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

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

It's the model, stupid. OpenAI just had its worst run of news in months, and Rick Watson thinks the fix is simpler than anyone inside OpenAI wants to admit.This week Rick and Jessica Lesesky work through three stories about platforms and owners deciding what business they actually want to be in.First, Shopify pulls the plug on vapes. Every merchant selling them got a notice to strip the products by July 8 or lose the store. States leaned on Shopify over illegal sales, and Shopify answered with a blanket ban rather than a state-by-state fix. To Rick, this is not Shopify taking a stand for entrepreneurs, it is Shopify reading the writing on the wall and staying on the right side of the law. If your category is drifting from gray to red, the payment processor is the pressure point, and you may want to own your software before someone else decides your business for you.This episode is sponsored by Avalara. Learn more at avalara.watsonweekly.comSecond, the great private equity traffic jam. Nearly a decade into the exit drought, PitchBook's midyear scorecard is not pretty. Per their data, the big buyout deals that were supposed to reach a quarter of all activity have slipped toward 19% by May. Rick counts roughly 13,000 US companies sitting in PE portfolios, an eleven year backlog at the current selling pace. Sellers don't know what their asset is worth. Buyers can afford to wait. His take is that the AI bubble may have to deflate at least a little before the ordinary software deals start moving again.Third, OpenAI's worst Thursday: lawsuits, exits, and a dead browser. Apple trade secret allegations, a top lieutenant stepping back for health reasons, the head of safety systems out the door, and the Atlas browser shut down less than a year after launch. The new model, ChatGPT 5.6, draws strong benchmarks and better press, and OpenAI positions it as beating Opus at a fraction of the cost. Rick argues the deeper problem is governance. The list of ex-OpenAI founders now running billion dollar startups keeps getting longer, and you cannot win a race this fast while your talent walks out the side door.Can a $20 subscription outrun Google's distribution, Apple's install base, and Anthropic's grip on enterprise? The magic eight ball points to no.#watsonweekly #shopify #openai #privateequity #apple #anthropic #ecommerce #chatgpt

PitchBook reported US venture funding of $412.7 billion in the first half of 2026, with AI deals dominating capital allocation. The total reflects larger average check sizes, more late-stage financings, and a concentration of dollars in mega-deals. Investors are prioritizing foundation model development, AI infrastructure, and semiconductor design, with venture firms and corporate investors both active. Outside AI, capital remains available where unit economics are strong and AI features create measurable value, but diligence and timelines have tightened. Deal structures include more inside rounds, extensions, and selective use of structured terms, while secondary markets offer limited liquidity. The exit environment shows tentative IPO activity and steady M&A under regulatory scrutiny, prompting late-stage companies to emphasize profitability and multi-year contracts.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Show Notes: Update since recording: Restack has since moved to one simple plan at $800 per seat per month, and now works across PowerPoint, Excel, and Word as a single agent. Harsh Aggarwal is the founder of Restack, an AI agent that works inside PowerPoint to turn a consulting or PE team's analysis into a client-ready deck. He was previously at Roland Berger and product lead at an $8B PE firm. Restack: From Thinking to a Client-ready Deck Harsh explains that Restack is an AI agent that works natively inside PowerPoint to automate formatting for consulting and private equity teams. The purpose of Restack is to help consulting teams focus on client relationships and actual consulting work by automating the grind of formatting. Harsh highlights the historical advantage big firms had due to their production capacity, using McKinsey as an example. He discusses how AI has closed the research gap but the production gap remains, which is where Restack aims to help by automating the process of turning thinking into a client-ready deck. A Restack Demonstration Harsh begins demonstrating Restack by sharing his screen and showing a standard PowerPoint deck. He explains that Restack opens as a sidebar inside PowerPoint and can handle tasks that would normally take hours, such as updating a deck with new data. Harsh shows how Restack can update a deck with new data without specific instructions on formatting or chart appearance. He emphasizes that Restack can handle complex tasks like verifying data from sources like the SEC EDGAR website and updating slides based on that data. Converting Decks into Code Harsh explains that Restack converts the entire PowerPoint deck into code, allowing it to make precise changes to the data. He compares this technology to popular tools like Cloud Code and Cursor, which are used in software engineering to augment code bases. Restack can update specific lines of code in the deck, making it highly precise and efficient.  Restack updates the slides and analyzes the uploaded data, showing the progress on screen. Restack Results Harsh shows the results of Restack updating the numbers in the slides, explaining that it also updates the storyline based on the new data. He highlights that Restack takes screenshots to verify its own results before presenting them to the analyst or associate. The charts created by Restack are fully editable and can be manipulated like normal charts in PowerPoint. Harsh recaps the task of updating the data in four slides and shows the final results, emphasizing the efficiency of Restack. Creating Slides from Scratch Harsh demonstrates another use case where Restack can create a new slide from scratch using a data model. He explains that Restack can read the existing deck to understand the context and formatting, creating new slides that match the existing deck's style.  When asked if Restack can create any kind of chart, Harsh confirms, showing how Restack can convert a chart to a different type, like a waterfall chart. Harsh emphasizes that Restack is a generalized tool that can handle various tasks, making it versatile for different consulting needs. Pricing and Subscription Information Harsh explains how to sign up for Restack, either by visiting the website or reaching out to him on LinkedIn. He offers a free sandbox for listeners to test Restack on live cases, encouraging them to reach out for access. Pricing is $800 per seat per month, billed monthly, with credits pooled across the team (a five-seat team is $4,000/month). One to five seats are self-serve; six or more is the Practice plan. Harsh compares the pricing to tools like PitchBook and Bloomberg, noting the time and cost savings Restack offers.  Timestamps: 01:51: Demonstration of Restack's Functionality  06:08: Technical Details of Restack's Operation  08:03: Results and Verification of Restack's Work  12:34: Additional Use Cases and Capabilities of Restack 22:38: Sign-Up and Pricing Information  Links: LinkedIn: http://linkedin.com/in/heyhiharsh Company website: http://re-stack.com/ This episode on Umbrex: https://umbrex.com/unleashed/episode-653-harsh-aggarwal-how-ai-lets-solo-consultants-take-on-bigger-work/ 60-second Demo: http://re-stack.com/demo   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com. *AI generated timestamps and show notes.  

Hans Swildens started Industry Ventures in 2000, and this is his first podcast since selling it to Goldman Sachs in 2026.Hans has been buying venture secondaries longer than almost anyone, and the combined business is one of the largest VC portfolios in the world: 525 firms, 1,600 funds, and over $22B in capital commitments.We get into why he sold to Goldman, the barbell market quietly killing middle-stage venture, how VC's are manufacturing their own exits, why most seed funds without a real angle have 5 years left, the LP base that's a completely different species every fund cycle, how he started Industry by acquiring funds at 99% discounts during the Dot Com Collapse, the day he passed on a multi-billion-dollar data center, and why the secondary market may eventually be multiples the size of the primary market.Thank you to Zach Coelius, Will Quist, Emily Zheng, and Andrea McGee for help brainstorming topics for the conversation.Thank you to Numeral, Flex, Amplitude, and Merge for supporting this episode.Numeral: The end-to-end platform for sales tax and compliance https://www.numeral.comFlex: Get premium banking and a net 60 day credit card at 0% APR https://home.flex.one/referral/bananacapitalAmplitude: AI analytics, all you have to do is ask https://www.amplitude.comMerge: Every model. One API. Total control. Check out Merge's Agent Handler. merge.dev/turnerTimestamps:(0:00) Inside Goldman's $22B venture bet(7:57) 1,600 funds over 525 firms(11:11) Why scale lets you “see the cube”(14:17) Most humbling lesson in 26 years(16:19) Three types of Seed funds(18:47) LP's evolve every fund cycle(22:00) VC is a sales game(24:38) A strong CRM is non-negotiable(29:06) Buying secondaries during the Dot Com Collapse(31:33) Triangulating opinions across GP's, founders, and LP's(37:22) Seed without differentiation doesn't work(42:16) Biggest mistakes by first-time GP's(44:31) Buying Enron's VC portfolio at a 99% discount(50:15) Entrepreneurial finance is underappreciated(52:15) Why asset managers are acquiring venture firms(58:47) Why it's hard to start venture programs(1:03:20) Secondaries: $250M to $150B market in 25 years(1:08:06) Continuation funds & debt structured secondaries(1:12:02) “Secondaries might be multiples bigger than primaries”(1:19:52) How to structure secondary transactions(1:26:00) What happens after SpaceX, OpenAI, Anthropic IPO's(1:30:35) How Seed survives the asset manager era(1:35:16) How to manufacture liquidity(1:41:41) How AI is impacting secondary marketsReferencedIndustry Ventures: https://www.industryventures.com/How Big Is The Secondary Market for Venture Capital?: https://www.industryventures.com/insight/2023-2025e-how-big-is-the-secondary-market-for-venture-capital/Exploring the Growth of Venture Secondaries: https://www.chronograph.pe/exploring-the-growth-of-venture-secondaries/Pitchbook's US Secondaries Market Watch: https://pitchbook.com/news/reports/2025-annual-us-vc-secondary-market-watchFollow HansTwitter: https://x.com/HansSwildensLinkedIn: https://www.linkedin.com/in/hansswildensFollow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

Your 401(k) is about to get access to private equity, but how, in what form, and at what price are all still TBD. Which is kind of a problem for such a big change. The pitch for putting private markets in retirement accounts is all about fairness: regular people got locked out of the hottest private companies, such as SpaceX, OpenAI, Anthropic, and Stripe. But the products actually getting built are something else entirely. They are semi-liquid, evergreen, with various fee wrappers, and most of what you'll be offered is actually private credit rather than private equity or venture capital. Devin is joined by return guest Nizar Tarhuni, PitchBook's EVP of Research & Market Intelligence, to get into how these structures work, what to look out for, what questions to ask, and how, in the right circumstances, privates can work well in retirement accounts. They both agree, however, it is not a matter of if, but when and how, privates are coming for your 401(k). PitchBook.com  PitchBook Research 

2026 is the year of the mega-IPO. SpaceX, Anthropic, and OpenAI are working their way through the pipeline as anticipation builds for their blockbuster debuts. Elon Musk's conglomerate is leading the shift from private to public markets. Demand is appearing to skyrocket to buy the companies the moment their stocks are publicly available. But is that the right move if you're investing for the long term? Or are there better opportunities among stocks that are down on fears of AI competition, such as software as a service, or SaaS, stocks? These big private firms' public arrivals will also affect index funds and 401(k)s.    So what should you make of all this? Paul Condra is the global head of private markets research for PitchBook, a Morningstar company. He and his colleagues are presenting this topic at the upcoming Morningstar Investment Conference in Chicago.   Why We Think the SpaceX IPO Is Overvalued   On this episode: 00:00:00 Welcome 00:01:46 Why SpaceX, Anthropic, and OpenAI Are Going Public Now 00:03:10 Why Morningstar Values SpaceX at Half the IPO Price 00:04:34 How Retail Investors Should Approach Buying SpaceX Stock 00:05:56Anthropic's IPO and Its Trillion-Dollar Valuation 00:07:12 OpenAI's Broken Economics and IPO Prospects 00:08:47 New Index Rules and What They Mean for Your 401(k)   Watch more from Morningstar: The Portfolio That Has Been Beating the Classic 60/40, and Why It Matters for You https://www.morningstar.com/portfolios/portfolio-that-has-been-beating-classic-6040-why-it-matters-you The Best Opportunities for Fund Investors Today https://www.morningstar.com/funds/best-opportunities-fund-investors-today Will Vacation Inflation Affect Your Summer Travel? Here's What to Know https://www.morningstar.com/stocks/will-vacation-inflation-affect-your-summer-travel-heres-what-know   Follow Morningstar on social: Facebook https://www.facebook.com/MorningstarInc/ X https://x.com/MorningstarInc Instagram https://www.instagram.com/morningstarinc/?hl=en LinkedIn https://www.linkedin.com/company/morningstar/posts/?feedView=all Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Details of SpaceX's IPO are expected to be made public this week as it accelerates plans for a stock debut that will likely make CEO Elon Musk the world's first trillionaire, but one analyst warned that trading may be volatile as the stock faces “substantial” downside risks. SpaceX, which filed confidential initial public offering registration with the Securities and Exchange Commission in April, will likely make public its paperwork—offering insight into the firm's operations and finances—this week as it plans for a June 12 debut on the Nasdaq, the Wall Street Journal reported, citing people familiar with the matter. Some have expressed concerns about SpaceX's stock listing, including PitchBook analyst Franco Granda, who wrote in March that SpaceX may act like Tesla's stock “on steroids,” suggesting trading volatility. Learn more about your ad choices. Visit megaphone.fm/adchoices

