Podcast appearances and mentions of andrew mcafee

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

My fellow pro-growth/progress/abundance Up Wingers in America and around the world:Will artificial intelligence displace workers or make them more valuable? Probably plenty of both. But how much in either direction, and how fast will all this change happen?Today on Faster, Please!—The Podcast, I am joined by Erik Brynjolfsson, one of the world's top economists studying how AI is reshaping productivity, jobs, and the American economy.Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and Senior Fellow at the Stanford Institute for Human-Centered AI, and Director of the Stanford Digital Economy Lab. He is also the co-author, along with Andrew McAfee, of Machine, Platform, Crowd, The Second Machine Age, and the classic Race Against the Machine. He is a co-founder of Workhelix, which helps large companies measure, track, and maximize the return of their AI investments.We explore what the next decade of AI could mean for workers, businesses, and the broader economy, and what the relationship between humans and intelligent machines may look like. We discuss why views from Silicon Valley and the East Coast differ so sharply on AI's impact, why the technology has produced dramatically different results across companies, and why some firms and departments are already seeing meaningful productivity gains while others have yet to unlock AI's full potential.In This Episode:* What AI brings to the table (0:35)* Does AI bring too much? (6:49)* Moving away from the Valley view (10:55)* How perspectives are formed (16:05)* Companies and productivity (23:01)* AI in the foreseeable future (29:21)A lightly edited transcript of our conversation will be appear in my Week in Review issue on Saturday. (Another option is using the Substack auto transcript function.)But here are some edited highlights from the chat:What's the positive case for workers in an AI future that currently sounds like it only benefits CEOs, tech firms, and data-center buildersThis is, I think, the best time to be alive if you're somebody who's got agency and ambition and intention and wants to do something, create new things for themselves and for the world. But that's not the story that's out there. …There's going to be a lot of new jobs created. You got to tell both parts of that story. Of course, you want to lean into the second part of the story about the new stuff that's being created [and not just job disruption and loss], because that's where people should be focusing. There's no point staring at the things that are disappearing… My company, Workhelix, is all about doing that. So I'm doing what I can for my part. I would love to see more people lean into that part of the story.Can someone coherently believe both that AI may become extremely powerful—possibly AGI or superintelligence—and that the future labor market can still be broad, humane, and full of useful work?I think we're going to have several decades worth of humans and machines working together. I'd like to extend that window where we can still have an important role for humans to contribute and for us to expand that pie, not simply automate what's already existing. We should probably be preparing for some further time in the future when there's less of a role for people. But most of my friends here in Silicon Valley, I think their timelines are way too short for when humans no longer have a role.If AI eventually becomes capable of doing almost all economically valuable work, would that actually be a desirable future for humanity, and what would make it a good society rather than a dystopia?We should start preparing for a period where AI can do almost everything and we need to come up with mechanisms so that we still have freedom and power in that kind of world. I don't think that's automatic. And one of my biggest concerns, to be frank, is not that we don't have abundance, I think we will, but it's that we don't have freedom and autonomy. That's something that's not to be taken for granted, and we need to put in place ways that we not only have the wealth, but we also have widely shared prosperity and widely shared decision making rights.Is AI already delivering real business value and productivity gains, or are the impressive lab results still mostly failing to show up in the economy?The returns (AI productivity gains) have been somewhat disappointing. To me, that's totally natural. That's totally understandable. As you know, I've done a lot of work, we call it the Productivity J Curve on the need for complementary investments for intangible investments in new business process design and new skills for the workforce, even new products and services. Those take time. With past general purpose technologies like the steam engine and electricity, it took literally decades before you got those returns.How should we think about AI's usefulness when some high-profile business uses have produced embarrassing hallucinations?They (AI) can also do wondrous things that are incredibly valuable. My advice is to keep a human in the loop. Ultimately, you, the person, is responsible for the output. You can identify where the good things are and not the bad things.Can AI progress happen so quickly that society can't adapt, and should policymakers worry about the speed of change, not just the destination?How fast do we want to go with this? It's not infinitely fast. We want to be able to digest and manage it. Now, the way I would handle that is I would put more resources into speeding up our ability to understand and adapt, and we're not doing enough of that. And that means, for instance, instead of cutting the budget for economic statistics, I would be massively boosting it so we get more visibility.On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe

Zal AI al onze jobs weg-automatiseren? Deze week is MIT-techdenker Andrew McAfee speciaal naar Brussel gevlogen om op ons event New Insights te komen praten over de maatschappelijke impact van artificiële intelligentie. McAfee is onder meer directeur van het MIT Initiative on the Digital Economy. Hij bestudeert hoe technologie de wereld verandert en heeft er ook al verschillende boeken over geschreven. In deze extra aflevering van De 7 spreekt host Roan Van Eyck met McAfee (in het Engels) over de jobvernietiging die artificiële intelligentie teweeg zal brengen, of er überhaupt winst mee te maken valt, en wat bedrijven nu best doen om AI zo slim mogelijk te integreren in hun werking. Lees: MIT-onderzoeker Andrew McAfee: ‘AI-bedrijven maken rampzalige reclame voor hun technologie’ See omnystudio.com/listener for privacy information.

