Podcast appearances and mentions of benedict evans

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

Adam Mosseri is the Head of Instagram, where he oversees an app used by over 3 billion people. He also leads the team building Threads. Adam has run Instagram for longer than its founders did, after taking over from Kevin Systrom and Mike Krieger in 2018. A designer by training, he spent over 15 years at Meta, starting as a designer on Facebook's mobile app, rising to lead Facebook's News Feed, and eventually chosen to lead Instagram. During his tenure, Instagram's user base has more than tripled.In our in-depth conversation, we discuss:1. How the canonical product team structure is changing in 2026, from baker's-dozen specialist teams to lean pods of four to six generalists2. The rise of the “product staff” role—a blending of PM, design, data science, and research into one generalist operator3. Why Adam is bullish on designers even as functional boundaries dissolve, and which roles are most at risk4. What the Instagram algorithm knows about you, and why it's only now catching up to what people assumed it knew years ago5. Why the rise of AI-generated content is a tailwind for Instagram, and how the company is thinking about creator identity in a synthetic-content world6. The two biggest product failures of Adam's career—Facebook Home and the first version of Reels—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more: https://workos.com/lennyMercury—Radically different banking, now with Command: https://mercury.com/command?utm_source=lennys&utm_medium=sponsored_newsletter&utm_campaign=26q3_brand_campaign—Episode transcript: https://www.lennysnewsletter.com/p/adam-mosseri-ai-is-a-tailwind-for—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Adam Mosseri:• X: https://x.com/mosseri• LinkedIn: linkedin.com/in/mosseri• Instagram: https://www.instagram.com/mosseri• Threads: https://www.threads.com/@mosseri—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Adam Mosseri(02:09) How product teams are changing inside Meta(05:48) Blurring roles and career anxiety(14:01) Hiring traits that matter now(16:48) How AI is resetting who succeeds at work(19:38) How Meta thinks about token spend and AI costs(23:23) Where human judgment still matters(25:56) Why AI is not automatically great at strategy(30:36) Why great product leaders are curators(34:23) What Instagram's algorithm actually knows about you(38:08) Why chronological feeds often disappoint users(40:56) Why AI content may be a tailwind for Instagram(43:42) The future of AI and human content in the feed(48:00) What Adam admires about other social platforms(52:05) How he handles public criticism(56:31) Lessons from the Instagram feed redesign backlash(01:00:21) Adam's biggest failure: Instagram on iPad(01:03:03) His approach to kids, screens, and social media(01:06:56) What Adam wants listeners to remember—Referenced:• What happens after coding is solved? | Fiona Fung (Manager of the Claude Code and Cowork Teams): https://www.lennysnewsletter.com/p/building-the-most-ai-pilled-engineering• Claude Code: https://www.anthropic.com/product/claude-code• Claude Cowork: https://www.anthropic.com/product/claude-cowork• Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens• A rational conversation on where AI is actually going | Benedict Evans: https://www.lennysnewsletter.com/p/a-rational-conversation-on-where• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Mythos: https://www.anthropic.com/claude/mythos• Fable: https://www.anthropic.com/claude/fable• Pluralistic: The Reverse-Centaur's Guide to Criticizing AI: https://pluralistic.net/2025/12/05/pop-that-bubble• Plastic Dream Sequence on Instagram: https://www.instagram.com/plasticdreamsequence• TikTok: https://www.tiktok.com• Facebook–Cambridge Analytica data scandal: https://en.wikipedia.org/wiki/Facebook%E2%80%93Cambridge_Analytica_data_scandal• Facebook Home: https://en.wikipedia.org/wiki/Facebook_Home—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Fresh out of the studio, Benedict Evans, independent technology analyst and author of AI Eats the World, returns to explore whether the AI model layer is becoming commodity infrastructure. Benedict argues there is no winner-takes-all effect in models yet, drawing parallels to telecoms, cloud, chips and the fiber bubble to ask where durable value actually accrues when everyone runs similar infrastructure on similar tokens. He unpacks why the chatbot remains a poor interface, introduces the "blank screen" and "jagged frontier" problems that keep software companies alive, and explains why large language models inherently give you "the average." Closing the conversation, Benedict reflects on the indicators that would show AI has truly eaten the world — and why the answer is better products, not better models."When you automate away work, you can always see the jobs that are going away because they're right there. And you don't know what the new jobs are going to be. Human needs are infinite. How many people are earning a living from making podcasts now? Imagine predicting that 10 years ago. There's a stage in the evolution of the market where like if you're still arguing about that, you're an idiot. But there's a stage at the beginning where you might have opinions about some of these questions, you're probably not even asking the right questions. That, I think, is where we are with this stuff today." — Benedict EvansEpisode Highlights: [00:00] Quote of the Day by Benedict Evans from AI Eats the World[01:16] The public market test: what are investors buying?[04:21] How far up the stack can models go?[05:30] Models can't build all the apps themselves[06:00] The thesis: models as commodity infrastructure[07:52] "All the value went up the stack"[08:24] Chips and Rock's Law: down to three players[11:23] The 1999 reseller story: one-time sales[13:28] The S-curve framing of technology[16:38] You're probably not asking the right questions on AI[18:02] "If this works, we're competing with a Mac"[20:25] Incumbents make it a feature[22:14] Big tech "killing startups" is overstated[24:39] Cowork as the new spreadsheet[26:01] The blank-screen and jagged-frontier problems[29:00] The hard part isn't writing the code[31:25] "What a good answer would probably look like"[33:38] The job displacement debate[37:38] Jevons paradox and the lump-of-labour fallacy[40:30] LLMs inherently give you the average[42:36] Why you really hire McKinsey[45:33] Punk versus prog rock: outside the training data[49:00] Automating ever-higher human functions[49:55] Why this is unanswerable: no theory of scaling[51:30] Indicators that AI has eaten the world[54:53] The solution isn't a better model[56:39] Where to find Benedict EvansProfile: Benedict Evans, Independent Technology AnalystLinkedIn: https://www.linkedin.com/in/benedictevans/Website: https://www.ben-evans.com/newsletterPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.Here are the links to watch or listen to our podcast.Analyse Podcast Main Site: https://analysepodcast.comAnalyse Podcast Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl Analyse Podcast Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245 Analyse Podcast LinkedIn: https://www.linkedin.com/company/analyse-podcast/Sign Up for Our This Week in Asia Newsletter: https://www.analysepodcast.com/#/portal/signup Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288

