Podcasts about series b

CuspAI, het Nederlands-Britse AI-bedrijf dat nieuwe materialen ontdekt met hulp van AI, heeft een Series B-financieringsronde van in totaal 450 miljoen dollar behaald. Daar doen naast Jeff Bezos, Nvidia en Meta ook onder meer Invest-NL en het Britse overheids-AI-fonds aan mee. Joe van Burik vertelt erover in deze Tech Update. Verder in deze Tech Update: Google zou een efficiëntere AI-chip hebben ontwikkeld onder de naam Frozen v2, zo meldt The Information, waar aandeelhouders enthousiast op reageren See omnystudio.com/listener for privacy information.

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

Prototypes and demoware.That was Monumental two years ago, at their $25m Series A.Yesterday they announced a $32m Series B led by Khosla Ventures. In between, their robots laid the entire brickwork on 100 houses, plus canal walls and commercial buildings, as a subcontractor carrying full liability for the work.The day after the announcement, co-founder Salar al Khafaji sat down with us.In his words:"The Series A was basically still like an R&D round... we had some prototypes, we had some like demoware basically.""The Series B was raised off actual success."And the line that will start arguments:"Everyone is obsessed with speed. Speed doesn't really matter."A bricklaying robot company telling you speed doesn't matter. His reasoning is worth the listen on its own.Also in this one:> Why SAM and Hadrian X were too early, and what changed> Day zero to day one of a robot crew arriving on site> The economics vs a traditional bricklaying gang> UK going commercial, US pilots starting this year> Why the entire facade is next. Not in a decade, within a couple of years.

Roope Heinilä perusti Smarpin opiskelukaverien kanssa vuonna 2011 ilman koodaustaitoja ja ilman ulkopuolista rahoitusta. Kymmenen vuotta myöhemmin firma oli ylittänyt 10 miljoonan ARR:n, myynyt miljoonan dollarin diilejä Googlelle ja Salesforcelle, ja päätynyt exitiin jenkkipääomasijoittajalle. Sen jälkeen Roope veti yhdistettyä 300 hengen organisaatiota 20 miljoonasta 40 miljoonaan ARR:iin – ja lähti sitten rakentamaan uutta.Optivian AI syntyi yhden ongelman ympärille: top 20 prosenttia myyjistä tuo 80 prosenttia tuloksesta, ja mikään työkalu, koulutus tai prosessi ei ole toistaiseksi pystynyt ratkaisemaan tätä kunnolla. Roopen näkemys on, että AI-aikakaudella se on ensimmäistä kertaa mahdollista – ei kertomalla myyjille mitä tehdä, vaan tekemällä se työ heidän puolestaan.Jaksossa pureudumme:- Smarpin rakentamiseen koulun penkiltä kansainväliseksi SaaS-firmaksi ja siihen, mitä pivotteja matkalla tehtiin- Miksi ensimmäinen Jenkki-laajennus New Yorkiin ei toiminut ja miten Atlanta toimi paremmin- Miten yksi myyntijohtajan rekrytointi muutti koko myynnin suunnan – hinnoittelumallia, uskomuksia ja prosesseja myöten- Mitä tarkoittaa myydä miljoonan euron diili Googlelle ja mitä siitä voi oppia- Miten private equity -omistuksen alla johtaminen eroaa VC-omistetusta kasvufirmasta- Miksi vanhoilla SaaS-firmoilla on vaikeuksia reagoida AI-murrokseen ja miksi se on samalla mahdollisuus uusille tekijöille- Miten Optivian AI on rakennettu ja mitä alkuvaiheen asiakaspalaute muutti tuotteen suunnassa- Mitä "context is the king" tarkoittaa käytännössä AI-tuotteen arkkitehtuurissaSmarp on yksi harvoista suomalaisista SaaS-firmoista, jotka ovat ylittäneet 10 miljoonan ARR:n, hankkineet Fortune 500 -asiakkaita Pohjoismaiden ulkopuolelta ja päätyneet merkittävään exitiin. Roopen tarina ei ole suoraviivainen menestyskulku vaan sarja vääriä suuntia, uudelleenrakennettuja oletuksia ja oppeja, jotka syntyivät tekemällä.Sisällysluettelo:00:00 Johdanto ja vieraan esittely01:24 Smarpin tarina: lähtökohdat ja ensimmäiset pivotit07:21 BBC World News -haastattelu 10 hengen firmalta11:28 Koulun penkiltä yrittäjäksi: mitä meni pieleen alussa13:35 Ensimmäinen ulkopuolinen sijoittaja Lontoosta16:10 Product market fit löytyy: prototyyppi asiakaspalaverissa19:48 Kasvu kiihtyy, avautuu UK ja Ruotsi27:02 Ensimmäinen Jenkki-laajennus New Yorkiin35:21 Miksi New York ei toiminut ja mitä opittiin38:27 Uusi myyntijohtaja muuttaa kaiken42:30 Näin myydään miljoonan diili Googlelle ja Salesforcelle45:52 Hinnoittelumallin muutos ja sen vaikutus kasvuun49:34 Series B vai exit – markkinamuutos pakottaa päätökseen57:16 Private equity -toimitusjohtajana: 20 M:stä 40 M ARR:iin01:05:35 AI-murros ja miksi se on vaikea vanhoille firmoille01:12:27 Optivian AI: idea, tuote ja alkuvaiheen opit01:21:45 Miten AI SaaS eroaa perinteisestä SaaS-rakentamisesta01:30:33 Konteksti on kaiken ydin – miten AI:ta pitää ruokkia01:38:10 Pipeline-konversio vs. pipelinen täyttäminen01:47:58 Missä Optivian AI on nyt ja miten sitä voi kokeillaMenestystä Etsimässä on podcast suomalaisesta ohjelmisto- ja SaaS-liiketoiminnasta. Keskustelut käsittelevät kasvua, kansainvälistymistä, liiketoimintamalleja ja päätöksiä, joita harvoin avataan julkisesti. Jakso on katsottavissa YouTubessa ja kuunneltavissa kaikissa podcast-palveluissa.Tutustu myös:Roope Heinilä (LinkedIn): https://www.linkedin.com/in/rheinila/Optivian AI: https://optivian.ai/Antti Pietilä (LinkedIn): https://www.linkedin.com/in/anttipietila/Kasvuvalmennus SaaS-yrityksille: https://calendly.com/antti-pietila/kasvuvalmennus-sparrausLoyalistic: https://loyalistic.com/fi/Loyalistic Studio: https://loyalistic.com/fi/studio/SaaS Finland: https://www.saasfinland.com/Podcast tehdään yhteistyössä Loyalisticin ja SaaS Finlandin kanssa.

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we delve into a series of significant advancements and challenges shaping the landscape of these dynamic sectors. Starting with Ipsen's Dysport, which has made notable strides in its Phase 3 trials for migraine prevention. The trials covered both episodic and chronic conditions, marking a first in the neurotoxin market. Dysport's success positions it as a formidable competitor to AbbVie's Botox, expanding therapeutic options for individuals battling migraine disorders. This achievement showcases the potential efficacy of botulinum toxin-based therapies in neurology and pain management, offering promising new avenues for patient care. In regulatory news, Boehringer Ingelheim has received approval from the Medicines and Healthcare products Regulatory Agency (MHRA) for Jascayd, a small molecule PDE4B inhibitor with antifibrotic properties. This approval marks a significant milestone in the treatment of idiopathic and progressive pulmonary fibrosis. Jascayd's addition to the therapeutic arsenal offers new hope for managing this debilitating condition, emphasizing the ongoing efforts to improve patient outcomes through innovative treatments. The arena of business development sees HanChorBio partnering with InxMed to advance oncology research. By combining HCB101 with FAK inhibitors and FAP-targeted ADCs, this collaboration aims to leverage antibody and small molecule drug discovery techniques. The goal is to develop innovative cancer treatments that could redefine therapeutic approaches in oncology. Keenova Therapeutics has also reported success with Xiaflex for plantar fibromatosis. This enzyme injection therapy offers a novel approach by targeting collagen, thus providing an innovative solution for musculoskeletal conditions. Similarly, Fate Therapeutics' FT819, an off-the-shelf CAR-T therapy, has shown early promise in tackling treatment-resistant systemic sclerosis, underscoring the potential of cell therapies beyond oncology. Meanwhile, MindRank's successful Series B funding round of $52 million highlights the growing role of AI platforms in drug discovery. The funding will propel its AI-discovered oral GLP-1 obesity pill into Phase III trials, exemplifying how technology-driven solutions are gaining traction in addressing metabolic diseases like obesity. On the regulatory front, Saol Therapeutics has resubmitted SL1009 (DCA) to the FDA for pyruvate dehydrogenase complex deficiency. This submission underscores ongoing efforts to address rare metabolic disorders using small molecule therapies. Additionally, Sanofi's concessions to the EU regarding flu vaccine marketing illustrate the complexities of competitive dynamics and regulatory scrutiny within the vaccine market. However, not all developments are favorable. AstraZeneca and Ionis Pharmaceuticals faced a setback as their drug Wainua failed its Phase 3 trial for transthyretin-mediated amyloid cardiomyopathy. This outcome highlights the challenges inherent in developing effective treatments for complex cardiovascular conditions. Meanwhile, regulatory processes remain contentious as the FDA pauses its release of complete response letters amid debates over proprietary information disclosures. In another noteworthy development, GSK has terminated its $2.2 billion collaboration with Alector after underwhelming results from Alzheimer's drug trials. This decision highlights both financial implications and strategic shifts as companies reassess risk tolerance in neurodegenerative disease research. Conversely, Roche's success with its KRAS G12C inhibitor divarasib in Phase 3 lung cancer trials underscores the promise of precision medicine. Divarasib outperformed competitors Amgen's Lumakras and Bristol Myers Squibb's Krazati, positioning Roche to potentially redefine standards of care based on genetic profiles. In a move reflecting industry trends towards collaboration and innovation risk-sharing models, AstraZeneca has partnered with Sino Biopharmaceutical on respiratory disease research. This strategic alliance represents a substantial investment aimed at expanding AstraZeneca's pipeline in respiratory therapeutics. Lastly, amidst these developments, psychedelic drugs are experiencing a renaissance in psychiatric care. Companies like Compass Pathways are pioneering clinical validation for their use in treating depression, signaling a potential paradigm shift from traditional SSRIs to newer therapeutic classes pending safety and efficacy data. Overall, these stories illustrate a dynamic interplay of scientific progress and regulatory navigation within the pharmaceutical and biotech sectors. While challenges persist—particularly in neurodegenerative disease treatment—the breakthroughs in oncology and metabolic disorder therapeutics offer hopeful prospects for improving patient care. As these industries continue evolving, integrating advanced technologies such as AI will likely play a pivotal role in shaping future therapeutic landscapes.Support the show

Will is the co-founder and CEO of Orbital, a legal AI platform built for the real estate industry. Founded in 2018, Orbital sits at the centre of property transactions, automating the legal work that has traditionally been slow and manual and connecting the parties who depend on it.  It now handles over 200,000 transactions a year for some of the UK's top law firms including Mishcon de Reya and works directly with the real estate businesses behind those deals: the developers, owner-operators, investors and REITs shaping the built environment.  By bringing law firms and their clients onto a single platform, Orbital is building the infrastructure for how real estate gets bought, sold and financed.  It opened a New York office in 2025 and, in January, raised a $60 million Series B to scale further across the US. 

