Podcasts about Arista

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

Set the controls for the heart of 1985 and the Dead's 20th anniversary shows at the Greek Theatre in Berkeley, with a look at the band's very busy year, plus Dead Head adventures aplenty.Guests: Dennis McNally, Len Dell'Amico, Rosie McGee, John Leopold, Dave Leopold, Johnny Dwork, David Lemieux, Nicholas Meriwether, Dave PerlisSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Host Jesse Jackson welcomes returning guest Scott Shea—author of All the Leaves Are Brown and a forthcoming Waylon Jennings book—to discuss the anniversary of Bruce Springsteen signing with Columbia Records in June 1972 and the recent death of Clive Davis. Shea highlights Springsteen's rare 54-year label loyalty, early rocky years, and how Born to Run marked progress before larger commercial success arrived with The River and “Hungry Heart.” He recounts the origin story of Springsteen's deal, focusing on manager Mike Appel's cold calls, John Hammond's pivotal audition, and Clive Davis approving the signing. They also discuss Columbia's artist roster, Davis's career arc from Columbia to founding Arista, Springsteen's evolving vocals and live arrangements, reactions to the latest tour's political tone, and Shea's ongoing research for his Waylon Jennings biography. 00:00 Podcast Welcome 00:51 Meet Scott Shea 02:13 Columbia Anniversary 04:51 Why Bruce Stayed 06:49 Band Loyalty Talk 08:50 Clive Davis Passing 09:59 Hammond and Appel Story 20:02 Cold Call Breakthrough 21:25 The Audition Moment 23:12 What If He Was Missed 23:44 Cafferty Career What Ifs 24:59 Clive Davis Origins 26:16 Columbia Breakthrough Signings 27:28 Arista Records Reinvention 28:56 Springsteen Tour Politics 30:40 Cranky Bruce And Banter 33:12 Born In USA Outtakes 36:44 Waylon Book Progress 39:02 Wrap Up And Plugs 40:05 Podcast Housekeeping Learn more about your ad choices. Visit megaphone.fm/adchoices

We explore the very high times of the Grateful Dead's 1966, touring Rancho Olompali with Rosie McGee, swan-diving into Owsley's magical banana box of mystery reels, and celebrating the feral young Dead heard on the new July 3rd, 1966 Fillmore Auditorium release.Guests: Rosie McGee, Ron Rakow, Bob Matthews, David Freiberg, David Lemieux, HawkSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

M51 EXTRA DE VERANO II El conde que desafió un imperio, Entre la traición y la historia. García Galíndez, conde de Aragón en el siglo IX, pasó a la historia con un apodo inquietante: “el Malo”. Pero, ¿quién fue realmente? En este episodio viajamos a los Pirineos para entender su historia: una humillación que desencadena una venganza, la ruptura con el poder franco, su alianza con Íñigo Arista y Musa ibn Musa, y su papel en la transformación de Aragón. Un relato donde historia y memoria se mezclan, y donde surge una pregunta clave: ¿fue realmente un villano… o alguien que la historia decidió condenar?

100 episodes baby! This episode, I'm joined by Nick Heyward and Blair Cunningham of Haircut 100, very appropriately for my 100th episode. The band are back, with their first studio album since 1982's masterpiece, “Pelican West”, that's not counting a 1984 album they made without Nick. The new album is called “Boxing The Compass” and it is sensational. I caught them play a tour warm-up recently and they are on incredible form in that department too.   Nick Heyward is from Beckenham, and started playing in early incarnations of the band in 1977. They eventually became Haircut 100 and signed to Arista in 1981. The band were ill-prepared for the success that followed, Nick in particular not dealing well with the pressures of fame, and by early ‘83 he was out the band, later citing stress and depression among the reasons. He went on to have a solo career, and Haircut 100 have reunited several times in more recent years, but this is the first time they're released a new record together.   Blair Cunningham is a world-class drummer, originally from Memphis, Tennessee. He's one of 13 children, and remarkably, and very sadly, his brother Carl was the drummer for Stax band The Bar-Kays, and died in the same plane crash that killed Otis Redding. After the original dissolution of Haircut 100, he went on to drum for The Pretenders, Sade, Mick Jagger, and loads of others, and notably was a member of Paul McCartney's band for a few years, so when he refers to “Paul” during our conversation, that's who he means.   I had a great time with these guys. The conversation is all over the place, but it was loads of fun, and I hope that comes across.   Also, a particularly special thank you to everyone that listens regularly. I appreciate the selection of guests is idiosyncratic and diverse, so it means a lot that you've stuck around. The show continues to be a fully-independent podcast, with no advertising, and a one man operation. I book the guests, do the interviews, edit the show together, and run the socials. I love the control this gives me, but it brings its own challenges, especially as I want to make podcasts, not spend hours generating content on social media, and so the algorithm doesn't help the show much. This means that anything you can do to help, telling your friends, following me on Instagram @sendingsignalspodcast, liking posts, leaving a star rating or review with your podcast provider; these things generally mean a lot to a show like mine.   Thank you all!

Tapping the barometer of news to see what's blistering or stormy, which this week includes … … “The Man can't bust our music!”: the crimes and misdemeanours of Clive Davis … the single biggest change in our lifetimes ... when did musicians become ‘artists'? … Johnny Marr's guitar habit … unlimited cash and what we'd spend it on … Madonna smoking at Paris Fashion Week hoping someone would try to stop her … why Dave doesn't own any Arista records … which five Paul Simon songs became film titles? … a Prime Minister who loves ‘Dragon New Warm Mountain I Believe In You' by Big Thief! … 56 year-old takes annual Dark Side of the Moon test: “and I still don't like it!” … are there more registered songwriters or lorry drivers? Plus the biopic boom and birthday guest Andrew Stocks has a senior moment.Help us to keep The Longest Continuous Conversation In Rock'n'Roll going: https://www.patreon.com/wordinyourear Hosted on Acast. See acast.com/privacy for more information.

Tapping the barometer of news to see what's blistering or stormy, which this week includes … … “The Man can't bust our music!”: the crimes and misdemeanours of Clive Davis … the single biggest change in our lifetimes ... when did musicians become ‘artists'? … Johnny Marr's guitar habit … unlimited cash and what we'd spend it on … Madonna smoking at Paris Fashion Week hoping someone would try to stop her … why Dave doesn't own any Arista records … which five Paul Simon songs became film titles? … a Prime Minister who loves ‘Dragon New Warm Mountain I Believe In You' by Big Thief! … 56 year-old takes annual Dark Side of the Moon test: “and I still don't like it!” … are there more registered songwriters or lorry drivers? Plus the biopic boom and birthday guest Andrew Stocks has a senior moment.Help us to keep The Longest Continuous Conversation In Rock'n'Roll going: https://www.patreon.com/wordinyourear Hosted on Acast. See acast.com/privacy for more information.

Tapping the barometer of news to see what's blistering or stormy, which this week includes … … “The Man can't bust our music!”: the crimes and misdemeanours of Clive Davis … the single biggest change in our lifetimes ... when did musicians become ‘artists'? … Johnny Marr's guitar habit … unlimited cash and what we'd spend it on … Madonna smoking at Paris Fashion Week hoping someone would try to stop her … why Dave doesn't own any Arista records … which five Paul Simon songs became film titles? … a Prime Minister who loves ‘Dragon New Warm Mountain I Believe In You' by Big Thief! … 56 year-old takes annual Dark Side of the Moon test: “and I still don't like it!” … are there more registered songwriters or lorry drivers? Plus the biopic boom and birthday guest Andrew Stocks has a senior moment.Help us to keep The Longest Continuous Conversation In Rock'n'Roll going: https://www.patreon.com/wordinyourear Hosted on Acast. See acast.com/privacy for more information.

The Deadcast explores Steal Your Face's iconic artwork & visits the Grateful Dead's June 1976 return to the road, including a tour of the Dead Head culture that bloomed in their absence.Guests: Richard Loren, John Scher, Ron Rakow, Eugene Dolgoff, Pat Lee, Johnny Dwork, Dave Davis, Rob Bleetstein, John Brackett, Starfinder Stanley, David LemieuxSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Legendary music mogul Clive Davis passed away at the age of 94. In 2017, while promoting a documentary about his life, Clive spoke to Andrew Carter. Photo Credit: (Photo by Andy Kropa/Invision/AP)

Take a Network Break! Our Red Alert covers critical vulnerabilities in Ivanti Sentry, including OS command injection and authentication bypass, for which patches are now available. On the news front, we dig into Arista's new 1.6Tbps rack-scale portfolio for AI infrastructure and Nokia's Deepfield Genome Shield, designed to proactively stop DDoS from residential proxy botnets. We... Read more »

Take a Network Break! Our Red Alert covers critical vulnerabilities in Ivanti Sentry, including OS command injection and authentication bypass, for which patches are now available. On the news front, we dig into Arista's new 1.6Tbps rack-scale portfolio for AI infrastructure and Nokia's Deepfield Genome Shield, designed to proactively stop DDoS from residential proxy botnets. We... Read more »

Take a Network Break! Our Red Alert covers critical vulnerabilities in Ivanti Sentry, including OS command injection and authentication bypass, for which patches are now available. On the news front, we dig into Arista's new 1.6Tbps rack-scale portfolio for AI infrastructure and Nokia's Deepfield Genome Shield, designed to proactively stop DDoS from residential proxy botnets. We... Read more »

This week Baxie talks with legendary bass player Tony Marsico! Tony was not only the bass player for The Plugz--one of the first predominantly Latino punk bands. The were also the band that scored the 1984 film “Repo Man”. After The Plugz the band rebranded themselves as the The Cruzados. The Cruzados were quickly signed by Clive Davis from Arista records and released two outstanding records until breaking up in 1987. But Tony hardly stopped there. Since the band's original break up Tony became one of the most in-demand session players in America. His list of credits includes the likes of Bob Dylan, Neil Young, Roger Daltry, Marianne Faithful, Willie Nelson, Linda Ronstadt, the Divinyls, Juliana Hatfield, Matthew Sweet, and many more. He also found time to release 25 solo albums, act in several feature films, write four books, and revive the Cruzados in 2021. Tony talks about all of that—and a whole lot more! Just amazing! Listen on Apple Podcasts, Spotify, YouTube, and on the Rock102 app! Brought to you by Metro Chrysler Dodge Jeep Ram of Chicopee!