Artificial intelligence is advancing quickly, but its real impact on productivity, jobs, and competitive advantage is still uncertain. In this four-part special series, we'll share conversations from the recent HBR Strategy Summit to help you get ahead. In this episode, Andrew McAfee, principal research scientist at MIT and cofounder and codirector of the MIT Initiative on the Digital Economy at the MIT Sloan School of Management, will explain why we're in a moment where “nobody knows anything” about how AI will ultimately reshape business—and what leaders should do anyway. Plus, he argues cutting entry-level hiring because of AI could be a major long-term mistake. HBR editor at large Adi Ignatius contributes audience questions.

After years of disappointing productivity growth, are we about to experience an AI-powered breakout? On this episode of The Permanent Problem podcast, Brink Lindsey welcomes Andrew McAfee, a principal research scientist at MIT Sloan School of Management and the author of (most recently) The Geek Way, to discuss the current state and future prospects of technological and economic dynamism. They start off by reviewing recent developments in AI and discussing whether LLMs will lead soon to superhuman machine intelligence. They then dive into the potential of current LLM technology to substitute for white-collar knowledge work, emphasizing the tortuous, trial-and-error process of technological diffusion and the distinction between eliminating tasks and eliminating jobs. Here McAfee points out how the new style of business organization he calls the "Geek Way" can accelerate this discovery process. Finally, Lindsey and McAfee review the political barriers to innovation erected by today's interest-group "vetocracy" and the daunting severity of the problem in western Europe.

What if there was one place left in the United States where Wi-Fi, cell towers, and constant wireless exposure simply did not exist? In this episode of the Healthier Tech Podcast, I sit down with Andrew McAfee, licensed electrician, EMF expert, and inventor of the Nuisance Current Blocker, to explore one of the most important and little-known places in America: the National Radio Quiet Zone in Green Bank, West Virginia. Originally protected by state law to shield one of the world's most sensitive radio telescopes, this region has quietly become a refuge for people who are electrically sensitive and overwhelmed by modern wireless exposure. Today, that refuge is under threat. Andrew explains what the Radio Quiet Zone is, why it matters for both science and human health, and how recent decisions to allow Wi-Fi inside the zone could permanently change it. We also discuss the legal effort now underway to update West Virginia law so it protects people, not just telescopes. This conversation goes far beyond EMFs. It touches on public health, autonomy, surveillance, and why preserving even one truly low-EMF environment may matter more than ever. In this episode, we cover: What the National Radio Quiet Zone is and why it was created Why electrically sensitive people are moving to Green Bank How wireless exposure affects the nervous system and immune health The difference between ambient wireless radiation and satellite communication Why allowing Wi-Fi inside the Quiet Zone could open the floodgates The legal fight to protect the Quiet Zone for future generations Why this area may be the last true low-EMF refuge in the U.S. Resources mentioned: Green Bank Safe Haven https://greenbanksafehaven.net Protect the National Radio Quiet Zone https://protectnationalquietzone.org Donate to Protect the National Radio Quiet Zone https://protectnationalquietzone.org/donate Children's Health Defense https://childrenshealthdefense.org If you care about the future of technology, health, and human resilience, this is a conversation you do not want to miss. Subscribe to the Healthier Tech Podcast for more conversations at the intersection of technology and well-being. Connect with R Blank: For more Healthier Tech Podcast episodes, and to download our Healthier Tech Quick Start Guide, visit https://HealthierTech.co and follow https://instagram.com/healthiertech  Additional Links: EMF Superstore: https://ShieldYourBody.com (save 15% with code “pod”) Digital Wellbeing with a Human Soul: https://Bagby.co (save 15% with code “pod”) Youtube: https://youtube.com/shieldyourbody Instagram: https://www.instagram.com/bagbybrand/ Tiktok: https://www.tiktok.com/@bagby.co Facebook: https://www.facebook.com/shieldyourbody This episode is brought to you by Shield Your Body—a global leader in EMF protection and digital wellness. Because real wellness means protecting your body, not just optimizing it. If you found this episode eye-opening, leave a review, share it with someone tech-curious, and don't forget to subscribe to Shield Your Body on YouTube for more insights on living healthier with technology.