What does it actually take to build an executive team from nothing? This week on The Data Minute, Ashley Neville fills in for Peter and sits down with Francois Ajenstat, Founder and CEO of Golden Analytics, to talk hiring at the earliest stages of a company, from seed through Series B.Francois spent over a decade as Chief Product Officer at Tableau before leading product at Amplitude, and recently launched Golden Analytics, an AI-native BI platform that just closed $21 million in total seed funding. He walks through why he sees fundraising as less about the check and more about finding long-term partners, why he never set out to build a foundational model, and why he thinks the fear around AI replacing data analysts has it backwards. He also breaks down his approach to those first few hires: starting with people he trusts completely, using Carta's own compensation data to build trust with candidates during offer negotiations, and the three-part test he runs on every new hire around AI fluency, taste, and ownership of outcomes.The conversation also covers Golden's unconventional customer feedback loop, the surprising order in which startups actually hire across functions, and Francois's long-running framework for job satisfaction: the work, the people, and the recognition.Subscribe to Carta's weekly Data Minute newsletter: https://carta.com/subscribe/data-newsletter-sign-up/Explore interactive startup and VC data, with Carta's Data Desk: https://carta.com/data-desk/Chapters: 01:17 – Announcing the $21M Seed: Fundraising Is About Partners, Not Just Capital 02:57 – Pitching Golden Analytics: Zig When Everyone Else Zags 06:06 – Why Golden Isn't Building Its Own Foundational Model 07:48 – The Privacy Question: Why Golden Never Sends Customer Data to the Models 09:17 – Will AI Replace the Data Analyst? (No, It Makes Them 10x) 10:57 – From CPO to "Solo" Founder: Why the Label Never Fit 13:14 – Hiring Employee One: The Former Tableau CTO 15:17 – Using Carta's Comp Data to Build Trust with Candidates 17:42 – Thinking About the ESOP from Day One 19:08 – The New Hiring Bar: AI Fluency, Taste, and Ownership 21:44 – Inside a Seven-Person Company Outshipping the Competition 22:46 – No Wall Between Customers and Engineers 24:33 – Making Customers Feel Like Founders 27:03 – An Unboxing: The Golden Analytics Coin 28:06 – The First Experience: What Happens When You Open Golden 29:33 – Surprising Data: Founders Hire Before They Raise 30:52 – The Order of Hires: Why CFOs Come Before Revenue 32:18 – Fractional vs. Full-Time: "Does This Make the Beer Taste Better?" 34:22 – What's Next to Hire: Engineers Ahead, Sales Behind 36:11 – Why Golden Skips the Middle: Senior Talent Paired With Junior Hunger 38:25 – Education, Fear, and Learning by Doing 41:15 – Building Carta's Own Report With AI, Faster 43:06 – Every Company Is a Data Company 44:27 – The Customer Data Francois Obsesses Over Daily 47:28 – Is SaaS Dead? Why the "Apocalypse" Headlines Miss the Point 49:21 – The Three-Factor Test for Job Satisfaction 51:47 – Redefining Appreciation: Experiences Over Titles 54:47 – What's Next for Golden Analytics 56:30 – OutroThis presentation contains general information only and eShares, Inc. dba Carta, Inc. (“Carta”) is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services, and is for informational purposes only.  This presentation is not a substitute for such professional advice or services nor should it be used as a basis for any decision or action that may affect your business or interests. © 2026 eShares, Inc., dba Carta, Inc. All rights reserved. In the interest of transparency, Golden Analytics is a customer of eShares, Inc. dba Carta, Inc. ("Carta").  While we have invited them here today to discuss their journey, please note that this is not an endorsement, solicitation, or recommendation for Golden Analytics or Carta. Carta does not assume any liability for reliance on the information provided during this podcast.

Build a new kind of rocket engine, and the world will beat a path to your door. Also, Blue Origin is raising $10 billion at a $130 billion pre-money valuation from Coatue Asset Management, Bezos himself and other big-name investors, the New York Times reported. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Today on the Invest In Her Podcast, host Catherine Gray talks with Sarah Lerner-Mantel, Managing Partner at Roll Tack Ventures, a Midwest-based venture capital firm investing in Series A and B B2B technology companies that drive growth, improve efficiency, and reduce risk. Before launching Roll Tack Ventures, Sarah helped scale Wayfair's 33-million-SKU marketplace and co-founded a med-tech company that developed an innovative ventilator during COVID-19, leading the company through acquisition. Her mission is to help exceptional founders grow industry-defining companies by providing not only capital, but meaningful strategic support and connections.  In this episode, Catherine and Sarah explore what it really takes for startups to raise venture capital, the difference between Series A and Series B funding, and why the Midwest is an untapped opportunity for innovation despite generating a quarter of U.S. GDP. Sarah shares how Roll Tack Ventures partners with founders by opening doors to customers—not just writing checks—and discusses her own entrepreneurial journey from startup founder to venture capitalist. They also discuss the importance of increasing women's representation in venture capital, how investors evaluate companies, why founders should better understand the venture funding process, and how education can encourage more women to become both founders and investors. Whether you're building a company, considering venture investing, or simply curious about how innovation gets funded, this conversation offers valuable insights from both sides of the investment table.  Websites Mentioned Roll Tack Ventures – https://rolltackventures.com Show Her The Money – https://showherthemoneymovie.com She Angel Investors – https://www.sheangelinvestors.com   Follow Us On Social Facebook @sheangelinvestors Twitter (X) @sheangelsinvest Instagram @sheangelinvestors & @catherinegray_investinher LinkedIn @catherinelgray & @sheangels   #InvestInHer #WomenInBusiness #WomenFounders #WomenEntrepreneurs #VentureCapital #VC #StartupFunding #SeriesA #SeriesB #FounderJourney #B2BTech #TechStartups #Innovation #FemaleInvestors #WomenInVC #AngelInvesting #WomenWhoInvest #BusinessGrowth #Leadership #Entrepreneurship #ScalingStartups #MidwestStartups #FounderLife #StartupSuccess #CatherineGray  

James Hygate, CEO and founder of Firefly, returns to The SAF Podcast for the first time since December 2023 — and a lot has happened. James brings his characteristic blend of technical depth, founder candour, and big-picture ambition to a brilliant wide ranging discussion.  James opens with a comprehensive update on Firefly's progress: the selection of Turkish engineering firm Altaca as their HTL technology partner, the securing of a site at an existing refinery in Harwich for the downstream hydrotreating facility, and supply agreements with water companies including Severn Trent and Anglian Water that already cover more feedstock than the first facility needs. He also touches on the significance of the Boeing investment, the Builders Vision backing, and the ongoing Series B round that Firefly hopes to close shortly.James also shares his observations from a recent visit to Shanghai on the pace of Chinese SAF and renewables infrastructure development, the implications of used cooking oil supply being internalised within China, and what lessons — if any — Western developers can draw from "China speed." We also explore the trajectory of the wider SAF industry, James's concerns about policy wobble and its chilling effect on infrastructure capital, and a frank view on whether some SAF projects are gaming mandates rather than genuinely pursuing decarbonisation.Throughout, James returns to the thread that has defined his 20-year career in low carbon fuels: the only thing worth doing is something that can move the needle on climate change. For Firefly, that means wet-waste-to-jet at gigaton scale — and James believes the technology stack to do it is now in place.

This week in Portland startup news, we start with the top 10 Silicon Florist posts from H1 2026 — the list readers built by clicking, not me — and the story it tells is worth pausing over: QSBS grabbed the top two slots, Dwayne Johnson's community read landed at number three, and Panthalassa's quiet $140 million Series B slotted in at four. From there, a midyear refresh of the "how to Portland startup community" primer — the front door for anyone who's been circling the edges and hasn't quite found their way in yet. CHAPTERS:00:00 Portland startup news02:05 Small Business resources07:10 Top 10 Oregon startup stories so far18:35 How to Portland startup community22:15 SecretsLINKS:Top 10 Silicon Florist posts, H1 2026 — https://siliconflorist.com/2026/07/01/top-10-silicon-florist-posts-for-the-first-half-of-2026/How to Portland startup community (midyear) — https://siliconflorist.com/2026/07/02/refresher-how-to-portland-startup-community-midyear-2026-quickstart-edition/Portland Startups Slack — https://pdxslack.com/Bricks Need Mortar × Portland Office of Small Business AI Shop Talk — https://siliconflorist.com/2026/06/29/run-a-small-business-curious-about-ai-bricks-need-mortar-can-help/The AI Conversation — register (Jul 16) — https://www.eventbrite.com/e/the-ai-conversation-thats-been-missing-for-small-business-tickets-1991933655184Our Place — https://siliconflorist.com/2026/07/01/helping-you-find-your-people-and-community-with-our-place/Our Place app — https://www.ourplace.community/LocalFirst PDX — https://siliconflorist.com/2026/07/01/portland-loves-local-small-business-localfirst-pdx-makes-them-easier-to-find/LocalFirst PDX site — https://localfirstpdx.com/FIND RICK TUROCZY ON THE INTERNET AT…- https://patreon.com/turoczy- https://linkedin.com/in/turoczy- Portland Oregon startup news on Apple Podcasts https://podcasts.apple.com/us/podcast/portland-oregon-startup-news-silicon-florist/id1711294699- Portland Oregon startup news Spotify https://open.spotify.com/show/2cmLDH8wrPdNMS2qtTnhcy?si=H627wrGOTvStxxKWRlRGLQ- Startup Stories on Spotify https://open.spotify.com/show/1Tk7bbzaNYowGouI9ucKC3- Startup Stories on Apple Podcasts https://podcasts.apple.com/us/podcast/startup-stories-with-silicon-florist/id1849468494- The Long Con on Apple Podcasts https://podcasts.apple.com/us/podcast/the-long-con/id1810923457- The Long Con on Spotify https://open.spotify.com/show/48oglyT5JNKxVH5lnWTYKA- https://bsky.app/profile/turoczy.bsky.social- https://siliconflorist.substack.com/- https://pdxslack.comABOUT SILICON FLORIST ----------For nearly two decades, Rick Turoczy has published Silicon Florist, a blog, newsletter, and podcast that covers entrepreneurs, founders, startups, entrepreneurship, tech, news, and events in the Portland, Oregon, startup community. Whether you're an aspiring entrepreneur, a startup or tech enthusiast, or simply intrigued by Portland's startup culture, Silicon Florist is your go-to source for the latest news, events, jobs, and opportunities in Portland Oregon's flourishing tech and startup scene. Join us in exploring the innovative world of startups in Portland, where creativity and collaboration meet.ABOUT RICK TUROCZY ----------Rick Turoczy has been working in, on, and around the Portland, Oregon, startup community for nearly 30 years. He has been recognized as one of the “OG”s of startup ecosystem building by the Kauffman Foundation. And he has been humbled by any number of opportunities to speak on stages from SXSW to INBOUND and from Kobe, Japan, to Muscat, Oman, including an opportunity to share his views on community building on the TEDxPortland stage (https://www.youtube.com/watch?v=Cj98mr_wUA0). All because of a blog. Weird.https://siliconflorist.com#pdx #portland #oregon #startup #entrepreneur