The Deadcast tells the dramatic story of when the Hells Angels put ex-Grateful Dead Records president Ron Rakow on trial for walking away from the Dead with $225,000 he believed the band owed him.Guests: Ron Rakow, Steve Brown, Terry Haggerty, John Scher, David Lemeiux See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

México duplica cobertura ante desastres naturales Boris se degrada, pero mantiene lluvias intensasMar de fondo golpea costas de ChiapasAjustan reglas para aeropuertos saturadosMás información en nuestro podcast#grc

Join the Inner Circle.  Crazy Crew, it's time to level up. Get closer to the show, unlock exclusive content, and stay connected with us beyond the mic. Tap in below: - Join On YouTube Memberships: https://wesoundcrazy.ffm.to/youtubemembers - Join On Patreon: https://wesoundcrazy.ffm.to/patreon - Subscribe to Email & SMS: https://wesoundcrazy.ffm.to/jointhewesoundcrazy-emailandsms Listen on your favorite podcast service: https://pods.to/wesoundcrazy Stream songs from the episode on our official We Sound Crazy playlists: https://lnkfi.re/8I8Drkfz In part one of this special two-part release, the We Sound Crazy podcast welcomes a true titan of the music industry: the legendary Antonio "L.A." Reid. Rising to prominence as the dynamic drummer for the hitmaking 1980s R&B band The Deal, Reid seamlessly transitioned from the stage to the boardroom, cementing his status as a visionary producer, iconic songwriter, and major label force. Throughout his illustrious career, his taste and leadership guided the culture as the co-founder of LaFace Records, and later as the chairman and CEO of some of the most powerful major labels in music history, including Arista, Island Def Jam, and Epic Records. Bringing his journey full-circle, he continues to nurture the next generation of musical innovators through Mega, his powerful label venture launched in partnership with global superstar Usher. Joining hosts Philionaire, Claude Kelly and Chuck Harmony of Louis York, and Tamone Bacon, Reid engages in an open, free-flowing conversation about what it takes to shape the sound of a generation. Right from the jump, the chemistry is electric as Reid jokingly auditions to become the podcast's official fifth cast member, setting a warm and celebratory tone for an episode dedicated to giving an industry hero his well-deserved flowers. This first installment dives straight into the mind of a musical architect, exploring Reid's eclectic taste in music and his enduring philosophy on talent. The hosts cue up special video questions from fans, prompting Reid to share his candid thoughts on everything from his dream biopic casting to how he embraces AI as the next evolutionary tool for creatives and drawing a fascinating parallel to his own early days mastering the drum machine in the 1980s. He also pulls back the curtain on his legendary ability to spot undeniable star power, describing how true icons "move the air" the moment they step into a room. As the conversation unfolds, listeners are treated to an inside look at the history-making partnerships that defined an era. Reid reflects on his early days with Kenny "Babyface" Edmonds in The Deal, surviving on "pride and being broke" while studying the mastery of Prince and Luther Vandross. The episode highlights the magical intuition behind some of his biggest triumphs, including the unorthodox breaking of Toni Braxton via the Boomerang soundtrack. Grounded in a deep, mutual respect for the craft, this first part captures the essence of a man who never let boxes define him, cementing a legacy built entirely on an uncompromising love for music. We Sound Crazy is your backstage pass to all things music and culture. Special thanks to our We Sound Crazy team!  Director: Malachi Fuller Director of Photography: Neither Camera Op: Andrew Meyers, Derek Reed, Malachi Fuller, Neither Gaffer: Tyler Holmes Set Design: Gina Dorsey Producer/A2: Jerel Duren Editor: Hyyer Creative Producer:  Lamont Baldwin, Aaron Walton Show Producer/Remixer: Michael "Roux" Johnson Assistant: Brittany Guydon Talent Producer: Micha "ML6" Logan Photography: Kirk McClain   PA: Keylon Hall, Jonaye Anderson, Ryan Lee Thank you to all of our listeners and watchers! Special thanks to Antonio "L.A." Reid! Subscribe to We Sound Crazy on Spotify, Apple Podcasts, and anywhere you get your favorite podcast. Follow We Sound Crazy on Social Media:  ~ Facebook: https://wesoundcrazy.ffm.to/wscfacebook ~ Instagram: https://wesoundcrazy.ffm.to/wscinstagram ~ Twitter: https://wesoundcrazy.ffm.to/wsctwitter ~ TikTok: https://wesoundcrazy.ffm.to/wsctiktok Subscribe to We Sound Crazy on YouTube: https://wesoundcrazy.ffm.to/wscyoutube-subscribe Visit the official We Sound Crazy website: https://wesoundcrazy.ffm.to/officialwebsite #WeSoundCrazy #AntonioL.A.Reid Learn more about your ad choices. Visit megaphone.fm/adchoices

The Deadcast uncovers the secrets of Steal Your Face, the Dead's 1976 live album with a checkered reputation, dramatic backstory, & sonic experimentation by Phil Lesh & Owsley Stanley. Guests: Ron Rakow, Al Teller, John Scher, Ned Lagin, David Lemeiux See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Mit Thomas Rappold bekommt der Heiko Thieme Club Silicon-Valley-Tiefgang: KI, Quantencomputing, Cybersecurity und Chips werden nicht als Hype abgehandelt, sondern als Machtfrage der nächsten Jahre. Rappolds Kernthese: Alphabet ist im KI-Rennen besonders stark, weil Google den gesamten Stack beherrscht - von Rechenzentren und eigenen Chips bis Gemini. Dahinter nennt er ASML, Palantir und Arista als wichtige KI-Pferde. Der Abverkauf vieler Softwarewerte sei kein Ende der Software, sondern womöglich die "Golden Opportunity". SAP und Intuit sieht Rappold als Beispiele, weil KI-Agenten Unternehmenssoftware eher mehr nutzen dürften. Heiko Thieme greift das auf: Disruption zerstört nicht jedes Geschäftsmodell, oft stärkt sie die richtigen Platzhirsche. Bei Quantum sieht Rappold den echten Vorteil in den nächsten zwei bis drei Jahren. CrowdStrike steht für Sicherheitsinfrastruktur, Intel muss jetzt liefern, Infineon bleibt stark, aber kein Neukauf mehr. Danach ordnet Heiko den Markt ein: DAX über 25.000, Iran-Risiko und Straße von Hormus aber langfristig bleibt er konstruktiv. Seine DAX-Liste: Heidelberg Materials klarer Kauf, Hannover Rück und Henkel bei Schwäche, Mercedes nur vorsichtig. Mehr dazu auf: https://www.heiko-thieme.club/club-ausgaben/