Elon Musk Reporter Theo Wayt talks with today's TITV Host Anita Ramaswamy about Elon Musk's mission to replace X staff with xAI's Grok and the role of the Siboliyev twins. We also talk with D.A. Davidson's Gil Luria and WorkHelix's Andrew McAfee about AI's accelerating impact on job cuts, particularly in white-collar professions, and the shift of wealth from big tech to NVIDIA. Warp CEO Zack Lloyd shares his data showing Google's Gemini 3.0 model's advantage over OpenAI's models in agentic coding. Lastly, KeyBanc Capital Markets' Jackson Ader provides an earnings preview for Zoom, Zscaler, and Salesforce, and The Information's Co-Executive Editor Martin Peers breaks down the unprecedented growth of NVIDIA's free cash flow and how the company is using it to fight competition.Articles discussed on this episode:https://www.theinformation.com/articles/twins-pushing-elon-musks-plans-replace-x-staff-grokhttps://www.theinformation.com/articles/nvidias-mushrooming-cash-pile-spotlights-spending-choicesTITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Subscribe to: - The Information on YouTube: https://www.youtube.com/@theinformation- The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agenda

En esta conversación con Andrew McAfee, cofundador y codirector del Initiative on the Digital Economy del MIT, exploramos cómo las empresas pueden integrar la Inteligencia Artificial (IA) para generar valor real, más allá de la innovación superficial.McAfee define el error más común en la adopción de la IA: dedicar demasiado tiempo a la planificación, la formación de comités y la reflexión sin llegar a la implementación. Por ello, lo más importante es empezar a usar la IA y monitorear su funcionamiento.Descubrirás las claves para una integración exitosa:• Maestría Digital: La combinación esencial de Capacidades Digitales (inversión efectiva en tecnología) y Capacidades de Liderazgo (una visión que impulse la transformación).• Priorización y Medición: La importancia de alinear las oportunidades de IA con las prioridades estratégicas de la organización y establecer indicadores de avance (KPIs) claros.• Adopción Obligatoria: El éxito en la adopción de la IA debe ser un OKR (Objetivo y Resultado Clave) en la evaluación de desempeño de los empleados.Si tu organización busca transformar la experiencia del cliente, explotar operaciones centrales o reinventar modelos de negocio a través de la IA, en este episodio encontrarás un marco estratégico para una ejecución que perdure.

A handful of places still let your nervous system breathe, and Green Bank, West Virginia, is one of them. We sit down with returning guest Andrew McAfee—author, inventor, teacher and licensed electrician—to unpack why the town's famed radio quiet sanctuary is suddenly vulnerable and what it will take to keep it intact. A recent policy shift around 2.4 GHz opens the door to Wi‑Fi meshes, smart meters and new towers that could erase the very conditions that make recovery possible for so many.There's a practical path forward. Children's Health Defense is engaged to push a targeted update to West Virginia state law so protections extend to people, not only the telescope. The strategy is concrete: fund the legal brief, sustain lobbying, and lock in a framework that prevents 2.4 GHz from seeding the very infrastructure Green Bank has long avoided. Andrew also shares how Safe Home helps electrically sensitive residents navigate housing and power-grid issues so they can actually live well in this unique low-EMF area.Your help can make the difference, please consider donating now to this important initiative:  https://childrenshealthdefense.org/support/protect-the-national-radio-quiet-zone/Avoid fees by mailing a check to:Children's Health Defense852 Franklin Ave., Suite 511Franklin Lakes, NJ 07417Include a note that the donation is specifically for the "Protect the NRQZ"These special prices are good only through the end of October 2025. Here's the link for 50% off the EMF Remedy Premium audio podcast only: https://emfremedypremium.supercast.com/subscriptions/new?code=36bd7df2-7f65-406e-a569-b72974adfd0cHere's the link for 60% off the EMF Remedy Premium audio podcast + One year of Keith's Substack: https://emfremedypremium.supercast.com/subscriptions/new?code=36bd7df2-7f65-406e-a569-b72974adfd0cSupport the showContinue the journey with the EMF Remedy Premium Podcast, with over 110 episodes and counting! Keith Cutter is President of EMF Remedy LLChttps://www.emfremedy.com/YouTube Channel: https://www.youtube.com/channel/UCp8jc5qb0kzFhMs4vtgmNlgKeith's SubstackThe EMF Remedy Podcast is a production of EMF Remedy LLC