Sebastian Mallaby (@scmallaby) is the Paul A. Volcker senior fellow for international economics at the Council on Foreign Relations, a two-time Pulitzer Prize finalist, and the author of six books, including More Money Than God, The Power Law, The Man Who Knew, and The World's Banker. His latest book is The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence.This episode is brought to you by:Eight Sleep Pod Cover 5 sleeping solution for dynamic cooling and heating: EightSleep.com/TimAG1 Pro all-in-one nutritional supplement: DrinkAG1.com/TimWealthfront high-yield cash account: Wealthfront.com/Tim Wealthfront disclaimer: New clients get 3.30% base APY from program banks + additional 0.75% boost for 3 months on your uninvested cash (max $150k balance). Terms and conditions apply. The Cash Account offered by Wealthfront Brokerage LLC (“WFB”) member FINRA/SIPC, not a bank. The base APY as of 1/30/26 is representative, can change, and requires no minimum. Tim Ferriss, a non-client, receives compensation from WFB for advertising and holds a non-controlling equity interest in the corporate parent of WFB, which creates a conflict of interest. Individual experiences and outcomes will differ. Instant withdrawals may be limited by your receiving firm and other factors. Investment advisory services provided by Wealthfront Advisers LLC, an SEC-registered investment adviser. Securities investments: not bank deposits, not bank-guaranteed or FDIC-insured, and may lose value.*Timestamps[00:00:00] Start.[00:02:11] The twinkly eyed polymath who became Sebastian's next book.[00:06:55] Picking the next book project the way a great VC picks a startup.[00:09:41] Why God keeps crashing the superintelligence party.[00:11:13] Shane Legg's grainy 2009 prophecy — and the nervous giggle.[00:13:11] Ilya Sutskever burns an effigy.[00:13:54] Demis at 4 a.m., hunting God's algorithm.[00:18:43] Super-abundance, Mad Max, and the China shock lesson.[00:22:39] The kitchen debate with Geoff Hinton that flipped Sebastian.[00:24:06] Why a zero-percent chance of doom is indefensible.[00:24:52] Will Washington seize the labs? The Mythos wake-up call.[00:27:18] Anthropic's bull case, bear case, and a dead parent's letter.[00:33:24] Where Sebastian and Benedict Evans part ways.[00:38:16] Is the SaaS apocalypse overdone? One word: Palantir.[00:39:53] The AI friend you'll never switch.[00:41:56] Does Google win consumer AI by default?[00:44:45] Four cities, eight days: China actually talks safety.[00:47:28] A Cold War non-proliferation playbook for AI.[00:49:45] Did the chip export controls actually work?[00:51:49] Burned doves: why Washington swears China won't talk.[00:54:56] "By 2028, the race is over" — one lab boss' bet.[00:59:11] Inside Hikvision: toddlers, sensors, and US sanctions.[01:01:07] Bill Gurley's Uber bet: venture capital perfected.[01:05:18] Luke Nosek bear-hugs DeepMind into existence.[01:10:52] Thiel's heresy: never invest by committee.[01:11:59] How Founders Fund nearly fumbled the deal of the century.[01:14:30] Selling to Google for $650M: a secret British heist?[01:16:41] The Traitorous Eight, gardening leave, and the UK's to-do list.[01:20:55] Ender's Game: "That's really how I see myself."[01:23:42] Too dumb for Gödel, Escher, Bach? Maybe an LLM can help.[01:25:19] If not Demis or Sam, then Dario.[01:26:04] My royalties cliff — and what dropped in late 2022.[01:27:47] Lila Sciences and the labs that run themselves.[01:31:13] Sebastian's billboard: "Prepare your mind."[01:35:14] The one thing Sebastian will never outsource to AI.[01:40:09] Parting thoughts.For show notes and past guests on The Tim Ferriss Show, please visit tim.blog/podcast.For deals from sponsors of The Tim Ferriss Show, please visit tim.blog/podcast-sponsorsSign up for Tim's email newsletter (5-Bullet Friday) at tim.blog/friday.For transcripts of episodes, go to tim.blog/transcripts.Discover Tim's books: tim.blog/books.Follow Tim:Twitter: twitter.com/tferriss Instagram: instagram.com/timferrissYouTube: youtube.com/timferrissFacebook: facebook.com/timferriss LinkedIn: linkedin.com/in/timferrissSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Todo mês surge uma nova tabela prometendo mapear quais empregos a IA vai destruir. Benedict Evans publicou um texto desmontando esse exercício: prever exposição de empregos à automação é impossível porque trabalho nunca é um conjunto estável de tarefas mensuráveis, e a história prova isso. A contabilidade foi automatizada por um século inteiro e o número de contadores continuou subindo. Mas o ponto mais profundo é outro: a gente nem consegue descrever adequadamente o que um trabalho é. Os artistas Holly Herndon e Trevor Paglen, em entrevista à revista Tote, estão descrevendo do lado da criação o mesmo fenômeno: o local da arte migrou do objeto final para os protocolos, datasets e sistemas de agentes. E um artigo da Jessica Brandt no New York Times sobre os memes iranianos gerados por IA durante o conflito recente fecha o argumento: a IA não acelerou a propaganda antiga, mudou a lógica do sistema inteiro. As tabelas de empregos em risco não estão erradas porque são pessimistas demais. Estão erradas porque fazem a pergunta errada. Apresentado por Bruno Natal.Assine a newsletter O Futuro Explicado: https://resumido.substack.com/subscribeFaça sua assinatura: https://resumido.cc/assinaturaLoja RESUMIDO: https://www.studiogeek.com.br/resumidoOuça mais: https://resumido.cc

Erik Torenberg speaks with tech analyst Benedict Evans about the current state of AI, what has changed over the past year, and which questions remain unanswered. The conversation covers coding agents, foundation models, AI infrastructure spending, software economics, and the tension between today's AI excitement and the long-term realities of technology adoption. Evans discusses why coding has emerged as AI's first breakout use case, how previous platform shifts can help frame the current moment, and why many of the most important questions about AI remain unresolved. Along the way, they explore the future of software, enterprise adoption, consumer behavior, and whether AI models ultimately capture value themselves or become infrastructure for the next generation of applications.   Resources: Follow Benedict Evans on X: https://x.com/benedictevans Follow Erik Torenberg on X: https://x.com/eriktorenberg Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Skippy and Doogles dig into Benedict Evans' latest AI deck that covers eye-popping AI valuations, data center capex, and whether LLMs become commodities. Then, Micron goes nowhere but up, Americans are falling behind on credit card bills, and a new Journal of Finance paper asks whether investors are actually risk-averse, or just trapped by tiny frictions and bad defaults.Join the premium Skippy and Doogles fan club. You can also get more details about the show at skippydoogles.com, show notes on our Substack, and send comments or questions to skippydoogles@gmail.com.

Benedict Evans is an independent analyst and former partner at Andreessen Horowitz, where he spent years as their in-house “thinker” tracking the most important technology trends. For the past six years, he's been publishing deeply researched presentations on where tech is heading, most recently focused on AI's transformation of the economy. His work is read by founders, investors, and operators trying to make sense of a noisy field. His most controversial opinion: AI is as big a deal as the internet or mobile—and only as big.In our in-depth conversation, we discuss:1. Why we're in “1997” for AI—early, exciting, and deeply uncertain about what comes next2. Where value will actually accrue in the AI stack3. The anti-AI backlash, and where it may lead4. The surprising boom in consulting and professional services at AI companies5. Why distribution is becoming the ultimate moat as software gets easier to build6. Why the right question about your job isn't “What percent can AI do?” but “Is this a task or a job?”7. Why things will probably be okay—and what you need to do to prepare—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more: https://workos.com/lennyVanta—Automate compliance, manage risk, and accelerate trust with AI: https://vanta.com/lenny—Episode transcript: https://www.lennysnewsletter.com/p/a-rational-conversation-on-where—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Benedict Evans:• LinkedIn: https://www.linkedin.com/in/benedictevans• Newsletter: https://www.ben-evans.com/newsletter• Website: https://www.ben-evans.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Benedict Evans(02:19) What people aren't pricing in about AI's impact(06:24) Why we're in the 1997 moment of AI(09:44) The unexpected boom in professional services and consultants(17:44) Why distribution is becoming the ultimate moat(23:17) The coming job transformation: what's real vs. panic(27:33) Why AGI definitions keep shifting(38:11) Where value will accrue: models vs. applications(42:55) Distribution wars: Google, Meta, Apple, and OpenAI(48:12) The anti-AI sentiment and backlash(53:11) How to raise kids in an AI future(58:27) What jobs to steer toward or away from(59:20) The question nobody's asking about AI(1:06:25) How to be successful in this coming future(1:08:43) AI corner(1:11:43) Lightning round—Referenced: https://www.lennysnewsletter.com/p/a-rational-conversation-on-where—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Is OpenAI trapped without a defensible moat? World-renowned independent tech analyst Benedict Evans returns to the MAD Podcast and argues that foundation models have zero network effects, making them closer to commodity infrastructure than the next iOS. We unpack OpenAI's "mile wide, inch deep" usage problem, why simply having a "better model" does not solve the core UX challenge, and whether the hyperscalers' massive CapEx spending is a sustainable strategy or a fast track to financial gravity.We also explore the reality behind the recent "SaaSpocalypse", the structural shift from traditional enterprise systems to "improvised" and "ephemeral" software, and where the actual white space lies for founders and investors navigating the artificial intelligence hype cycle.(00:00) Intro(01:06) OpenAI's Focus Shift (03:12) ChatGPT usage: a "mile wide, inch deep"(09:03) Why better models do not solve the real problem(13:58) Why AI product teams are strategy takers, not strategy setters(15:38) Do agents help create defensibility?(20:06) OpenClaw and the "Desktop Linux" moment for AI(25:52) Why "everyone will build their own software" is completely wrong(28:09) Improvised software vs. institutionalized software(29:23) The Jevons Paradox: Why there will be more software, not less(36:15) Are we heading toward value destruction before value creation?(38:03) Circular revenue, leverage, and AI bubble dynamics(38:53) Big Tech's Trillion-Dollar CapEx Crisis & Financial Gravity(45:23) Why AI job exposure charts can be misleading(52:15) How Fortune 500 Execs are actually deploying AI today(56:45) The White Space: What this means for founders and investors