Brandon Sedloff and Aaron Gershenberg sit down in California's wine country to explore three decades of venture capital evolution. Aaron, CEO and managing partner at Pinegrove Venture Partners, shares his unconventional path from economic development work and real estate consulting to building SVB Capital into a $10 billion platform, then relaunching after the bank's collapse as Pinegrove, backed by Sequoia Heritage and Brookfield. They discuss: - How Aaron used Monte Carlo analysis to project fund performance and justify additional capital during the dot-com crash - Why he structured distribution-only fee models for anchor LPs instead of traditional management fees and carry - The mechanics of becoming a FOIA blocker to attract pension fund capital - How Pinegrove positions across seed, Series A, Series B, credit, and secondaries to capture alpha at different stages - Why the 2024–2026 vintage may deliver the fastest value creation Aaron has seen in his career This episode offers a roadmap for institutional investors navigating venture exposure, LP sentiment shifts, and the structural innovations that have reshaped fund economics over the past 25 years. Topics: (00:00:00) - Intro (00:03:20) - Growing up between Uganda, Kenya and New Jersey (00:10:50) - Africa's influence and giving back (00:15:40) - Question authority and the unstructured path (00:18:30) - Real estate consulting and the sales pivot (00:21:10) - Breaking into venture in the mid-'90s (00:32:00) - Joining Silicon Valley Bank (00:33:30) - Building community through cycling and kiteboarding (00:37:00) - Launching SVB Capital (00:40:15) - Innovative fund structures and FOIA blockers (00:44:20) - Scaling through three eras: 2000–2023 (00:49:00) - SVB's collapse and rebuilding as Pine Grove (00:50:20) - Partnering with Sequoia Heritage and Brookfield (00:56:30) - Pine Grove's platform and strategies today (01:02:30) - AI conviction and the 2024–2026 vintage (01:09:10) - What keeps you up at night Links: Aaron on LinkedIn - https://www.linkedin.com/in/aaron-gershenberg-7361b23/ Pinegrove Venture Partners - https://pinegrove.vc/ Brandon on LinkedIn - https://www.linkedin.com/in/brandonsedloff/ Juniper Square - https://www.junipersquare.com/

Chris Gomes set out to hire four AI product managers. Seven months later, his biggest lesson was not about AI at all.In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Chris Gomes, VP of Product at Conveyor, the Series B startup that automates responses to security questionnaires and RFPs. Chris walks through the seven months he spent hiring four AI PMs, why he spent so long defining what an “AI product manager” even means, and the realization that culture fit, not AI skill, was the thing that actually predicted success.They explore how he rebuilt a stalled interview process by pulling the most important screens to the front, the MOC framework he uses to map each interview step to specific competencies, his case for work trials and customer role-plays, how he treats references and back-channels, and why the gut-level question of whether you'd enjoy working with someone deserves more weight than most rubrics give it.If you're a hiring manager trying to run a tighter, higher-signal process, a founder thinking about your first product hires, or a PM preparing for interviews and wondering what teams are really evaluating, this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Paul Erlanger is the Co-Founder and CEO of FOMO, the social-first trading platform building the future of on-chain investing. Since founding the company in 2025, Paul has raised approximately $94 million, including a $17 million Series A led by Benchmark and a $75 million Series B led by Index Ventures with participation from USV, valuing the company at $550 million. Today, FOMO has grown to 600,000 users, processed over $4 billion in trading volume, and is adding thousands of new users every day—all with a team of just 17 people. AGENDA:  00:00 – Building a $550M Company with No Salaries, No Managers & No 1:1s 03:58 – Why Traditional Brokerages Will Lose in the Next 10 Years 09:30 – Why Robinhood's Strategy Is Wrong; The End of the Financial Super App?  13:05 – "Markets Aren't a Casino" — The Case for Retail Investors Fighting Wall Street 16:45 – The Radical Hiring Bet: Giving Employees Founder-Level Equity 23:40 – AI Kills Org Charts: Why FOMO Will Stay Under 25 Employees 29:30 – Why Taste Beats AI & The Biggest Mistake Most Consumer Startups Make 33:10 – The Social Media Playbook That Every Startup Gets Wrong 39:20 – How Benchmark, Index & USV Won the Deal—and the VC Advice Founders Need to Hear 46:10 – The Future of Investing: Social Trading, Creator Economies & Financial Networks  

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we explore the dynamic shifts and breakthroughs shaping the industry, from major acquisitions to groundbreaking scientific advancements. Merck KGaA has made headlines with its bold $11.3 billion acquisition of Bio-Techne Corporation. This marks Merck's most significant deal since purchasing Sigma-Aldrich in 2015, reinforcing its strategic focus on expanding its life sciences tools portfolio. The acquisition aims to accelerate innovation in drug development and diagnostics, highlighting Merck's commitment to enhancing its capabilities in biotechnology under the leadership of CEO Kai Beckmann. Such strategic moves underscore a broader trend towards bolstering biotech portfolios through mergers and acquisitions as companies aim to remain competitive in an ever-evolving market landscape. In regulatory news, the FDA has approved a pioneering combination therapy involving Gilead's Trodelvy and Merck & Co.'s Keytruda for the first-line treatment of triple-negative breast cancer (TNBC). This aggressive cancer subtype has historically had limited treatment options, making this approval particularly significant. The combination therapy leverages an antibody-drug conjugate targeting Trop-2 alongside a PD-1 inhibitor, offering a promising new strategy that could substantially improve patient survival outcomes. This development also highlights the growing role of antibody-drug conjugates in oncology, illustrating how innovative therapeutic combinations can enhance treatment efficacy. Meanwhile, Pfizer's Ibrance has received FDA approval for label expansion to treat HR-positive, HER2-positive metastatic breast cancer. As a CDK4/6 inhibitor crucial in cell cycle regulation, Ibrance's expanded use reflects ongoing advancements in targeted therapies that personalize cancer treatment based on specific molecular characteristics. Such expansions demonstrate the importance of continuous clinical evaluation and regulatory engagement in extending the lifecycle and applications of existing drugs. Ionis Pharmaceuticals has gained FDA approval for Tryngolza for severe hypertriglyceridemia, marking a significant milestone for antisense oligonucleotide therapies. By targeting apolipoprotein C-III, Tryngolza offers a novel approach to managing metabolic conditions linked to pancreatitis risks. This approval underscores the growing importance of antisense technology in addressing complex lipid disorders and highlights Ionis' strategic efforts to expand market reach through global partnerships. On the business development front, Boehringer Ingelheim's partnership with Immunai aims to leverage artificial intelligence in T-cell target discovery for cancer and autoimmune diseases. The integration of AI/ML technologies into drug discovery processes is increasingly seen as essential for enhancing precision and efficiency. This collaboration reflects an industry-wide shift towards embracing technology to improve research and development outcomes. In clinical trials, Otsuka's centanafadine shows promise for adults with ADHD and comorbid anxiety following successful Phase 3b trials. As a small molecule reuptake inhibitor, centanafadine could provide dual therapeutic benefits for patients with these overlapping conditions. Such developments highlight ongoing innovation in neuropsychiatric treatments aimed at addressing mental health conditions with greater precision. Financially, Oblenio Bio's $62 million Series B funding round will support advancing its tri-specific autoimmune T-cell engager into trials, potentially offering new solutions for autoimmune diseases through innovative immunotherapy approaches. These financial movements illustrate how companies are strategically positioning themselves to capitalize on emerging therapeutic opportunities. Amid these developments, regulatory dynamics continue to evolve, as seen with the FDA's pilot program aimed at streamlining drug approval processes. Initiatives like these are pivotal in restoring confidence in regulatory frameworks while adapting to new scientific insights and technological advancements. Overall, these developments underscore the pharmaceutical and biotech sectors' dynamic nature, characterized by strategic collaborations, regulatory milestones, and innovative treatment options poised to enhance patient care and strengthen drug development pipelines. The ongoing integration of cutting-edge technologies such as AI signifies an evolution towards more personalized and efficient healthcare solutions. Thank you for tuning into Pharma Daily, where we bring you the latest insights from the forefront of pharmaceutical and biotech innovation. Join us next time as we continue to explore the trends shaping the future of healthcare globally.Support the show