Episode SummaryIn this 101 episode, Cheryl and Maxine go deep on the fundamentals of pre-seed investing, from how the stage came to exist to why the perceived risk gap between pre-seed and seed is much smaller than most investors think.They break down the history of how venture stages evolved from single rounds to alphabetized series, how seed investors eventually got pushed up the stack, and why pre-seed emerged around 2017 to 2019 as a distinct category. They unpack why PitchBook's definition of pre-seed as "whatever the investor calls it" muddies the water, how seed preempts from larger funds are inflating average valuations, and why thinking in risk stages rather than round labels is a better framework for evaluating early companies.You'll also hear why graduation rates from pre-seed to seed don't support the idea that pre-seed is two to three times riskier, why the Australian ecosystem is sitting on a talent surplus with a capital gap at pre-seed, and why this stage is particularly well suited for angels building diversified portfolios of 20 to 40 companies. Cheryl shares her framework for evaluating pre-seed opportunities through the lens of problem pain, frequency, and market size, and Maxine walks through how to think about return profiles, dilution, and valuation at a stage where there are no outputs to measure.Time Stamps00:00 Intro01:56 – A brief history of venture stages: how pre-seed became a thing07:14 – Why round labels are broken and risk stages are a better framework09:23 – Seed preempts: how big funds are blurring the line between pre-seed and seed11:48 – Is pre-seed actually riskier than seed? The case that it's not16:41 – Graduation rates: what the data says about pre-seed to seed conversion22:59 – Valuation dynamics: what pre-seed rounds look like in Australia vs the US28:36 – Why Australian founders are leaving for the US at pre-seed and what that means31:29 – How to evaluate pre-seed companies: inputs over outputs35:05 – Cheryl's pain framework: frequency, intensity, and willingness to pay38:28 – Why pre-seed is the best stage for diversifying who gets funded43:44 – The AI revenue problem: why getting in early matters more than everSponsors:First Cheque is supported by our wonderful sponsors:Deel: Founders scale faster on Deel. Set up payroll for any country in minutes, hire anyone anywhere, and get visas handled fast, so you stay focused on scaling. Deel takes care of onboarding, HR, IT, EOR, benefits, and compliance, so your team can grow without borders.It's why more than 40,000 fast-growing companies trust Deel to move fast. Visit https://www.deel.com/dayoneFirst Cheque is part of Day One.Day One helps founders and startup operators make better business decisions more often. To learn more, join our newsletter to be notified of new First Cheque episodes and upcoming shows.This podcast uses the following third-party services for analysis: Podtrac - https://analytics.podtrac.com/privacy-policy-gdrpSpotify Ad Analytics - https://www.spotify.com/us/legal/ad-analytics-privacy-policy/

Dave "CAC" Kellogg and Ray "Growth" Rike dig into the Redpoint Ventures 2026 Software and AI Market Update - a 69-page report built on proprietary CIO survey data from 141 respondents, plus public market data from Qatalyst, Pitchbook, Goldman Sachs, RBC, and McKinsey. Big report with even bigger implications. Ray and Dave unpack the data that matter most for B2B SaaS and AI-native software operators.WHAT WE COVER IN THIS EPISODEThe AI Build-Out Is Real and It's Not the Dot-Com BubbleHyperscaler CapEx is projected to hit $765B in 2026, up nearly 50% year over year. More than 90% of new data center capacity is already pre-committed. Compare that to the dot-com era when fiber utilization was under 3%. The other critical difference: today's infrastructure spend is funded primarily by free cash flow, not debt. The more important signal is demand. AI has reached 1 billion monthly active users in four years. The internet took far longer to reach 70 million. The demand is real. The risk of speculative overbuild is also real.The Agent Maturity Curve and Why Most of the Value Is Still AheadPage 7 of the report maps the four phases of agent maturity by runtime: co-pilots (seconds), task agents (minutes), workflow agents (hours), autonomous agents (days). Co-pilots represent roughly $500B in software spend. Task agents, where coding tools live today, push that to $1.2T. Workflow agents expand the TAM to $2.8T. Autonomous agents take it to $6.1T. Coding has been the beachhead use case for good reasons: structured training data, instant verification, self-improving feedback loops. The real enterprise revenue opportunity is still in phases three and four.What the CIO Survey Actually Says This is the buried lead of the report. 54% of CIOs are actively consolidating vendors. 45% of AI budgets are coming from existing software budgets, not net-new spend. 58% say AI feature additions are the top driver of incremental software spend. 54% prefer to stay with incumbent vendors if they deliver on AI. Only 13% have a strong preference for AI-native software. The 33% who are neutral are the swing vote. Incumbents are winning the preference battle but losing the execution battle — the CIO feedback on Agentforce, Copilot, and ServiceNow AI in the survey is not flattering.Terminal Value Is the Real SaaS Valuation StoryThe public SaaS median NTM revenue multiple sits at 4.1x (Meritech says 3.1x), the lowest since the global financial crisis. In a SaaS DCF, 85 to 95% of enterprise value comes from terminal value, not the five-year forecast. The implied long-term growth rate embedded in current SaaS valuations has collapsed from 4.7% to 1.1%. Short-term beats like ServiceNow's recent quarter do almost nothing to move the stock because the market's concern is not next year. It's year ten and beyond. That is a terminal value story, not a growth story.ARR Per Employee - The Benchmark EvolvesCursor and Anthropic hit $100M ARR in roughly two years. Slack took three. Salesforce and Adobe took four to five. ServiceNow took seven to eight. AI-native companies have made $1M revenue per FTE the new floor. The P&L transformation model in slide 39 projects R&D costs down 15 to 20%, sales costs down 15 to 20%, COGS increasing due to inference spend but offset by reductions in customer support and customer success. Net result: potential EBITDA expansion of 100 to 250% on the same revenue base over three to five years.Private Markets Are in an AI Love FestAI-native deals represent nearly 100% of new VC activity in Q1 2026. Deal concentration is accelerating: the top 20 deals captured 44% of total funding in 2025, up from 31% in 2024 and 7% in 2022. At the model layer, dollars and valuations are concentrated while deal volume belongs to the application layer (61% of deals). The model competition is effectively over. The only question is rank order. The application layer is where the volume plays out, and AI-native vendors are winning that battle.Redpoint 2026 Software and AI Market Update: https://www.redpoint.com/reports/2026-market-updateABOUT THE METRICS BROTHERS Ray Rike is the Founder and CEO of Benchmarkit, the leading B2B SaaS and AI-native software benchmarking company. Dave Kellogg is an EIR at Balderton Capital, independent consultant, and author of Kellblog. Together they bring a CFO-meets-GTM lens to the metrics and benchmarks that drive efficient revenue growth and enterprise value.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

How is the ongoing Middle East conflict reshaping the global economy, energy markets, and the tech sector? In this episode of the BRAVE Southeast Asia Tech Podcast, Jeremy Au sits down with Kristie Neo, PitchBook's newly appointed Asia Editor, to unpack the macroeconomic shockwaves hitting Southeast Asia and the Gulf. From surging oil prices and the fuel emergency in the Philippines to the sudden acceleration in green energy and defense tech investments across Singapore and the UAE, we cover what founders and investors need to know to navigate this crisis. Tune in for deep-dive insights on venture capital shifts, global capital flight, the resilience of safe-haven economies, and why times of conflict often forge the strongest technological innovations. 00:00 - Introduction & PitchBook's APAC Expansion 03:10 - The Middle East Crisis & Macro Impacts 06:45 - How the Conflict Affects UAE Venture Capital 09:30 - Inflation, Supply Chains & Asia's Energy Security 12:28 - Surging Demand for Renewables & Nuclear Power 15:28 - Tech Capital, LPs, & Diversification out of the Gulf 22:48 - The Rise of Defense Tech in Singapore & Israel Parallels Watch, listen or read the full insight at https://www.bravesea.com/blog/kristie-neo-global-energy-crisis Get transcripts, startup resources & community discussions at https://www.bravesea.com WhatsApp: https://whatsapp.com/channel/0029VakR55X6BIElUEvkN02e TikTok: https://www.tiktok.com/@jeremyau Instagram: https://www.instagram.com/jeremyauz Twitter X : https://x.com/jeremyau LinkedIn: https://www.linkedin.com/company/bravesea English: Spotify | YouTube | Apple Podcasts Bahasa Indonesia: Spotify | YouTube | Apple Podcasts Chinese: Spotify | YouTube | Apple Podcasts #Singapore #TechNews #StartupNews #Business #Podcast #southeastasia #techpodcast

This week, we chat with Marlon Nichols!Marlon is the co-founder and managing general partner of MaC Venture Capital, a leading seed-stage firm backing visionary founders who are redefining industries and shaping the future. Under his leadership, MaC has grown into one of North America's largest seed-stage venture firms, with over $600 million in assets under management.He's backed an incredible portfolio of companies including MongoDB, Gimlet Media, Thrive Market, Blavity, and Pipe—consistently identifying cultural and technological shifts before they hit the mainstream. His ability to spot transformative opportunities has earned him recognition on Business Insider's Seed 100 and PitchBook's top VCs to watch.Before founding MaC Venture Capital, Marlon launched Cross Culture Ventures and served as an investment director at Intel Capital, developing a sharp lens on the intersection of culture, technology, and consumer behavior.A former professional athlete, Marlon brings a leadership style rooted in discipline and long-term vision, and is deeply committed to expanding access in venture capital through his work with Kauffman Fellows.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Marlon Nichols @MarlonCNicholsMaC Ventures: MaCVentureCap@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

