com/twist today!