A lot of jobs in the modern economy don't pay a living wage, and some of those jobs may be wiped out by new technologies. So what's to be done? We revisit an episode from 2016 for a potential solution. SOURCES:Erik Brynjolfsson, professor of economics at Stanford University.Evelyn Forget, professor of economics and community health sciences at the University of Manitoba.Sam Altman, C.E.O. of OpenAI.Robert Gordon, professor emeritus of economics at Northwestern University.Greger Larson, professor of archeology at the University of Oxford. RESOURCES:"Here's what a Sam Altman-backed basic income experiment found," by Megan Cerullo (CBS News, 2024).Utopia for Realists, by Rutger Bregman. The Correspondent (2016).The Second Machine Age, by Erik Brynjolfsson and Andrew McAfee (2014)."The Town With No Poverty: Using Health Administration Data To Revisit Outcomes of a Canadian Guaranteed Annual Income Field Experiment," by Evelyn Forget (Canadian Public Policy, 2011)."The Negative Income Tax and the Evolution of U.S. Welfare Policy," by Robert Moffitt (Journal of Economic Perspectives, 2003).Capitalism and Freedom, by Milton Freidman (2002)."Lesson from the Income Maintenance Experiments," (Federal Reserve Bank of Boston and The Brookings Institution, 1986).Law, Legislation and Liberty, Volume 3: The Political Order of A Free People, by Frederick Hayek (1981)."Daniel Moynihan and President-elect Nixon: How charity didn't begin at home," by Peter Passell and Leonard Ross (New York Times, 1973)."Income Maintenance Programs," (Hearings Before The Subcommittee On Fiscal Policy Of The Joint Economic Committee Congress Of The United States, 1968). EXTRAS:"President Nixon Unveils the Family Assistance Program," (1969)."Milton Friedman interview with William F Buckley Jr.," (1968)."Martin Luther King Jr. advocates for Guaranteed Income at Stanford," (1967). Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What happens when every company becomes a tech company—and the geeks take the wheel? Renowned economist, bestselling author, and MIT Principal Research Scientist Andrew McAfee unpacks how AI is transforming corporate strategy—from cement makers to software engineers. Drawing on insights from his book The Geek Way and his startup Workhelix, McAfee shares what he's hearing from executives around the world, where AI is delivering real ROI, and why understanding how we measure impact is just as important as what we measure. From call centers to material science labs, from spreadsheet power users to the future of education, McAfee examines how generative AI is changing who wins—and how people can avoid getting left behind. He also weighs in on the biggest AI misconceptions, the risks that actually matter, and why Silicon Valley still holds the crown in the age of innovation. Topics include: The management playbook of the future Real-world studies on AI's impact in the workplace Why AI helps some workers more than others The “credibility revolution” in measuring ROI What companies get wrong about scaling AI Whether writing—and even coding—will still matter in 10 years Andrew McAfee is the Co-Director of the IDE and a Principal Research Scientist at the MIT Sloan School of Management. His research investigates how information technology changes the way companies perform, organize themselves, and compete. He is a NYT bestselling author and writes a widely read blog, which is at times one of the 10,000 most popular in the world. Prior to joining MIT Sloan, McAfee was a professor at Harvard Business School. Connect with CSAIL Alliances: On our site: https://cap.csail.mit.edu/about-us/meet-our-team On X: https://x.com/csail_alliances On LinkedIn: https://linkedin.com/company/mit-csail #MITCSAIL #AI #GenerativeAI #Leadership #Technology #CSAILPodcast

"A balcony to watch the moon!" ShaoLan tells the world leading economist - Andrew McAfee the literal romantic meaning of platform, and the two explore the ways in which other kinds of platforms are shaping the world we live in. ✨ BIG NEWS ✨ Our brand new Talk Chineasy App, is now live on the App Store! Free to download and perfect for building your speaking confidence from Day 1. portaly.cc/chineasy Visit our website for more info about the app.

What's Chinese for Money?! Economics expert Andrew McAfee talks finance with ShaoLan, how to make the pronunciation perfect and how to light-heartedly ask for money! ✨ BIG NEWS ✨ Our brand new Talk Chineasy App, is now live on the App Store! Free to download and perfect for building your speaking confidence from Day 1. portaly.cc/chineasy Visit our website for more info about the app.

This is the last and amongst the liveliest of my interviews at Munich's DLD Conference this year. An old friend who has appeared on KEEN ON several times before, Andrew McAfee is a MIT professor who co-wrote the 2014 classic The Second Machine Age. In our conversation, celebrating the 20th anniversary of the DLD Conference, McAfee reflects on the technological changes of the past 20 years,. He acknowledges that while he accurately predicted the broad trajectory of technological advancement, he underestimated AI's capabilities in areas like language processing and creative tasks. McAfee discusses the emergence of deep learning around 2012 and its evolution into today's generative AI. While maintaining overall optimism about technology's impact, he expresses concern about increasing social polarization and anxiety, particularly related to social media use, though he notes these trends actually preceded current technology. On economic matters, McAfee challenges the notion that tech innovation is stagnating, pointing to newcomers like Nvidia and OpenAI as evidence of continued inventive dynamism. He discusses Europe's technological lag behind the United States, citing regulatory challenges like GDPR as potential factors. Regarding climate change, McAfee believes technological solutions, particularly nuclear fusion, could address environmental challenges, though he acknowledges the severity of the crisis. He concludes by warning how traditional companies must adapt to survive in an era of rapid technological change, particularly facing competition from more agile, tech-savvy competitors.Andrew McAfee (@amcafee) is a Principal Research Scientist at the MIT Sloan School of Management, co-founder and co-director of MIT's Initiative on the Digital Economy, and the inaugural Visiting Fellow at the Technology and Society organization at Google. He studies how technological progress changes the world. His next book, The Geek Way, will be published by Little, Brown in 2023. His previous books include More from Less and, with Erik Brynjolfsson, The Second Machine Age. McAfee has written for publications including Foreign Affairs, Harvard Business Review, The Economist, The Wall Street Journal, and The New York Times. He's talked about his work on CNN and 60 Minutes, at the World Economic Forum, TED, and the Aspen Ideas Festival, with Tom Friedman and Fareed Zakaria, and in front of many international and domestic audiences. He's also advised many of the world's largest corporations and organizations ranging from the IMF to the Boston Red Sox to the US Intelligence Community. McAfee and his frequent coauthor Erik Brynjolfsson are othe nly people named to both the Thinkers50 list of the world's top management thinkers and the Politico 50 group of people transforming American politics.Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children.Keen On is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