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we delve into a series of impactful developments shaping the future of medical innovation and patient care. The landscape of pharmaceutical and biotech industries is currently experiencing notable shifts driven by scientific advancements, regulatory updates, and strategic collaborations. One of the more controversial events involves the retraction of a high-profile study in Nature Medicine. This study initially suggested that the timing of PD-1 inhibitor administration had significant impacts on survival rates for non-small cell lung cancer patients. Early-day administration was linked to improved outcomes compared to later in the day. However, after a four-month investigation, concerns over methodological validity led to its retraction. This incident serves as a stark reminder of the necessity for rigorous peer review and transparency in clinical research, which are essential for influencing treatment protocols effectively. In industry news, Eli Lilly has entered into a major $1.9 billion partnership with Abbisko Therapeutics to harness Abbisko's drug discovery capabilities, particularly in oncology. This collaboration highlights an increasing trend where Western pharmaceutical companies team up with Asian biotech firms to accelerate drug development and tap into innovative therapeutic platforms. Eli Lilly is also recalibrating its strategy for launching its oral obesity treatment, Foundayo, in Europe, as it navigates the complexities introduced by the Most Favored Nation pricing agreement with the U.S. This underscores a broader challenge within the industry: balancing pricing regulations with expanding access through digital health channels like telehealth. ADC Therapeutics is taking steps to address safety concerns surrounding its antibody-drug conjugate, Zynlonta, by reducing its workforce by 17%. This strategic realignment demonstrates the delicate balance companies must maintain between advancing promising therapies and ensuring patient safety through vigilant clinical oversight. On the regulatory front, Incyte's decision to drop its lawsuit against CMS over drug classification issues involving its JAK inhibitor Opzelura highlights ongoing negotiations between pharmaceutical companies and regulatory bodies. These classifications have direct implications for market access and reimbursement strategies. Shifting focus to infrastructure, Advancell's move to establish its U.S. headquarters near Boston for radiopharmaceutical production underscores an emphasis on localizing drug manufacturing facilities to enhance supply chain resilience. This decision aligns with broader efforts to support domestic clinical trials for innovative therapies targeting prostate cancer. In terms of technological innovation, Novartis has invested $105 million upfront in Antares Therapeutics to target historically undruggable cancer proteins through small molecule development. This move reflects an industry-wide push towards exploiting cutting-edge technologies like AI-driven drug discovery to meet unmet medical needs in oncology. Precision medicine continues to gain traction, exemplified by Abbott's partnership with AlzPath to develop blood-based diagnostic tests for Alzheimer's disease. Collaborations such as these are pivotal in enhancing early diagnosis and personalized treatment approaches for neurodegenerative disorders. Meanwhile, the industry's financial dynamics continue to evolve with significant fundraising activities. Serapha Bio's public debut through a reverse merger with Boundless Bio raised $230 million, highlighting a growing trend of utilizing reverse mergers as a pathway to public markets. This financial boost comes alongside their licensing of a gene editing technology from China, underscoring the global nature of biotech collaborations. In oncology, Eli Lilly's extended partnership with Abbisko Therapeutics underscores the ongoing commitment to precision medicine, aiming to harness small molecule innovations targeting specific cancer pathways. Concurrently, the European Medicines Agency approved Astellas' Padcev combined with Merck & Co.'s Keytruda for muscle-invasive bladder cancer treatment based on promising Phase 3 results. Ophthalmology research is also seeing substantial investment with Ollin Biosciences raising $330 million in Series B funding aimed at developing therapies that challenge existing treatments like Vabysmo for eye diseases. Such investments indicate strong confidence in novel therapies that could redefine standards in treating conditions like wet age-related macular degeneration. In conclusion, these developments reflect a vibrant biotech and pharma landscape characterized by strategic partnerships, innovative financing mechanisms, and regulatory milestones that collectively drive forward scientific progress and enhance therapeutic options available worldwide. As these sectors continue to evolve, integrating cutting-edge technologies like gene editing and precision oncology will be pivotal in shaping healthcare delivery's future trajectory while improving patient outcomes globally.Support the show

Unser heutiger Gast ist gelernter Fluggerätmechaniker, Wirtschaftsingenieur, täglich Meditierender und Gründer eines der am schnellsten wachsenden SaaS-Startups in Deutschland. Keine gewöhnliche Kombination – aber genau das macht seine Geschichte so spannend. Julian Wiedenhaus begann mit einem dualen Studium bei Airbus in Bremen. Er baute Flugzeuge, lernte, was Produktionstechnik bedeutet, und wechselte für den Master an die TU Hamburg – bewusst, weil dort Entrepreneurship im Lehrplan stand. Dort traf er Alexander Noll, einen Bauingenieur, dessen Vater eine Zimmerei in Niedersachsen betreibt. Und genau dort, zwischen Werkstatt und Büro, sahen die beiden, was Hunderttausende Handwerksbetriebe in Deutschland jeden Tag erleben: veraltete Software, Excel-Tabellen, Stift und Papier. Gleichzeitig ein enormer Fachkräftemangel, steigender Kostendruck und eine Branche, auf die wir alle angewiesen sind – für jede Sanierung, jeden Neubau, jede Wärmepumpe. Im Februar 2020 gründeten sie mit dem Entwickler Richard Keil Plancraft. Die erste Tischlerei in Hamburg-Ottensen ging im Sommer als Pilotkunde live. Heute, fünf Jahre später, nutzen über 20.000 Kunden in elf Ländern die Software, das Team ist auf über 130 Mitarbeitende gewachsen, und mit mehr als 50 Millionen Euro Finanzierung – zuletzt eine Series B über 38 Millionen, angeführt von Headline – spielt Plancraft in der ersten Liga europäischer ConstructionTech-Startups. Die Vision: das europäische Betriebssystem für das Handwerk. Weniger Büro, mehr Handwerk. Doch was Julian Wiedenhaus besonders macht, zeigt sich nicht in den Zahlen, sondern in der Kultur. Er meditiert seit über fünf Jahren jeden Morgen, hat mit dem „Weekly Fight Club" ein gemeinsames Achtsamkeitsritual im Team etabliert und führt nach dem Prinzip: Vertrauen gegen Engagement. Die Unternehmenswerte bei Plancraft heißen #stoked, #together, #humble. Als er 2024 drei Wochen auf Sri Lanka verbrachte, schrieb er auf LinkedIn offen darüber, was es bedeutet, als CEO loszulassen und seinem Team zu vertrauen. Seit mehr als neun Jahren beschäftigen wir uns in diesem Podcast mit der Frage, wie Arbeit den Menschen stärkt, statt ihn zu schwächen. Wir haben in über 500 Episoden mit fast 700 Persönlichkeiten darüber gesprochen, was sich bereits verändert hat und was sich weiter ändern muss. Fünf Millionen Menschen arbeiten im deutschen Handwerk, die meisten in Betrieben mit weniger als zwanzig Mitarbeitenden. Wie verändert sich Arbeit, wenn eine Branche, die Jahrhunderte lang analog funktioniert hat, plötzlich digital denken muss – und kann? Plancraft entwickelt sich zunehmend zum KI-Unternehmen. Der neue Telefonassistent PORTA nimmt Anrufe an, dokumentiert Anfragen, koordiniert Termine. Wenn die Vision lautet, dass Handwerker bald nur noch ihre Stimme brauchen – was bedeutet das für die Rolle des Menschen im Betrieb? Und wie baut man als junger Gründer eine Unternehmenskultur, die gleichzeitig Höchstleistung und Menschlichkeit trägt – mit Meditation im Kalender, Vertrauen als Führungsprinzip und dem Mut, als CEO drei Wochen zu verschwinden? Fest steht: Für die Lösung unserer aktuellen Herausforderungen brauchen wir neue Impulse. Wir suchen weiter nach Methoden, Vorbildern, Erfahrungen, Tools und Ideen, die uns dem Kern von New Work näher bringen. Darüber hinaus beschäftigt uns von Anfang an die Frage, ob wirklich alle Menschen das finden und leben können, was sie im Innersten wirklich, wirklich wollen. Ihr seid bei On the Way to New Work – heute mit Julian Wiedenhaus. [Hier](https://linktr.ee/onthewaytonewwork) findet ihr alle Links zum Podcast und unseren aktuellen Werbepartnern

Nick Turner is the CEO of Dreamdata. Nick is a seasoned B2B software leader with nearly two decades of experience building and scaling go-to-market teams, helping companies grow from early traction to tens of millions in revenue. Before stepping into the CEO role, Nick served as Chief Revenue Officer at Dreamdata, where he played a key role in shaping the company's growth strategy and expanding its presence in the U.S. market. He later transitioned into the CEO seat, leading the company through a pivotal phase of scale and transformation. Under his leadership, Dreamdata recently raised a $55 million Series B round led by PeakSpan Capital—fueling its mission to become the go-to platform for B2B marketers to connect data, attribution, and revenue in the AI era.  In this episode, we'll explore what it takes to step into the CEO role, how to lead through rapid growth and funding milestones, and Nick's perspective on building modern go-to-market teams in an increasingly data-driven world.