The Deadcast concludes its extended 2-part tribute to Bobby Weir, ranging into the evolution of his songwriting, stage persona, guitar playing, and unexpected career beyond the Grateful Dead.Guests: Bobby Weir, David Lemieux, Jeff Chimenti, Scott Metzger, Don Was, Gary Lambert, Tim Stevens, Tony Italiano, William Keats, Bretty PauleySee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Anthropic is making the AI infrastructure race look less like software and more like an industrial arms race. The company reportedly committed $200 billion to Google Cloud over five years, stacked on top of its existing Amazon compute arrangement, while also striking a separate agreement tied to SpaceX and xAI's Colossus supercomputer infrastructure. Cerebras is blowing past expectations in what could become the largest tech IPO of 2026. The wafer-scale AI chip company is reportedly oversubscribed more than 20x, with a potential valuation up to $26.6 billion. After years of NVIDIA dominating the AI hardware story, public markets are now showing real appetite for the next layer of AI infrastructure.Meta is cutting roughly 8,000 jobs and canceling another 6,000 open roles as it redirects up to $135 billion toward AI infrastructure in 2026. The AI labor transition is no longer theoretical. The Cleveland Fed's inflation nowcast is projecting May headline CPI at 3.89%, while the S&P 500's Shiller P/E ratio reached 41.83, the second-highest reading in more than a century of market history. Stocks are expensive, inflation is reaccelerating, and rate cuts are moving further out of reach. Stagflation risk is back in the market conversation.Runner-up: The Trump-Xi summit is reportedly adding AI to the agenda for the first formal U.S.-China bilateral AI dialogue. The talks are expected to focus on autonomous weapons, frontier model behavior, and risks from open-source models in the hands of nonstate actors. Runner-up: OpenAI released GPT-5.5 Instant as the new default ChatGPT model, with a focus on reducing hallucinations in legal, medical, and financial use cases. The model wars are increasingly being fought on reliability, latency, and enterprise trust rather than raw benchmark dominance.Runner-up: Google, Microsoft, and xAI joined OpenAI and Anthropic in giving the U.S. Commerce Department's AI standards office pre-release access to frontier models for evaluation. Government review is becoming part of the frontier AI release process.Runner-up: AMD and Arista both posted blowout Q1 results, showing that the AI capex boom is spreading beyond NVIDIA into accelerators, networking, optics, power, and cooling. The picks-and-shovels trade is broadening.If you want a prize, send us a DM on Instagram:http://instagram.com/rickerandbonttps://www.tiktok.com/@rickerandbon

The Grateful Deadcast returns for its 13th season, beginning with a 2-part tribute to the great Bobby Weir, mixing interviews with archival audio to tell the story of how a teenage Atherton folkie found his singular jazz-informed musical voice (dropping a few water balloons en route).Guests: Bobby Weir, David Lemieux, David Nelson, Gary Lambert, Rhoney Stanley, Graeme BooneSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Host Nicholas Wardroup interviews zoning consultants Arista Strungys and Chris Jennette of Camiros about their collaborative work with division staff planners for the Unified Development Code (UDC) update. They bring their collective decades of experience working with other planning departments across the country to informing recomendations on changes in written zoning guidance and explanations here in Memphis and Shelby County. Arista and Chris share how they find satisfaction in helping municipalities simplify zoning for their residents and prepare for future development and growth.This episode is the second part in a two-part series about the Unified Development Code (UDC) update and adoption process.Have questions for Nicholas, Arista, or Chris? Email them to buildingbeat@memphistn.gov, and you'll get an answer on a future episode.

Windows tips, In The Trenches this week, Arista, Unlock Outlook account

I sat down with the legendary record executive Clive Davis. We looked back on the career-defining moments that made him one of the most influential figures in music. He shares memories from the Beverly Hills Hotel, where we recorded the conversation. We also discussed the evolution of his famed pre-Grammy gala, and the philosophy behind honoring artists in the room. Davis also revisits being pushed out at Columbia, Arista, launching J Records, and the story behind Whitney Houston recording “Why Does It Hurt So Bad.” It's a conversation about instinct, reinvention, and what it takes to keep going at the highest levels in business. CHAPTERS 04:21 Clive Davis' Pre-Grammy Gala 11:30 Lessons Learned at Columbia 15:38 From Arista to J Records 23:41 When Whitney Houston and Clive Disagreed SPONSORS Chartmetric: Listen in for our Stat of the Week Symphonic: Distribute your music to one of the largest networks in the industry. Symphonic delivers your music to over 200 digital service providers ensuring that you're monetizing every stream and use of your music on Spotify, TikTok, YouTube, and more TRAPITAL Where technology shapes culture. New episodes and memos every week. Sign up here for free.

Are the hyperscalers including Alphabet the ultimate cybersecurity investment for 2026? While hyperscalers like Microsoft—boasting a projected $37 billion in fiscal 2025 revenue—Google, and Amazon are the safest entry points , pure-play companies like CrowdStrike often provide more robust, fast-moving products for organizations that prioritize high-level data security. We explore how the shift toward AI agents and platform-based models is driving massive vendor consolidation across the industry.Learn how to identify the winners in this era of disruption using a specific qualitative and quantitative checklist. We explain why metrics like Return on Equity (ROE) are more effective than ROIC for measuring the performance of acquisitive leaders like Palo Alto Networks. From the rise of AI-native startups to the importance of monitoring stock-based compensation, get the insights you need to determine which cybersecurity stocks will thrive and which will merely survive.Watch the cybersecurity video of 2026 here: https://youtu.be/foj_QXksKWEJoin us on Discord with Semiconductor Insider, sign up on our website: www.chipstockinvestor.com/membershipSupercharge your analysis with AI! Get 15% of your membership with our special link here: https://fiscal.ai/csi/Sign Up For Our Newsletter: https://mailchi.mp/b1228c12f284/sign-up-landing-page-short-formChapters:00:00 - Starting with Hyperscalers (Microsoft, Google, AWS) 01:00 - Pure Plays: Why Specialized Security Still Wins 02:11 - AI Agents & The Next Wave of Disruption 02:50 - Strategy Duel: Palo Alto Networks vs. Fortinet 03:55 - Legacy Giants: Broadcom, Cisco, & Arista 05:10 - The 2026 Investment Checklist 06:55 - Platform Dominance vs. Point Solutions 10:55 - Valuation Tip: Why ROE Beats ROIC for SoftwareIf you found this video useful, please make sure to like and subscribe!*********************************************************Affiliate links that are sprinkled in throughout this video. If something catches your eye and you decide to buy it, we might earn a little coffee money. Thanks for helping us (Kasey) fuel our caffeine addiction!Content in this video is for general information or entertainment only and is not specific or individual investment advice. Forecasts and information presented may not develop as predicted and there is no guarantee any strategies presented will be successful. All investing involves risk, and you could lose some or all of your principal. #Cybersecurity #Investing #StockMarket2026 #Microsoft #PaloAltoNetworks #CrowdStrike #AIAgents #TechStocks #SoftwareAsAServiceNick and Kasey own shares of PANW, FTFT, CRWD, GOOG, AMZN

Er is meer onder de zon dan de bedrijven van taalmodellen. Tijd voor een verkenning door de AI-industrie. Tech-expert Marc Langeveld is te gast en brengt de hele AI-waardeketen in beeld. Van de halfgeleiders tot de datacenters en de enterprise data-waardeketen. Dat gaat dus over TSMC, Nvidia, ASML, Arista, CrowdStrike en nog veel meer! Maar … hoe moet je snelle groeiers in deze industrie waarderen? Marc weet raad! ► Uitgebreide show notes en achtergrondinformatie: https://jongbeleggendepodcast.nl/215-ai-waardeketen-met-marc-langeveld ► Word Vriend: https://portfoliodividendtracker.com ► Updates via Instagram: https://www.instagram.com/jongbeleggen ► Mijn volledige portfolio: https://app.portfoliodividendtracker.com/p/jongbeleggen 1) We maken gebruik van programmatic advertising, wat inhoudt dat we geen invloed hebben op de spots die in de podcast worden afgespeeld. Dit is vergelijkbaar met tv, YouTube, radio en de krant, uiteraard met uitzondering van de advertenties die we zelf hebben ingesproken. 2) Deze podcast is 100% expertise-vrij en alleen geschikt voor amusementsdoeleinden. De inhoud mag niet worden beschouwd als financieel advies.See omnystudio.com/listener for privacy information.

In this episode John and Stewart sit down for a chat with friend and mentor Rick Chertoff. Rick is a Producer and songwriter whose career started in A&R for Clive Davis's newly minted Arista records in 1974. From there, he went on to be senior VP of A&R at Columbia records, started his own Blue Gorilla record Label at Polygram, and all the while produced songs and albums for Cyndi Lauper, The Band, Joan Osborne, Sophie B Hawkins, and many others. In this interview Rick discusses his path through the music industry, gets in depth about the writing and recording of Cyndi Lauper's smash first album She's So Unusual, and shares behind the scenes stories from a career filled with hit records.

Take a Network Break! We start with listener follow-up on data centers in space, and sound the Red Alert about a sandbox failure in Claude Code and a rash of Microsoft zero-days. On the news front, Cisco announces a 102.4Tbps switch ASIC in its Silicon One line of homegrown chips, and adds AI agent monitoring... Read more »

Take a Network Break! We start with listener follow-up on data centers in space, and sound the Red Alert about a sandbox failure in Claude Code and a rash of Microsoft zero-days. On the news front, Cisco announces a 102.4Tbps switch ASIC in its Silicon One line of homegrown chips, and adds AI agent monitoring... Read more »

Take a Network Break! We start with listener follow-up on data centers in space, and sound the Red Alert about a sandbox failure in Claude Code and a rash of Microsoft zero-days. On the news front, Cisco announces a 102.4Tbps switch ASIC in its Silicon One line of homegrown chips, and adds AI agent monitoring... Read more »

In this episode, Ben and Jay discuss various topics related to the tech industry, focusing on hyperscalers, cloud computing, and the memory market. They analyze the earnings of Nebius and CoreWeave, the implications of heavy asset businesses, and the dynamics of AI and memory. The conversation also covers the performance of Applied Materials and networking companies like Cisco and Arista, highlighting the challenges and opportunities in these sectors.