AI products are shipping faster than ever. But shipping isn't impact. The teams pulling ahead aren't the ones with the best models — they're the ones who can prove their product moves the business. This edition is about that gap. How to measure what matters, where the biggest barriers to impact are hiding, and what the latest research says about getting AI products to actually drive growth. Because the real competitive advantage isn't AI. It's knowing whether your AI is working.What You'll Learn in This EditionThis edition cuts through the noise to focus on the measurement gap — the difference between shipping AI and proving AI drives growth.* The Power/Speed/Impact/Joy bullseye — a calibration framework for AI products that actually drive growth* A Nature paper reveals why removing friction from AI may be destroying the learning your team needs* John Maeda on why design teams are being hollowed out — and why PMs are next* Benedict Evans on why even OpenAI can't solve product-market fit with capability alone* Research that should change how your team thinks about AI-assisted skill buildingThanks for reading Product Impact | AI Strategy, Value Creation, AI UX! This post is public so feel free to share it.Episode 1: Why Your AI Metrics Are Lying to You - Framework for improving AI product performanceYour AI product might be fast, capable, and technically impressive — and still not drive the growth your business needs. In this episode, Brittany Hobbs and I introduce the Power, Speed, Impact, and Joy bullseye — a calibration framework borrowed from F1 racing. The teams winning aren't shipping more features. They're measuring different things entirely. We break down a three-layer eval approach and why most completion metrics are hiding the signals that matter.“Success does not mean satisfaction. If someone stops engaging, does that mean they solved their problem — or that they were frustrated and left?” — Brittany HobbsListen on Spotify | Apple Podcasts | YouTubeYour Role Isn't Shrinking. It's Being Hollowed Out.John Maeda — Three major tech companies have restructured design teams into “prompt engineering pods.” Maeda's #DesignInTech 2026 calls it what it is: the elimination of design judgment from the product process. “When you replace a designer with a prompt, you don't lose the pixels. You lose the questions that should have been asked before anyone opened a tool.” This applies to product managers too — if your PM's job becomes prompt-wrangling instead of deciding what to build and why, you've automated the wrong layer. The roles aren't disappearing. The judgment inside them is.Featured Resource: Strategy for Measuring & Improving AI ProductsThe gap between what AI products ship and what they prove is where growth stalls. This framework moves teams from tracking activity — token counts, completion rates, session length — to defining and measuring the outcomes that actually drive business impact. Most teams ship features and assume engagement means success. It doesn't. If your team can't answer “is this AI feature making the business better?” with data, you're flying blind. The framework covers product discovery through scale, with concrete steps for building measurement into your AI product from the start — not bolting it on after launch.Read the full resource at ph1.caWaterfall: we'll build you a car in 18 months. Agile: here's a skateboard, we'll iterate. AI: here's a photorealistic render of a Lamborghini that doesn't start. We've never made it easier to build something that looks incredible and does absolutely nothing. AI development doesn't need more iteration — it needs someone asking “does this thing actually drive?”If your team is celebrating demos instead of outcomes, you're already behind the teams that measure first and ship second.Two years of capability gains. Almost no reliability improvement. This is the chart that should be on every product team's wall — because it explains why your AI demos brilliantly and fails in production. Capability without reliability isn't a product. It's a liability.If your team can't name which type of AI they're building, they can't measure whether it's working. Six categories that force precision. — Narain JashanmalProduct Impact ResourcesThe resources in this edition make one thing clear: the teams investing in measurement and deliberate friction are pulling ahead, while the ones chasing capability are stalling. These resources challenge the assumption that faster and more capable automatically means better outcomes.* Removing struggle from AI workflows destroys the learning that builds expertise. Teams should audit which friction to keep and which to cut. Against Frictionless AI — Inzlicht & Bloom in Nature* AI users learned 17% less without any efficiency gains. How your team uses AI matters more than whether they use it. How AI Impacts Skill Formation — Shen & Tamkin RCT* Two years of capability gains with only modest reliability improvement. The barrier to growth isn't what models can do — it's whether you can trust them. The Capability-Reliability Gap — Narayanan et al.* Polished AI outputs reduce critical evaluation by users. Build in friction points that force your team to think before accepting. (Anthropic studying its own product — read accordingly.) Anthropic AI Fluency Index* AI forces strategic clarity because you cannot delegate logic you haven't articulated. That's a feature, not a bug. Strategy as Protocol — Schwarzmann via Scaman* Six functional AI categories that sharpen how teams talk about what they're building. Precision in language is precision in product decisions. AI Taxonomy — Jashanmal* Mapping 50 AI startups across six pricing models reveals that pricing is a product decision, not a finance one. Get it wrong and adoption stalls regardless of quality. How to Price AI Products — Gupta* Wade Foster shut Zapier down for a week-long AI hackathon. Adoption went from 10% to 50% in five days. Adoption follows experience, not mandates. Zapier's Code Red HackathonProduct Impact NewsThis is the news that matters. Reliability failures are making headlines, benchmark credibility is collapsing, and even the market leaders can't prove product-market fit. The gap between what AI can do and what it can prove is widening, not closing.* ChatGPT missed diabetic ketoacidosis and respiratory failure in 52% of emergency cases. Suicide-risk alerts fired inconsistently. Reliability is the product, not a feature to ship later. ChatGPT Health Under-Triaged 52% of Emergencies* LLMs chose nuclear strikes in 95% of simulated crises. The nuclear taboo is no impediment to AI escalation — a stark reminder that evaluation stakes extend beyond product. AI Models Chose Nuclear Strikes in 95% of Simulated Crises* Google patent US12536233B1 lets it generate its own landing page from your product feed if yours scores below threshold. Own your experience or someone else will. Google Patented AI Landing Pages That Replace Your Storefront* 84% of the world has never used AI. Only 0.3% pay for it. The growth opportunity is massive — but only for teams that solve adoption, not just access. 84% of the World Has Never Used AI* 80% of ChatGPT users sent fewer than 1,000 messages in 2025. Even the market leader hasn't solved product-market fit. Capability alone isn't enough. OpenAI Has No Moat and Engagement an Inch Deep* RCT shows AI tools made experienced developers work faster and take on broader tasks — without measurable output gains. Speed is not productivity. METR: Experienced Devs Saw Zero Productivity Gain* NIST finds standard benchmarks conflate different performance measures. Models with different scores may perform identically in production. Build your own evals. NIST: AI Benchmarks Don't Measure What They Claim* MIT reviewed 300+ AI implementations: 85% failed, 91% of models degrade silently. The 5% that succeeded built measurement into the product from day one. 85% of AI Projects Fail, 91% of Models Degrade SilentlyKey takeawaysThe throughline across this edition is unmistakable: capability without measurement is theater. From the METR study showing zero productivity gains for experienced developers to MIT's finding that 85% of AI projects fail, the evidence converges on one point — the teams that win are the ones that prove their AI works.* Measure outcomes, not activity. Completion rates, token counts, and session length tell you your AI is running — not that it's working. Define what “working” means for your business before you ship.* Protect judgment. Automate everything else. The roles being hollowed out aren't the ones doing rote work — they're the ones asking the hard questions. If you're automating decisions instead of tasks, you're cutting the wrong layer.* Friction is a feature. Research consistently shows that removing struggle from AI workflows destroys learning and degrades skill. Build in the friction that keeps your team sharp, and strip out the friction that just wastes time.If your AI product ships well but you can't prove it drives growth, that's the gap PH1 closes. We help teams define what success looks like for AI experiences and build the measurement systems to prove it — from product discovery through scale. ph1.caThank you for supporting the Product Impact PodcastEvery episode tackles the gap between what AI products promise and what they actually deliver. Brittany and I bring in the builders, researchers, and leaders who are closing that gap — with frameworks, evidence, and hard-won lessons. If an episode shifted how you think about your product, share it. Follow the show so you never miss one. That's how we grow this community.* Episode 1: Why Your AI Metrics Are Lying to You* Vibe Coding Will Disrupt Product — Base44's Path to $80M* AI Trap: Hard Truths About the Job MarketBrowse all episodes at productimpactpod.com — filter by topic to find the episode that fits what you're working on right now. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