Bobbie Racette started with $300 at her kitchen table. Nine years later, she became the first Indigenous woman in Canada to build, scale, and sell a tech startup. In this episode - the first time Bobbie has dug into the details of the sale on a podcast - host Colleen O'Connell-Campbell sits down with the founder of Virtual Gurus, an AI-powered inclusive talent marketplace that matched underrepresented talent with businesses including Mastercard, Telus, and BMO. Bobbie shares the full arc: bootstrapping to $1.8 million in revenue before raising a cent, hearing 170 no's before closing a seed round, scaling through three funding rounds during COVID, becoming the first Indigenous woman in Canada to close a Series A, navigating founder fatigue, stepping down as CEO before the exit, and ultimately selling to a U.S. private equity firm that rolled Virtual Gurus into North America's largest virtual assistant platform - with the AI sold separately to a Calgary company. This is a conversation about what it takes to build something from nothing, what it costs personally, and what comes next when the mission is bigger than the transaction.   Key Takeaways:   Bobbie created Virtual Gurus in 2016 after being laid off in oil and gas and unable to find a job. She is Cree Métis, queer, and covered in tattoos - and nobody would hire her. The business started as a way to create a job for herself and evolved into a platform providing remote work to marginalized talent across Canada and the U.S.   She bootstrapped to approximately $1.8 million in annual revenue before seeking external funding. The seed round took over two years and 170 investor rejections before closing at $1.25 million. The Series A, two years later, was significantly easier.   Virtual Gurus scaled past $40 million in revenue and closed three funding rounds during COVID. Total capital raised was $14-20 million.   The exit was not originally planned. For the first four years, Bobbie intended to keep the company as a legacy business. The shift came around 2022 when the scale of the operation began to outpace the original mission. The board recognized that an acquisition was likely the best path forward.   The company was simultaneously pursuing a Series B and fielding acquisition offers - a dual-track process. The data room was already built for the fundraise, which accelerated due diligence to approximately five months. The acquisition by a U.S. private equity firm closed in November 2025.   The AI platform was sold separately to a Calgary-based company - effectively a double sale. The core business was rolled into the acquirer's larger virtual assistant platform.   Bobbie had stepped down from CEO to president in May 2025, with her COO becoming successor CEO. The successor stayed with the company through and after the acquisition. Bobbie's role during due diligence was primarily support - being available for the team mentally, emotionally, and strategically, while the finance team and executive team drove the process.   Founder fatigue and decision fatigue were real and significant. Bobbie emphasizes that founders need to talk about this more openly, and that boards and investors need to be supportive during those low periods rather than adding pressure.   Retention of employees during due diligence was one of the hardest parts. Bobbie's culture at Virtual Gurus was built on honesty and transparency, and not being able to tell her leadership team about the acquisition felt deeply uncomfortable.   Post-exit, Bobbie has retired her parents (her mother was her first angel investor, contributing her last $20,000), bought a new home, and is investing time and capital into the next generation. She is now an angel investor in five businesses - all founded by people from underserved communities, including Indigenous and LGBTQ+ entrepreneurs.   She has launched Tapwe (Cree for "truth"), a platform to support underserved founders with financial literacy, mentorship, AI-powered matching, and startup scaling resources. A documentary is in production. Her newsletter, The Fire Report, scaled to 4,000 subscribers almost immediately. She is also doing regular paid advisory sessions each week through her website.   Bobbie's story is a reminder that a cash-rich exit can be deeply values-driven, inclusive, and barrier-breaking - and still set you up for whatever comes next. If today's episode has you thinking about your own journey, whether you are at the kitchen table, scaling fast, or quietly eyeing your exit, book a one-on-one Wealth Gap Analysis with Colleen O'Connell-Campbell via LinkedIn or email    Please leave a five-star rating and review to help more founders find this show. *** The Cash Rich Exit Podcast is brought to you by O'Connell-Campbell Wealth Management at RBC Dominion Securities.   All opinions expressed by the host, Colleen O'Connell-Campbell, and podcast guests are solely their own opinions and do not reflect the opinion of RBC Dominion Securities.   This podcast is for informational purposes only before taking any action based on information in this podcast you should consult with a qualified professional.   Colleen O'Connell-Campbell is a Wealth Advisor at RBC Dominion Securities, a member of the Canadian Investor Protection Fund.

Tyler talks with Bailey Stockdale about their $13M Series B announcement. — This episode is presented by Ambrook. — Links Leaf - https://withleaf.io Leaf's Series B - https://www.agnavigator.com/Article/2026/06/11/bayer-invests-in-ai-backbone-company-leafs-series-b-round/ 

On this week's episode, Graig Suvannavejh, Eric Schmidt, Paul Matteis and Financial Times' Oliver Barnes kicked off with the biotech market, with the XBI in positive territory and 12 biotech IPOs completed so far this year. They expected the IPO window to remain open for high-quality private companies. The group also overviewed recent financings, including SonoThera's $125 million Series B, City Therapeutics' $100 million Series B, Ethyreal's $101 million Series A, and Summit's decision to cancel a $500 million secondary offering. In data news, the co-hosts covered Tango's combination data with Revolution Medicines' RAS inhibitor. They also discussed Incyte's acquisition of Vega Therapeutics as a pipeline-building move ahead of Jakafi's 2028 patent expiration and J&J's acquisition of Firefly, with the RAS inhibitor space expected to remain hot. The group also discussed GSK's acquisition of Nuvalent -- its largest deal to date -- for two late-stage lung cancer assets. Oliver added perspective on biotech deal leaks, following the Incyte/Vega deal and GSK/Nuvalent deals this week. In partnership updates, Novartis expanded its molecular glue work with Orionis, Lilly licensed an Alzheimer's candidate from AlzeCure, and Corvus supported China partner Angel Pharmaceuticals. The episode concluded with the latest in rare disease and gene therapy, covering Novartis' FSHD program, FDA flexibility, Rett syndrome programs, and Sensorion's exit from hearing loss development. *This episode aired on June 12, 2026. 

Most AI failures won't come from a bad model. They'll come from bad data.Shashank Saxena spent most of his career on the buying side of enterprise technology before founding VNDLY which was acquired by Workday for $510 million. He then joined Sierra as a Managing Partner before going full time as Co-founder and CEO of Pantomath, a data operations center for enterprises that are betting their future on AI agents.We discuss why data quality is becoming one of the biggest challenges in enterprise AI. An AI agent fed bad data for 12 hours doesn't go rogue. It just makes 12 hours of wrong decisions: rejecting insurance claims, issuing credit cards, or drilling in the wrong location. As more business decisions are delegated to AI systems, companies will need far greater visibility into what is happening across their data infrastructure.Shashank also shares the decisions that led to VNDLY's acquisition, the advice he'd give founders evaluating acquisition offers today, and why a Michael Jordan analogy continues to motivate him as a second-time founder.If you're building enterprise software, selling to large companies, or trying to figure out whether experience is an asset or a liability in the AI era, this episode is for you.0:00 - Trailer01:00 - How Shashank became a second-time founder07:20 - Where Pantomath sits in the data stack10:55 - How a broken Tableau report turns mission-critical with AI12:55 - Who Pantomath sells to15:35 - Solving for a problem that doesn't exist yet19:03 - How have founder expectations changed today?20:31 - Series B companies pre- and post-AI21:26 - The Michael Jordan example23:57 - How a repeat founder chooses investors25:10 - What value Snowflake adds as a strategic investor27:05 - Data is not an open category today28:34 - The astounding Databricks outcome29:08 - The reality of the $100 million ARR number31:48 - Will non-human workers 100x in the next few years?36:00 - How to protect data in motion37:26 - How comfortable are we giving full access to agents?39:47 - Where is automation fastest today?42:09 - Why entrepreneurs tend to like uncertainty43:28 - Why Shashank chose to be a founder45:48 - A customer-driven $510M acquisition48:32 - Employees vs contractors in any organization51:22 - Building from Ohio vs the Bay Area53:14 - Learnings from selling to enterprises56:31 - How Shashank raised from Tier 1 US VCs59:19 - Heads down or network as a founder?1:02:47 - First-time vs second-time founder edge in AI1:06:22 - Hiring as a repeat founder1:08:08 - How enterprise sales has changed1:10:52 - How do you sell for a problem that isn't visible today?1:12:58 - Best piece of advice1:16:27 - The only advice for a founder considering M&A1:21:06 - Position yourself to be capable of taking risks1:24:51 - What matters to an enterprise buyer?-------------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

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. The pharmaceutical and biotech industries are undergoing significant transformations, driven by scientific advancements, regulatory changes, and strategic investments. These developments are shaping the landscape of drug development and patient care in profound ways. In recent news, Pfizer's CEO, Albert Bourla, is reconsidering investments in Germany due to proposed healthcare reforms. These reforms have sparked concerns about their potential impact on the pharmaceutical industry. This situation highlights the intricate balance between regulatory frameworks and corporate strategies, illustrating how policy changes can influence investment decisions and operational strategies within the pharma sector. The tension between regulatory environments and corporate interests is a recurring theme that continues to shape strategic directions within the industry. Meanwhile, heightened scrutiny over biotechnology operations is evident with Wuxi AppTec's inclusion on the Pentagon's blacklist under the Biosecure Act. This move reflects growing concerns about biosecurity and the necessity for stringent oversight in handling sensitive biotechnological advancements. Such actions underscore a global focus on safeguarding national security while fostering scientific innovation. Teva Pharmaceuticals is navigating restructuring efforts by laying off 250 employees at its Active Pharmaceutical Ingredients unit as it seeks a new owner. This restructuring underscores the challenges companies face in maintaining operational efficiency amid ownership transitions. These challenges are emblematic of broader industry dynamics where companies strive to adapt to changing market conditions while ensuring stability and growth. On the scientific front, Novo Nordisk's cagrisema and Eli Lilly's retatrutide are emerging as next-generation incretin therapies. Although early comparisons have been made, Novo Nordisk's chief scientific officer suggests it is premature to declare a definitive leader. This competition reflects the dynamic nature of drug development as companies strive to innovate and improve treatment options continuously. Additionally, Sonothera's successful $125 million Series B funding round for its bubble-based genetic delivery system highlights the biotech industry's momentum fueled by mergers and acquisitions (M&A) and partnerships. Such technologies promise to advance genetic therapies by enhancing delivery mechanisms, potentially transforming treatment paradigms for various genetic disorders. AbbVie's Skyrizi narrowly surpassing Johnson & Johnson's Tremfya in May drug ad spending underscores the competitive nature of pharmaceutical marketing. Despite a general slump in advertising expenditures among leading drugs, strategic marketing remains crucial for maintaining brand presence and market share. Increased M&A activity and partnerships are further bolstering the industry's growth trajectory. The resurgence of Initial Public Offerings (IPOs) and venture capital funding is fostering innovation and expansion within the sector, providing fuel for continued advancement in biotech. On the regulatory front, Johnson & Johnson's Darzalex received a new endorsement from NICE after a prior reversal. Such regulatory updates emphasize the evolving nature of drug approvals and market access strategies essential for pharmaceutical companies' success. Novartis' second deal with Orionis Biosciences worth up to $1.4 billion exemplifies strategic investments aimed at expanding research capabilities and addressing unmet medical needs through molecular glue technologies targeting challenging therapeutic areas. Conversely, Sanofi's decision to halt a Phase 3 autoimmune trial due to insufficient efficacy highlights the inherent risks in drug development pipelines. These setbacks emphasize the importance of robust clinical trial designs and adaptability in R&D strategies. Emerging insights into GLP-1 drugs like Novo Nordisk's semaglutide reveal potential antidepressant effects linked to gut microbiota modulation. These findings open new avenues for exploring psychiatric applications of metabolic drugs, although conflicting data necessitates further investigation. Overall, these developments illustrate a complex interplay of scientific innovation, regulatory dynamics, and strategic corporate actions driving the future of pharmaceuticals and biotechnology. The sector continues to navigate challenges while capitalizing on opportunities to enhance patient care through advanced therapeutic solutions. The industry's trajectory promises transformative impacts on patient care through novel therapies designed not only to treat symptoms but also address root causes via innovative science-driven solutions. As these advancements unfold, they herald a new era of targeted, effective treatments that hold promise for improving patient outcomes across diverse medical landscapes.Support the show

Parshat Shelach continues now with part 2 Enjoy! 