Story of the Week (DR):Elon Musk's SpaceX set to go public in $1 trillion share listingElon Musk's rocket and satellite company SpaceX has confidentially filed for an initial public offering with the Securities and Exchange CommissionThe firm could seek a valuation of $1.75 trillion with a public listing around June.A confidential filing means that SpaceX will submit its financials to the SEC before revealing them to the public, which must occur at least 15 days before the IPO roadshow.Musk owns 42% of the SpaceX now, according to Pitchbook, though that figure will change with the IPO when new owners are issued shares.Among current SpaceX owners is Donald Trump Jr, the president's oldest son. He owns a shares through 1789 Capital. That venture capital firm made him a partner shortly after his father won the presidency for a second time and has been buying up federal contractors seeking to win taxpayer money ever since.The White House and Trump himself have repeatedly denied there are any conflicts of interest between his role as president and his family's businesses.Public investors may get low-vote shares, while insiders could hold super-voting stock with roughly 10 to 20 votes per share, if the reported structure is adopted.Reports suggest SpaceX has been adding board members as it prepares for the IPO process.The company's board has historically included Elon Musk, Gwynne Shotwell, Antonio Gracias, Luke Nosek, Steve Jurvetson, and Donald Harrison in reporting about its governance.Gwynne Shotwell is widely reported as president and COO, and Bret Johnsen as CFOBig Banks Seeking a Piece of SpaceX's I.P.O. Must Subscribe to Elon Musk'sMusk is requiring Wall Street firms to purchase subscriptions to his A.I. chatbot if they want to advise on one of the largest initial public offerings in history.Air Canada CEO will retire this year after his English-only crash message was criticizedMichael Rousseau is stepping down following a massive public outcry after he delivered a condolence video almost entirely in English regarding a fatal plane crash that killed a French-speaking pilot.Critics and politicians, including Quebec's Premier, were outraged that Rousseau failed to fulfill a high-profile 2021 promise to learn French, viewing his English-only response to a tragedy as a sign of deep cultural disrespect.Air Canada's board has launched a global search for a successor and explicitly stated that fluency in both English and French is now a non-negotiable requirement for the next CEO.The company clarified that while a "comprehensive internal development program" has been in place for two years, the recent controversy accelerated the timeline for his departure.Rousseau will officially retire at the end of the third quarter (September 30, 2026), staying on until then to ensure a "seamless transition" and assist the board during the handover.Air Canada CEO Michael Rousseau initially stated he did not intend to step down following backlash over an English-only video regarding a runway incidentElon Musk called the decision “crazy” and suggested “it is not reciprocal.”“There are many one-sided laws in Canada that mandate French at the expense of English,” he posted to X, along with a Grok answering his request to provide a list of Canada's French language laws and explain “how this is hypocritical compared to no English mandate laws.”“Extremely hypocritical and unfair!”Oracle fired up to 30,000 workers via email after a 95% profit surge. Tech companies are cutting almost 1,000 jobs/day DROracle Corp.'s mass layoffs on Tuesday were part of the company's cost-cutting measures as it continues to build out expensive data centers for powering artificial intelligence.But one aspect of the mass layoffs — which were estimated to be as many as 30,000 people — was alerting workers over email at 6 a.m. Eastern that Tuesday would be their last day.The terse message, sent to workers in multiple regions and time zones, carried no executive name and was instead signed off simply as 'Oracle Leadership.'“We are sharing some difficult news regarding your position.After careful consideration of Oracle's current business needs, we have made the decision to eliminate your role as part of a broader organizational change. As a result, today is your last working day.We are grateful for your dedication, hard work, and the impact you have made during your time with us.After signing your termination paperwork, you will be eligible to receive a severance package subject to the terms and conditions of the severance plan. You will receive an email from DocuSign to your Oracle email address with details on your severance and termination date.Immediate Action RequiredTo receive important follow-up information, including FAQs and separation documents to help you through this transition, you must provide a personal email address.Please click here to submit a personal email address immediately. If you make a submission error, please re-submit a new form. Please Note: The personal email address will only be used for correspondence regarding separation-related information and severance agreements.Access to your computer, email, voicemail, and files will be deactivated soon, and you will be unable to log into your computer. As a reminder, you are prohibited from downloading, copying or retaining (including emailing yourself) any Oracle confidential information.Thank you for your contributions to our organization. If you have additional questions, please reach out to the HR team via the Ask HR page or at (888) 404-2494.Oracle Leadership”“After careful consideration of Oracle's current business needs, we have made the decision to eliminate your role,” an email to one affected employee, obtained by MarketWatch, read.Survivors of the cuts were allegedly told by senior management that they would need to 'ramp up efficiency' and 'stretch' to cover the workload left by departed colleagues, a suggestion that many are resisting.Allegations that automated tools influenced redundancy decisions have become a central issue in the fallout.Iran Claims Oracle Strike in UAE as Dubai Attack Fears EscalateAnti-DEI crusade:Trump ousts Pam Bondi as attorney generalTrump Tells Karoline Leavitt She's 'Doing a Terrible Job,' Asks 'Should We Keep Her?'Is Kash Patel Getting Fired? FBI Director Might Be Next After Pam Bondi OustingHegseth ousts top Army generalArmy Chief of State Gen. Randy George.Defense Secretary Pete Hegseth and the Army's chief of staff had recently clashed over promotions, leading to his eventual ouster.Hegseth reportedly told Gen. Randy George to pull the names of four Army officers from a list of promotions to the rank of one-star general. The list consisted of about three dozen officers, most of whom were white men. However, two of them were Black and two were women, and those were the names Hegseth wanted removed.According to The New York Times, George refused, citing the officers' history of exemplary service. George reportedly asked Hegseth to meet two weeks ago to discuss the matter, but Hegseth declined. The defense secretary then struck the officers' names from the promotion list, even though it's not clear he has the authority to do so, per The Times.Hegseth has repeatedly taken steps to block or delay the promotions of Black and female senior officers in all four branches of the military.Secretary of the ArmyLabor Secretary Lori Chavez-DeRemerArmy Secretary Daniel Driscoll (26th Secretary of the Army)2004–2007 Student (B.S. Business Administration)2007–2011 Military service: Officer2011 Investment Banking Associate2011–2014 JDCandidateYale Law School2014–2015 Judicial Clerk2016–2019 Venture Capital Executive Winston-Salem, NC2020Congressional Candidate (NC-11)US House of Representatives (Campaign)2021–2023 Chief Operating Officer (COO) Flex Capital Management LLC2023–2024 Chief Strategy Officer On Call Physician StaffingJ.D. Vance / Senior Advisor 2024 Senior Advisor Donald Trump Presidential Campaign2025–26th Secretary of the ArmyChristine Wormuth (25th Secretary of the Army)1995–1996 Presidential Management Intern Department of Defense1996–2002 Policy Officer / French Desk Officer Office of the Secretary of Defense2002–2006 Principal (Consulting) DFI Government Services2007–2008 Staff Director (Jones Commission) Independent Commission on Iraq Security Forces2008–2009 Senior Fellow Center for Strategic & International Studies (CSIS)2009–2010 Prin. Dep. Asst. Secretary (Homeland Defense) US Department of Defense2010–2012 Special Asst. to the President / Senior Director National Security Council (White House)2012–2014 Dep. Under Secretary (Strategy, Plans, Forces) US Department of Defense2014–2016 Under Secretary of Defense for Policy US Department of Defense2017–2021 Director, International Defense & Security Center RAND Corporation2021–2025 25th Secretary of the Army Goodliest of the Week (MM/DR):DR: Judge rules Trump order eliminating NPR, PBS funding is unconstitutionalDR: United Airlines and flight attendants reached a tentative deal with $740 million in bonusesMM: Amazon to add 3.5% fuel and logistics surcharge for sellers as Iran war drives up energy pricesGO TO A LOCAL STORE!Assholiest of the Week (MM):Lying-iestChevron and Microsoft Team Up for Giant Texas Gas Power PlantTeam includes Chevron, Microsoft, and ENGINE NO 1Microsoft pledged to be carbon NEGATIVE by 2050Since they keep doing things like building gas plants, they're relying on carbon credits through reforestation to hit their targetSo they went out to buy the credits and picked a company called Mombak, a startup that has signed massive reforestation deals for Amazon reforestation but has yet to actually produce a carbon credit yet, has only started in theory, and the company admits there's still little information on how to quantify the carbon absorption in restoration projects.Despite that, Microsoft and Google both made massive investments to look green as they build out data farms for AI no one asked forEngine No 1, meanwhile, after its climate-darling turn at Exxon 5 years ago, has taken its all white male executive team AND board with climate investment banking and VC/PE expertise to partner with Chevron, who celebrated the Big Bullshit BIll that rolled back renewables and decided to happily take Venezuelan oil at the behest of TrumpInvestor-iestFirst, the results from investors at Starbucks:Average 95.7% approve of the boardMarissa Mayer, the new and highly interlocked director, got a team high 99% approvalResults were more correlated with drink disclosures by directors than performance metricsDespite campaigns by New York State, NYC, Mercyside, Trillium, and others to target Beth Ford and Jorgen Knudstorp, as well as advice from ISS to target just Beth Ford (absurd), given labor issues, Andy Campion instead had the lowest vote total at 87% for reasons that are unclearAnd of course…Then, the reason why there was a campaign to vote out directors in the first place:Starbucks to offer baristas up to $1,200 a year in bonusesWith this nugget:Baristas at unionized locations are unlikely to see the bonus program right away. At approximately five percent of its U.S. locations where employees have union representation, Starbucks acknowledged that federal labor law requires the bonus program to go through the collective bargaining process before it can take effect. According to CNBC, the two sides have not made meaningful progress at the bargaining table in over a yearAI-iestJack Dorsey says he wants 6,000 Block employees reporting straight to himThey already do asshat, you have dual class controlSam Altman says he 'miscalibrated' the mood of distrust toward AI and the government in the Pentagon dealNvidia CEO Jensen Huang's advice to workers scared of AI: You're just confusing your job with the tools you use to do itLarry Ellison Says AI Now Does Oracle's Coding Amid Mass Layoffs—3 Strategic Moves for Tech Workers (Oracle Fires 30,000 With a Cold 6 a.m. Email: Here's What It Said That Devastated Teams)Marc Andreessen says AI layoffs are a farce: Companies are 75% overstaffed, and AI is the ‘silver bullet excuse' to clean house DR“Essentially, every large company is overstaffed,” he said. “It's at least overstaffed by 25%. I think most large companies are overstaffed by 50%. I think a lot of them are overstaffed by 75%.” He added, “Now they all have the silver bullet excuse: Ah, it's AI.”So despite record profits every single year, increasing CEO pay, companies are OVERSTAFFED? They get paid less than inflation, and they have TOO MANY people? Some populist math:Assume “every large company” is companies with market cap > $20bn (~415 companies)Total employment as of last year: 27,795,346Total estimated employed people in US: 162,900,000 (62% labor participation)“Every large company” is 17% of all US employmentCurrently, 7,239,000 unemployed in USAndreessens mid point - “most large companies are overstaffed by 50%” - means he thinks they'll blame AI but that they “overemployed” by 13,897,717He is suggesting they are ALL FIREABLE because they are OVERSTAFFEDEmployment goes from 162,900,000 to 149,002,283, unemployment goes to 21,136,717, and the unemployment rate goes to 12% overnight - a 3x increase on the 4% it's at nowBecause Marc Andreessen thinks we're overstaffed… I wonder why…Studies historically have shown that the few days after layoffs stocks are down - but it depends on the reason for the layoff. Proactive layoffs (not a result of down financials, for instance) are rewardedRecent studies show that layoffs actually push stocks UP as time goes one - up to 22% cumulative return over normal 30 days out, and 5% 10 days out. Let's assume a 5% bump for all the proactive AI job cut assholes - the Block and Oracles of the world Other studies show that CEO pay goes up after layoffs if performance improves - so cutting staff for AI pushes stock up, stock up is better performance, CEO pay goes up Using the CEO pay ratio, the “cost savings” of cutting 14m employees is ~$1.4 TRILLION dollars (that's $1.4tn no longer in the hands of people who would be buying stuff like food and houses and gas and rubber chickens and inflatable pool floats)The cuts would add $3tn to market cap of all companies, save $1.4tn in employee costs - the average CEO pay ratio would go from 306 to 319, and the average CEO would make $22m moreThis isn't about overstaffing or AI - this is about CEOs getting paidHeadliniest of the WeekDR: CEO of Epic Games apologizes after laying off employee with terminal brain cancerDR: BlackRock CEO admits 'woke' era went too farDR: Raising Cane's CEO says he doesn't care for this one menu item, but had to sell it anyway: he always substitutes coleslaw for an extra piece of toastMM: New lawsuit alleges DraftKings and FanDuel are digital heroinMM: Scientists Say Half the World Could Be Nearsighted by 2050, and It's Not Just Screens. This Indoor Habit May Be WhySITTING IN THE DARK. This is where we're at as a society.Jamie Dimon Says…Jamie Dimon's warning: More geopolitical risk for America than since WWIIJamie Dimon blasts remote work as JPMorgan staff revolts over office mandateJamie Dimon says JPMorgan could do prediction markets — with big guardrailsJamie Dimon says the American Dream is ‘slipping out of reach'—and JPMorgan is spending billions to fix itJPMorgan's Jamie Dimon predicts AI will cut the working week to 3.5 days, cure cancers, and free up time for hobbiesWho Won the Week?DR: Angry French people in QuebecMM: Headhunting firms who suddenly can expect as much as 75% of large company employees to be calling them to find them workPredictionsDR: Air Canada hires a woman who speaks 845 languages who continually apologies for something she never didMM: Jamie Dimon says speaking French is stupid

PitchBook's Franco Granda and Elon Musk Reporter Theo Wayt talk with TITV Host Akash Pasricha about SpaceX's confidential IPO filing and whether a $1.75 trillion valuation is actually justifiable. We also talk with Financial Analysis Columnist Anita Ramaswamy about Blackstone's exposure to the SaaS sell-off and the $400 million confidence vote from its own employees, and Emergence Capital's Yazan El-Baba about why the AI sector must pivot to efficiency before hitting the public markets. Finally, we get into the existential regulatory battle facing prediction markets with our Senior Finance Editor Ken Brown.Articles discussed on this episode: https://www.theinformation.com/articles/blackstone-private-credit-fears-miss-big-picturehttps://www.theinformation.com/newsletters/the-briefing/mixed-openai-investor-signalsSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

What does e-commerce actually look like heading into 2026? In this episode, Lonnie Bloom, Partner and Co-Lead of Withum's E-Commerce Sector, sits down with Eric Bellomo from PitchBook to break down the private market data and consumer trends shaping the year ahead.From capital concentration and the wellness brand explosion to tariff pressure, AI attribution challenges, and the rise of agentic shopping, this conversation covers the full picture for operators, investors, and brand builders in the e-commerce space. Whether you are a brand founder, e-commerce operator, investor, or platform builder, this episode delivers the data and perspective you need to plan for 2026.

Il cambio di strategia di OpenAI e la chiusura di Sora. Il leak del modello di Anthropic troppo pericoloso. Il film con la replica di Val Kilmer. Le cause perse di Meta. La città irlandese coi bambini senza smartphone. Queste e molte altre le notizie tech commentate nella puntata di questa settimana.Dallo studio distribuito di digitalia:Franco Solerio, Michele Di Maio, Giulio CupiniProduttori esecutivi:Filippo Brancaleoni, Valerio Bendotti, Fiorenzo Pilla, Enrico De Anna, Stefano Augusto Innocenti, Fabrizio Reina, Paolo Bernardini, Valerio Galano, Arzigogolo, Alessandro Grossi, Davide Tinti, Manuel Zavatta, Christian Schwarz, Gabriele Gambini, Umberto Marcello, Vito Astone, Giulio Magnifico, Akagrinta@Fountain.Fm, Angelo Travaglione, Matteo Tarabini, Alessandro Morales, Roberto Basile, Fabrizio Mele, Ligea Technology Di D'esposito Antonio, Cristian Pastori, Elisa Emaldi - Marco Crosa, Simone Magnaschi, Giuliano Arcinotti, Silvano Carradori, Fabio Filisetti, Antonio Gargiulo, Riccardo Peruzzini, Edoardo Volpi Kellerman, Luca Di Stefano, Franco, Massimo Pollastri, Alessandro Lago, Davide Bellia, Piero Alberto Mazzo, Fabio Zappa, Nicola Bisceglie, Mattia Lanzoni, Isacco Tacchella, Roberto Tarzia, Gabriele Marinelli, Cristian De Solda, Giuseppe Brusadelli, Paola Bellini, Joanpiretz@Fountain.Fm, Mirto Tondini, Andrea Malesani, Andrea Bottaro, Marcello Spinelli, Fabio Brunelli, Antonio Manna, Sabino Menduni, Jh4Ckal@Fountain.Fm, Claudio Galante, Mattia Vailati, Beconsulting, Marco Siviero, Gianfranco Di Summa, Sandro Acinapura, Alessandro Blasi, Giorgio Puglisi, Douglas WhitingSponsor:Squarespace.com - utilizzate il codice coupon "DIGITALIA" per avere il 10% di sconto sul costo del primo acquisto.Links:OpenAI drops plans to release an adult chatbotOpenAI Plans Launch of Desktop SuperappAnthropic Leaks Claude Mythos (Too Dangerous to Release)We're saying goodbye to the Sora app.OpenAI Will Shut Down Sora Video PlatformDisney's Sora Disaster Shows AI Will Not Revolutionize HollywoodLa prima terribile settimana del nuovo capo di DisneyThe hunger for ‘content' is keeping us culturally stuckVal Kilmer resuscitato dall'AIDopo 80 miliardi Zuckerberg ha chiuso il metaversoSweden's Digital ID System HackedMeta Found Negligent in Social Media Addiction CaseJury says Meta knowingly harmed children for profitMeta condannata per non aver protetto i minorenniMeta Verdict Could End Anonymous Internet AccessAnthropic pentagon risk injunctionLa città di bambini senza smartphoneGoogle Just Patented The End Of Your WebsiteI built an ICE tracking app that can't be pulled from the App StoreThis is Android's advanced flow for sideloading appsBalancing openness and choice with safetyIn futuro i semafori avranno un quarto colore?Gingilli del giorno:PitchBook - leggi i bilanciFineTune - controllo audio avanzato per MacHTTP Shortcuts - chiamate API su AndroidSupporta Digitalia, diventa produttore esecutivo.