In the security news: Viral AI prompts Things to do in your home security lab I can open your garage door They call me DKnife Beyondtrust RCE Cool AI device Robots need your body Meta is just full of scams, phishing, and malware Claude Opus 4.6 found more than 500 high-severity vulnerabilities Arista next gen firewalls and command injection Secure Boot updates The RCE AMD won't fix and why the article went away End of support means get it off the network Accidentally giving away $44 billion of Bitcoin Visit https://www.securityweekly.com/psw for all the latest episodes! Show Notes: https://securityweekly.com/psw-913

In the security news: Viral AI prompts Things to do in your home security lab I can open your garage door They call me DKnife Beyondtrust RCE Cool AI device Robots need your body Meta is just full of scams, phishing, and malware Claude Opus 4.6 found more than 500 high-severity vulnerabilities Arista next gen firewalls and command injection Secure Boot updates The RCE AMD won't fix and why the article went away End of support means get it off the network Accidentally giving away $44 billion of Bitcoin Show Notes: https://securityweekly.com/psw-913

In the security news: Viral AI prompts Things to do in your home security lab I can open your garage door They call me DKnife Beyondtrust RCE Cool AI device Robots need your body Meta is just full of scams, phishing, and malware Claude Opus 4.6 found more than 500 high-severity vulnerabilities Arista next gen firewalls and command injection Secure Boot updates The RCE AMD won't fix and why the article went away End of support means get it off the network Accidentally giving away $44 billion of Bitcoin Visit https://www.securityweekly.com/psw for all the latest episodes! Show Notes: https://securityweekly.com/psw-913