This episode originally appeared on the Network State Podcast. Balaji Srinivasan and Benedict Evans sit down in Singapore for a wide-ranging conversation on the mechanics of disruption. Evans, a former Andreessen Horowitz partner who now writes one of tech's most-read newsletters, argues that the conversation about any technology peaks during the transition—not at 0% or 100% adoption. They cover AI's real capabilities and limits, the politics of technological disruption, why crypto's killer metric is block space, and what smart glasses, elevator attendants, and the elephant graph reveal about how change works.  Resources:Follow Benedict Evans on LinkedIn: https://www.linkedin.com/in/benedictevans/Check out Benedict's Newsletter: https://www.ben-evans.com/newsletterFollow Balaji Srinivasan on X: https://x.com/balajisCheck out Network State Podcast: https://www.youtube.com/@nspodcastHigh Output Management: https://www.amazon.com/High-Output-Management-Andrew-Grove-ebook/dp/B015VACHOK/eHang: https://www.youtube.com/watch?v=nUTu4_8QznEThe Deep Research Problem: https://www.ben-evans.com/benedictevans/2025/2/17/the-deep-research-problemARC AGI: https://arcprize.org/arc-agiUber and Airbnb didn't sell software: https://www.ben-evans.com/benedictevans/2025/3/14/what-kind-of-disruptionAI Use cases: https://www.ben-evans.com/benedictevans/2024/4/19/looking-for-ai-use-casesStablecoin surpasses Visa & Mastercard: https://crypto.news/ark-invest-stablecoin-transaction-value-in-2024-surpasses-visa-and-mastercard/Senate passes stablecoin bill: https://www.reuters.com/sustainability/boards-policy-regulation/us-senate-passes-stablecoin-bill-milestone-crypto-industry-2025-06-17/ Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of The Metrics Brothers, Ray Rike and Dave Kellogg unpack Benedict Evans' latest landmark presentation, AI Eats the World, and explore why this moment may rival or even surpass the original “software is eating the world” era. Drawing parallels to Marc Andreessen's 2011 thesis, they examine how AI is no longer just another platform shift, but a force capable of reshaping labor, capital allocation, and entire industries at once.The conversation spans the explosive rise in AI infrastructure spending, from hyperscaler capex surging past $400B to the growing strain on power, compute, and supply chains. Ray and Dave discuss why this moment feels different from past tech cycles, not just because of scale, but because AI directly targets labor, which represents more than half of global GDP. They explore whether AI is creating real moats or accelerating commoditization, and why many enterprises are still stuck in experimentation rather than true deployment.The episode also dives into historical parallels from elevators and telephone operators to cloud computing highlighting how software enabled automation always feels threatening before it quietly becomes invisible. Along the way, they unpack the strategic tension facing AI leaders: go down the stack for scale or up the stack for value capture. With insights on hyperscalers, OpenAI, Oracle, and the economics of AI adoption, this episode challenges leaders to rethink how value will actually be created and captured in the age of AI.If you want to understand what's hype, what's durable, and why “AI eating the world” may be the most consequential shift since the internet itself, this episode is a must-listen.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

For this week's episode of Enterprise Security Weekly, there wasn't a lot of time to prepare. I had to do 5 podcasts in about 8 days leading up to the holiday break, so I decided to just roll with a general chat and see how it went. Also, apologies, for any audio quality issues, as the meal I promised to make for dinner this day required a lot of prep, so I was in the kitchen for the whole episode! For reference, I made the recipe for morisqueta michoacana from Rick Martinez's cookbook, Mi Cocina. I used the wrong peppers (availability issue), so it came out green instead of red, but was VERY delicious. As for the episode, we discuss what we've been up to, with Jackie sharing her experiences fighting against Meta (allegedly, through some shell companies) building an AI datacenter in her town. We then get into discussing the limitations of AI, the potential of the AI bubble popping, and general limitations of AI that are becoming obvious. One of the key limitations is AI's inability to apply personal experience, have strong opinions, or any sense of 'taste'. I think I shared my observation that AI is becoming a sort of 'digital junk food'. "NO AI" has become a common phrase used by creators - a source of pride that media consumers seem to be celebrating and seeking out. Segment Resources: Kagi absolutely did NOT sponsor this episode. I have become a big fan of paying for search so that I am not the product. There are other players in this market, but I've settled on Kagi. We mention Ira Glass's bit on taste, which is a small bit of a longer talk he did on storytelling. The shorter bit is here, and is less than 2 minutes long. The full talk is split into 4 parts and posted on a YouTube channel called "War Photography" for some reason. Part 1: https://youtu.be/5pFI9UuC_fc Part 2: https://youtu.be/dx2cI-2FJRs Part 3: https://youtu.be/X2wLP0izeJE Part 4: https://youtu.be/sp8pwkgR8 Finally, we also bring up a talk we also discussed on episode 437, Benedict Evans' AI Eats the World Visit https://www.securityweekly.com/esw for all the latest episodes! Show Notes: https://securityweekly.com/esw-439

In this conversation, Ben Bajarin and Jay Goldberg engage with Benedict Evans to explore the current state of AI development, its historical context, and future predictions. They discuss the potential for an AI bubble, the importance of productization for user adoption, and the varying levels of AI integration across different industries. The conversation also touches on the comparison between Nvidia and Sun Microsystems, highlighting the challenges and opportunities in the AI landscape.

For this week's episode of Enterprise Security Weekly, there wasn't a lot of time to prepare. I had to do 5 podcasts in about 8 days leading up to the holiday break, so I decided to just roll with a general chat and see how it went. Also, apologies, for any audio quality issues, as the meal I promised to make for dinner this day required a lot of prep, so I was in the kitchen for the whole episode! For reference, I made the recipe for morisqueta michoacana from Rick Martinez's cookbook, Mi Cocina. I used the wrong peppers (availability issue), so it came out green instead of red, but was VERY delicious. As for the episode, we discuss what we've been up to, with Jackie sharing her experiences fighting against Meta (allegedly, through some shell companies) building an AI datacenter in her town. We then get into discussing the limitations of AI, the potential of the AI bubble popping, and general limitations of AI that are becoming obvious. One of the key limitations is AI's inability to apply personal experience, have strong opinions, or any sense of 'taste'. I think I shared my observation that AI is becoming a sort of 'digital junk food'. "NO AI" has become a common phrase used by creators - a source of pride that media consumers seem to be celebrating and seeking out. Segment Resources: Kagi absolutely did NOT sponsor this episode. I have become a big fan of paying for search so that I am not the product. There are other players in this market, but I've settled on Kagi. We mention Ira Glass's bit on taste, which is a small bit of a longer talk he did on storytelling. The shorter bit is here, and is less than 2 minutes long. The full talk is split into 4 parts and posted on a YouTube channel called "War Photography" for some reason. Part 1: https://youtu.be/5pFI9UuC_fc Part 2: https://youtu.be/dx2cI-2FJRs Part 3: https://youtu.be/X2wLP0izeJE Part 4: https://youtu.be/sp8pwkgR8 Finally, we also bring up a talk we also discussed on episode 437, Benedict Evans' AI Eats the World Visit https://www.securityweekly.com/esw for all the latest episodes! Show Notes: https://securityweekly.com/esw-439