I have always felt the best shabbat table talk on the Prasha comes from parents and children who know and are confident with the ins and puts of the details in the weekly Parsha. So many of my students never take advantage of this because they either never learned it or do not have the time to review it weekly. Enter the BEST SERIES! You are about to master the Parsha with four, fun and engaging quick Shiurim each week. give me 20 minutes or less and I will give you the Parsha! ENJOY! 

Parshat Shelach continues with Shiur 3, Enjoy!

The Finale of Parshat Shelach- Enjoy!

Autonomous vehicles may be the closest real-world example of AI operating in life-and-death situations at scale. Justin Norden believes healthcare has a lot to learn from how that industry approached safety, testing, adoption, and trust. This week, Michael and Halle sit down with the founder and CEO of Qualified Health, fresh off the company's $125 million Series B, to discuss why healthcare organizations need to think differently about deploying AI. Justin shares how his experience at Stanford, Apple, Waymo, and in healthcare investing shaped his view that health systems need AI infrastructure, governance, and workforce buy-in, not just another point solution.We cover:What healthcare can learn from Waymo's approach to safe AI deploymentWhat founders need to understand about building around EpicWhy health systems need to treat AI as a CEO-level priority, not an innovation projectHow Qualified Health is helping systems deploy, monitor, and measure AI workflowsWhy governance, safety, and ROI matter as much as model performanceWhy clinicians are right to be skeptical about AI liabilityAbout our guest:Justin Norden, MD is Co-Founder and CEO of Qualified Health building the trusted platform for health system AI. Additionally, he has been an Adjunct Professor at Stanford Medicine in the Department of Biomedical Informatics Research where his research and teaching focused on AI in medicine and digital health where he founded and still teaches courses on digital health and generative AI in medicine. Previously, Dr. Norden was Co-Founder and CEO of Trustworthy AI, a company focused on algorithm safety and trust, which was acquired by Waymo (Google Self-Driving). He was a Partner at GSR Ventures leading investments in healthcare and AI, worked on the healthcare team at Apple, and helped start the Stanford Center for Digital Health. Dr. Justin Norden received an MD and MBA from Stanford University, an MPhil in Computational Biology from the University of Cambridge, and a BA in Computer Science from Carleton College.—

https://novacut.ai/  https://genaimeetup.com/  Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM     Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h  Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/  OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens  Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d   Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/    https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073    Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales.   https://www.minimax.io/models/text/m3  MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops.   https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/  Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/  JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8    https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html      Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal.  https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench  ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%.   Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/  The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.

Physical retail is under pressure to become as measurable and responsive as e-commerce. While retailers have spent years optimizing digital channels with real-time data, store teams have often had to make decisions with incomplete inventory visibility and delayed operational signals. That gap matters because stores still account for 80% of U.S. retail sales, making better store-level intelligence a revenue, margin, and customer experience issue — not just a technology upgrade. As RFID adoption matures and AI raises the stakes for cleaner operational data, item-level visibility is becoming a more important layer of retail infrastructure. Radar, a retail technology company that recently raised $170 million in Series B funding at a billion-dollar valuation, reflects that shift, pointing to renewed confidence in tools that help retailers understand not only what inventory they have, but where it is, how it moves, and how associates can act on it in real time.The shift is being driven by a practical question for retailers: if stores remain central to the business, how can they operate with the same speed, accuracy, and intelligence as digital channels?On this episode of Retail Refined, host Melissa Gonzalez speaks with Spencer Hewett, founder and CEO of Radar, about how retailers can make physical stores more measurable, responsive, and operationally intelligent. The conversation explores how Radar's ceiling-mounted sensors and software platform help retailers track inventory in real time, locate products inside stores, support omnichannel fulfillment, and use item-level data to improve store operations, merchandising, demand planning, and customer experience.Key highlights from the talk…Radar's role in closing the store data gap: Hewett explains how the platform counts inventory continuously and locates items in real time, giving retailers a clearer view of what is available, where it is, and how products move throughout the store.Why inventory accuracy is foundational: The discussion highlights how inaccurate inventory can create out-of-stocks, fulfillment issues, missed sales, and flawed demand planning. Hewett argues that improving inventory accuracy gives retailers better data for decision-making and future AI applications.How store intelligence supports associates and operations: Gonzalez and Hewett discuss how item-location data can help associates find products faster, fulfill buy online, pick up in store orders more efficiently, and spend more time serving customers instead of searching for merchandise.Spencer Hewett is the founder and CEO of Radar, a retail technology company building RF sensing technology to automate inventory, analytics, and checkout in physical stores. Since founding the company in 2013, he has led its evolution from an autonomous checkout concept into a broader platform for item-level intelligence, working with retailers representing more than $100 billion in annual sales. Hewett is also a Thiel Fellow and Forbes 30 Under 30 honoree, with earlier experience in RFID localization, signal processing, e-commerce technology, and startup development.

Anjali Sardana grew up in northern Virginia, studied biology at Georgetown, worked at Bain Capital — and then, without telling her parents, flew to India and founded Pronto: a platform building the world's largest labor organization network, starting with home services.In this episode of Unstarted, Anjali breaks down how she picked an operations business over a product business (and why), why she sees India's informal labor market as a trillion-dollar opportunity, and the founder mindset that got her through the messy, chaotic, sleep-deprived early days.She also gets brutally honest about faking confidence, hiring missionaries not mercenaries, and why she thinks most human limitations are completely made up.Chapters0:00 Intro — Meet Anjali Sardana1:20 Growing up in Virginia, studying biology at Georgetown3:10 The evolution framework that shaped her business thinking5:00 Product vs. operations vs. distribution — how she chose8:30 Why India? The labor-market thesis12:00 Moving to India with zero experience — and hiding it from her parents15:40 Fake it till you make it: raising a seed round at Bain Capital19:15 Running pilots, vibe-coding the app, and getting the first bookings24:00 The Kapil story — recruiting 30 workers in one afternoon28:00 Operating 24/7 with 5 people, sleeping in shifts31:30 Building culture: missionaries vs. mercenaries36:00 Urgency as a core value — actions beget information39:30 Conviction vs. market signals — how to balance both

On this episode, Jhave and Scott tackle the intersection of artificial intelligence, theology, and human dignity, sparked by Pope Leo XIV's landmark AI encyclical, Magnifica Humanitas. From philosophical debates over the "TESCREAL" ideology and Isomorphic Labs' massive $2.1 billion funding and how to navigate mundane office politics using AI ethics, which Scott tires himself with a real time conversation with Claude. References Davies, H., McKernan, B., & Sabbagh, D. (2024, April 3). ‘The machine did it coldly': Israel used AI to identify 37,000 Hamas targets. The Guardian. https://www.theguardian.com/world/2024/apr/03/israel-gaza-ai-database-lavenderFuture of Life Institute. (2023, March 22). Pause giant AI experiments: An open letter. https://futureoflife.org/open-letter/pause-giant-ai-experiments/Gebru, T., & Torres, É. P. (2024). The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence. First Monday, 29(4). https://doi.org/10.5210/fm.v29i4.13636Isomorphic Labs. (2026, May 13). Isomorphic Labs announces $2.1 Billion in Series B funding. https://www.isomorphiclabs.com/articles/isomorphic-labs-announces-series-b-investment-roundKurzweil, R. (1999). The age of spiritual machines: When computers exceed human intelligence. Viking Press.Pope Leo XIV. (2026, May 25). Magnifica humanitas [Encyclical letter]. The Holy See. https://www.vatican.va

Craig Rosenberg, Chief Platform Officer at Scale Venture Partners and co-founder of Topo, joins AJ Bruno and Asad Zaman to take on the question every founder is wrestling with: can you still build a world-class sales team when OpenAI and Anthropic are handing individual contributors $10 million equity packages? Craig argues you do not have to compete head-on, then lays out the hiring profile to chase instead, the quota-to-comp discipline that keeps packages sane, and why founder brand has become the most reliable pipeline play left as CAC keeps climbing. Topics include enterprise AE compensation, where private equity is still winning the GTM talent war, the Topo playbook for events and data-as-moat, and a bull-versus-bear debate on whether Gong goes public in the next 36 months. Plus, a Quiz Pro Quo on the real customer counts behind Salesforce, HubSpot, and ZoomInfo. Key Takeaways: - Rather than try to outbid OpenAI and Anthropic for talent, build your own farm system and develop people into the role. As Craig Rosenberg, Chief Platform Officer at Scale Venture Partners, put it: "You have to change your hiring profile to a unique profile that's unique to your business, but then you gotta coach 'em up." - A resume from a hot AI lab is not a guarantee of success at your company. As Craig Rosenberg noted, "The person that is going to do well at Anthropic may not do well at Series B," so hire for the stage and the hunger rather than the logo. - On compensation, Craig anchors the package to the role's real value: "you pay for what your wedge costs… if you feel like you have to pay $10 million, then you have a huge problem and you gotta go back to the drawing board." If the number runs away from you, the model is broken. - With CAC climbing and most channels breaking down, founder brand has become the highest-leverage pipeline play. As Craig Rosenberg said, "The value of building a founder brand, when you look at the data, it's amazing," pointing to gains in both pipeline and deal size. Connect with the Hosts & Guests: Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/ Host: Asad Zaman, CEO at Sales Talent Agency - https://www.linkedin.com/in/azaman1/ Guest: Craig Rosenberg, Chief Platform Officer at Scale Venture Partners - https://www.linkedin.com/in/craigrosenberg/ Topline is more than a YouTube Channel: Subscribe to Topline Newsletter: https://toplinemedia.substack.com/ Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters: 00:00 Introducing Craig Rosenberg 02:34 Can Anyone Out-Hire The AI Labs? 04:33 Why Craig Isn't Worried 06:52 Enterprise AE Comp Is Climbing 08:21 Founders Overpay For Star CROs 10:53 Why AI Reps Struggle At Series B 14:00 Hire The Slighted CRO 14:42 Quota-To-Comp And Attainment 18:45 Can AI Labs Sustain Growth? 22:20 Where PE Still Wins GTM Talent 27:17 Major Runs Reshape GTM 32:36 The Topo GTM Playbook 37:55 Quiz Pro Quo 47:45 Founder Brand And Rising CAC 58:42 Bulls and Bears