Today's guest on The Long View is Hilary Wiek. Hilary is a principal analyst at PitchBook, where she leads PitchBook's coverage of fund strategies and performance, publishing primary research on the alternative space. Hilary also leads PitchBook's coverage of the ESG and impact investing space. Hilary has over 20 years of experience in asset owner, manager, and advisory roles. Prior to joining PitchBook, she was the director of investments at the Saint Paul & Minnesota Foundations, where she handled portfolio management, impact and ESG investment, investment due diligence and monitoring, and investment operations. Before that, she worked in senior positions at Segal Rogerscasey, the South Carolina Retirement Systems Investment Commission, Buckingham Financial Group, Dayton Power & Light, and KeyCorp. Wiek received a master's degree in finance and economics from Case Western Reserve University and a bachelor's degree in business leadership and finance from the University of Puget Sound. She is based in PitchBook's Seattle office. PitchBook is a Morningstar company. Episode Highlights 00:00:00 Background in the Private Markets and Joining PitchBook 00:04:49 Drivers of Private Market Slowdown in 2026 and Pockets of Outperformance 00:14:15 Key Lessons for Investing in Private Market Funds 00:18:12 Private Market Fees, Hidden Volatility, and Valuations 00:20:38 Evergreen Investment Growth, Interval Funds, and Questions Investors Should Ask 00:32:26 Is It Worth It to Invest in Private Markets? 00:36:50 ESG, Impacting Investing, and Key Themes for 2026 00:41:05 Private Market Exposure in 401(k)s PitchBook Reports Discussed Benchmarking and Returns: Why Are There So Many Numbers? Evergreen Funds: We Have Questions The Evergreen Evolution The New Face of Private Markets in Your 401(k) US Evergreen Fund Landscape 2025 Impact Investing Update If you have a comment or a guest idea, please email us at TheLongView@Morningstar.com. Follow Christine Benz (@christine_benz) and Ben Johnson (@MstarBenJohnson) on X, and Christine Benz, Amy Arnott, and Ben Johnson on LinkedIn. Visit Morningstar.com for new research and insights from Christine, Ben, and Amy. Subscribe to Christine's weekly newsletter, Improving Your Finances. If you want more Morningstar podcasts, check out The Morning Filter and Investing Insights. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Your host, Sebastian Hassinger, talks with Alumni Ventures managing partner Chris Sklarin about how one of the most active US venture firms is building a quantum portfolio while “democratizing” access to VC as an asset class for individual investors. They dig into Alumni Ventures' co‑investor model, how the firm thinks about quantum hardware, software, and sensing, and why quantum should be viewed as a long‑term platform with near‑term pockets of commercial value. Chris also explains how accredited investors can start seeing quantum deal flow through Alumni Ventures' syndicate.Chris' background and Alumni Ventures in a nutshellChris is an MIT‑trained engineer who spent years in software startups before moving into venture more than 20 years ago.Alumni Ventures is a roughly decade‑old firm focused on “democratizing venture capital” for individual investors, with over 11,000 LPs, more than 1.5 billion dollars raised, and about 1,300 active portfolio companies.The firm has been repeatedly recognized as a highly active VC by CB Insights, PitchBook, Stanford GSB, and Time magazine.How Alumni Ventures structures access for individualsMost investors come in as individuals into LLC‑structured funds rather than traditional GP/LP funds.Alumni Ventures always co‑invests alongside a lead VC, using the lead's conviction, sector expertise, and diligence as a key signal.The platform also offers a syndicate where accredited investors can opt in to see and back individual deals, including those tagged for quantum.Quantum in the Alumni Ventures portfolioAlumni Ventures has 5–6 quantum‑related investments spanning hardware, software, and applications, including Rigetti, Atom Computing, Q‑CTRL, Classiq, and quantum‑error‑mitigation startup Qedma/Cadmus.Rigetti was one of the firm's earliest quantum investments; the team followed on across multiple rounds and was able to return capital to investors after Rigetti's SPAC and a strong period in the public markets.Chris also highlights interest in Cycle Dre (a new company from Rigetti's former CTO) and application‑layer companies like InQ and quantum sensing players.Barbell funding and the “3–5 year” viewChris responds to the now‑familiar “barbell” funding picture in quantum— a few heavily funded players and a long tail of small companies—by emphasizing near‑term revenue over pure science experiments.He sees quantum entering an era where companies must show real products, customers, and revenue, not just qubit counts.Over the next 3–5 years, he expects meaningful commercial traction first in areas like quantum sensing, navigation, and point solutions in chemistry and materials, with full‑blown fault‑tolerant systems further out.Hybrid compute and NVIDIA's signal to the marketChris points to Jensen Huang's GTC 2025 keynote slide on NVIDIA's hybrid quantum–GPU ecosystem, where Alumni Ventures portfolio companies such as Atom Computing, Classiq, and Rigetti appeared.He notes that NVIDIA will not put “science projects” on that slide—those partnerships reflect a view that quantum processors will sit tightly coupled next to GPUs to handle specific workloads.He also mentions a large commercial deal between NVIDIA and Groq (a classical AI chip company in his portfolio) as another sign of a more heterogeneous compute future that quantum will plug into.Where near‑term quantum revenue shows upChris expects early commercial wins in sensing, GPS‑denied navigation, and other narrow but valuable applications before broad “quantum advantage” in general‑purpose computing.Software and middleware players can generate revenue sooner by making today's hardware more stable, more efficient, or easier to program, and by integrating into classical and AI workflows.He stresses that investors love clear revenue paths that fit into the 10‑year life of a typical venture fund.University spin‑outs, clustering, and deal flowAlumni Ventures certainly sees clustering around strong quantum schools like MIT, Harvard, and Yale, but Chris emphasizes that the “alumni angle” is secondary to the quality of the venture deal.Mature tech‑transfer offices and standard Delaware C‑corps mean spinning out quantum IP from universities is now a well‑trodden path.Chris leans heavily on network effects—Alumni Ventures' 800,000‑person network and 1,300‑company CEO base—as a key channel for discovering the most interesting quantum startups.Managing risk in a 100‑hardware‑company worldWith dozens of hardware approaches now in play, Chris uses Alumni Ventures' co‑investor model and lead‑investor diligence as a filter rather than picking purely on physics bets.He looks for teams with credible near‑term commercial pathways and for mechanisms like sensing or middleware that can create value even if fault‑tolerant systems arrive later than hoped.He compares quantum to past enabling waves like nanotech, where the biggest impact often shows up as incremental improvements rather than a single “big bang” moment.Democratizing access to quantum ventureAlumni Ventures allows accredited investors to join its free syndicate, self‑attest accreditation, and then see deal materials—watermarked and under NDA—for individual investments, including quantum.Chris encourages people to think in terms of diversified funds (20–30 deals per fund year) rather than only picking single names in what is a power‑law asset class.He frames quantum as a long‑duration infrastructure play with near‑term pockets of usefulness, where venture can help investors participate in the upside without getting ahead of reality.

A single email can cost millions of dollars. Not because of what it says, but because it didn't reach the right people at the right time. Most companies treat content as marketing fluff until it fails spectacularly. Then suddenly everyone realizes it's the invisible infrastructure holding together every digital experience.Join hosts Chuck Moxley and Nick Paladino as they sit down with Rafael Carranza, who's spent his career proving that content isn't just words on a page. Starting at a wire service during the dot-com boom when thousands of websites suddenly needed live content, Rafael moved to Microsoft where he helped open their content platform to publishers. He then went to Amazon building decision-making systems for thousands of sellers navigating complex rules, and now to PitchBook where data trust drives financial decisions. We explore why trust is the foundation of all content operations, why Microsoft pivoted from being a media company to becoming a platform, and when content stops being marketing and becomes integral to the product itself. Rafael argues that frictionless isn't about improving processes or deploying better technology, it's about how deeply you understand the customer on the other side.Key Actionable Takeaways:Build content governance foundations before implementing AI - Clean your content libraries, audit outdated information, establish clear tagging systems, and align terminology across departments; LLMs can't generate accurate responses from messy, ungoverned dataTreat content as product infrastructure, not just marketing - Critical information about rules, procedures, and product usage directly impacts customer success and costs real money when missing or wrong at decision-making momentsPrioritize quality gates over speed when stakes are high - Create intentional friction through approval processes and pushback mechanisms to maintain quality standards; moving fast without accuracy can trigger legal issues, government involvement, and million-dollar failuresWant more tips and strategies about creating frictionless digital experiences? Subscribe to our newsletter! https://www.thefrictionlessexperience.com/frictionless/ Download the Black Friday/Cyber Monday eBook: http://bluetriangle.com/ebook Rafael Carranza's LinkedIn: https://linkedin.com/in/rafaelcarranza Nick Paladino's LinkedIn: https://linkedin.com/in/npaladino Chuck Moxley's LinkedIn: https://www.linkedin.com/in/chuckmoxley/Chapters:(00:00) Introduction(02:43) Journalism origins(03:15) Wire service dot-com boom(04:30) Microsoft partnership(05:30) Learning user trust(07:15) Trust across organizations(08:35) Microsoft media pivot(09:45) Platform over content(10:30) Content as product(11:15) Amazon seller information(12:30) Operationalizing at scale(13:15) Governance structures(14:30) AI hallucination risks(15:15) Content accuracy guardrails(17:15) Windows to Linux journey(18:15) Business adoption limits(20:00) Human-AI collaboration(21:30) Innovation vs trust balance(22:00) B2B vs B2C content(23:30) Right content right time(24:30) When content fails(25:30) Million-dollar mistakes(26:45) Intentional friction benefits(27:30) Quality over speed(28:45) Biggest misconception(29:30) Conclusion

HSBC Innovations is the global bank's financing arm for American and European startups, especially in the healthcare and life sciences industries. The bank's semi-annual Venture Healthcare Reports document trends in the investment market.Key Tech's Andy Rogers welcomes the report's author, HSBC Innovation Managing Director Jon Norris in Episode 43 of the MedTech Speed to Data podcast.Need to know·       Four core market segments — HSBC Innovation's Venture Healthcare Reports cover investments and exits in Biopharma, Dx/Tools, Med Device, and Healthtech.·       Sourcing investment data — Norris enriches Pitchbook data with additional structure and analyses, making the report more relevant to these market segments.·       Sourcing exit data — Norris supplements media and industry publications with market research and conversations with industry leaders.·       An investment data tapestry — The reports provide “an honest picture of what's going on in the market” so investors and innovators alike “can make targeted smart decisions.”The nitty-grittyAndy and Norris discuss the investment market's recent history before exploring drivers of today's investment headwinds.“2021 was a record-setting year,” Norris recalls. “Every record that could be set for deals and dollars was set across all the sectors.” Things changed in 2022 as new BioTech IPOs struggled, prompting investment reprioritizations.“VCs had done all these… frothy valuations,” Norris says. “They had to go back and look at their own portfolios and say, does this company have enough capital? How do you want to put money to work?”Investments rebounded in 2024, but not the number of deals. Investors poured money into their existing portfolios to boost their exit chances, resulting in today's nine-figure megadeals.“Basically, they're smooshing two rounds together and extending the investors coming in to support that round,” Norris says.Headwinds stiffened in 2025 as tariffs, a more litigious competitive space, and other factors amplified business uncertainty.Norris attributes this progression to the psychology of venture capital. “When you think about what makes these folks tick,” Norris explains, “they want to continue to raise new venture funds because they get paid management fees. But in order to raise their new venture funds, they have to show their investors that they've actually gotten returns.”That means reaching an acquisition or IPO. “They're very focused on getting to exit right now. That's why they're so focused on their existing portfolio. And because of that, they haven't been doing as many new investments.”New investments still happen, of course, but the criteria have changed. “While the dollars are actually up in some of these sectors, especially Med Device,” Norris says, “you're seeing that being put to work on later-stage deals because they'd rather get a shorter time to exit.”Data that made the difference:Norris' insights from the HSBC Venture Healthcare Report let him advise startups fighting today's investment headwinds.Adopt a megaround mentality. “Series B has been extremely difficult,” Norris says. “[Raising] sub two million, that's one thing. But if you're looking to raise five million, it's almost better to raise twelve.”Find investors outside the mainstream. “Traditional venture investors don't want to write small checks.” Norris sees angel groups, innovation centers, and other small investors funding these early rounds.Explore acquisition exits, but be careful. “On the device side, most of the corporates have been pretty darn active,” Norris says. However, some litigate to block emerging competition, especially in the Dx/Tools sector. Norris' recommends researching potential acquirers before taking meetings.Download the HSBC Venture Healthcare Report for Norris' complete analysis, and watch the video below for insights into the Medical Device and Dx/Tools sectors, AI's role in MedTech, and more.