For this week's episode of Enterprise Security Weekly, there wasn't a lot of time to prepare. I had to do 5 podcasts in about 8 days leading up to the holiday break, so I decided to just roll with a general chat and see how it went. Also, apologies, for any audio quality issues, as the meal I promised to make for dinner this day required a lot of prep, so I was in the kitchen for the whole episode! For reference, I made the recipe for morisqueta michoacana from Rick Martinez's cookbook, Mi Cocina. I used the wrong peppers (availability issue), so it came out green instead of red, but was VERY delicious. As for the episode, we discuss what we've been up to, with Jackie sharing her experiences fighting against Meta (allegedly, through some shell companies) building an AI datacenter in her town. We then get into discussing the limitations of AI, the potential of the AI bubble popping, and general limitations of AI that are becoming obvious. One of the key limitations is AI's inability to apply personal experience, have strong opinions, or any sense of 'taste'. I think I shared my observation that AI is becoming a sort of 'digital junk food'. "NO AI" has become a common phrase used by creators - a source of pride that media consumers seem to be celebrating and seeking out. Segment Resources: Kagi absolutely did NOT sponsor this episode. I have become a big fan of paying for search so that I am not the product. There are other players in this market, but I've settled on Kagi. We mention Ira Glass's bit on taste, which is a small bit of a longer talk he did on storytelling. The shorter bit is here, and is less than 2 minutes long. The full talk is split into 4 parts and posted on a YouTube channel called "War Photography" for some reason. Part 1: https://youtu.be/5pFI9UuC_fc Part 2: https://youtu.be/dx2cI-2FJRs Part 3: https://youtu.be/X2wLP0izeJE Part 4: https://youtu.be/sp8pwkgR8 Finally, we also bring up a talk we also discussed on episode 437, Benedict Evans' AI Eats the World Show Notes: https://securityweekly.com/esw-439

For this week's episode of Enterprise Security Weekly, there wasn't a lot of time to prepare. I had to do 5 podcasts in about 8 days leading up to the holiday break, so I decided to just roll with a general chat and see how it went. Also, apologies, for any audio quality issues, as the meal I promised to make for dinner this day required a lot of prep, so I was in the kitchen for the whole episode! For reference, I made the recipe for morisqueta michoacana from Rick Martinez's cookbook, Mi Cocina. I used the wrong peppers (availability issue), so it came out green instead of red, but was VERY delicious. As for the episode, we discuss what we've been up to, with Jackie sharing her experiences fighting against Meta (allegedly, through some shell companies) building an AI datacenter in her town. We then get into discussing the limitations of AI, the potential of the AI bubble popping, and general limitations of AI that are becoming obvious. One of the key limitations is AI's inability to apply personal experience, have strong opinions, or any sense of 'taste'. I think I shared my observation that AI is becoming a sort of 'digital junk food'. "NO AI" has become a common phrase used by creators - a source of pride that media consumers seem to be celebrating and seeking out. Segment Resources: Kagi absolutely did NOT sponsor this episode. I have become a big fan of paying for search so that I am not the product. There are other players in this market, but I've settled on Kagi. We mention Ira Glass's bit on taste, which is a small bit of a longer talk he did on storytelling. The shorter bit is here, and is less than 2 minutes long. The full talk is split into 4 parts and posted on a YouTube channel called "War Photography" for some reason. Part 1: https://youtu.be/5pFI9UuC_fc Part 2: https://youtu.be/dx2cI-2FJRs Part 3: https://youtu.be/X2wLP0izeJE Part 4: https://youtu.be/sp8pwkgR8 Finally, we also bring up a talk we also discussed on episode 437, Benedict Evans' AI Eats the World Show Notes: https://securityweekly.com/esw-439

AI is reshaping the tech landscape, but a big question remains: is this just another platform shift, or something closer to electricity or computing in scale and impact? Some industries may be transformed. Others may barely feel it. Tech giants are racing to reorient their strategies, yet most people still struggle to find an everyday use case. That tension tells us something important about where we actually are.In this episode, technology analyst and former a16z partner Benedict Evans joins General Partner Erik Torenberg to break down what is real, what is hype, and how much history can guide us. They explore bottlenecks in compute, the surprising products that still do not exist, and how companies like Google, Meta, Apple, Amazon, and OpenAI are positioning themselves.Finally, they look ahead at what would need to happen for AI to one day be considered even more transformative than the internet.Timestamps: 0:00 – Introduction 0:17 – Defining AI and Platform Shifts1:50 – Patterns in Technology Adoption6:04 – AI: Hype, Bubbles, and Uncertainty13:25 – Winners, Losers, and Industry Impact19:00 – AI Adoption: Use Cases and Bottlenecks24:00 – Comparisons to Past Tech Waves32:00 – The Role of Products and Workflows40:00 – Consumer vs. Enterprise AI46:00 – Competitive Landscape: Tech Giants & Startups51:00 – Open Questions & The Future of AIResources:Follow Benedict on LinkedIn: https://www.linkedin.com/in/benedictevans/ Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Technology analyst Benedict Evans joins Edelman's Sat Dayal to discuss the findings from the new Edelman Trust Barometer Flash Poll on AI. They explore why optimism and adoption are higher in China and Brazil than in developed markets, how AI is reshaping the workplace, and what companies can do to build trust and make the … Continue reading "Tech Analyst Benedict Evans on How AI Is Reshaping Work, Trust, and Daily Life"

We met in a conference room at an office in Barrington, IL. A place where sometime later a couple guys thought they'd screw me in a business deal. I came out ahead in the end, but the place has mixed memories.   This meeting involved thinking about the future of asset data and systems interoperability. We had a system diagram. The idea was to solve a huge problem for owner/operators of process manufacturing enterprises—flowing engineering data into other software systems for operations, maintenance, and enterprise. The incumbent system was a morass of paper (or pdf documents which was much the same thing).   We did trademark searches and domain name searches and eventually settled on the Open Industrial Interoperability Ecosystem—OIIE.   I plot this history for context for the conference I attended recently—the 2nd ADIF Workshop at Texas A&M University dubbed Driving Asset Data and Systems Interoperability Toward an Open and Neutral Data Ecosystem.   This workshop brought together owner/operators, EPCs, System Integrators, university researchers, standards organizations, and software vendors. Each group conducted a panel discussion of its needs and successes. I was there for a short presentation and to moderate the standards panel.   Professor David Jeong from Texas A&M and the session leader previewed the discussions. One of his colleagues later presented research his team has performed to provide a method for taking P&ID documentation into a standard format usable by other software systems.   The message that came to me from the panel of owner/operators (grossly summarized, as will be all the discussions) included two key words—collaborate and operationalize. They are impatient about solving this data interoperability problem. One panelist quipped, "We know the project is finished when the large van backs into the loading dock and disgorges mountains of paper."   What blows my mind is that I was moved to a position called Data Manager in 1977 to tackle the (much smaller) mountain of paper our product engineering department provided to operations, accounting, and inventory management. I led a digitalization effort in 1978 to tackle the problem. The problem not only remains, but it is immensely more complicated and critical.   The EPCs basically said that their hands were tied by the owner/operators mandating which design and engineering software to use and the inflexibility of the vendors of said design and engineering software. When owner/operators had requested digital documentation, they had responded with pdfs. Hardly interoperable data.   Our standards panel included the leader of DEXPI, whose organization has developed a method of changing P&ID data into an xlsx (Excel) format. That, of course, is a good start.   An organization called CFIHOS (see-foss) presented their take on standards. I'm afraid I got a bit lost in the slides (note: more research needed). What I gathered was that they were attempting one overriding standard—and that that work was years away. Interesting that I listened to Benedict Evans' podcast this morning. He is a long-time tech industry analyst. He remarked in another context, "It seems that where there are 10 standards and someone comes along with a standard to encompass them all, you wind up with 11 standards."   The ISA-95 was presented. This messaging (and more) standard is incorporated with the OIIE, which was presented next. Dr. Markus Stumptner of the University of South Australia presented his research work on proof of concept of the OIIE.   If we can get enough momentum focusing on this area and find some SIs willing to take the OIIE to an owner/operator, perhaps we can finally prove the business case of asset data and systems interoperability.