Infrastructure was passé…uncool. Difficult to get dollars from Private Equity and Growth funds, and almost impossible to get a VC fund interested. Now?! Now, it's cool. Infrastructure seems to be having a Renaissance, a full on Rebirth, not just fueled by commercial interests (e.g. advent of AI), but also by industrial policy and geopolitical considerations. In this episode of Tech Deciphered, we explore what's cool in the infrastructure spaces, including mega trends in semiconductors, energy, networking & connectivity, manufacturing Navigation: Intro We're back to building things Why now: the 5 forces behind the renaissance Semiconductors: compute is the new oil Networking & connectivity: digital highways get rebuilt Energy: rebuilding the power stack (not just renewables) Manufacturing: the return of “atoms + bits” Wrap: what it means for startups, incumbents, and investors Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Nuno Gonçalves Pedro Introduction Welcome to episode 73 of Tech Deciphered, Infrastructure, the Rebirth or Renaissance. Infrastructure was passé, it wasn’t cool, but all of a sudden now everyone’s talking about network, talking about compute and semiconductors, talking about logistics, talking about energy. What gives? What’s happened? It was impossible in the past to get any funds, venture capital, even, to be honest, some private equity funds or growth funds interested in some of these areas, but now all of a sudden everyone thinks it’s cool. The infrastructure seems to be having a renaissance, a full-on rebirth. In this episode, we will explore in which cool ways the infrastructure spaces are moving and what’s leading to it. We will deep dive into the forces that are leading us to this. We will deep dive into semiconductors, networking and connectivity, energy, manufacturing, and then we’ll wrap up. Bertrand, so infrastructure is cool now. Bertrand Schmitt We're back to building things Yes. I thought software was going to eat the world. I cannot believe it was then, maybe even 15 years ago, from Andreessen, that quote about software eating the world. I guess it’s an eternal balance. Sometimes you go ahead of yourself, you build a lot of software stack, and at some point, you need the hardware to run this software stack, and there is only so much the bits can do in a world of atoms. Nuno Gonçalves Pedro Obviously, we’ve gone through some of this before. I think what we’re going through right now is AI is eating the world, and because AI is eating the world, it’s driving a lot of this infrastructure building that we need. We don’t have enough energy to be consumed by all these big data centers and hyperscalers. We need to be innovative around network as well because of the consumption in terms of network bandwidth that is linked to that consumption as well. In some ways, it’s not software eating the world, AI is eating the world. Because AI is eating the world, we need to rethink everything around infrastructure and infrastructure becoming cool again. Bertrand Schmitt There is something deeper in this. It’s that the past 10, even 15 years were all about SaaS before AI. SaaS, interestingly enough, was very energy-efficient. When I say SaaS, I mean cloud computing at large. What I mean by energy-efficient is that actually cloud computing help make energy use more efficient because instead of companies having their own separate data centers in many locations, sometimes poorly run from an industrial perspective, replace their own privately run data center with data center run by the super scalers, the hyperscalers of the world. These data centers were run much better in terms of how you manage the coolings, the energy efficiency, the rack density, all of this stuff. Actually, the cloud revolution didn’t increase the use of electricity. The cloud revolution was actually a replacement from your private data center to the hyperscaler data center, which was energy efficient. That’s why we didn’t, even if we are always talking about that growth of cloud computing, we were never feeling the pinch in term of electricity. As you say, we say it all changed because with AI, it was not a simple “Replacement” of locally run infrastructure to a hyperscaler run infrastructure. It was truly adding on top of an existing infrastructure, a new computing infrastructure in a way out of nowhere. Not just any computing infrastructure, an energy infrastructure that was really, really voracious in term of energy use. Nuno Gonçalves Pedro There was one other effect. Obviously, we’ve discussed before, we are in a bubble. We won’t go too much into that today. But the previous big bubble in tech, which is in the late ’90s, there was a lot of infrastructure built. We thought the internet was going to take over back then. It didn’t take over immediately, but there was a lot of network connectivity, bandwidth built back in the day. Companies imploded because of that as well, or had to restructure and go in their chapter 11. A lot of the big telco companies had their own issues back then, etc., but a lot of infrastructure was built back then for this advent of the internet, which would then take a long time to come. In some ways, to your point, there was a lot of latent supply that was built that was around that for a while wasn’t used, but then it was. Now it’s been used, and now we need new stuff. That’s why I feel now we’re having the new moment of infrastructure, new moment of moving forward, aligned a little bit with what you just said around cloud computing and the advent of SaaS, but also around the fact that we had a lot of buildup back in the late ’90s, early ’90s, which we’re now still reaping the benefits on in today’s world. Bertrand Schmitt Yeah, that’s actually a great point because what was built in the late ’90s, there was a lot of fibre that was built. Laying out the fibre either across countries, inside countries. This fibre, interestingly enough, you could just change the computing on both sides of the fibre, the routing, the modems, and upgrade the capacity of the fibre. But the fibre was the same in between. The big investment, CapEx investment, was really lying down that fibre, but then you could really upgrade easily. Even if both ends of the fibre were either using very old infrastructure from the ’90s or were actually dark and not being put to use, step by step, it was being put to use, equipment was replaced, and step by step, you could keep using more and more of this fibre. It was a very interesting development, as you say, because it could be expanded over the years, where if we talk about GPUs, use for AI, GPUs, the interesting part is actually it’s totally the opposite. After a few years, it’s useless. Some like Google, will argue that they can depreciate over 5, 6 years, even some GPUs. But at the end of the day, the difference in perf and energy efficiency of the GPUs means that if you are energy constrained, you just want to replace the old one even as young as three-year-old. You have to look at Nvidia increasing spec, generation after generation. It’s pretty insane. It’s usually at least 3X year over year in term of performance. Nuno Gonçalves Pedro At this moment in time, it’s very clear that it’s happening. Why now: the 5 forces behind the renaissance Maybe let’s deep dive into why it’s happening now. What are the key forces around this? We’ve identified, I think, five forces that are particularly vital that lead to the world we’re in right now. One we’ve already talked about, which is AI, the demand shock and everything that’s happened because of AI. Data centers drive power demand, drive grid upgrades, drive innovative ways of getting energy, drive chips, drive networking, drive cooling, drive manufacturing, drive all the things that we’re going to talk in just a bit. One second element that we could probably highlight in terms of the forces that are behind this is obviously where we are in terms of cost curves around technology. Obviously, a lot of things are becoming much cheaper. The simulation of physical behaviours has become a lot more cheap, which in itself, this becomes almost a vicious cycle in of itself, then drives the adoption of more and more AI and stuff. But anyway, the simulation is becoming more and more accessible, so you can do a lot of simulation with digital twins and other things off the real world before you go into the real world. Robotics itself is becoming, obviously, cheaper. Hardware, a lot of the hardware is becoming cheaper. Computer has become cheaper as well. Obviously, there’s a lot of cost curves that have aligned that, and that’s maybe the second force that I would highlight. Obviously, funds are catching up. We’ll leave that a little bit to the end. We’ll do a wrap-up and talk a little bit about the implications to investors. But there’s a lot of capital out there, some capital related to industrial policy, other capital related to private initiative, private equity, growth funds, even venture capital, to be honest, and a few other elements on that. That would be a third force that I would highlight. Bertrand Schmitt Yes. Interestingly enough, in terms of capital use, and we’ll talk more about this, but some firms, if we are talking about energy investment, it was very difficult to invest if you are not investing in green energy. Now I think more and more firms and banks are willing to invest or support different type of energy infrastructure, not just, “Green energy.” That’s an interesting development because at some point it became near impossible to invest more in gas development, in oil development in the US or in most Western countries. At least in the US, this is dramatically changing the framework. Nuno Gonçalves Pedro Maybe to add the two last forces that I think we see behind the renaissance of what’s happening in infrastructure. They go hand in hand. One is the geopolitics of the world right now. Obviously, the world was global flat, and now it’s becoming increasingly siloed, so people are playing it to their own interests. There’s a lot of replication of infrastructure as well because people want to be autonomous, and they want to drive their own ability to serve end consumers, businesses, etc., in terms of data centers and everything else. That ability has led to things like, for example, chips shortage. The fact that there are semiconductors, there are shortages across the board, like memory shortages, where everything is packed up until 2027 of 2028. A lot of the memory that was being produced is already spoken for, which is shocking. There’s obviously generation of supply chain fragilities, obviously, some of it because of policies, for example, in the US with tariffs, etc, security of energy, etc. Then the last force directly linked to the geopolitics is the opposite of it, which is the policy as an accelerant, so to speak, as something that is accelerating development, where because of those silos, individual countries, as part their industrial policy, then want to put capital behind their local ecosystems, their local companies, so that their local companies and their local systems are for sure the winners, or at least, at the very least, serve their own local markets. I think that’s true of a lot of the things we’re seeing, for example, in the US with the Chips Act, for semiconductors, with IGA, IRA, and other elements of what we’ve seen in terms of practices, policies that have been implemented even in Europe, China, and other parts of the world. Bertrand Schmitt Talking about chips shortages, it’s pretty insane what has been happening with memory. Just the past few weeks, I have seen a close to 3X increase in price in memory prices in a matter of weeks. Apparently, it started with a huge order from OpenAI. Apparently, they have tried to corner the memory market. Interestingly enough, it has flat-footed the entire industry, and that includes Google, that includes Microsoft. There are rumours of their teams now having moved to South Korea, so they are closer to the action in terms of memory factories and memory decision-making. There are rumours of execs who got fired because they didn’t prepare for this type of eventuality or didn’t lock in some of the supply chain because that memory was initially for AI, but obviously, it impacts everything because factories making memories, you have to plan years in advance to build memories. You cannot open new lines of manufacturing like this. All factories that are going to open, we know when they are going to open because they’ve been built up for years. There is no extra capacity suddenly. At the very best, you can change a bit your line of production from one type of memory to another type. But that’s probably about it. Nuno Gonçalves Pedro Just to be clear, all these transformations we’re seeing isn’t to say just hardware is back, right? It’s not just hardware. There’s physicality. The buildings are coming back, right? It’s full stack. Software is here. That’s why everything is happening. Policy is here. Finance is here. It’s a little bit like the name of the movie, right? Everything everywhere all at once. Everything’s happening. It was in some ways driven by the upper stacks, by the app layers, by the platform layers. But now we need new infrastructure. We need more infrastructure. We need it very, very quickly. We need it today. We’re already lacking in it. Semiconductors: compute is the new oil Maybe that’s a good segue into the first piece of the whole infrastructure thing that’s driving now the most valuable company in the world, NVIDIA, which is semiconductors. Semiconductors are driving compute. Semis are the foundation of infrastructure as a compute. Everyone needs it for every thing, for every activity, not just for compute, but even for sensors, for actuators, everything else. That’s the beginning of it all. Semiconductor is one of the key pieces around the infrastructure stack that’s being built at scale at this moment in time. Bertrand Schmitt Yes. What’s interesting is that if we look at the market gap of Semis versus software as a service, cloud companies, there has been a widening gap the past year. I forgot the exact numbers, but we were talking about plus 20, 25% for Semis in term of market gap and minus 5, minus 10 for SaaS companies. That’s another trend that’s happening. Why is this happening? One, because semiconductors are core to the AI build-up, you cannot go around without them. But two, it’s also raising a lot of questions about the durability of the SaaS, a software-as-a-service business model. Because if suddenly we have better AI, and that’s all everyone is talking about to justify the investment in AI, that it keeps getting better, and it keeps improving, and it’s going to replace your engineers, your software engineers. Then maybe all of this moat that software companies built up over the years or decades, sometimes, might unravel under the pressure of newly coded, newly built, cheaper alternatives built from the ground up with AI support. It’s not just that, yes, semiconductors are doing great. It’s also as a result of that AI underlying trend that software is doing worse right now. Nuno Gonçalves Pedro At the end of the day, this foundational piece of infrastructure, semiconductor, is obviously getting manifest to many things, fabrication, manufacturing, packaging, materials, equipment. Everything’s being driven, ASML, etc. There are all these different players around the world that are having skyrocket valuations now, it’s because they’re all part of the value chain. Just to be very, very clear, there’s two elements of this that I think are very important for us to remember at this point in time. One, it’s the entire value chains are being shifted. It’s not just the chips that basically lead to computing in the strict sense of it. It’s like chips, for example, that drive, for example, network switching. We’re going to talk about networking a bit, but you need chips to drive better network switching. That’s getting revolutionised as well. For example, we have an investment in that space, a company called the eridu.ai, and they’re revolutionising one of the pieces around that stack. Second part of the puzzle, so obviously, besides the holistic view of the world that’s changing in terms of value change, the second piece of the puzzle is, as we discussed before, there’s industrial policy. We already mentioned the CHIPS Act, which is something, for example, that has been done in the US, which I think is 52 billion in incentives across a variety of things, grants, loans, and other mechanisms to incentivise players to scale capacity quick and to scale capacity locally in the US. One of the effects of that now is obviously we had the TSMC, US expansion with a factory here in the US. We have other levels of expansion going on with Intel, Samsung, and others that are happening as we speak. Again, it’s this two by two. It’s market forces that drive the need for fundamental shifts in the value chain. On the other industrial policy and actual money put forward by states, by governments, by entities that want to revolutionise their own local markets. Bertrand Schmitt Yes. When you talk about networking, it makes me think about what NVIDIA did more than six years ago when they acquired Mellanox. At the time, it was largest acquisition for NVIDIA in 2019, and it was networking for the data center. Not networking across data center, but inside the data center, and basically making sure that your GPUs, the different computers, can talk as fast as possible between each of them. I think that’s one piece of the puzzle that a lot of companies are missing, by the way, about NVIDIA is that they are truly providing full systems. They are not just providing a GPU. Some of their competitors are just providing GPUs. But NVIDIA can provide you the full rack. Now, they move to liquid-cool computing as well. They design their systems with liquid cooling in mind. They have a very different approach in the industry. It’s a systematic system-level approach to how do you optimize your data center. Quite frankly, that’s a bit hard to beat. Nuno Gonçalves Pedro For those listening, you’d be like, this is all very different. Semiconductors, networking, energy, manufacturing, this is all different. Then all of a sudden, as Bertrand is saying, well, there are some players that are acting across the stack. Then you see in the same sentence, you’re talking about nuclear power in Microsoft or nuclear power in Google, and you’re like, what happened? Why are these guys in the same sentence? It’s like they’re tech companies. Why are they talking about energy? It’s the nature of that. These ecosystems need to go hand in hand. The value chains are very deep. For you to actually reap the benefits of more and more, for example, semiconductor availability, you have to have better and better networking connectivity, and you have to have more and more energy at lower and lower costs, and all of that. All these things are intrinsically linked. That’s why you see all these big tech companies working across stack, NVIDIA being a great example of that in trying to create truly a systems approach to the world, as Bertrand was mentioning. Networking & connectivity: digital highways get rebuilt On the networking and connectivity side, as we said, we had a lot of fibre that was put down, etc, but there’s still more build-out needs to be done. 5G in terms of its densification is still happening. We’re now starting to talk, obviously, about 6G. I’m not sure most telcos are very happy about that because they just have been doing all this CapEx and all this deployment into 5G, and now people already started talking about 6G and what’s next. Obviously, data center interconnect is quite important, and all the hubbing that needs to happen around data centers is very, very important. We are seeing a lot movements around connectivity that are particularly important. Network gear and the emergence of players like Broadcom in terms of the semiconductor side of the fence, obviously, Cisco, Juniper, Arista, and others that are very much present in this space. As I said, we made an investment on the semiconductor side of networking as well, realizing that there’s still a lot of bottlenecks happening there. But obviously, the networking and connectivity stack still needs to be built at all levels within the data centers, outside of the data centers in terms of last mile, across the board in terms of fibre. We’re seeing a lot of movements still around the space. It’s what connects everything. At the end of the day, if there’s too much latency in these systems, if the bandwidths are not high enough, then we’re going to have huge bottlenecks that are going to be put at the table by a networking providers. Obviously, that doesn’t help anyone. If there’s a button like anywhere, it doesn’t work. All of this doesn’t work. Bertrand Schmitt Yes. Interestingly enough, I know we said for this episode, we not talk too much about space, but when you talk about 6G, it make me think about, of course, Starlink. That’s really your last mile delivery that’s being built as well. It’s a massive investment. We’re talking about thousands of satellites that are interconnected between each other through laser system. This is changing dramatically how companies can operate, how individuals can operate. For companies, you can have great connectivity from anywhere in the world. For military, it’s the same. For individuals, suddenly, you won’t have dead space, wide zones. This is also a part of changing how we could do things. It’s quite important even in the development of AI because, yes, you can have AI at the edge, but that interconnect to the rest of the system is quite critical. Having that availability of a network link, high-quality network link from anywhere is a great combo. Nuno Gonçalves Pedro Then you start seeing regions of the world that want to differentiate to attract digital nomads by saying, “We have submarine cables that come and hub through us, and therefore, our connectivity is amazing.” I was just in Madeira, and they were talking about that in Portugal. One of the islands of Portugal. We have some Marine cables. You have great connectivity. We’re getting into that discussion where people are like, I don’t care. I mean, I don’t know. I assume I have decent connectivity. People actually care about decent connectivity. This discussion is not just happening at corporate level, at enterprise level? Etc. Even consumers, even people that want to work remotely or be based somewhere else in the world. It’s like, This is important Where is there a great connectivity for me so that I can have access to the services I need? Etc. Everyone becomes aware of everything. We had a cloud flare mishap more recently that the CEO had to jump online and explain deeply, technically and deeply, what happened. Because we’re in their heads. If Cloudflare goes down, there’s a lot of websites that don’t work. All of this, I think, is now becoming du jour rather than just an afterthought. Maybe we’ll think about that in the future. Bertrand Schmitt Totally. I think your life is being changed for network connectivity, so life of individuals, companies. I mean, everything. Look at airlines and ships and cruise ships. Now is the advent of satellite connectivity. It’s dramatically changing our experience. Nuno Gonçalves Pedro Indeed. Energy: rebuilding the power stack (not just renewables) Moving maybe to energy. We’ve talked about energy quite a bit in the past. Maybe we start with the one that we didn’t talk as much, although we did mention it, which was, let’s call it the fossil infrastructure, what’s happening around there. Everyone was saying, it’s all going to be renewables and green. We’ve had a shift of power, geopolitics. Honestly, I the writing was on the wall that we needed a lot more energy creation. It wasn’t either or. We needed other sources to be as efficient as possible. Obviously, we see a lot of work happening around there that many would have thought, Well, all this infrastructure doesn’t matter anymore. Now we’re seeing LNG terminals, pipelines, petrochemical capacity being pushed up, a lot of stuff happening around markets in terms of export, and not only around export, but also around overall distribution and increases and improvements so that there’s less leakage, distribution of energy, etc. In some ways, people say, it’s controversial, but it’s like we don’t have enough energy to spare. We’re already behind, so we need as much as we can. We need to figure out the way to really extract as much as we can from even natural resources, which In many people’s mind, it’s almost like blasphemous to talk about, but it is where we are. Obviously, there’s a lot of renaissance also happening on the fossil infrastructure basis, so to speak. Bertrand Schmitt Personally, I’m ecstatic that there is a renaissance going regarding what is called fossil infrastructure. Oil and gas, it’s critical to humanity well-being. You never had growth of countries without energy growth and nothing else can come close. Nuclear could come close, but it takes decades to deploy. I think it’s great. It’s great for developed economies so that they do better, they can expand faster. It’s great for third-world countries who have no realistic other choice. I really don’t know what happened the past 10, 15 years and why this was suddenly blasphemous. But I’m glad that, strangely, thanks to AI, we are back to a more rational mindset about energy and making sure we get efficient energy where we can. Obviously, nuclear is getting a second act. Nuno Gonçalves Pedro I know you would be. We’ve been talking about for a long time, and you’ve been talking about it in particular for a very long time. Bertrand Schmitt Yes, definitely. It’s been one area of interest of mine for 25 years. I don’t know. I’ve been shocked about what happened in Europe, that willingness destruction of energy infrastructure, especially in Germany. Just a few months ago, they keep destroying on live TV some nuclear station in perfect working condition and replacing them with coal. I’m not sure there is a better definition of insanity at this stage. It looks like it’s only the Germans going that hardcore for some reason, but at least the French have stopped their program of decommissioning. America, it seems to be doing the same, so it’s great. On top of it, there are new generations that could be put to use. The Chinese are building up a very large nuclear reactor program, more than 100 reactors in construction for the next 10 years. I think everybody has to catch up because at some point, this is the most efficient energy solution. Especially if you don’t build crazy constraints around the construction of these nuclear reactors. If we are rational about permits, about energy, about safety, there are great things we could be doing with nuclear. That might be one of the only solution if we want to be competitive, because when energy prices go down like crazy, like in China, they will do once they have reach delivery of their significant build-up of nuclear reactors, we better be ready to have similar options from a cost perspective. Nuno Gonçalves Pedro From the outside, at the very least, nuclear seems to be probably in the energy one of the areas that’s more being innovated at this moment in time. You have startups in the space, you have a lot really money going into it, not just your classic industrial development. That’s very exciting. Moving maybe to the carbonization and what’s happening. The CCUS, and for those who don’t know what it is, carbon capture, utilization, and storage. There’s a lot of stuff happening around that space. That’s the area that deals with the ability to capture CO₂ emissions from industrial sources and/or the atmosphere and preventing their release. There’s a lot of things happening in that space. There’s also a lot of things happening around hydrogen and geothermal and really creating the ability to storage or to store, rather, energy that then can be put back into the grids at the right time. There’s a lot of interesting pieces happening around this. There’s some startup movement in the space. It’s been a long time coming, the reuse of a lot of these industrial sources. Not sure it’s as much on the news as nuclear, and oil and gas, but certainly there’s a lot of exciting things happening there. Bertrand Schmitt I’m a bit more dubious here, but I think geothermal makes sense if it’s available at reasonable price. I don’t think hydrogen technology has proven its value. Concerning carbon capture, I’m not sure how much it’s really going to provide in terms of energy needs, but why not? Nuno Gonçalves Pedro Fuels niche, again, from the outside, we’re not energy experts, but certainly, there are movements in the space. We’ll see what’s happening. One area where there’s definitely a lot of movement is this notion of grid and storage. On the one hand, that transmission needs to be built out. It needs to be better. We’ve had issues of blackouts in the US. We’ve had issues of blackouts all around the world, almost. Portugal as well, for a significant part of the time. The ability to work around transmission lines, transformers, substations, the modernization of some of this infrastructure, and the move forward of it is pretty critical. But at the other end, there’s the edge. Then, on the edge, you have the ability to store. We should have, better mechanisms to store energy that are less leaky in terms of energy storage. Obviously, there’s a lot of movement around that. Some of it driven just by commercial stuff, like Tesla a lot with their storage stuff, etc. Some of it really driven at scale by energy players that have the interest that, for example, some of the storage starts happening closer to the consumption as well. But there’s a lot of exciting things happening in that space, and that is a transformative space. In some ways, the bottleneck of energy is also around transmission and then ultimately the access to energy by homes, by businesses, by industries, etc. Bertrand Schmitt I would say some of the blackout are truly man-made. If I pick on California, for instance. That’s the logical conclusion of the regulatory system in place in California. On one side, you limit price that energy supplier can sell. The utility company can sell, too. On the other side, you force them to decommission the most energy-efficient and least expensive energy source. That means you cap the revenues, you make the cost increase. What is the result? The result is you cannot invest anymore to support a grid and to support transmission. That’s 100% obvious. That’s what happened, at least in many places. The solution is stop crazy regulations that makes no economic sense whatsoever. Then, strangely enough, you can invest again in transmission, in maintenance, and all I love this stuff. Maybe another piece, if we pick in California, if you authorize building construction in areas where fires are easy, that’s also a very costly to support from utility perspective, because then you are creating more risk. You are forced buy the state to connect these new constructions to the grid. You have more maintenance. If it fails, you can create fire. If you create fire, you have to pay billions of fees. I just want to highlight that some of this is not a technological issue, is not per se an investment issue, but it’s simply the result of very bad regulations. I hope that some will learn, and some change will be made so that utilities can do their job better. Nuno Gonçalves Pedro Then last, but not the least, on the energy side, energy is becoming more and more digitally defined in some ways. It’s like the analogy to networks that they’ve become more, and more software defined, where you have, at the edge is things like smart meters. There’s a lot of things you can do around the key elements of the business model, like dynamic pricing and other elements. Demand response, one of the areas that I invested in, I invest in a company called Omconnect that’s now merged with what used to be Google Nest. Where to deploy that ability to do demand response and also pass it to consumers so that consumers can reduce their consumption at times where is the least price effective or the less green or the less good for the energy companies to produce energy. We have other things that are happening, which are interesting. Obviously, we have a lot more electric vehicles in cars, etc. These are also elements of storage. They don’t look like elements of storage, but the car has electricity in it once you charge it. Once it’s charged, what do you do with it? Could you do something else? Like the whole reverse charging piece that we also see now today in mobile devices and other edge devices, so to speak. That also changes the architecture of what we’re seeing around the space. With AI, there’s a lot of elements that change around the value chain. The ability to do forecasting, the ability to have, for example, virtual power plans because of just designated storage out there, etc. Interesting times happening. Not sure all utilities around the world, all energy providers around the world are innovating at the same pace and in the same way. But certainly just looking at the industry and talking to a lot of players that are CEOs of some of these companies. That are leading innovation for some of these companies, there’s definitely a lot more happening now in the last few years than maybe over the last few decades. Very exciting times. Bertrand Schmitt I think there are two interesting points in what you say. Talking about EVs, for instance, a Cybertruck is able to send electricity back to your home if your home is able to receive electricity from that source. Usually, you have some changes to make to the meter system, to your panel. That’s one great way to potentially use your car battery. Another piece of the puzzle is that, strangely enough, most strangely enough, there has been a big push to EV, but at the same time, there has not been a push to provide more electricity. But if you replace cars that use gasoline by electric vehicles that use electricity, you need to deliver more electricity. It doesn’t require a PhD to get that. But, strangely enough, nothing was done. Nuno Gonçalves Pedro Apparently, it does. Bertrand Schmitt I remember that study in France where they say that, if people were all to switch to EV, we will need 10 more nuclear reactors just on the way from Paris to Nice to the Côte d’Azur, the French Rivière, in order to provide electricity to the cars going there during the summer vacation. But I mean, guess what? No nuclear plant is being built along the way. Good luck charging your vehicles. I think that’s another limit that has been happening to the grid is more electric vehicles that require charging when the related infrastructure has not been upgraded to support more. Actually, it has quite the opposite. In many cases, we had situation of nuclear reactors closing down, so other facilities closing down. Obviously, the end result is an increase in price of electricity, at least in some states and countries that have not sold that fully out. Nuno Gonçalves Pedro Manufacturing: the return of “atoms + bits” Moving to manufacturing and what’s happening around manufacturing, manufacturing technology. There’s maybe the case to be made that manufacturing is getting replatformed, right? It’s getting redefined. Some of it is very obvious, and it’s already been ongoing for a couple of decades, which is the advent of and more and more either robotic augmented factories or just fully roboticized factories, where there’s very little presence of human beings. There’s elements of that. There’s the element of software definition on top of it, like simulation. A lot of automation is going on. A lot of AI has been applied to some lines in terms of vision, safety. We have an investment in a company called Sauter Analytics that is very focused on that from the perspective of employees and when they’re still humans in the loop, so to speak, and the ability to really figure out when people are at risk and other elements of what’s happening occurring from that. But there’s more than that. There’s a little bit of a renaissance in and of itself. Factories are, initially, if we go back a couple of decades ago, factories were, and manufacturing was very much defined from the setup. Now it’s difficult to innovate, it’s difficult to shift the line, it’s difficult to change how things are done in the line. With the advent of new factories that have less legacy, that have more flexible systems, not only in terms of software, but also in terms of hardware and robotics, it allows us to, for example, change and shift lines much more easily to different functions, which will hopefully, over time, not only reduce dramatically the cost of production. But also increase dramatically the yield, it increases dramatically the production itself. A lot of cool stuff happening in that space. Bertrand Schmitt It’s exciting to see that. One thing this current administration in the US has been betting on is not just hoping for construction renaissance. Especially on the factory side, up of factories, but their mindset was two things. One, should I force more companies to build locally because it would be cheaper? Two, increase output and supply of energy so that running factories here in the US would be cheaper than anywhere else. Maybe not cheaper than China, but certainly we get is cheaper than Europe. But three, it’s also the belief that thanks to AI, we will be able to have more efficient factories. There is always that question, do Americans to still keep making clothes, for instance, in factories. That used to be the case maybe 50 years ago, but this move to China, this move to Bangladesh, this move to different places. That’s not the goal. But it can make sense that indeed there is ability, thanks to robots and AI, to have more automated factories, and these factories could be run more efficiently, and as a result, it would be priced-competitive, even if run in the US. When you want to think about it, that has been, for instance, the South Korean playbook. More automated factories, robotics, all of this, because that was the only way to compete against China, which has a near infinite or used to have a near infinite supply of cheaper labour. I think that all of this combined can make a lot of sense. In a way, it’s probably creating a perfect storm. Maybe another piece of the puzzle this administration has been working on pretty hard is simplifying all the permitting process. Because a big chunk of the problem is that if your permitting is very complex, very expensive, what take two years to build become four years, five years, 10 years. The investment mass is not the same in that situation. I think that’s a very important part of the puzzle. It’s use this opportunity to reduce regulatory state, make sure that things are more efficient. Also, things are less at risk of bribery and fraud because all these regulations, there might be ways around. I think it’s quite critical to really be careful about this. Maybe last piece of the puzzle is the way accounting works. There are new rules now in 2026 in the US where you can fully depreciate your CapEx much faster than before. That’s a big win for manufacturing in the US. Suddenly, you can depreciate much faster some of your CapEx investment in manufacturing. Nuno Gonçalves Pedro Just going back to a point you made and then moving it forward, even China, with being now probably the country in the world with the highest rate of innovation and take up of industrial robots. Because of demographic issues a little bit what led Japan the first place to be one of the real big innovators around robots in general. The fact that demographics, you’re having an aging population, less and less children. How are you going to replace all these people? Moving that into big winners, who becomes a big winner in a space where manufacturing is fundamentally changing? Obviously, there’s the big four of robots, which is ABB, FANUC, KUKA, and Yaskawa. Epson, I think, is now in there, although it’s not considered one of the big four. Kawasaki, Denso, Universal Robots. There’s a really big robotics, industrial robotic companies in the space from different origins, FANUC and Yaskawa, and Epson from Japan, KUKA from Germany, ABB from Switzerland, Sweden. A lot of now emerging companies from China, and what’s happening in that space is quite interesting. On the other hand, also, other winners will include players that will be integrators that will build some of the rest of the infrastructure that goes into manufacturing, the Siemens of the world, the Schneider’s, the Rockwell’s that will lead to fundamental industrial automation. Some big winners in there that whose names are well known, so probably not a huge amount of surprises there. There’s movements. As I said, we’re still going to see the big Chinese players emerging in the world. There are startups that are innovating around a lot of the edges that are significant in this space. We’ll see if this is a space that will just be continued to be dominated by the big foreign robotics and by a couple of others and by the big integrators or not. Bertrand Schmitt I think you are right to remind about China because China has been moving very fast in robotics. Some Chinese companies are world-class in their use of robotics. You have this strange mix of some older industries where robotics might not be so much put to use and typically state-owned, versus some private companies, typically some tech companies that are reconverting into hardware in some situation. That went all in terms of robotics use and their demonstrations, an example of what’s happening in China. Definitely, the Chinese are not resting. Everyone smart enough is playing that game from the Americans, the Chinese, Japanese, the South Koreans. Nuno Gonçalves Pedro Exciting things are manufacturing, and maybe to bring it all together, what does it mean for all the big players out there? If we talk with startups and talk about startups, we didn’t mention a ton of startups today, right? Maybe incumbent wind across the board. But on a more serious note, we did mention a few. For example, in nuclear energy, there’s a lot of startups that have been, some of them, incredibly well-funded at this moment in time. Wrap: what it means for startups, incumbents, and investors There might be some big disruptions that will come out of startups, for example, in that space. On the chipset side, we talked about the big gorillas, the NVIDIAs, AMDs, Intel, etc., of the world. But we didn’t quite talk about the fact that there’s a lot of innovation, again, happening on the edges with new players going after very large niches, be it in networking and switching. Be it in compute and other areas that will need different, more specialized solutions. Potentially in terms of compute or in terms of semiconductor deployments. I think there’s still some opportunities there, maybe not to be the winner takes all thing, but certainly around a lot of very significant niches that might grow very fast. Manufacturing, we mentioned the same. Some of the incumbents seem to be in the driving seat. We’ll see what happens if some startups will come in and take some of the momentum there, probably less likely. There are spaces where the value chains are very tightly built around the OEMs and then the suppliers overall, classically the tier one suppliers across value chains. Maybe there is some startup investment play. We certainly have played in the couple of the spaces. I mentioned already some of them today, but this is maybe where the incumbents have it all to lose. It’s more for them to lose rather than for the startups to win just because of the scale of what needs to be done and what needs to be deployed. Bertrand Schmitt I know. That’s interesting point. I think some players in energy production, for instance, are moving very fast and behaving not only like startups. Usually, it’s independent energy suppliers who are not kept by too much regulations that get moved faster. Utility companies, as we just discussed, have more constraints. I would like to say that if you take semiconductor space, there has been quite a lot of startup activities way more than usual, and there have been some incredible success. Just a few weeks ago, Rock got more or less acquired. Now, you have to play games. It’s not an outright acquisition, but $20 billion for an IP licensing agreement that’s close to an acquisition. That’s an incredible success for a company. Started maybe 10 years ago. You have another Cerebras, one of the competitor valued, I believe, quite a lot in similar range. I think there is definitely some activity. It’s definitely a different game compared to your software startup in terms of investment. But as we have seen with AI in general, the need for investment might be larger these days. Yes, it might be either traditional players if they can move fast enough, to be frank, because some of them, when you have decades of being run as a slow-moving company, it’s hard to change things. At the same time, it looks like VCs are getting bigger. Wall Street is getting more ready to finance some of these companies. I think there will be opportunities for startups, but definitely different types of startups in terms of profile. Nuno Gonçalves Pedro Exactly. From an investor standpoint, I think on the VC side, at least our core belief is that it’s more niche. It’s more around big niches that need to be fundamentally disrupted or solutions that require fundamental interoperability and integration where the incumbents have no motivation to do it. Things that are a little bit more either packaging on the semiconductor side or other elements of actual interoperability. Even at the software layer side that feeds into infrastructure. If you’re a growth investor, a private equity investor, there’s other plays that are available to you. A lot of these projects need to be funded and need to be scaled. Now we’re seeing projects being funded even for a very large, we mentioned it in one of the previous episodes, for a very large tech companies. When Meta, for example, is going to the market to get funding for data centers, etc. There’s projects to be funded there because just the quantum and scale of some of these projects, either because of financial interest for specifically the tech companies or for other reasons, but they need to be funded by the market. There’s other place right now, certainly if you’re a larger private equity growth investor, and you want to come into the market and do projects. Even public-private financing is now available for a lot of things. Definitely, there’s a lot of things emanating that require a lot of funding, even for large-scale projects. Which means the advent of some of these projects and where realization is hopefully more of a given than in other circumstances, because there’s actual commercial capital behind it and private capital behind it to fuel it as well, not just industrial policy and money from governments. Bertrand Schmitt There was this quite incredible stat. I guess everyone heard about that incredible growth in GDP in Q3 in the US at 4.4%. Apparently, half of that growth, so around 2.2% point, has been coming from AI and related infrastructure investment. That’s pretty massive. Half of your GDP growth coming from something that was not there three years ago or there, but not at this intensity of investment. That’s the numbers we are talking about. I’m hearing that there is a good chance that in 2026, we’re talking about five, even potentially 6% GDP growth. Again, half of it potentially coming from AI and all the related infrastructure growth that’s coming with AI. As a conclusion for this episode on infrastructure, as we just said, it’s not just AI, it’s a whole stack, and it’s manufacturing in general as well. Definitely in the US, in China, there is a lot going on. As we have seen, computing needs connectivity, networks, need power, energy and grid, and all of this needs production capacity and manufacturing. Manufacturing can benefit from AI as well. That way the loop is fully going back on itself. Infrastructure is the next big thing. It’s an opportunity, probably more for incumbents, but certainly, as usual, with such big growth opportunities for startups as well. Thank you, Nuno. Nuno Gonçalves Pedro Thank you, Bertrand.