Insurance tech Corgi announced today an $106 million Series B1 raise, valuing the company at $2.6 billion, just three weeks after announcing a $160 million Series B. Also, Anthropic has closed a $65 billion Series H round at a $965 billion post-money valuation, marking what could be the AI startup's final private fundraise before a highly anticipated IPO. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Stord offers a network of physical warehouses and inventory management software for e-commerce. It bills itself as a sort of anti-Amazon, giving brands "the speed to compete" while still owning their customer relationships. Also, OpenRouter has raised a $113 million Series B led by CapitalG. Its 5x growth in usage over six months indicates the multi-AI-model future is here. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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THIS WEEK IN REC TECH is sponsored by https://www.dalia.co/ SEATTLE — May 21, 2026 — Humanly, the AI hiring platform for hourly, frontline, and high-volume recruiting, today announced $25 million in Series B funding.  https://hrtechfeed.com/humanly-raises-25m-series-b-to-help-companies-hire-faster-retain-and-stay-fully-staffed/ NEW YORK — Saile, a physician-founded workforce platform targeting the bureaucratic bottlenecks of healthcare staffing, has emerged from stealth mode with $2.2 million in pre-seed funding. https://hrtechfeed.com/healthcare-staffing-platform-lands-2-2m/ LONDON — RemotePass, the global employment, payroll, and spend platform, has raised $17.4 million in Series B funding led by the EBRD Venture Capital, with participation from 500 other investors  https://hrtechfeed.com/eor-platform-raises-17-4m-series-b/ MINNEAPOLIS — Match2, the platform powering the Universal Candidate Profile™, today announced its integration with the Phenom Marketplace… This partnership brings Match2's Universal Candidate Profile™, Direct Talent Network™ (DTN), and Talent Connector™ platform into the Phenom hiring ecosystem https://hrtechfeed.com/match2-joins-phenom-marketplace/ Contrario announced its official launch. The platform helps companies at every stage hire faster by combining expert recruiters with AI agents that take on the operational work.  https://hrtechfeed.com/new-hr-tech-contrario-juicebox/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Researchers crack Apple's M5 memory protections with a kernel exploit. An IBM Security executive emerges as a possible CISA pick. Researchers uncover four malicious npm packages.  AI-generated “slop” floods bug bounty programs. Major healthcare breaches hit the HHS tracker, 7-Eleven confirms a breach, and chained OpenClaw AI flaws could enable full host compromise. Santa Clara County sues Meta over alleged scam ads on Facebook and Instagram. Monday business breakdown. Our guest is Jason Madigan, Director of Commercial Cloud Security at Booz Allen, discussing the tension between resilience and data residency laws. A fond farewell for a security pioneer.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest On today's Industry Voices segment we are joined by Jason Madigan, Director of Commercial Cloud Security at Booz Allen, discussing the tension between resilience and data residency laws. If you enjoyed this conversation, check out the full interview here. Selected Reading First public macOS kernel memory corruption exploit on Apple M5 (Calif) IBM executive floated for CISA director as concerns persist for agency (SC Media) Former CISA nominee Sean Plankey named US CEO of defense startup (CyberScoop) New Actors Deploy Shai-Hulud Clones: TeamPCP Copycats Are Here (OX Security) ‘Never-ending' AI slop strains corporate hacking reward schemes (Financial Times) Millions Impacted Across Several US Healthcare Data Breaches (SecurityWeek) 7-Eleven Data Breach Confirmed After ShinyHunters Ransom Demand (SecurityWeek) 'Claw Chain' OpenClaw Flaws Allow Sandbox Escape, Backdoor Delivery (SecurityWeek) Santa Clara County sues Meta over alleged scam ads (San José Spotlight) Exaforce raises $125 million in Series B funding. (N2K Pro Business Briefing) Peter G. Neumann, Who Warned of Computer Security Risks, Dies at 93 (The New York Times) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Hollywood has a new favourite hero, and it is not a warrior, a wizard, or a spy. It is a founder, a CEO, a disruptor.In this episode, we name and explore a brand new film genre: the capitalist procedural. From startup biopics to corporate origin stories, business movies have quietly taken over cinema and streaming, and we want to know why.We break down what defines the genre, why studios keep greenlighting these films, and what it says about our culture that we are now paying to watch board meetings, product launches, and Series B funding rounds play out on the big screen.Has hustle culture replaced the hero's journey? Are we using business stories to inject meaning into capitalism? Or have we just run out of ideas?Topics covered: capitalist procedural, business movies, startup films, Hollywood trends, cinema culture, film genre, hustle culture, founder mythology, cultural criticism, film analysis Hosted on Acast. See acast.com/privacy for more information.

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we're diving into some of the latest news shaping the industry, from breakthroughs in cancer therapies to advancements in AI-driven drug discovery. Starting with regulatory updates, the potential appointment of Richard Pazdur, M.D., as the new FDA Commissioner is causing quite a stir. Following Marty Makary's resignation, Pazdur has emerged as a prominent candidate due to his extensive background in oncology drug regulation. Known for his commitment to accelerating cancer therapy approvals, his potential leadership could maintain or even amplify the focus on expediting innovative treatments for cancer patients. In a significant regulatory achievement, Beone Medicines celebrated the FDA's approval of Beqalzi, marking it as the first BCL-2 inhibitor approved for mantle cell lymphoma. This approval challenges AbbVie's Venclexta and underscores a growing trend towards targeted cancer therapies that offer new treatment avenues for patients. The oncology space continues to be fiercely competitive, with companies striving to deliver more precise and effective cancer treatments. Turning to clinical trials, AstraZeneca's Imfinzi has shown promising results in a phase 3 trial focused on bladder cancer patients who are not eligible for cisplatin-based chemotherapy. These findings position Imfinzi as a strong competitor to Merck's Keytruda and reinforce AstraZeneca's strategic focus on expanding its oncology portfolio through novel combinations and indications. In the realm of genetic therapies, Regenxbio has achieved a milestone with its gene therapy for Duchenne muscular dystrophy. This therapy met its primary endpoint in pivotal trials, highlighting the potential of gene therapies to address rare diseases with limited treatment options. Such successes are likely to encourage further investment in gene editing technologies, which hold significant promise for tackling conditions once deemed untreatable. The FDA is also exploring frameworks to repurpose existing drugs for new uses by leveraging existing safety data. This could streamline drug development processes and offer cost-effective solutions for patients with complex conditions. However, this approach will need rigorous validation of efficacy in new indications to ensure patient safety and therapeutic effectiveness. Despite setbacks in its Alzheimer's research, Biogen remains steadfast in its efforts. While their tau-targeting candidate did not meet primary endpoints in a phase 2 trial, reductions in tau pathology and cognitive benefits were observed. This perseverance showcases Biogen's commitment to finding innovative approaches to tackle Alzheimer's disease despite ongoing challenges. On the operational front, Taiwan's Bora Group is acquiring Macrogenics' CDMO operations for up to $127.5 million. This move reflects a broader trend of consolidation within the CDMO space as companies aim to enhance their production capabilities and streamline operations. Quality control remains a critical concern as evidenced by Sun Pharma's recent recall of a chemotherapy batch due to glass particle contamination. Incidents like these underline the importance of stringent quality assurance measures throughout the manufacturing process to ensure patient safety. Moreover, Viz.ai has launched an AI-powered pulmonary care platform aimed at integrating acute and chronic care workflows. This development signals an increasing adoption of artificial intelligence in healthcare, promising improvements in diagnostics and patient management efficiency. AI continues to gain traction as Isomorphic Labs recently secured $2.1 billion in Series B funding aimed at enhancing AI-driven drug design models. Similarly, Charles River has introduced an AI-powered digital pathology platform poised to Support the show

Today we have Marlena Sarunac is a marketing strategist who helps early stage startups in complex industries turn forgettable products into unforgettable brands. As cofounder of The Company Advice, a fractional marketing and design studio, she's acted as a stealth marketing executive for health tech, fintech, and insurtech startups navigating inflection points. Before that, she led marketing for Fortune 100 giants like MasterCard and fast growing startups like Particle Health, where she built GTM playbooks, scaled pipeline, and reshaped how an entire industry talked about health data. Known for her #PlaybookNicely approach, Marlena blends analytics with bold storytelling to help founders clarify their message, tighten their product positioning, and build real traction. She's not here to dress up pitch decks. She's here to make sure your brand lands, sticks, and sells. Whether you're in stealth mode or Series B, Marlena brings the clarity and creative firepower to make the market care. Our website: https://www.thecompanyadvice.com/   The Friction Finder: https://www.thecompanyadvice.com/friction-finder   Our LinkedIn page to follow along our latest thought leadership + projects: https://www.linkedin.com/company/the-company-advice   My LinkedIn: https://www.linkedin.com/in/marlenasarunac/ Ramblings of a Designer podcast is a monthly design news and discussion podcast hosted by Laszlo Lazuer and Terri Rodriguez-Hong (@flaxenink, insta: alohathletesco) LinkedIn Page: https://www.linkedin.com/company/ramblings-of-a-designer/ Facebook: https://www.facebook.com/Ramblings-of-a-Designer-Podcast-2347296798835079/ Send us feedback! ramblingsofadesignerpod@gmail.com Support us on Patreon! patreon.com/ramblingsofadesigner  