Daniel Lewis joins us this week to trace a path from Ravel Law to LexisNexis to LegalOn, with a throughline of data-driven thinking and practical outcomes for lawyers. Stanford roots shaped early work on judicial analytics, then a front-row view inside a global publisher broadened focus to content, guidance, and the daily reality of in-house teams. That experience pointed straight at contract review as a top pain for corporate counsel, which led to LegalOn's product mission and global push.Data access still shapes progress. Case law digitization advanced through projects like Harvard's archive, yet comprehensive coverage, secondary sources, and news remain guarded by incumbents. Daniel explains why large datasets give scale, why startups face steep hurdles, and why thoughtful product scope matters. The lesson, build where data, workflow, and user value intersect.LegalOn's hybrid approach blends large models with attorney-built playbooks, practice notes, and suggested clause language. Consistency matters more than clever one-offs, so reviews align to standards, not model whimsy. Daniel shares a memorable demo from a rival where a phantom “California Code section 17” alert appeared, a cautionary tale that underscores the need for guardrails, verification, and explainability.Conversation turns to multi-step agents and matter management. Picture an intake email from sales, missing key fields. An agent requests what is needed, opens a matter, applies a tailored playbook, highlights non-negotiables and fallbacks, then keeps stakeholders informed as work progresses. LegalOn also converts existing playbooks and prior redlines into AI-ready guidance, reducing setup chores while preserving organizational risk preferences.Finally, Daniel outlines new muscles for legal teams. Daily AI usage shifts time from line-by-line edits to judgment, negotiation strategy, and process leadership. Tech fluency, business orientation, and change leadership rise in importance, along with a steady diet of outside-legal analysis from voices like Ben Thompson and Benedict Evans. The message, free lawyers from sludge, raise the ceiling on strategic work, and build for long-term improvement across the legal function.Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.] ⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠ Transcript:

Benedict Evans has been calling tech shifts for decades. Now he says forget the hype: AI isn't the new electricity. It's the biggest change since the iPhone, and that's plenty big enough. We talk about why everyone gets platform shifts wrong, where Google's actually vulnerable, and what real people do with AI when nobody's watching. Evans sees patterns others don't. This conversation will change how you think about what's actually happening versus what everyone says is happening. ----- Approximate Timestamps: (00:00) Introduction (01:04) What's your Most Controversial Take On AI? (05:11) Platform Shifts - The Rise Of Automatic Elevators (10:07) Profit Margins In AI (26:37) What Are The Questions We Aren't Asking About AI (39:41) What Benedict Uses AI For (44:21) Thinking By Writing (47:35) Can AI Make Something Original? (52:31) Advice for Students In The Age Of AI? (59:32) Who Will Win The AI Race? (1:11:09) What Is Success For You? ----- Thanks to our sponsors for this episode: SHOPIFY: Sign up for your one-dollar-per-month trial period at ⁠www.shopify.com/knowledgeproject ⁠ ReMarkable for sponsoring this episode. Get your paper tablet at ⁠⁠reMarkable.com⁠⁠ today NOTION MAIL: Get Notion Mail for free right now at ⁠⁠notion.com/knowledgeproject ----- Upgrade: Get a hand edited transcripts and ad free experiences along with my thoughts and reflections at the end of every conversation. Learn more @ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠fs.blog/membership⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ------ Newsletter: The Brain Food newsletter delivers actionable insights and thoughtful ideas every Sunday. It takes 5 minutes to read, and it's completely free. Learn more and sign up at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠fs.blog/newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠ ------ Follow Shane Parrish X ⁠⁠⁠⁠@ShaneAParrish⁠⁠⁠⁠ Insta ⁠⁠⁠⁠@farnamstreet⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

In this weeks episode Nick talks to Benedict EvansBenedict Evans is an independent analyst with over 25 years' experience in mobile, media, and technology. Evans recounts his career journey, from equity analysis of mobile operators, to strategic roles in media and telecoms, and later his work at venture capital firm Andreessen Horowitz.Nick and Benedict conversations explores the evolution of artificial intelligence, clarifying definitions of AI, machine learning, and generative AI, and drawing historical parallels with past technology shifts such as smartphones, spreadsheets, and the internet. Evans discusses AI's strengths, limitations, and public misconceptions, emphasising that its current utility lies in domains like software development and marketing. He notes that while AI can produce impressive results, it operates on probabilistic reasoning rather than human-like understanding, making it well-suited for some tasks but unreliable for others requiring precise factual accuracy.The discussion also addresses societal responses to new technologies, including moral panics, misuse by bad actors, and challenges in regulation. Evans stresses the importance of distinguishing hype from genuine capability and identifying where AI adds the most value. Looking ahead, he outlines adoption patterns, the integration of AI into everyday workflows, and the ongoing debate over whether large language models represent a fundamental shift in computing or simply another software evolution.Follow Benedict and subscribe to his newsletter here. This content is issued by Zeus Capital Limited (“Zeus”) (Incorporated in England & Wales No. 4417845), which is authorised and regulated in the United Kingdom by the Financial Conduct Authority (“FCA”) for designated investment business, (Reg No. 224621) and is a member firm of the London Stock Exchange. This content is for information purposes only and neither the information contained, nor the opinions expressed within, constitute or are to be construed as an offer or a solicitation of an offer to buy or sell the securities or other instruments mentioned in it. Zeus shall not be liable for any direct or indirect damages, including lost profits arising in any way from the information contained in this material. This material is for the use of intended recipients only.

AI and AI-Native companies are changing the software industry and the metrics to measure AI market momentum are still evolving.During this weeks episode, our co-hosts Dave "CAC" Kellogg and Ray "Growth" Rike discuss a recent article on AI Metrics by Benedict Evans. AI measurements and metrics discussed include:Daily Active Users / Monthly Active Users (DAU/MAU)Tokens - what they are and how they are calculatedWeekly User RetentionDave and Ray take us on a stroll down "metrics memory lane" as they discuss the early days of the internet, and some of the metrics that were used in the early days, that evolved and changed as the use of the internet began to mature - some of those metrics included:Internet HostsHitsPage ViewsEyeballsLastly, the term Occam's Razor was introduced to discuss why sometimes the simplest metrics that require the lowest number of assumptions is a good place to start when initially measuring new things.The Metrics Brothers are excited to expand their metrics-centric analysis and insights into the rapidly evolving world of AI, and specifically AI software and AI metrics!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

What if the “AI revolution” is actually… stuck in the messy middle? In this episode, Benedict Evans returns to tackle the big question we left hanging a year ago: Is AI a true paradigm shift, or just another tech platform shift like mobile or cloud? One year later, the answer is more complicated — and more revealing — than anyone expected.Benedict pulls back the curtain on why, despite all the hype and model upgrades, the core LLMs are starting to look like commodities. We dig into the real battlegrounds: distribution, brand, and the race to build sticky applications. Why is ChatGPT still topping the App Store charts while Perplexity and Claude barely register outside Silicon Valley? Why did OpenAI just hire a CEO of Applications, and what does that signal about the future of AI products?We go deep on the “probabilistic” nature of LLMs, why error rates are still the elephant in the room, the future of consumer AI (is there a killer app beyond chatbots and image generators?), the impact of generative content on e-commerce and advertising, and whether “AI agents” are the next big thing — or just another overhyped demo.And, we ask: What happened to AI doomerism? Why did the existential risk debate suddenly vanish, and what risks should we actually care about?Benedict EvansLinkedIn - https://www.linkedin.com/in/benedictevansThreads - https://www.threads.net/@benedictevansFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (01:47) Is AI a Platform Shift or a Paradigm Shift? (07:21) Error Rates and Trust in AI (15:07) Adapting to AI's Capabilities (19:18) Generational Shifts in AI Usage (22:10) The Commoditization of AI Models (27:02) Are Brand and Distribution the Real Moats in AI? (29:38) OpenAI: Research Lab or Application Company? (33:26) Big Tech's AI Strategies: Apple, Google, Meta, AWS (39:00) AI and Search: Is ChatGPT a Search Engine? (42:41) Consumer AI Apps: Where's the Breakout? (45:51) The Need for a GUI for AI (48:38) Generative AI in Social and Content (51:02) The Business Model of AI: Ads, Memory, and Moats (55:26) Enterprise AI: SaaS, Pilots, and Adoption (01:00:08) The Future of AI in Business (01:05:11) Infinite Content, Infinite SKUs: AI and E-commerce (01:09:42) Doomerism, Risks, and the Future of AI

Welcome back! Today, we're joined by one of the most insightful voices in tech - Benedict Evans, former Andreessen Horowitz partner and founder of Benedict's Newsletter. Over the last ten years, Benedict has independently predicted huge waves in tech, often before others. If you've followed the evolution of technology, it's likely you've read his work. Benedict has spent 20 years analysing media and technology, and worked across equity research, strategy, consulting and venture capital. He's now an independent analyst.His newsletter now attracts around 200,000 subscribers, he writes essays, creates annual presentations and writes a weekly column analysing the new questions and ideas shaping society and technology. In his newsletter he asks the questions: what matters in tech? What does it mean? And what will happen next? And that's what we will talk about with him today - especially regarding AI, product strategy, and who is actually using which AI tools. Plus, we find out what led him to his career and what the parallels are between the emergence of the web and this new wave of technology.Building a purpose driven company? Read more about Giant Ventures at www.Giant.vc.Music credits: Bubble King written and produced by Cameron McLain and Stevan Cablayan aka Vector_XING. Please note: The content of this podcast is for informational and entertainment purposes only. It should not be considered financial, legal, or investment advice. Always consult a licensed professional before making any investment decisions.