इस एपिसोड में, Nicolai Tangen की बातचीत अरिस्टा नेटवर्क्स की मशहूर CEO जयश्री उल्लाल से होती है। जयश्री समझाती हैं कि कैसे अरिस्टा आज के AI सिस्टम के लिए बड़े-बड़े नेटवर्क चलाता है, क्यों AI का ट्रैफिक पहले के सभी ट्रैफिक से बिल्कुल अलग है, और क्यों अब सबसे बड़ी रुकावट हार्डवेयर नहीं बल्कि बिजली की कमी है। वे अरिस्टा की कहानी बताती हैं—कैसे एक छोटी इंजीनियरिंग टीम जिसकी कमाई शून्य थी, दुनियाभर में लीडर बन गई, वो कल्चर जिसने कंपनी को सफल बनाया, और इलेक्ट्रिकल इंजीनियर से CEO बनने तक का उनका अपना सफर। यह एक खुली और गहरी बातचीत है जो लीडरशिप, नई सोच और AI की दुनिया में नेटवर्किंग के भविष्य के बारे में है।——————Jayshree Ullal - CEO of Arista Networks In this episode, Nicolai Tangen sits down with Jayshree Ullal, the influential CEO of Arista Networks. Jayshree explains how Arista powers the demanding networks behind today's AI systems, why AI traffic is fundamentally different from anything that came before, and why power—not hardware—is now the biggest constraint. She reflects on Arista's evolution from a small engineering team with zero revenue to a global leader, the culture that shaped its success, and her own path from electrical engineer to CEO. A candid and insightful conversation about leadership, innovation, and the future of networking in an AI-driven world. Hosted on Acast. See acast.com/privacy for more information.