In this episode of The Unmistakable Creative, Srini Rao interviews Andrew McAfee, author of "The Geek Way: The Radical Mindset that Drives Extraordinary Results." They discuss the social dynamics of high school, flaws in the education system, and the importance of cultural evolution. McAfee emphasizes the significance of confidence, observability, and autonomy in achieving success. He explores the role of status and the impact of overconfidence. The conversation delves into the geek norms of science, ownership, and speed, offering valuable insights for personal and professional growth. Discover how to apply these principles in your daily life and become unmistakable. Subscribe for ad-free interviews and bonus episodes https://plus.acast.com/s/the-unmistakable-creative-podcast. Hosted on Acast. See acast.com/privacy for more information.

Kara and Scott are back with part two of Pivot's special series on the basics of artificial intelligence. How is AI revolutionizing the workplace? And what are the skills workers need to learn to stay ahead? Andrew McAfee, MIT research scientist and co-founder of Workhelix, explains why he doesn't think AI is a job killer, and shares the advice he gives to business leaders about integrating AI into workflows. Follow us on Instagram and Threads at @pivotpodcastofficial. Follow us on TikTok at @pivotpodcast. Send us your questions by calling us at 855-51-PIVOT, or at nymag.com/pivot. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Today, we're honored to have a true trailblazer in the realm of electromagnetic field (EMF) protection and home wellness – Andrew McAfee. Based in Raleigh, North Carolina, Andrew's journey is a testament to turning personal challenges into a quest for solutions.   He is also the inventor of the ground-breaking NCB, which is available at Shield Your Body. This is the world's first product to safely strip dirty electricity and contact current from the grounding conductor. Andrew explains today how the NCB is different from other dirty electricity filters, what dirty electricity is, and how it can often manifest in electro-sensitivity.    In this episode, you will hear:  What electro-sensitivity is, how it manifested for Andrew and his wife, and the levels of perception.  Defining dirty electricity (DE).  How electricity affects the infections in our bodies.  Things power companies, homeowners, and corporations can do to help clean up dirty electricity.  The necessity for health standards, not just fire safety standards in building codes.  The NCB Pro and why it addresses the grounding conductor, unlike other dirty electricity filters.  The key difference between the NCB and the NCB Pro.  Handling dirty energy in your apartment.  Diseases and other conditions caused by electrical sensitivity.  Your grounding is dirty and dangerous until proven otherwise.    A seasoned EMF consultant, Andrew's story began with a realization—health issues tied to wiring problems in his own home.    After moving into a brand new home Andrew and his wife became electrically sensitive in 2001. He was fortunate to have Charles Keen and Karl Riley as early mentors. Beginning in 2010 Andrew successfully petitioned the NC Utility Commission to order Duke Energy to provide a no-cost, non-emitting meter for its customers.   After 15 years as Principal Horn of the NC Symphony, earning a master's degree in conducting, and 10 years as a professor of music at UNC Chapel Hill, he left music to help others with ES. Andrew was featured in a 2014 TIME docu-film “Searching for a Golden Cage” about electro-sensitivity.   In 2017, he made the leap to become a full-time EMF consultant, channeling his knowledge into creating safer living spaces for all.   Utilizing electrical training materials from Mike Holt, Andrew earned a Residential Electrician's Career Diploma in Penn Foster's year-long program in 2019.  In 2020, he wrote 6 short books as a part of his Killing Current series to raise awareness about the dangers of contact current, and in 2021 co-created a web course “Staying Healthy in a 5G World.”    Fast forward to today, and Andrew stands as the innovative mind behind the NCB Pro, a groundbreaking solution reshaping the landscape of home grounding safety. As a career diplomat in residential electricity, he brings a unique blend of expertise and passion to the world of EMF protection.   He currently works full-time for Bonneville Electric as a project manager and service technician and has earned an OSHA 10 safety certification.   He is also the inventor of the ground-breaking NCB, which is available at Shield Your Body.   Connect with Andrew McAfee: Website: https://www.homeemftracing.com/    Find out more about the NCB at: https://shieldyourbody.com/ground      Connect with R Blank and Stephanie Warner:  For more Healthier Tech Podcast episodes, and to download our Healthier Tech Quick Start Guide, visit https://HealthierTech.co and follow https://instagram.com/healthiertech   Additional Links: Shield Your Body website: https://ShieldYourBody.com Shield Your Body Youtube Channel: https://youtube.com/shieldyourbody Host R Blank on LinkedIn: https://www.linkedin.com/in/rblank9/ Shield Your Body on Instagram: https://instagram.com/shieldyourbody