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover 1) Satya Nadella's criticism of AI benchmark hacking 2) Ex-OpenAI CTO Mira Murati's new Thinking Machines Lab startup 3) There are too many AI startups 4) Why foundation models have commoditized 5) Did Google 'DeepSeek' itself? 6) Grok3 arrives 7) How do you evaluate whether models are good? 8) Grok3 at the top of Chatbot arena 9) Benedict Evans on Deep Research 10) Does using AI tools make our brains atrophy? 11) Amazon's incoming Alexa upgrade 12) Actually, voice AI helps during marital disputes 13) RIP Humane Pin Join the Big Technology Discord here: https://www.bigtechnology.com/p/lets-talk-deepseek-ai-etc-on-big

AI models are getting cheaper, faster, and more accessible – but what does that mean for businesses and product builders? Renowned tech analyst Benedict Evans joins us to unpack the latest AI shifts, from DeepSeek's rapid rise to the ongoing debate between deterministic and probabilistic AI. He chats with our Co-founder and Chief Strategy Officer Des Traynor about the implications of falling compute costs, the race to refine AI agents, and how companies should think about building in an unpredictable landscape.Watch this episode on YouTube: https://youtu.be/QkpqBCaUvS4

Bill Maher excoriates the software industry for making our lives more difficult, two professors from the University of Washington put together a curriculum to help us manage life in the ChatGPT world, Daniel Delaney thinks deeply on chat as a dev tool UI, Benedict Evans explores our assumptions that computers be 'correct' & the Thoughtbot team writes up six cases when not to refactor.

Bill Maher excoriates the software industry for making our lives more difficult, two professors from the University of Washington put together a curriculum to help us manage life in the ChatGPT world, Daniel Delaney thinks deeply on chat as a dev tool UI, Benedict Evans explores our assumptions that computers be 'correct' & the Thoughtbot team writes up six cases when not to refactor.

Bill Maher excoriates the software industry for making our lives more difficult, two professors from the University of Washington put together a curriculum to help us manage life in the ChatGPT world, Daniel Delaney thinks deeply on chat as a dev tool UI, Benedict Evans explores our assumptions that computers be 'correct' & the Thoughtbot team writes up six cases when not to refactor.

How are the largest VCs viewing the early stages of the AI Era, from the perspective of investment, technology moats, economics, early adoption and future use-cases.  SHOW: 879SHOW TRANSCRIPT: The Cloudcast #879 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW NOTES:AI Eats the World (Presentation - Benedict Evans)IS SILICON VALLEY STILL THE CENTER OF TECH INNOVATION?Companies are investing tons of moneyBreakthrough results haven't emerged yet (business models, profits)It's not clear that there is a technology moat; but maybe a capital moatModel training costs are expected to rise 5x to 10x - worse economics??Lots of VC investment and vendor 2nd-order investmentsLLM costs are creating marginal cost of software (been since the mainframe)Model quality vs. price is improving, but price of the services (e.g. ChatGPT-Pro) is increasing - how much extra value is being delivered?How will open source impact AI? “If anything in life is certain, semiconductors are cyclical, commodity tech goes to marginal cost, and every new tech produces a bubble.”Today's GenAI question - is it accurate and useful? How can we tell, and how can it improve (or does it need to)?Start with a simple concept - AI gives us unlimited interns - how can you extrapolate that? How would this have been extrapolated for the original internet (create content, translate language, write code, etc.)Use cases are still not easy to see beyond Chatbots (and variants), Coding AssistantsConsulting revenue from GenAI is bigger than technology - and still most/many projects still in trials. Technology can take a long time to adopt - Cloud still only has 30% of workloads (15yrs old)66% of CEO's don't expect their first GenAI app in production until sometime in 2025, 50% at least 2H of 2025.[Shadow AI] SaaS AI will accelerate adoption, if it follows Cloud pattern - external forces are more motivated to attack business “change” than internal teams[Build vs. Ecosystem] Do the LLM vendors become the application vendors? Where does the LLM start and stop (infra, platform, API, apps, etc.)[Learning from the customers] Do the LLM vendors use their knowledge advantage to build the apps? GenAI Apps Categories - Make something better, Replace something, Just do the thing“AI is just whatever is wrong/broken now” - How well does AI understand “broken”Will people be the biggest problem in AI progress? [Decoupling] Looks at global markets for Internet today - ecommerce/retail, food delivery, advertising, media, autonomous driving, [Elevator Example] Automation gets rid of peopleNo real conclusionFEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Is the AI hype justified, or is there more to the story? Hear renowned industry analyst Benedict Evans as he cuts through the noise and reveals what AI really means for industries like customer service.Watch this episode here: https://events.intercom.com/on-demand/69b135d5-09ad-4023-a52e-b8a4ea0ee939/?referrer_page=de35aee1-a86c-48bb-8967-f5620d766eabTo check out all the sessions in full, check out: https://pioneer.intercom.com/Newsletters:Sign up for The Ticket: A twice-monthly newsletter bursting with all the insights, trends, tips, and assets your team needs to embrace the future of customer service. https://www.intercom.com/blog/newsletterSign up for Intercom on Product: a monthly newsletter sharing our latest thinking on building and designing great products, and how that's changing in the age of AI.https://inter.com/productpodcastSay hi on

Last week, we hosted Pioneer, our first ever AI customer service summit where we brought together industry leaders and experts to explore how AI is revolutionizing customer service. In this episode, we bring you highlights from the event, including insights from Intercom Co-founder and CEO Eoghan McCabe, Co-founder and Chief Strategy Officer Des Traynor, Chief Product Officer Paul Adams, and renowned industry analyst Benedict Evans. We also hear from Intercom customers Natalie Hurst from Nuuly, Constantina Samara from Synthesia, and Angelo Livanos from Lightspeed Commerce, who share the results they're already seeing from Intercom's Fin AI Agent.To check out all the sessions in full, check out: https://events.intercom.com/pioneer-2024/Newsletters:Sign up for The Ticket: A twice-monthly newsletter bursting with all the insights, trends, tips, and assets your team needs to embrace the future of customer service. https://www.intercom.com/blog/newsletterSign up for Intercom on Product: a monthly newsletter sharing our latest thinking on building and designing great products, and how that's changing in the age of AI.https://inter.com/productpodcastSay hi on

In episode 79 of Topline, Sam and Asad sit down with Benedict Evans to discuss the rapid evolution of AI. Together, they explore AI's growing impact on businesses and the economy, with Benedict offering expert insights on the scalability of AI models and the challenges companies face in staying competitive. The conversation also delves into AI's implications for the job market and its role in shaping the future of enterprise software. Topline by Pavilion is also proud to debut The Revenue Leadership Podcast with Kyle Norton. Listen to the first two episodes now. Want more? Join the Topline Slack channel to engage with hosts, guests, and other listeners and subscribe to Topline Newsletter.  