The Church are a bit of an enigma in the rock world. Though best known for their breakout 1988 song Under The Milky Way off of Starfish. Prior to that they'd had an unexpected Australian anthem in An Unguarded Moment. But they didn't want to be a pop band - they saw themselves as members of the new psychedelic movement. So the cover of Heyday (released in Australia late 1985 but in the US January 28, 1986) you can see the lads sporting some paisley shirts against a Persian Rug. But at that point, they'd already abandoned their psychedelic leanings for their own AOR stylings. The album Heyday, produced by Peter Walsh (Simple Minds) provides some entrancing guitar work with lyrics from Steve Kilbey that go from seeing behind the curtain of fame (Disenchanted) to unending sadness (Tristesse) to vain plastic surgery junkies (Youth Worshipper). Peter Koppes and Marty Wilson-Piper offer intricate and jangling guitars which make for 120 Minutes gold on MTV and can even put you into a bit of a trance. Myrrh and Tantalized proved to be all time favorites of Church fans and are still part of the band's setlist to this day. However, though songs like Columbus and Already Yesterday may have been enjoyed by fans of the band, they failed to crack the charts the way the record company had hoped. Still, the band were able to tour the US with Echo & The Bunnymen which helped them break down some doors and win some fans. Though they were dropped by their record companies after Heyday, this led to them being picked up by Arista, which led to Starfish and success in the US and around the world. It may not be multi-platinum but Heyday would help define the sound of The Church, allowed them to write songs together and create a foundation that built towards greater success. Check out our new website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ugly American Werewolf in London Website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Threads⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LInkTree⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.pantheonpodcasts.com⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