Ireland's cybersecurity sector delivered its strongest year in 2024, closing 40% more VC deals than in 2023 while European cybersecurity funding fell 9.5%, according to a new report powered by PitchBook data and published by Enterprise Ireland. This performance helped Ireland maintain its position as first or second in Europe for cybersecurity VC deal count per capita every year since 2017. Enterprise Ireland participated in more than three-quarters of all deals over the past decade, making it Europe's leading cybersecurity investor by deal count. This sustained ecosystem support has enabled Irish cybersecurity companies to launch, scale rapidly and attract world-class global investors - most recently demonstrated by Tines' €120.7 million Series C raise in Q1 2025, which was led by Goldman Sachs Alternatives, one of the largest venture rounds ever secured by an Irish-founded company. Key highlights from the report: • Since 2014, Irish cybersecurity companies have raised over €450 million across more than 100 VC transactions. • Irish firms such as Tines, Siren, UrbanFox, Cytidel and Vaultree are winning global enterprise and government clients with automation workflow, investigative intelligence, AI-driven threat detection, vulnerability prioritisation and fully encrypted data-in-use solutions. • Ireland is home to over 140 pure-play cybersecurity companies and has ranked first or second in Europe for cybersecurity VC deal count per capita every year since 2017. • The sector currently employs more than 8,100 professionals, projected to grow to 17,000 by 2030. Anna-Marie Turley, Head of Fintech, Financial Services and Cybersecurity at Enterprise Ireland, commented: "Ireland continues to punch well above its weight in European cybersecurity investment, consistently attracting top-tier global investors. With Irish solutions now trusted by leading enterprises and governments worldwide for AI-driven threat detection, investigative intelligence and regulatory compliance, we are seeing unprecedented international demand. Looking ahead, cybersecurity is set to become an absolute non-negotiable priority for organisations everywhere. This positions Ireland's ecosystem for sustained high-growth investment and global leadership in the years to come." The full report, Ireland's Cybersecurity Landscape, is available for immediate download at Ireland's Cybersecurity Landscape | Enterprise Ireland See more stories here. More about Irish Tech News Irish Tech News are Ireland's No. 1 Online Tech Publication and often Ireland's No.1 Tech Podcast too. You can find hundreds of fantastic previous episodes and subscribe using whatever platform you like via our Anchor.fm page here: https://anchor.fm/irish-tech-news If you'd like to be featured in an upcoming Podcast email us at Simon@IrishTechNews.ie now to discuss. Irish Tech News have a range of services available to help promote your business. Why not drop us a line at Info@IrishTechNews.ie now to find out more about how we can help you reach our audience. You can also find and follow us on Twitter, LinkedIn, Facebook, Instagram, TikTok and Snapchat.

SummaryIn this episode of the In/organic Podcast, co-host Christian Hassold shares insights from the KPMG Technology M&A Conference, discussing the current landscape of mergers and acquisitions, particularly in the tech sector. In this episode, Christian shares highlights from the conference, including the pervasive influence of AI on M&A decisions, the challenges and opportunities presented by the AI investing landscape, and the importance of creative deal structures in navigating the current market dynamics. The episode also covers the “operator's dilemma” faced by CEOs - that is, the rise in peer pressure to do M&A, and what are the best practices are from leading strategics. Finally, Hassold provides an overview of current B2B SaaS deal activity and market trends based on Pitchbook data.TakeawaysThe KPMG M&A Conference provided valuable insights into current market dynamics.AI is a major factor influencing M&A decisions and strategies.VCs are increasingly making investments in AI startups without getting governance rights, and not always checking the underlying economics of the businessThe operator's dilemma highlights the challenges that CEOs face in mergers and acquisitions (M&A).Corporate development roles are seeing a significant increase in demand.Top CEOs simplify their M&A strategies to focus on core problems.Deal activity in the tech sector is on the rise, indicating a healthy market.Earnouts are becoming a significant component of deal structures.Chapters00:00 Introduction and Context of the Episode02:50 Insights from the KPMG M&A and Tech Conference06:04 AI's Pervasive Influence on Tech and M&A08:54 The AI Investing Landscape11:40 Deal Structures Sparking Innovation16:42 The Operator's Dilemma in M&A21:48 Corporate Development and Deal Activity24:38 Priorities in M&A for Corporates vs. Private Equity29:19 Case Studies of Successful M&A Strategies32:59 Market Update on Deal Activity and EarnoutsConnect with Christian and AyeletAyelet's LinkedIn: https://www.linkedin.com/in/ayelet-shipley-b16330149/Christian's LinkedIn: https://www.linkedin.com/in/hassold/Web: https://www.inorganicpodcast.coIn/organic on YouTube: https://www.youtube.com/@InorganicPodcast/featuredEpisode ReferencesKPMG M&A Conference AgendaKPMG 2025 Deal Market Study (buyer priorities)Kirkland & Ellis M&A Bring Down Report 2025 (earnout data) Hosted on Acast. See acast.com/privacy for more information.

In Q3 2025, private equity activity surged, achieving a record US$310b in deal value as firms capitalized on narrowing valuation gaps and renewed market confidence. With 156 deals announced, including six exceeding US$10b, the sector is pivoting towards larger transactions. Improved financing conditions and creative deal structures are facilitating this momentum. Looking ahead, 61% of firms anticipate increased exit activity, signalling a robust outlook as the market embraces a "risk on" approach, balancing optimism with discipline.  All data contained in this document is sourced from Dealogic, PitchBook, and EY analysis unless otherwise noted. The Dealogic data in this report are under license by ION. ION retains and reserves all rights in such data.

Join Marina Franklin on the latest Friends Like Us podcast with special guests Jon Laster & Marlon Nichols as they dive into the world of venture capital, diversity in tech, and the untapped potential in Africa. Don't miss this insightful episode!  Jon Laster is known for One Bedroom (2018), No More Mr Nice Guy (2018) and The 2019 ESPY Awards (2019) and check out his new series An Astute Woman Web Series. He is the Founder and C.E.O. of Blapp. Blapp is a Google-based, geo-located app that will tell you all the Black businesses that are right around you. Marlon Nichols is the co-founder and managing general partner of MaC Venture Capital, a leading seed-stage firm renowned for backing visionary founders who redefine industries. Under his leadership, MaC has grown into one of North America's largest seed-stage venture firms, surpassing $600 million in assets under management (AUM). In October 2024, the firm announced the closing of its third fund ($150 million), further solidifying its influence in the early-stage investment landscape. Marlon's portfolio includes industry-defining companies such as Airspace, Blavity, FINESSE, Gimlet Media, MongoDB, Pipe, Purestream, Thrive Market, and Shekel Mobility, among others. His keen eye for transformative opportunities has earned him widespread recognition, including consecutive placements on Los Angeles Business Journal's LA500 (2022–2025) and Business Insider's Seed 100 (Top Early-Stage Investors) for four years. Additionally, he ranks 25th on the Kauffman Fellows Fund Returners Index and has been featured in PitchBook's 25 Black Founders and VCs to Watch for six years. His expertise is frequently sought by top media outlets such as Axios, CNBC, Fortune, and more. A passionate advocate for diversity and inclusion, Marlon serves on the board of Kauffman Fellows, working to expand representation for underrepresented minorities in venture capital. With a unique blend of technology acumen and leadership principles shaped by his athletic background, he actively mentors CEOs, fosters strategic partnerships, and helps founders scale their businesses into market leaders. Always hosted by Marina Franklin - One Hour Comedy Special: Single Black Female ( Amazon Prime, CW Network), TBS's The Last O.G, Last Week Tonight with John Oliver, Hysterical on FX, The Movie Trainwreck, Louie Season V, The Jim Gaffigan Show, Conan O'Brien, Stephen Colbert, HBO's Crashing, and The Breaks with Michelle Wolf. Writer for HBO's 'Divorce' and the new Tracy Morgan show on Paramount Plus: 'Crutch  

UC Berkeley is widely considered a leader in innovation and startups. Pitchbook university rankings from 2025 announced, for the third year in a row, that Berkeley graduates have founded more venture-backed companies than undergraduate alumni from any other university in the world. Some might wonder, says Chancellor Rich Lyons, if this entrepreneurial energy clashes with Berkeley's tradition of top-tier research and teaching. But Lyons sees it differently: These forces fuel each other, combining to drive the campus's ultimate goal of making a lasting difference in the world. It's a dynamic duo, he says, that keeps the campus pushing boundaries and shaping the future. In this Berkeley Talks episode, a panel of prominent Berkeley faculty and an alum join Lyons to discuss how the campus's startup culture has powered their work and encourages the next generation of scholars to grow their ideas. The panel, which took place on Oct. 6 during Homecoming weekend, includes: Ana Claudia Arias, professor of electrical engineering and computer sciencesKen Goldberg, professor of engineeringMarco Lobba, alum and co-founder and CEO of CatenaBio Chancellor Rich Lyons (moderator)Watch a video of the conversation. Read more about the event.Listen to the episode and read the transcript on UC Berkeley News (news.berkeley.edu/podcasts/berkeley-talks).Music by HoliznaCC0.UC Berkeley photo by Keegan Houser. Hosted on Acast. See acast.com/privacy for more information.

Episode OverviewIn this episode, Alex Rawlings welcomes Michael Von Bevern, Co-Managing Director for Sunterra Fund Services, to discuss the evolving landscape of fund administration in private equity. Michael shares nearly two decades of experience, working with over 500 fund managers, and offers invaluable insight into common mistakes first-time managers make, the growing role of fund administration, and why outsourcing has become the gold standard for operational efficiency and investor confidence.

Ahead of the historic Steelers Vs. Vikings NFL game this Sunday (28th September) in Dublin, the Irish government's trade and innovation agency, Enterprise Ireland has launched a report, powered by PItchBook data on Ireland's sports tech sector. The report points to Ireland's robust domestic sports tech sector, home to 93 VC-backed sports tech companies, including 40 homegrown global innovators. These companies include Orreco, Kitman Labs and Output Sports, who joined Enterprise Ireland for the launch of the report. All three companies are working with NFL teams and many other global sporting brands, including NBA, NHL, PGA Tour, F1, Olympians, Premier League and Premiership Rugby. Orreco the global leader in bio-analytics solutions for professional athletes, also announced today that they are supporting the Harvard Football Players Health Study, funded by the NFL and NFLPA. The study leads efforts to develop and support innovative research that has the potential to impact the health of current, former, and future NFL players. Examining the VC level of activity in Irish sports tech, the report, powered by PitchBook data reports that Ireland's sports tech ecosystem has notably defied the recent downward trend in VC activity seen in other regions since 2022. Irish sports tech VC deal count grew by more than 50% in 2023, compared with a drop of more than 30% for Europe overall in the same period. In 2024, Ireland's deal count grew an additional 47.1%, while broader European deal count declined again by 15.5%. Since 2014, Irish sports tech start-ups have collectively raised over €224 million in VC funding across 181 transactions. Notable VC funding rounds include Kitman Labs (€71.1 million raised) and Xtremepush (€18.6 million) and Output Sports with €4.5 million raised in 2025. Year to date 2025 data show that Ireland ranks seventh in Europe for total sports tech VC deal count, despite its relatively small population. On a per-capita basis, this ranking rises to first, and Ireland has maintained a top four ranking nearly every year over the past decade. Enterprise Ireland has emerged as a central force in shaping the sports tech investment landscape, not only in Ireland but across Europe. Since 2024, it has led the region in deal activity with 19 sports tech investments - more than triple the number recorded by the next most active investors. Commenting on the report, Keith Brock, Enterprise Ireland's Senior Client Advisor for Enterprise Solutions, said: "The Irish sports tech industry has expanded rapidly over the past ten years, both in terms of the number of companies, but also in the level of investment they have attracted and the impact they are achieving internationally working with global sports leagues. Across Enterprise Ireland's portfolio of sports tech companies, there are 89 active or pending patents covering wearable devices, augmented reality, diagnostic, exercise equipment and equine health. This level of patents points to the strong level of R&D and innovation happening in the sector, which in turn is accelerating the international sports industry from athlete performance to fan engagement. "We are proud of Enterprise Ireland's role in supporting the Irish sports tech industry, leading sports tech investment not only in Ireland, but in Europe, with 19 sports tech investment since 2024 alone. However, while Enterprise Ireland has been backing the Irish sports tech industry, the industry has also attracted major global VC investment including from Octopus Ventures (UK), Partech (France) and Apex Capital (Portugal)." Irish companies working with NFL Teams: Kitman: Kitman Labs' Intelligence Platform (iP) is an advanced operating system for performance, health, and talent development, unifying data across medical, performance, coaching, and operational domains to deliver AI-driven, evidence-based insights. iP enables leagues, federations, and elite organisations to connect data, streamline workflows, and optimise ath...