What does it mean to be geeky -- and how are geeks changing the world? Andrew McAfee joins Vasant Dhar in episode 87 of Brave New World to share his insights on how geeks have created a brave new innovation culture. Useful resources: 1. Andrew McAfee on Twitter, LinkedIn, Amazon, MIT and his own website. 2. The Geek Way -- Andrew McAfee. 3. The Second Machine Age -- Erik Brynjolfsson and Andrew McAfee. 4. Elon Musk -- Walter Isaacson. 5. No Rules Rules -- Reed Hastings and Erin Meyer. 6. Regional Advantage: Culture and Competition in Silicon Valley and Route 128 -- AnnaLee Saxenian. 7. The New Argonauts -- AnnaLee Saxenian. 8. What the Dormouse Said -- John Markoff. 9. John's Markoff's interview of Raj Reddy. 10. The Secret of Our Success -- Joseph Henrich. 11. The Knowledge Machine -- Michael Strevens. 12. When It Comes to Culture, Does Your Company Walk the Talk? -- Donald Sull, Stefano Turconi and Charles Sull. 13. The Paradigm Shifts in Artificial Intelligence -- Vasant Dhar. Check out Vasant Dhar's newsletter on Substack. Subscription is free!

The highly entertaining, world-leading economist Andrew McAfee returns to share his thoughts on the way in which “the crowd” is changing the world that we live in. ShaoLan ensures that his Chinese pronunciation of the word is spot on!

In this episode of The Unmistakable Creative, Srini Rao interviews Andrew McAfee, author of "The Geek Way: The Radical Mindset that Drives Extraordinary Results." They discuss the social dynamics of high school, flaws in the education system, and the importance of cultural evolution. McAfee emphasizes the significance of confidence, observability, and autonomy in achieving success. He explores the role of status and the impact of overconfidence. The conversation delves into the geek norms of science, ownership, and speed, offering valuable insights for personal and professional growth. Discover how to apply these principles in your daily life and become unmistakable. Subscribe for ad-free interviews and bonus episodes https://plus.acast.com/s/the-unmistakable-creative-podcast. Hosted on Acast. See acast.com/privacy for more information.

"A balcony to watch the moon!" ShaoLan tells the world leading economist - Andrew McAfee the literal romantic meaning of platform, and the two explore the ways in which other kinds of platforms are shaping the world we live in.

Welcome to episode #923 of Six Pixels of Separation - The ThinkersOne Podcast. Here it is: Six Pixels of Separation - The ThinkersOne Podcast - Episode #923. He's a hugely respected thought leader and practitioner at the intersection of technology and business. Andrew McAfee offers a compelling exploration of The Geek Way in his latest book, which redefines our approach to innovation and leadership. As a Principal Research Scientist at the MIT Sloan School of Management and the co-founder of MIT's Initiative on the Digital Economy, Andy has been at the forefront of how technological progress reshapes our world. Andy unpacks the essence of The Geek Way, revealing it as more than just a cultural shift. It's a transformative approach to achieving extraordinary results across industries. The book, characterized by an unwavering commitment to science, speed, ownership, and openness, emerges not only as a pathway to success but as a better model for realizing company goals and fostering innovation. As geek culture transitions from the fringes to the mainstream (look no further than Marvel movies), admired for its dedication to evidence-based decision-making and problem-solving, Andy highlights the profound impact of this mindset on business practices and societal progress. One of the most compelling aspects of Andy's work is the application of The Geek Way to the realm of artificial intelligence. In an era where AI's potential to revolutionize industries is often met with equal parts enthusiasm and apprehension, Andy provides a balanced perspective. He acknowledges the transformative power of AI as a tool for economic progress while addressing the societal implications of job displacement, advocating for iterative learning and adaptation as keys to harnessing AI's benefits. His previous books include More From Less, Machine. Platform. Crowd, The Second Machine Age (with Erik Brynjolfsson - which I adored), Race Against The Machine and Enterprise 2.0. For leaders, innovators, and anyone curious about the intersection of technology and business, this podcast and Andy's insights are indispensable. Enjoy the conversation... Running time: 1:04:06. Hello from beautiful Montreal. Subscribe over at Apple Podcasts. Please visit and leave comments on the blog - Six Pixels of Separation. Feel free to connect to me directly on Facebook here: Mitch Joel on Facebook. Check out ThinkersOne. or you can connect on LinkedIn. ...or on Twitter. Here is my conversation with Andrew McAfee. The Geek Way. Second Machine Age. Race Against The Machine. More From Less. Machine. Platform. Crowd. Enterprise 2.0. MIT's Initiative on the Digital Economy. MIT Sloan School of Management. Follow Andrew on X. Follow Andrew on LinkedIn. This week's music: David Usher 'St. Lawrence River'. Takeaways: Geek culture has evolved from being stigmatized to being admired and accepted. The Geek Way is characterized by norms such as science, speed, ownership, and openness. The Geek Way can lead to better outcomes for companies and is more effective in achieving goals. Leadership plays a crucial role in driving the adoption of the Geek Way and overcoming challenges.  Visionary leaders are not essential for the 'geek way' to thrive in various industries. Artificial intelligence is a powerful tool that can accelerate economic progress. Concerns about job displacement and societal implications of AI. Letting go of personal hangups is crucial for embracing new opportunities and growth. Chapters: 00:00 - Introduction and Geek Culture 03:34 - The Evolution of Geek and Geek Culture 09:44 - The Geek Way and Business Geeks 14:11 - The Geek Way and Big Tech 19:11 - The Geek Way and the Post-Pandemic Workforce 25:34 - The Geek Way and Technology Impact 30:53 - Geek Leaders and Their Characteristics 36:55 - The Geek Way in Other Industries 45:21 - The Heart of Science 46:09 - Geek Way in Solving Wicked Problems 47:38 - Geek Way in Prosaic Industries 47:46 - Artificial Intelligence and its Impact 53:27 - Concerns and Optimism about Artificial Intelligence 56:23 - The Role of Critical and Emergent Thinking 59:48 - Letting Go of Hangups