John Graunt was a shopkeeper in 17th-century London who followed his own curiosity to a rather grand result. His work gave rise to the fields of demography and epidemiology. Research: Berke, Olaf, et al. “Celebration day: 400th birthday of John Graunt, citizen scientist of London.” Environmental Health Review. 63(3): 67-69. 2020. https://doi.org/10.5864/d2020-018 Britannica Editors. "John Graunt". Encyclopedia Britannica, 20 Apr. 2025, https://www.britannica.com/biography/John-Graunt Britannica, The Editors of Encyclopaedia. "Sir William Petty." Encyclopedia Britannica, 11 Apr. 2026, https://www.britannica.com/money/William-Petty Clark, Andrew. “Aubrey’s ‘Brief Lives.’” Oxford. Clarendon Press. 1898. https://dn790003.ca.archive.org/0/items/briefliveschiefl01aubruoft/briefliveschiefl01aubruoft.pdf Connor, Henry. “John Graunt F.R.S. (1620-74): The founding father of human demography, epidemiology and vital statistics.” Journal of medical biography 32,1 (2024): 57-69. doi:10.1177/09677720221079826 Eschner, Kat. “People Have Been Using Big Data Since the 1600s.” Smithsonian. April 24, 2017. https://www.smithsonianmag.com/smart-news/people-have-been-using-big-data-1600s-180962949/ Glass, D.V., et al. “John Graunt and His Natural and Political Observations [and Discussion].” Proceedings of the Royal Society of London. Series B, Biological Sciences, Vol. 159, No. 974, A Discussion on Demography (Dec. 10, 1963), pp. 2-37 Published by: The Royal Society Stable URL: https://www.jstor.org/stable/90480 Graunt, John. “Natural and political observations mentioned in a following index, and made upon the Bills of mortality.” Oxford : Printed by William Hall, for John Martyn, and James Allestry, printers to the Royal Society MDCLXV [1665]. http://resource.nlm.nih.gov/2356017R KARGON, ROBERT. “John Graunt, Francis Bacon, and the Royal Society: The Reception of Statistics.” Journal of the History of Medicine and Allied Sciences, vol. 18, no. 4, 1963, pp. 337–48. JSTOR, http://www.jstor.org/stable/24621352 Kelsey, Holly. “Sovereign and the Sick City in 1603.” Shakespeare Birthplace Trust. Aug. 23, 2016. https://www.shakespeare.org.uk/explore-shakespeare/blogs/sovereign-and-sick-city-1603/ Lewin, C. G. "Graunt, John (1620–1674), statistician." Oxford Dictionary of National Biography. August 08, 2024. Oxford University Press. https://www.oxforddnb.com/view/10.1093/ref:odnb/9780198614128.001.0001/odnb-9780198614128-e-11306 Pepys, Samuel. “The Diary of Samuel Pepys.” GEORGE BELL & SONS. London. 1893. Accessed online: https://www.gutenberg.org/cache/epub/4200/pg4200.txt Smith, R.M. (2008). “Graunt, John (1620–1674).” The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95121-5_758-2 See omnystudio.com/listener for privacy information.

In this episode, host Sandy Vance chats with Isaiah Granet, co-founder and CEO of Bland, for a sharp and eye-opening conversation about one of the most overlooked bottlenecks in healthcare: the phone call. Bland now handles 3.5 million phone calls a week, has raised over $100 million, including a $40 million Series B, and is backed by Emergence Capital, Scale, and Y Combinator. Isaiah brings a refreshingly honest take on what it actually takes to get voice AI into production in healthcare, why most vendors are just talking about it rather than doing it, and why the security risks hiding in third-party AI dependencies should be keeping every healthcare CIO up at night. In this episode, they talk about: Most people call a call center because they are at the end of the line and cannot solve their problem any other way The best voice AI systems conform to the caller, not the other way around Intake is the fastest path to ROI for health systems deploying voice AI for the first time Bland tracks emotional sentiment, call escalation rates, and a unique metric called utterances to measure patient experience quality Bland does not use OpenAI or any third-party LLM under the hood, meaning PHI never touches an outside vendor Health systems should demand that calls go live within 30 days and measurable automation within 60 days A single third-party dependency, three steps removed from a vendor, recently led to a class action lawsuit Always declare that it is an AI agent on the call; deceptive practices destroy the trust that voice AI depends on The CIO role is becoming one of the most important in any healthcare organization, as AI decisions multiply A Little About Isaiah: Isaiah values community, family, and impact above all else. He believes that building for impact is what makes life. In addition to being the cofounder and CEO of Bland, he also sits on the board of the nonprofit he founded, the San Diego Chill.

I'm sitting down with Jonathan Ronzio, who scaled Trainual from an idea to $30M ARR—while building a company known for its culture and still finding time to climb mountains, run marathons, and live a full life outside of work. What stood out to me in this conversation is how intentional he's been about building systems—not just in the business, but in his life. We talk about why most founders document the wrong things early, how structure actually creates freedom, and how AI is completely reshaping how companies build, sell, and operate. We also get into how he thinks about balance versus alignment, what changes (and what doesn't) after raising capital, and why the most defining moments in business are usually the ones you never planned for. If you're trying to scale without becoming consumed by your business, there's a lot here worth paying attention to. Key Takeaways  (00:00) Introduction (01:28) Summiting Aconcagua vs Closing a Series B (03:01) What Is Trainual and Why It Exists (03:52) The #1 Thing SaaS Founders Document Too Late (04:59) When to Create Company Core Values? (06:48) Why Structure Actually Creates Freedom (09:57) Which Processes Deserve SOPs? (11:43) How AI Transformed Trainual's Product Roadmap (15:16) Will AI Kill SaaS? His Honest Take (18:49) Figure Out How to Disrupt Your Business (20:38) Agentic AI and the New Outbound Playbook (22:34) The Exact AI Tech Stack His Team Uses (26:05) Data Security in the Age of AI (30:11) $400K in Credit Card Debt for FB Ads  (32:27) Balance vs. Alignment  (34:31) Why Daymond John Joined the Cap Table (38:05) Cultural Practices That Actually Work (39:48) Project Management & Communication Tools (43:36) How to Define Culture at Scale (46:30) Mountaineering Lessons That Made Him a Better Leader (53:08) Living an Adventurous Life  (58:50) Obsessive Compulsive Creative Disorder (59:43) Advice for Founders Torn Between Focus and Exploration   Watch on YouTube: https://youtu.be/q17qPXHSEC0     Let's Connect: Website | Instagram | YouTube | TikTok | Twitter | Facebook

Introduction What happens when a decade-long carrier executive decides that the best way to fix insurance operations is to stop advising from the inside and start building from the outside? Vijay Laknidhi spent his career at Travelers and Amtrust, sitting in the rooms where technology decisions stalled, procurement cycles stretched past usefulness, and AI pilots died in committee. Now, as GM of Commercial Insurance at Liberate, a voice AI company built exclusively for P&C, he runs what he calls "a Series A company inside a Series B company," tasked with scaling a P&L dramatically in a single year. In this episode of the Insurtech Leadership Podcast, host Joshua Hollander sits down with Vijay to unpack what it actually looks like to cross from buyer to builder, why commercial insurance is uniquely ripe for AI disruption, and what separates production-grade insurance AI from a compelling demo. Guest Bio Vijay Laknidhi is the General Manager of Commercial Insurance at Liberate, a voice AI company focused exclusively on property and casualty insurance. Before joining Liberate, Vijay spent over a decade in executive roles at Travelers and Amtrust, where he led underwriting, product, and operational functions across commercial lines. His carrier-side experience gives him rare dual fluency: he understands the internal politics, compliance requirements, and procurement friction that slow AI adoption at large insurers, and he now builds the products designed to break through those barriers. At Liberate, he operates with startup autonomy and carrier-grade expectations. Key Topics • The carrier-to-startup leap - Why a successful insurance executive would leave the stability of a Top 10 carrier to join a Series B startup, and what that transition actually demands • Voice AI in P&C operations - How Liberate applies voice AI to claims intake, FNOL, and policy servicing, replacing legacy IVR and manual call center workflows • Why commercial insurance is the AI beachhead - The structural reasons (submission volume, manual underwriting, broker friction) that make commercial lines more amenable to AI than personal lines • The demo-to-production gap - What separates an impressive AI proof-of-concept from a system that handles edge cases, compliance, and carrier-grade uptime in production • Selling to the buyers you used to be - How Vijay's decade on the carrier side shapes his approach to navigating procurement, legal review, and stakeholder alignment at prospect companies • Why every insurance leader must get hands-on with AI - The argument against delegating AI strategy to innovation teams or consultants, and why executives need direct fluency • AI-native architecture vs. legacy tech debt - Why recent startups like Liberate have a structural advantage over incumbents trying to bolt AI onto decades-old policy admin systems Notable Quotes -"I'm running a Series A company inside a Series B company. I own the P&L, I own the roadmap, and I have one year to prove the commercial insurance thesis." -"When you've sat in the buyer's chair for a decade, you know exactly which objections are real and which ones are just procurement theater." -"The gap between an AI demo and a production deployment in insurance is compliance, edge cases, and the willingness to handle the 2% of calls that don't fit a script." -"If you're a carrier executive delegating AI to your innovation team, you've already lost. You need hands-on fluency, not a briefing deck." Resources Guest: • Liberate: https://www.liberatetech.ai/ • Vijay Laknidhi on LinkedIn: https://www.linkedin.com/in/vijaylaknidhi/ Host & Organization: • Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ • Horton International (USA): https://www.horton-usa.com/ • Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show Subscribe & Review If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Apple Podcasts, and Spotify.

In this episode, Madelyn O'Farrell sits down with Ryan Soskin, Co-Founder and CEO of GoodShip, to explore how the company is transforming freight orchestration and procurement for large shippers. Ryan shares his journey through Coyote, Convoy, and Stord, and how those experiences shaped Goodship's focus on disciplined capital deployment, high hiring standards, and truly shipper-centric tools. They dive into why shipper decision-making is much messier than simple rate-based bids, how GoodShip connects procurement and day-to-day network orchestration, and the role of Laney, their new AI transportation analyst, in turning complex transportation data into fast, actionable insights. The conversation also covers why neutrality (remaining a pure software layer, not a freight participant) is critical to earning shipper trust, how the freight tech stack is likely to consolidate into a single operating system powered by AI, and what Ryan has learned stepping into the founder role while scaling GoodsShip after a $25M Series B. Highlights from their conversation include: Introduction and What GoodShip Does (0:29)   Ryan's Path Into Supply Chain and Early Career (2:34)   Learnings From Convoy and Stord For Building GoodShip (4:49)   How Shippers Actually Make Freight Decisions (5:40)   Connecting Procurement and Orchestration In Practice (7:45)   Launching Laney, the AI Transportation Analyst (8:52)   Human in the Loop and Goodship's Agentic Strategy (11:35)   Why Neutrality Matters in Freight Technology Platforms (13:08)   The Future Freight Tech Stack and Role of AI (15:00)   Fundraising, Series B, and What the Team Says Yes or No To (17:18)   Founder Lessons, Talent, and Financial Discipline (19:23)   Dynamo Ventures is a venture firm backing founders upgrading the physical economy. As intelligence moves into critical infrastructure and technology collides with physics, industry is entering a new era of transformation - the industrial renaissance. Born from the dirt and grit of supply chains and shaped by operations, not spreadsheets, Dynamo focuses on the complex realities of building in the real world. We invest in companies transforming infrastructure, manufacturing, logistics, transportation, and the systems that power global commerce. Dynamo works closely with founders who combine ambition with a bias to action, bringing a builder mindset to venture capital through deep operational insight, systematic pressure-testing and hands-on partnership. Our purpose is simple: to back the relentless shaping the industrial renaissance. Learn more at  www.dynamo.vc Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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.