Esta semana hablamos de la salida a bolsa de Nutrisa, de PitchBook, del desempeño de Pandora en México, de la repatriación de capital y de los espacios para construir data centers.10:26 - Nutrisa17:37 - PitchBook27:50 - Pandora31:44 - repatriación de capital 36:47 - data centersPrueba Whitepaper 30 días gratisCompra tu gorra o ilustraciones de Whitepaper aquí

Christina Cyr spoke with us about building cell phones, entrepreneurship, social purpose corporations, awards, lithium recycling, and her interesting career path.  We talked about Christina's Cyrcle Phone, the related kit from dTOOR, and her CES Innovation Award. We also mentioned Fairphone in the section about social purpose corporation. There is a great paper from Nature about lithium-ion battery recycling: The evolution of lithium-ion battery recycling | Nature Reviews Clean Technology Christina Cyr Personal Website  Wellfound (formerly AngelList) is a startup focused job site that may lead to non-fulltime positions. Crunchbase may help you figure out is the startup has capital (also Pitchbook thought that generally has a cost). ADH connectors by JST and the SparkFun JST Battery Removal Tool The quote was from Hemlock & Silver by T Kingfisher and it was a lovely fantasy mystery with an incredible first chapter. Note: there are some audio artifacts on Christina's track, we apologize as there was a technical issue that couldn't be resolved. We've tried to clean it up with post-processing. There's nothing wrong with your headphones :) Transcript                                If you're interested in how 3D printing is changing design engineering, Mouser Electronics has some great resources to check out. Their Empowering Innovation Together platform is taking a deep dive into additive manufacturing—covering smarter production, faster prototyping, and breakthrough materials that move ideas beyond prototypes into real-world products. You'll find podcasts, expert articles, and videos that keep you informed and inspired. Sound like your thing? Head to Mouser.com/empowering-innovation and explore.

Welcome back to the Alt Goes Mainstream podcast.Today's podcast was a conversation that was recorded live at Morningstar's Investment Conference in Chicago earlier this year.Morningstar CEO Kunal Kapoor took time out of his packed schedule at the event to sit down with me for a thought-provoking conversation that dove into the nuances of many of the trends that are shaping private markets today.Morningstar and Kunal have quite an interesting perch in the market. They occupy a critically important function in the market: helping investors understand the data, structures, and trends in public and private markets. They provide fund ratings, investment analysis, and market data to both individual and institutional investors.As public and private markets experience increasing convergence, Morningstar finds itself at the intersection of markets that are undergoing rapid evolutions across product structures, asset allocation frameworks, and weighty questions around conceptual frameworks of liquidity, risk, volatility, concentration that are on the minds of many. Amongst the wide range of topics Kunal and I covered, one stood out: Morningstar is fiercely on the side of the investor.If there's anyone who has a deep understanding of Morningstar's DNA, it's Kunal. Kunal started at Morningstar in 1997 as a data analyst, holding a variety of roles at the firm, including leadership positions in research and innovation. He served as director of mutual fund research and was part of the team that launched Morningstar Investment Services, Inc., before moving on to other roles including director of business strategy for international operations, and later, president and chief investment officer of Morningstar Investment Services. During his tenure, he has also led Morningstar.com® and the firm's data business as well as its global products and client solutions group.Kunal and I had a fascinating and lively conversation. We covered a number of the most pressing topics in private markets today: the convergence of public and private, liquidity vs illiquidity, investor education, the importance of transparency, and the why, what, and how behind evergreen funds.Thanks Kunal for coming on the show to share your wisdom, expertise, and passion for public and private markets.A word from AGM podcast sponsor, Ultimus Fund SolutionsThis episode of Alt Goes Mainstream is brought to you by Ultimus Fund Solutions, a leading full-service fund administrator for asset managers in private and public markets. As private markets continue to move into the mainstream, the industry requires infrastructure solutions that help funds and investors keep pace. In an increasingly sophisticated financial marketplace, investment managers must navigate a growing array of challenges: elaborate fund structures, specialized strategies, evolving compliance requirements, a growing need for sophisticated reporting, and intensifying demands for transparency.To assist with these challenging opportunities, more and more fund sponsors and asset managers are turning to Ultimus, a leading service provider that blends high tech and high touch in unique and customized fund administration and middle office solutions for a diverse and growing universe of over 450 clients and 1,800 funds, representing $500 billion assets under administration, all handled by a team of over 1,000 professionals. Ultimus offers a wide range of capabilities across registered funds, private funds and public plans, as well as outsourced middle office services. Delivering operational excellence, Ultimus helps firms manage the ever-changing regulatory environment while meeting the needs of their institutional and retail investors. Ultimus provides comprehensive operational support and fund governance services to help managers successfully launch retail alternative products.Visit www.ultimusfundsolutions.com to learn more about Ultimus' technology enhanced services and solutions or contact Ultimus Executive Vice President of Business Development Gary Harris on email at gharris@ultimusfundsolutions.com.We thank Ultimus for their support of alts going mainstream.Show Notes00:00 Introduction to our Sponsor, Ultimus01:18 Podcast Opening and Theme01:55 Welcome to the Morningstar Investment Conference02:29 Convergence of Public and Private Markets03:12 Challenges in Transitioning to Private Markets05:26 Morningstar's Evolution and Impact06:59 Morningstar's Role in Reducing Costs08:15 Evergreen Funds and Transparency08:48 Complexities in Private Market Structures09:36 Liquidity and Innovation in Private Markets12:27 Investor Education and Common Language14:34 Comparing Public and Private Market Investments16:28 Standardized Documentation and Regulation18:00 Educating Investors on Private Markets18:52 Morningstar's Style Box for Private Markets19:14 Data Availability and Analysis20:24 Evaluating Different Investment Structures21:09 Public-Private Partnerships and Transparency21:38 Philosophical Questions on Private Markets22:58 Behavioral Aspects of Illiquidity24:00 Evergreen Funds as Buy and Hold Vehicles24:15 Asset Allocation and Evergreen Structures25:16 Investor Behavior and Market Volatility25:25 Individual Investors vs. Advisors26:32 Stability of Retail Assets26:56 Retail Brokerage Apps and Crypto Trading27:15 Impact of Social Media on Young Investors27:29 Exposure to Private Markets28:01 Market Drawdowns and Young Investors28:27 Advisor-Led Models vs. Self-Directed Investing28:57 Investor Behavior Across Different Age Groups30:06 Morningstar's Role in Investor Validation30:50 Morningstar's Independent Voice32:01 Transparency in Private Markets32:24 PitchBook and Data Transparency33:02 Challenges in Private Market Data33:26 Tipping Point in Transparency34:54 Private Market Indices35:37 Challenges in Benchmarking Private Markets36:29 Lessons from Public Markets37:12 Evolution of Private Markets37:37 Future of Private Markets38:41 Fee Structures in Private Markets39:38 Operational Burden in Private Markets40:50 Pre-Trade Market Structure41:16 Access to Private Markets for All Investors43:06 Returns and Diversification in Private Markets44:51 Building Portfolios in a Lower Return Environment47:15 Brand vs. Performance in Alternative Assets49:18 Favorite Alternative InvestmentsEditing and post-production work for this episode was provided by The Podcast Consultant.

Meta is readying smartglasses with a display, but also, a look at their theoretical and prototype roadmap for the future of headware hardware generally. Is all the money in the world not actually going to get Zuck what he wants in AI? And is the acquihire in all but name trend of recent months breaking Silicon Valley's fundamental business model? Links: Apple's Vision Pro Is Suffering From a Lack of Immersive Video (Bloomberg) Meta Tiramisu "Hyperrealistic VR" Hands-On: A Stunning Window Into Another World (UploadVR) Meta Boba 3 Prototype Hands-On: Ultra-Wide Field Of View Without Compromise (UploadVR) Startup down rounds are at a 10 year high, according to PitchBook data (Fortune) Meta Plans Fourth Restructuring of AI Efforts in Six Months (The Information) Zuckerberg Squandered His AI Talent. Now He's Spending Billions To Replace It. (Forbes) Big Tech Is Eating Itself in Talent War (WSJ) Enough is enough—I dumped Google's worsening search for Kagi (ArsTechnica) Learn more about your ad choices. Visit megaphone.fm/adchoices

Show Notes: Sid Masson, co-founder and CEO of Wokelo.ai explains that Wokelo is an agentic platform for investment research and commercial due diligence, automating market research and desk research activities performed by consulting firms, investment banks,  private equity analysts and so on. It offers  private market research and allows the user to pass through hundreds and 1000s of data sets in a matter of minutes, but beyond just research, it automates end-to-end deliverables, all the way to a well formatted PowerPoint deck in a format of your choice. How Wokelo.ai Works Sid mentions that Wokelo has been in production for two and a half years and commercially launched in November 2023. The platform has 40+ paying customers, including big four consulting firms like KPMG, investment banks, and venture capital firms. Pricing starts at $30,000 annually for five seats and proportionate usage. Sid explains that  larger enterprises use bespoke models which cost more and cases where certain boutique consulting firms who may not have may not need five or 10 seats and are offered customized pricing. Wokelo also ensures various security levels, including SOC 2 compliant cloud, private cloud instances, and on-prem deployments. A Demonstration of Wokelo Sid explains Wokelo's web application, which offers several workflows for different tasks. The platform includes standardized workflows like company research, industry research, and market maps, as well as custom workflows designed by users. He demonstrates the process of creating a live report for a company, including adding company attributes, uploading files, and generating insights. The platform generates a detailed, editable notebook with insights, sources, and charts, which can be exported in various formats. Sid lists the data sources Wokelo uses, including third-party data partnerships, public data scraping, and user-uploaded data. The platform has partnerships with CrunchBase, PitchBook, SNP Cap IQ, and IEP Query for patent data. Wokelo's proprietary private company database includes detailed information beyond firmographics, such as product catalogs and management profiles. Wokelo's Custom Workflow Feature Sid explains the custom workflow feature, which allows users to design their own bespoke workflows to mimic their existing methodologies. Custom workflows can include custom analysis, synergy potential mapping, and IC memos, tailored to specific user needs. The platform's user interface is designed to be easy to use, with guardrails and standardized constraints to ensure high-quality outputs. Wokelo's editable notebooks and charts are designed to be user-friendly and customizable, allowing for detailed and professional reports. The Wokelo Team Sid shares the background of the Wokelo team, including his and his co-founder's experience in management consulting and AI. The team has grown from 10 to 25 members in the last 12 months, with a focus on building a solid product and team. Wokelo has raised two rounds of funding: a pre-seed round in 2023 and a seed round in September 2022, totaling $5.5 million. The funding has helped the team build a solid product and team, focusing on quality and value rather than excessive funding. Sid discusses the challenges of selling to large firms and the initial skepticism they face. Wokelo plans to continue iterating and improving the platform, focusing on user experience and domain expertise. The team aims to expand their customer base and offer more customized solutions to meet the evolving needs of their clients. Timestamps: 00:02: Overview of Wokelo and its purpose  02:47: Customer base and pricing  05:51: Demonstration of Wokelo's features  08:50: Data sources and security  19:24: Custom workflows and user interface 27:04: Team background and funding  35:46: Challenges and future plans  Links:  https://www.wokelo.ai/ Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.  