When we think of geeks, we tend to think of the people who built the tech we use – from our smartphones to search engines to AI.   But if we just focus on the tech, we're missing out on a lot. We're overlooking how these same geeks reinvented corporate culture using a repeatable set of norms that ensure sustainable innovation. Andrew McAfee is a principal research scientist at the MIT Sloan School of Management and cofounder and codirector of the MIT Initiative on the Digital Economy. He's been studying innovative companies for decades, and he's taken what he's learned and written about it in his latest book, The Geek Way: The Radical Mindset that Drives Extraordinary Results. I'm convinced what Andrew's learned about the geek way – and its four key norms – is a roadmap for where today's – and tomorrow's - companies are headed. Episode Links The Geek Way New Book Explains the ‘Geek Way' to Manage a Company Forward Thinking on How Geeks are Changing the World Interview with Roger Martin The Team Learn more about host, Gayle Allen, and producer, Rob Mancabelli, here. Support the Podcast If you like the show, please rate and review it on iTunes or wherever you subscribe, and tell a friend or family member about the show. Subscribe Click here and then scroll down to see a sample of sites where you can subscribe.

Today, MIT's Andrew McAfee stops by to share a few key insights from his recent book "The Geek Way: The Radical Mindset that Drives Extraordinary Results."

  In his new bestseller, “The Geek Way,” Andrew McAfee makes the fascinating case that the most important technological revolution of our time isn't what companies make, it's in how they're being managed. And by his definition, being geeky isn't a pejorative but rather a clear description of leaders who are perennially curious, not afraid […] The post Andrew McAfee: Being “Geeky” Happens To Be Good Leadership Form appeared first on Mark C. Crowley.

What's Chinese for Money?! Economics expert Andrew McAfee talks finance with ShaoLan, how to make the pronunciation perfect and how to light-heartedly ask for money!