Send me a Text Message hereFULL SHOW NOTES https://podcast.nz365guy.com/591 Can AI really transform the way businesses and governments operate? Find out as Ana Welch, Andrew Welch, Chris Huntingford and William Dorrington dive into the latest trends and initiatives driving AI adoption, particularly in the wake of Microsoft's fiscal year priorities and the post-election AI surge in the UK government. Listen to tech guru Benedict Evans dissect the hype versus the real-world application of AI, and get inspired by Sainsbury's innovative use of AI for weather-based inventory management. This episode is brimming with valuable insights that will help you understand the practical implications and benefits of integrating AI into your organization.But that's not all—we tackle the intricacies of data modeling and application development on platforms like Dynamics and Power Platform. Discover why solid data models are crucial and the common pitfalls citizen developers encounter, such as over-reliance on text fields for key data points. We stress the importance of professional software development practices, including application lifecycle management, testing, and security. Moreover, we highlight the critical role partners play in guiding organizations through these complexities, ensuring effective governance and productivity.Finally, we address the multifaceted challenges and immense opportunities that come with AI implementation in business. From substantial returns on investment to the essential need for upskilling staff, this episode covers it all. We also examine concerns regarding the accuracy of AI usage metrics and the phenomenon of "AI washing." Concluding with innovative strategies for software growth, we encourage you to keep pushing the boundaries and creating value in your tech endeavors. This episode promises to keep you at the cutting edge of AI and software development, packed with actionable insights and forward-thinking strategies.90 Day Mentoring Challenge 10% off code use MBAP at checkout https://ako.nz365guy.comSupport the Show.If you want to get in touch with me, you can message me here on Linkedin.Thanks for listening

Several Microsoft apps for macOS have been found to have security vulnerabilities. Fortnite returns to the iPhone in the EU. Select people have received demos of the upcoming visionOS 2.0. 9th-gen iPads are available at Amazon and Best Buy for the lowest price ever: $199! And what could be Apple's biggest competition in India? It couldn't be itself and used iPhone sellers, could it? The English Premier League will ditch its hated VAR Offside Tech for a fleet of iPhones. Procreate takes a stand against generative AI, vows to never incorporate the tech into its products. Flaws in Microsoft apps could let attackers spy on users. Jason's interview with Zach Gage. Fortnite is back on the iPhone — with a whole app store in tow. Competing in search — Benedict Evans. "Apple is apparently giving Secret (or just "private") Demos showing off VisionOS 2.0 features that have yet to appear in beta, plus some kind of enterprise AR demos." California driver's licenses to be available on Apple, Google wallets. Apple iPad 9th Gen hits $199 for first time. Apple Podcasts launches on the web. Apple's first India-made iPhone Pro models coming this year. iPhone 16 Pro's new color could be called 'Desert Titanium'. Apple's biggest competition in India? Used iPhone sellers. Apple's HomePod and iPad hybrid tabletop robot with an arm spotted in the supply chain, estimated to cost $1,000. Picks of the Week: Leo's Pick: The Famous Internet Cafe Andy's Pick: "Sounds of Apple" & the Twenty Thousand Hertz Podcast Alex's Pick: Deliciously Ella Jason's Pick: Carbon Copy Cloner 7 Hosts: Leo Laporte, Alex Lindsay, Andy Ihnatko, and Jason Snell Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsor: cachefly.com/twit

Several Microsoft apps for macOS have been found to have security vulnerabilities. Fortnite returns to the iPhone in the EU. Select people have received demos of the upcoming visionOS 2.0. 9th-gen iPads are available at Amazon and Best Buy for the lowest price ever: $199! And what could be Apple's biggest competition in India? It couldn't be itself and used iPhone sellers, could it? The English Premier League will ditch its hated VAR Offside Tech for a fleet of iPhones. Procreate takes a stand against generative AI, vows to never incorporate the tech into its products. Flaws in Microsoft apps could let attackers spy on users. Jason's interview with Zach Gage. Fortnite is back on the iPhone — with a whole app store in tow. Competing in search — Benedict Evans. "Apple is apparently giving Secret (or just "private") Demos showing off VisionOS 2.0 features that have yet to appear in beta, plus some kind of enterprise AR demos." California driver's licenses to be available on Apple, Google wallets. Apple iPad 9th Gen hits $199 for first time. Apple Podcasts launches on the web. Apple's first India-made iPhone Pro models coming this year. iPhone 16 Pro's new color could be called 'Desert Titanium'. Apple's biggest competition in India? Used iPhone sellers. Apple's HomePod and iPad hybrid tabletop robot with an arm spotted in the supply chain, estimated to cost $1,000. Picks of the Week: Leo's Pick: The Famous Internet Cafe Andy's Pick: "Sounds of Apple" & the Twenty Thousand Hertz Podcast Alex's Pick: Deliciously Ella Jason's Pick: Carbon Copy Cloner 7 Hosts: Leo Laporte, Alex Lindsay, Andy Ihnatko, and Jason Snell Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsor: cachefly.com/twit

Several Microsoft apps for macOS have been found to have security vulnerabilities. Fortnite returns to the iPhone in the EU. Select people have received demos of the upcoming visionOS 2.0. 9th-gen iPads are available at Amazon and Best Buy for the lowest price ever: $199! And what could be Apple's biggest competition in India? It couldn't be itself and used iPhone sellers, could it? The English Premier League will ditch its hated VAR Offside Tech for a fleet of iPhones. Procreate takes a stand against generative AI, vows to never incorporate the tech into its products. Flaws in Microsoft apps could let attackers spy on users. Jason's interview with Zach Gage. Fortnite is back on the iPhone — with a whole app store in tow. Competing in search — Benedict Evans. "Apple is apparently giving Secret (or just "private") Demos showing off VisionOS 2.0 features that have yet to appear in beta, plus some kind of enterprise AR demos." California driver's licenses to be available on Apple, Google wallets. Apple iPad 9th Gen hits $199 for first time. Apple Podcasts launches on the web. Apple's first India-made iPhone Pro models coming this year. iPhone 16 Pro's new color could be called 'Desert Titanium'. Apple's biggest competition in India? Used iPhone sellers. Apple's HomePod and iPad hybrid tabletop robot with an arm spotted in the supply chain, estimated to cost $1,000. Picks of the Week: Leo's Pick: The Famous Internet Cafe Andy's Pick: "Sounds of Apple" & the Twenty Thousand Hertz Podcast Alex's Pick: Deliciously Ella Jason's Pick: Carbon Copy Cloner 7 Hosts: Leo Laporte, Alex Lindsay, Andy Ihnatko, and Jason Snell Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsor: cachefly.com/twit

Several Microsoft apps for macOS have been found to have security vulnerabilities. Fortnite returns to the iPhone in the EU. Select people have received demos of the upcoming visionOS 2.0. 9th-gen iPads are available at Amazon and Best Buy for the lowest price ever: $199! And what could be Apple's biggest competition in India? It couldn't be itself and used iPhone sellers, could it? The English Premier League will ditch its hated VAR Offside Tech for a fleet of iPhones. Procreate takes a stand against generative AI, vows to never incorporate the tech into its products. Flaws in Microsoft apps could let attackers spy on users. Jason's interview with Zach Gage. Fortnite is back on the iPhone — with a whole app store in tow. Competing in search — Benedict Evans. "Apple is apparently giving Secret (or just "private") Demos showing off VisionOS 2.0 features that have yet to appear in beta, plus some kind of enterprise AR demos." California driver's licenses to be available on Apple, Google wallets. Apple iPad 9th Gen hits $199 for first time. Apple Podcasts launches on the web. Apple's first India-made iPhone Pro models coming this year. iPhone 16 Pro's new color could be called 'Desert Titanium'. Apple's biggest competition in India? Used iPhone sellers. Apple's HomePod and iPad hybrid tabletop robot with an arm spotted in the supply chain, estimated to cost $1,000. Picks of the Week: Leo's Pick: The Famous Internet Cafe Andy's Pick: "Sounds of Apple" & the Twenty Thousand Hertz Podcast Alex's Pick: Deliciously Ella Jason's Pick: Carbon Copy Cloner 7 Hosts: Leo Laporte, Alex Lindsay, Andy Ihnatko, and Jason Snell Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsor: cachefly.com/twit

Explore why AI is the fastest adopted work tech ever, yet still not fast enough for some. A nuanced discussion on AI's current place in the hype cycle, insights from former a16z partner Benedict Evans, and the gap between managerial and user perceptions of AI. Concerned about being spied on? Tired of censored responses? Check out ⁠⁠⁠⁠⁠⁠Venice.ai⁠⁠⁠⁠⁠⁠ for private, uncensored AI alternative. Learn how to use AI with the world's biggest library of fun and useful tutorials: https://besuper.ai/ Use code 'podcast' for 50% off your first month. The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614 Subscribe to the newsletter: https://aidailybrief.beehiiv.com/ Join our Discord: https://bit.ly/aibreakdown