In this episode, Duane Mancini welcomes to the show Aaron DeGagne, Healthcare Senior Analyst at PitchBook. From quarterly reports to market dynamics, Aaron sheds light on significant healthcare investments, including the rise in surgical tools, devices, and the blurring lines between healthtech and medtech. They delve into the impact of global market uncertainties, interest rates, and the noteworthy deals like Neuralink's $600 million raise. The conversation also explores themes like consumer health advancements, cancer diagnostics, and the potential shifts in IPO activities.Aaron DeGagne LinkedInPitchBook WebsiteDuane Mancini LinkedInProject Medtech WebsiteProject Medtech LinkedIn

Pitchbook reports a modest rebound in private equity deals in Q1 of 2025, and JPalmer Collective's Jennifer Palmer sees reasons for optimism for mission-driven brands and women-owned and -led brands. But both warn the current uncertainty is the new norm

In this episode of Tank Talks, we're joined by Gaurav Jain, co-founder of Afore Capital, one of the earliest and most respected players in the pre-seed investing space. Gaurav shares how growing up in a small town in India, moving to Canada, and working at Blackberry, Amazon, and Google helped him understand the value of momentum, iteration, and building products that truly matter.He walks us through how a random dinner at Harvard led to meeting his future co-founder, how they built Afore around the belief that the best founders are often overlooked too early, and why the firm exclusively focuses on investing before there's a product or sometimes even an idea.Gaurav dives into what makes a great founder at the earliest stage, why he believes momentum is the only moat, and how the rise of AI has only accelerated opportunities for young, technical entrepreneurs to build enduring companies with less capital. He also opens up about the firm's "Founder-in-Residence" and "UTransfer" programs, his view on the Canadian tech scene, and the power of bespoke, high-conviction investing.We explore:* Why is momentum the only true moat in early-stage startups?* Can pre-seed investing still deliver alpha now that it's crowded?* Is seed-strapping the future of venture capital?* How do you identify founders before they've found their idea?* What happens when you give 19-year-olds the capital to build?Building a Pre-Seed Fund Before “Pre-Seed” Was a Thing (00:03:54)* Interning globally to chase experience and perspective* The turning point: joining Founder Collective* Meeting co-founder Anamitra through a lucky dinner at Foundation Capital* Launching Afore in 2016 to fill the pre-seed voidFounder Empathy & Early-Stage VC Lessons (00:08:17)* Mistakes from being a first-time founder* Learning that exits don't matter, products and pain points do* Why Canadian angel advice focused too much on sales, not software* Why product-led growth is a must-have, not a nice-to-haveThe 10,000 Coffees Rule of Venture (00:11:27)* How judgment is built: time, exposure, and repetition* Why investing based on ideas (not teams) is a rookie mistake* Filtering “this could work” vs. “this must work”* The real constraint in VC: time, not capitalAfore's Mission: Investing Before the Idea (00:15:00)* The “Too Early” problem founders face and why Afore exists* How FIR (Founder in Residence) and Transfer University fund ideation* Building a support system, not a portfolio of call options* Why being idea-stage isn't a red flag, it's a sign of ambitionConvincing LPs That Pre-Seed Was Real (00:19:19)* LP skepticism: “Isn't this just the bad deals no one else wants?”* How talking to founders not seed managers won over investors* Working with PitchBook and Crunchbase to split out pre-seed data* Making pre-seed visible helped founders self-identify and alignSeed-Strapping and the Rise of Efficient Startups (00:24:00)* How AI-native startups are hitting $1M ARR 2x faster* Case study: Gamma's hypergrowth on ultra-low burn* Why founders can delay growth rounds longer than ever* Capital efficiency is now a competitive edgeMomentum Is the Only Moat (00:26:07)* How Android's rise taught Gaurav speed = survival* Lessons from RIM's downfall: never rest on product laurels* Why the AI era is reshaping iteration timelines* Pre-seed startups now move at the speed of launches, not quartersPivot-as-a-Service in the AI World (00:34:16)* FIR teams pivoting from speech therapy to CX platforms* Younger founders = more raw talent, less domain bias* Startups pivoting every 6–8 weeks—and why that's healthy* Embracing pivots as a feature, not a flawScaling Afore with Purpose (00:35:21)* Fund IV, $500M+ AUM, and 150+ companies later* Why concentrated portfolios beat spray-and-pray* The dangers of being too dogmatic on stage or valuation* Supporting breakout talent like Neo, Gamma, and BenchGlobal Perspective: Canada's Role in Venture (00:41:19)* Why Canada produces world-class engineering talent* The upside and limits of building in the North* Hybrid models: Canada for R&D, U.S. for GTM* Afore's belief in serving Canadian founders, wherever they buildFailure may define most early-stage startups, but for Gaurav Jain, the real story starts before the pitch, before the product, even before the idea. With Afore Capital, he is betting on people over polish, instinct over perfection, and helping founders build long before the rest of the world is watching. His journey reminds us that great companies don't always start with traction; they start with trust.About Gaurav JainCo-founder and Managing Partner at Afore Capital. Ex-Android, BlackBerry, and founder of Polar Mobile. Afore is known for being one of the first firms dedicated to pre-seed, supporting founders before they even have an idea.Connect with Gaurav Jain on LinkedIn: https://www.linkedin.com/in/gjainvcVisit Afore Capital Website: https://www.afore.vc/Connect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

John and Glen ruminate on a pair of major events reshuffling the fintech world- The Circle IPO's impact on venture funding, and the Big Beautiful Bill's unorthodox approach to throttling AI regulation. Also- John gets “spun out on quantum,” and admits a surprising change of heart regarding his outlook for AI.           Links related to this episode:   Glen's blog on the Circle and Chime IPOs: https://www.big-fintech.com/what-the-chime-and-circle-ipos-mean-for-fintech-investment/ The Senate alters the House budget provision curtailing states' ability to regulate AI: https://rollcall.com/2025/06/05/ai-regulation-moratorium-dropped-in-senate-budget-package/  Axios' story about AI's newfound ability to blackmail: https://www.axios.com/2025/05/23/anthropic-ai-deception-risk  CNBC's take on the Genius Act: https://www.cnbc.com/2025/06/13/what-the-genius-act-could-mean-for-crypto-and-other-investors.html  TechCrunch's recap of Chime's circuitous path to a successful IPO: https://techcrunch.com/2025/06/12/chime-almost-died-in-2016-turned-down-by-100-vcs-today-it-ipod-at-14-5b/  Pitchbook's take on the inevitability of Chime's “down round” IPO: https://pitchbook.com/news/articles/chimes-ipo-signals-down-rounds-are-here-to-stay     Join us for our next CU Town Hall- Wednesday July 9 at 3pm ET/Noon PT- for a live and lively interactive conversation tackling the major issues facing credit unions today. Industry developments keep coming fast and furious- the CU Town Hall is the place to make sense of these items together. It's free to attend, but advance registration is required:  https://www.cutownhall.com/   Join us on Bluesky!  @bigfintech.bsky.social;  @154advisors.bsky.social (Glen); @jbfintech.bsky.social (John) And connect on LinkedIn for insights like the Friday Fintech Five: https://www.linkedin.com/company/best-innovation-group/  https://www.linkedin.com/in/jbfintech/ https://www.linkedin.com/in/glensarvady/

Good morning from Pharma and Biotech daily: the podcast that gives you only what's important to hear in the Pharma and Biotech world.Jefferies predicts an increase in small tuck-in deals in the biotech industry as companies face challenges accessing capital. Companies and industry groups are offering solutions to mitigate the impact of Trump tariffs on rare disease, cell, and gene therapy. Pitchbook suggests a shift towards more sustainable investing in biotech VC firms. Gilead is gearing up to challenge J&J in the $20 billion multiple myeloma CAR-T market. PTP's generative AI is revolutionizing data summaries for biotech QC workflows. Sanofi recently acquired Blueprint for $9.5 billion, while BMS has committed up to $11 billion with Biontech. Lilly has signed a deal worth up to $870 million, and Regeneron is investing nearly $2 billion in a Chinese obesity drug. Merck's CEO is emphasizing diversity in operations following the defeat of an anti-DEI measure. Immuno-oncology drugs Keytruda and Opdivo may face scrutiny in the near future.In other news, Vigil Neuroscience's Trem2 antibody for a rare brain disease failed in a Phase II trial shortly after Sanofi's acquisition of the company. Analysts believe the results were not surprising and should not impact the deal. Lilly has signed a deal worth up to $870 million to develop a long-acting GLP-1 obesity drug, while the FDA is committed to making rare disease drugs available at the first sign of promise. Pharma tuck-in deals are increasing after a slow first quarter for small biotechs. BioAgc Biologics will be attending Bio International in Boston to discuss their global drug production capabilities.Stay tuned for more updates on investing in research, welcoming global talent, the biotech VC cycle, Gilead's challenge to J&J in the multiple myeloma CAR-T market, and much more. Upcoming events and job listings in the pharmaceutical industry are also featured in our newsletter.Thank you for tuning in to Pharma and Biotech daily.

Kern Egan is the founder and CEO of Multiplier, an agency that shapes culture to build brands. Multiplier manages the cultural marketing initiatives for a roster of world-class brands, including Bridgestone, Caterpillar, Chase Sapphire, Invisalign, JP Morgan Payments, On Running, PitchBook, Winnebago, and Wyndham Hotels, among others. Multiplier Ventures is a limited partner in Sapphire Sport Ventures and Elysian Park Golf Ventures and has made direct investments in Leeds United, Overtime, and TMRW Sports, among others. Kern is an advisor for Cal-Berkeley's SkyDeck technology accelerator and is the founder and chairman of Dallas Influencers in Sports and Entertainment (DISE), the area's leading industry nonprofit, granting over $1,000,000 to 46 local youth charities. He is also the former Chairman of the Heart of Dallas Bowl at the historic Cotton Bowl Stadium and served on the North Texas Super Bowl XLV Host Committee. Kern, a graduate of Indiana University, was named a Forty Under 40 honoree by both the SportsBusiness Journal and Dallas Business Journal.

Our analysts Vishy Tirupattur and Joyce Jiang discuss the health of private credit as default pressures are building for borrowers amid weaker growth, fewer rate cuts and policy uncertainty.Read more insights from Morgan Stanley.----- Transcript -----Vishy Tirupattur: Welcome to Thoughts on the Market. I am Vishy Tirupattur, Morgan Stanley's Chief Fixed Income Strategist.Joyce Jiang: And I'm Joyce Jiang, U.S. Leverage Finance Strategist.Vishy Tirupattur: Today we'll take a look at private credit markets. Will it stay resilient in the current macro conditions? Or a reckoning is ahead of us.It's Tuesday, May 13th at 10am in New York.Tariffs and policy uncertainty are on the top of mind for people with an eye on the economy and markets. Certainly, a frequent topic of discussion for us on this podcast. In this environment, there has been growing concern about the health of corporate credit – and within corporate credit direct lending or middle market segments, where companies tend to be smaller in size and have weaker fundamentals are of particular concern. The business models of these companies are sensitive to slower growth.Joyce, can you map out the risks associated with private credit companies?Joyce Jiang: To your point, risks are rising in private credit, but I think these risks would be measured given the still resilient fundamental backdrop. Looking at fundamental trends, there is no clear sign of leverage building up in the system yet, and multiple data sources actually show that the leverage ratios among direct lending companies have either improved or remained flat. And that's very different from the previous cycles where excessive corporate leverage set the stage for the eventual downturn.So, this time around credit, including both public credit and private credit, is not the source of the problem. But, of course, these direct lending companies would be impacted by higher tariffs. So, Vishy what's your view on the tariff impact?Vishy Tirupattur: So, the direct impact of tariffs, Joyce, we think is likely to be muted. It's quite hard to quantify this exposure, but if you look at a number of different data sources, we find that the direct lending loans are more skewed towards defensive and service-oriented sectors.For example, sectors such as a technology, business services and healthcare account for over half of the loans in typical BDC portfolios or Business Development Company portfolios of direct lending loans. But that said, even though the direct impact could be somewhat limited, there could be second order effects because there is higher uncertainty and weaker confidence, and that could weigh on demand. There could be a tail cohort that could be developing.So, some data from Lincoln International, for example, shows that about 15 per cent of direct lending companies have EBITDA interest coverage ratio below 1x. Another way of looking at tail cohort is by looking at companies generating negative free operating cash flow. According to S&P data, that's about 40 per cent. These tail cohorts are stretched and are weakly positioned to weather macro challenges ahead.So, Joyce, another thing that comes up frequently when we talk about private credit is Payment In Kind interest or the so-called PIK interest. Can you walk us through what is a PIK and why is it a concern?Joyce Jiang: So, Payment In Kind interest – it occurs when the company stops paying interest in cash, but instead the interest is accrued and added to the principal balance. It is quite common for companies under liquidity stress to switch to PIKs for cash preservation, But in many cases, PIKs don't really clean up the company's balance sheet, and the companies may still end up in a conventional default. So, PIK is generally considered as a leading indicator of default by market participants.And to be clear, not all PIK loans are bad. PIK toggles are actually a key feature that distinguishes direct lending loans from syndicated loans because it provides non-distressed companies the flexibility to reallocate cash for other business needs. So, PIKs do not necessarily signal higher defaults. And in fact, data showed that BDCs or Business Development Companies with a higher PIK income don't always see a greater increase in nonaccruals. So, in other words, the relationship between PIK income and defaults is not persistently strong.Vishy Tirupattur: So, to summarize, overall fundamentals are on a relatively strong footing, but risks in private credit are rising, especially if we have a potential economic slowdown ahead. On the other hand, there are a few structural features with the private credit loans that could potentially help mitigate some of the vulnerabilities we've just talked about.First thing, direct lending loans are not marked to market by design, so they have lower volatility and are relatively immune from daily price moves. And really related to that, redemption risk of private credit funds has been fairly contained so far. These funds usually have tools like lockup periods and redemption caps to guard against unexpected large outflows.But of course, the effectiveness of these mechanisms has not yet been tested in severe downturns. Moreover, the capital that is going into private credit is relatively sticky capital. Key investors, such as insurance companies and pension funds are hold-to-maturity type buyers, and they're entering in the space for the attractiveness of the higher yields and to harvest illiquidity premia embedded in these loans. So, with that long-term investment horizon, they would be more willing to support companies through temporary liquidity challenges. Also, small lender groups in direct lending market makes it easier to negotiate restructurings.Joyce Jiang: Lastly, there is also ample dry powder. According to PitchBook, there is $570 billion of dry powder in private debt fund, and another $2 trillion in private equity funds. And this capital can be deployed to backstop distressed companies and help keeping defaults in check. And in terms of defaults, we are expecting syndicated loan defaults to end the year at 4 per cent. And that's our base case.And based on the historical relationship, that implies a like for like default rate for perfect credit at 5 per cent, which means a mild uptake from the current level, but is still below the COVID peak.Vishy Tirupattur: Joyce, thanks for taking the time to talk about this.Joyce Jiang: Thanks for having me, Vishy.Vishy Tirupattur: And to our listeners, thank you for your attention. Let us know what you think of this podcast and the topics we cover. And if you think a friend or a colleague might find this information useful, please share Thoughts on the Market with them today.