We're diving deep into the world of iconic album cover art with our special guest, Frm. Elektra Records Art Director Bob Heimall.  A name behind some of the most memorable visuals in music history. From his humble beginnings at Elektra Records in the late 1960s to becoming the youngest art director in the business, Bob Heimall's creativity has graced records by legends like Carly Simon, Jim Croce, The Doors, Bread, Iggy Pop, and even Yoko Ono.You'll hear Bob Heimall share personal stories, like joining Jim Morrison, Jimi Hendrix, and Janis Joplin for an unforgettable moment in a New York penthouse, rubbing elbows with rock royalty, working with Carly Simon while she breastfed her son, and being the sole audience for Jim Croce's final album performance just two weeks before tragedy struck. He'll reveal behind-the-scenes anecdotes about album art decisions—some even leading to legendary band debates—describe the step-by-step design process before Photoshop, and recount the emotional impact these collaborations left on him.Plus, Bob Heimall discusses the cutthroat world of record labels, his transition from Elektra to Arista under Clive Davis, and reflects on the vital role music—and its packaging—plays in shaping our memories. Whether you're a vinyl enthusiast, design lover, or music history buff, this episode is packed with untold stories, industry insights, and the passion that goes into creating the artwork we all grew up with.(0:00) "Starting at Elektra Records"(4:14) "Music Legends at the Hilton"(9:14) "Redefining Album Cover Art"(11:45) "Early Album Cover Design Process"(15:41) Carly's Jingles and Brother(18:19) "Unplanned Success, Captured Moment"(22:04) "Music, Photos, and Choices"24:39 "Following the Music"(28:45) "Rejected Naked Silhouette Cover"(30:17) "Innovative Multi-Fold Album Design"(33:30) "Reflecting on Jim Croce's Death"(38:13) "Asthma, Draft Exception, Jersey Shore"(41:40) "QuadSound and Career Transition"(43:59) "High-Stakes Creative Meetings"(46:15) "Jack's Artistic Integrity Struggle"(48:45) "Pool Nights in the Office"(53:56) "The Band's Big Pink Album Cover Story"(56:19) "The Doors Strange Days Album Cover Controversy"(59:19) "Cover Stories Book"You can download or stream every episode of AIRCHECK from Apple Podcasts, and Spotify. You can also listen on YouTube. Ask your Smart Speaker to “Play Aircheck Podcast”.If you're a radio vet with a story to tell we want to hear from you.Email us at Aircheckme@gmail.comFollow us on Facebook: facebook.com/aircheckmeTell us what you think and your favorite episode!

Take a Network Break! The virtual donut factory is back from hiatus, and we’ve got a fresh batch to pass around as we discuss the week’s tech news. We start with an emergency patch for Cisco ISE, then dig into a set of new product announcements from Arista including a new ability to deploy wireless... Read more »

Take a Network Break! The virtual donut factory is back from hiatus, and we’ve got a fresh batch to pass around as we discuss the week’s tech news. We start with an emergency patch for Cisco ISE, then dig into a set of new product announcements from Arista including a new ability to deploy wireless... Read more »

Take a Network Break! The virtual donut factory is back from hiatus, and we’ve got a fresh batch to pass around as we discuss the week’s tech news. We start with an emergency patch for Cisco ISE, then dig into a set of new product announcements from Arista including a new ability to deploy wireless... Read more »