Send us a textWe explore the life and career of fascinating and worldly sideshow performer Francesco "Frank" Lentini aka The Great Lentini, born with three legs, four feet and two functional sets of genitalia! Join us on our deep dive into his early life, varied show skills and interesting perspectives proclaimed by this famed exhibitor. {This episode contains explicit language}LENTINI'S "PITCHBOOK": https://www.missioncreep.com/mundie/gallery/gallery11.htmMORE : https://www.youtube.com/watch?v=7iDqmwuKiWk**Check out our IG for images we discuss during the episode!**Head to www.magicmind.com/CIRCUSAPR and enter the code CIRCUSAPR20% off your first order or 48% off a subscription! Support the show+Follow Us on IG @circus.stories+Email us: circusstoriespodcast@gmail.comRate, Review and Subscribe where ever you Listen!Thanks for Listening + Check those Boilers !!

It's the last Friday in April and it's time for Marketplace Tech Bytes Week in Review. This week, we'll talk about how the Federal Trade Commission is suing Uber over its subscription service.Plus, how the VC world is navigating the uncertainty created by the trade war.But first, a nonprofit pivot is facing some challenges. Open AI, the maker of ChatGPT was founded about a decade ago as a nonprofit research lab. It's now looking to restructure as a for-profit — specifically, a public benefit corporationBut that transformation is facing resistance. About 10 former Open AI employees, along with several Nobel laureates and other experts, have written an open letter asking regulators in California and Delaware to block the change. They argue that nonprofit control is crucial to Open AI's mission, which is to “ensure that artificial general intelligence benefits all of humanity."Marketplace's Stephanie Hughes spoke with Jewel Burks Solomon, managing partner at Collab Capital, about how unusual it is to see this kind of conversion. YouTube Video of Marketplace Tech BytesMore on everything we talked aboutAn Open Letter - Not For Private GainEx-OpenAI workers ask California and Delaware AGs to block for-profit conversion of ChatGPT maker - from the Associated PressOpenAI's Latest Funding Round Comes With a $20 Billion Catch - from the Wall Street JournalFTC Takes Action Against Uber for Deceptive Billing and Cancellation Practices - from the Federal Trade CommissionFTC sues Uber over difficulty of canceling subscriptions, “false” claims - from ArsTechnicaWhite House Considers Slashing China Tariffs to De-Escalate Trade War - from the Wall Street JournalVC manufacturing deals were already declining before tariffs entered the picture - from Pitchbook

It's the last Friday in April and it's time for Marketplace Tech Bytes Week in Review. This week, we'll talk about how the Federal Trade Commission is suing Uber over its subscription service.Plus, how the VC world is navigating the uncertainty created by the trade war.But first, a nonprofit pivot is facing some challenges. Open AI, the maker of ChatGPT was founded about a decade ago as a nonprofit research lab. It's now looking to restructure as a for-profit — specifically, a public benefit corporationBut that transformation is facing resistance. About 10 former Open AI employees, along with several Nobel laureates and other experts, have written an open letter asking regulators in California and Delaware to block the change. They argue that nonprofit control is crucial to Open AI's mission, which is to “ensure that artificial general intelligence benefits all of humanity."Marketplace's Stephanie Hughes spoke with Jewel Burks Solomon, managing partner at Collab Capital, about how unusual it is to see this kind of conversion. YouTube Video of Marketplace Tech BytesMore on everything we talked aboutAn Open Letter - Not For Private GainEx-OpenAI workers ask California and Delaware AGs to block for-profit conversion of ChatGPT maker - from the Associated PressOpenAI's Latest Funding Round Comes With a $20 Billion Catch - from the Wall Street JournalFTC Takes Action Against Uber for Deceptive Billing and Cancellation Practices - from the Federal Trade CommissionFTC sues Uber over difficulty of canceling subscriptions, “false” claims - from ArsTechnicaWhite House Considers Slashing China Tariffs to De-Escalate Trade War - from the Wall Street JournalVC manufacturing deals were already declining before tariffs entered the picture - from Pitchbook

Emily Roland, co-chief investment strategist at John Hancock Investment Management, says she is minimizing geopolitical inputs right now because it's impossible to make investment decisions around uncertainty. She says it's particularly important right now to focus on fundamentals and what's real — "We're investing in companies not countries" — and she is not buying the long-term hype on Europe because she says the recent rally doesn't have a strong foundation to stand on. That's one of four interviews from FutureProof Citywide in Miami Beach for today's show. Chuck also talks emerging markets and global income investing with Dan Shaykevich, head of Multi Sector Strategy, co-head of Emerging Markets and Sovereign Debt with Vanguard, discusses the evolution  of new financial products with Alec Davis, head of enterprise reporting at Pitchbook, and covers the stock market and being a patient investor in impatient times with Eddy Elfenbein, editor of the Crossing Wall Street blog and portfolio strategist for the AdvisorShares Focused Equity ETF.

Welcome to another insightful episode of Coffee with a Journalist, brought to you by the team at One Pitch. This week, we're excited to have Kia Kokalitcheva, the Senior Editor of VC News at PitchBook, join us from San Francisco. Kia dives into PitchBook's editorial focus on the deal-making world, shares her approach to managing an influx of press releases, and offers advice for publicists looking to effectively engage with her team. With her extensive background at Axios and Fortune, Kia also highlights the critical elements she considers when covering funding announcements. So grab your coffee and get ready to learn about the power of data-driven journalism and how to navigate the fast-paced media landscape. Enjoy the episode!

Pitchbook reports that of venture capital deals in 2024, roughly 30% of them were down rounds or flat, meaning their valuation of the companies either went backwards or were the same round to round. It's a trend that will continue, so how can entrepreneurs break the cycle? Matt Tyner, managing partner of America's most active venture capital firm – Elevate Ventures, joins today to make sense of what's ahead and how innovators can succeed. He gets into: The current state of venture capital – and the criticality of taking a step back to understand where things sit today Investors' increased focus on profitability and not being able to cut your way to growth Artificial intelligence as an enabler – not a vertical Does the future include a shift to debt versus venture The cost of innovation and the role of an investor in this era of venture capital What the current conversation with portfolio companies looks like for Elevate Ventures What Matt sees as emerging trends in agbioscience The most important jobs to be done in the industry

Pitchbook reports that of venture capital deals in 2024, roughly 30% of them were down rounds or flat, meaning their valuation of the companies either went backwards or were the same round to round. It's a trend that will continue, so how can entrepreneurs break the cycle? Matt Tyner, managing partner of America's most active venture capital firm – Elevate Ventures, joins today to make sense of what's ahead and how innovators can succeed. He gets into:  The current state of venture capital – and the criticality of taking a step back to understand where things sit today Investors' increased focus on profitability and not being able to cut your way to growth Artificial intelligence as an enabler – not a vertical  Does the future include a shift to debt versus venture The cost of innovation and the role of an investor in this era of venture capital What the current conversation with portfolio companies looks like for Elevate Ventures What Matt sees as emerging trends in agbioscience The most important jobs to be done in the industry 

Nikola Lazarov is the co-founder and CEO of Eilla AI, a tool that provides AI workers for private market intelligence. Nikola is an AI engineer who started his career at a London-based hedge fund, Marble Bar Asset Management, where he worked as a quant. He realized the value of AI in structuring unstructured data for private companies and decided to start a company almost three years ago. What Eilla AI Does While Nikola mentions that their target clients are investors and investment bankers, Eilla AI's tool does various tasks, such as finding competitors, analyzing their USP, target market, and financials. It also offers a solution for finding comparable transactions and conducting valuation reports. By searching for similar companies, it can determine their multiples, revenues, and valuations. The tool collects data from various data providers, including CrunchBase Zero and PitchBook, and scrapes it on its own. One of the most exciting solutions offered by Eilla AI is finding comparable transactions and doing valuation reports. This involves finding similar companies, analyzing their financials, average multiples, and what is driving these valuations. The tool automatically gathers and compares the data, providing valuable insights for startups, investors, and investment bankers. How Eilla AI Works The conversation turns to how it works. Nikola talks through using the software and explains the visuals on the screen, which includes tabs such as company, profile, competitor, research, buyer, selection, investment highlights, key questions, risks and mitigates, and a one-pager. The company profile page provides a consolidated set of information about the company, including its headquarters location, number of employees, founding status, total raised, and last transaction. The company description, industry, problem solved, key team members, funding, product, clients, business model, digital intelligence, and news are all included. The platform is similar to CrunchBase and other data aggregators, but it aggregates data from various sources, such as LinkedIn, their website, CrunchBase, and Capita. The platform also offers footnotes for each piece of data, allowing users to hover over it to see the source of the information. The platform also provides information on the website traffic, such as the source and the number of followers. Aggregating Data from Various Sources Nikola explains how the tool works using competitor research as the example to find the closest competitors to Pay Hawk. He explains that this process saves time and helps save time by aggregating data.  However, what differentiates Eilla AI is what happens on top of this aggregated data. It uses a proprietary database of in-depth product information to gather information from over 7 million companies, ranking them based on funding, cat count, and other factors. AI is used to determine the number of competitors and similar companies.  A Vertical View of Information Users can select a few companies to dive deeper into, and a vertical view allows for a comparison table. The table includes company name type, description, product description, headquarters location, team, year of founding, last round of funding, status, ownership status, detailed offering, unique selling proposition, and target market. The information is organized in a way that would take weeks to pull together. Users can use the vertical view to see the companies side by side. The platform also includes green dots on product descriptions to indicate high similarity and source information. This tool is unique in that it not only provides data but also replicates the workflow of competitor research. It offers insights such as a SWOT analysis on the strengths, weaknesses, opportunities, and threats of Pay Hawk versus its competitors. Product and Services The platform also includes a Products and Services tab with bullet points around PayHawk versus its competitors. Each product has a footnote where users can click to see the sources and scroll down to understand the differences between the two companies. Nikola also mentions the upcoming release of Cap IQ Financial, which includes important information like revenue, beta, valuation, and financials. The buyer selection tab is particularly interesting, as it shows all similar companies to Payco, including acquisitions and mergers. These companies are split into potential strategic buyers, competitors, and financial buyers. The tool also highlights the similarities between Pay Hawk and other companies, such as Visma and Instant, a platform that automates control for secure payments and trustworthy suppliers. The platform also assesses the financial capabilities of the company to buy companies like Pay Hawk. Eilla AI Features Eilla AI  Nikola explains that the platform aims to replicate the workflow of investors and investment bankers by breaking down complex workflows into simpler steps. This is done by breaking down data from various sources, such as data providers, CRMs, emails, and nodes. The goal is to provide a comprehensive overview of the company's funding, team, head count, product, services, USP, and detailed offering. The platform also offers a one-pager, which can be easily downloaded and viewed as a PDF. This information provides a detailed overview of the company's funding, team, head count, product, and services, as well as its unique selling points. The platform also provides a free seven-day trial for potential customers, such as corporate executives or business consultants looking for acquisitions. Eilla AI Pricing Schedule The pricing schedule is based on the number of requests per user and the amount of time spent on due diligence. For larger companies, the standard price is $98 per month per seat, while for smaller companies, it is $300 per month per user. The platform also offers a free seven-day trial for those interested in trying out the product without the need for sales meetings. Timestamps: 02:38: Overview of Eilla AI's Services  04:46: Demonstration of Eilla AI's Capabilities  09:50: Competitor Research and Insights  16:08: Buyer Selection and Investment Highlights  20:18: Key Questions and Risks Mitigation  22:09: Customer Base and Pricing  24:50: Conclusion and Next Steps    Links: https://Eilla.ai   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.

Venture investments fell in the first quarter of 2024 to a near five-year low, PitchBook says. Funds started falling when the Federal Reserve first raised interest rates, and large exits have slowed in the past couple of years. Plus, “another test for the community”: Where Baltimore port workers and nearby businesses stand. Also, how campaign ads shape voters' economic views and what the Realtors settlement means for buyers and sellers.