Why are humans the only species on the planet that's been able to cooperate on such a massive scale and continuously reinvent our culture? Andrew McAfee is the co-director of the Initiative on the Digital Economy and a principal research scientist at the MIT Sloan School of Management. His books, such as the Machine trilogy and The Geek Way, examine how technology and cultural evolution have shaped the modern workplace.He and Greg discuss what has allowed humans to evolve to be these super collaborators, how that evolution translates to organizational culture, and why the education system might be in need of an overhaul. *unSILOed Podcast is produced by University FM.*Episode Quotes:What does science do with overconfidence?27:00: What science does that is brilliant is it says to us, overconfident human beings, "You're going to win. You're so smart. Your evidence is going to be right. Go collect the evidence; you're going to be right," and we over overconfidently march off and go do all that. So the amazing thing that happens, the jiujitsu that happens, is that science takes our overconfidence and channels it exactly where it should be. Which is doing the hard work to gather evidence and then confidently getting up in front of your peers and presenting it and have them kick you in the teeth over and over again. It ain't fun, but that's what we signed up for. And what I think is going on at geek companies is they're importing that ground rule to make their decisions. That's why their batting average is higher.What is it that allows humans to do this thing unique on the planet?05:12: We human beings, this weird species, have two superpowers. One of them is that we come together and cooperate intensely with large numbers of individuals who we are not related to and who are not our kin…[05:34]The other one is that we evolve our cultures much more rapidly than any other species on the planet.Navigating disagreement and safetyism in higher education33:53: If we're not training people about how to debate, disagree, argue, and do it without being jerks or without being completely thin-skinned about it, we're not doing people a service. We're doing them a real disservice. So I think there has been increased safetyism, especially on college campuses. And I think that is not serving young people well for a whole bunch of reasons.Is our politics and bureaucracy complements or substitutes?45:53: We want status, and that's where bureaucracy comes from. I'm going to figure out a need to be involved in this work. That gives me status. I honestly believe that's the deepest reason for this stultifying bureaucracy that we come across. The CEO of most companies, if they look at what the processes are like inside their company, they go, "How did things get this bad? What is going on here?" This is not anything close to what I want, but that's because the people in the organization create that encroachment or that encumbrance all the time.Show Links:Recommended Resources:Amy EdmondsonAmy Edmondson on unSILOedChris ArgyrisThe Ape that Understood the Universe: How the Mind and Culture Evolve by Steve Stewart-WilliamsThe Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter by Joseph HenrichThe Knowledge Machine: How Irrationality Created Modern Science by Michael StrevensFinal Accounting: Ambition, Greed and the Fall of Arthur Andersen by Barbara TofflerMaria MontessoriGuest Profile:Faculty Profile at MITProfessional WebsiteAndrew McAfee on TEDxBoston 2012His Work:The Geek Way: The Radical Mindset that Drives Extraordinary ResultsThe Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies Machine, Platform, Crowd: Harnessing Our Digital FutureRace Against the Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the EconomyEnterprise 2.0: New Collaborative Tools for Your Organization's Toughest ChallengesMore from Less: The Surprising Story of How We Learned to Prosper Using Fewer Resources―and What Happens Next

838: There's no shame in being called a “geek”. In fact, according to Andrew McAfee, it's actually a compliment. In this episode, Andrew, co-founder of MIT's Initiative on the Digital Economy, shares insight into the research he's conducted while writing his latest book “The Geek Way”. He explains the ‘Four Norms' of geeks; science, ownership, speed, & openness; and how companies can foster and navigate a culture that follows ‘the geek way'. Andrew discusses what it means to adopt Agile, how to leverage Agile practices to accelerate the pace of innovation, and why companies often get trapped in planning-heavy processes. Finally, Andrew looks back on his career and the learnings he has drawn from his writing process.

838: There's no shame in being called a “geek”. In fact, according to Andrew McAfee, it's actually a compliment. In this episode, Andrew, co-founder of MIT's Initiative on the Digital Economy, shares insight into the research he's conducted while writing his latest book “The Geek Way”. He explains the ‘Four Norms' of geeks; science, ownership, speed, & openness; and how companies can foster and navigate a culture that follows ‘the geek way'. Andrew discusses what it means to adopt Agile, how to leverage Agile practices to accelerate the pace of innovation, and why companies often get trapped in planning-heavy processes. Finally, Andrew looks back on his career and the learnings he has drawn from his writing process.

Today, we're honored to have a true trailblazer in the realm of electromagnetic field (EMF) protection and home wellness – Andrew McAfee. Based in Raleigh, North Carolina, Andrew's journey is a testament to turning personal challenges into a quest for solutions. He is also the inventor of the ground-breaking NCB, which is available at Shield Your Body. This is the world's first product to safely strip dirty electricity and contact current from the grounding conductor. Andrew explains today how the NCB is different from other dirty electricity filters, what dirty electricity is, and how it can often manifest in electro-sensitivity.    In this episode, you will hear:  What electro-sensitivity is, how it manifested for Andrew and his wife, and the levels of perception.  Defining dirty electricity (DE).  How electricity affects the infections in our bodies.  Things power companies, homeowners, and corporations can do to help clean up dirty electricity.  The necessity for health standards, not just fire safety standards in building codes.  The NCB Pro and why it addresses the grounding conductor, unlike other dirty electricity filters.  The key difference between the NCB and the NCB Pro.  Handling dirty energy in your apartment.  Diseases and other conditions caused by electrical sensitivity.  Your grounding is dirty and dangerous until proven otherwise.    A seasoned EMF consultant, Andrew's story began with a realization—health issues tied to wiring problems in his own home.  After moving into a brand new home Andrew and his wife became electrically sensitive in 2001. He was fortunate to have Charles Keen and Karl Riley as early mentors. Beginning in 2010 Andrew successfully petitioned the NC Utility Commission to order Duke Energy to provide a no-cost, non-emitting meter for its customers. After 15 years as Principal Horn of the NC Symphony, earning a master's degree in conducting and 10 years as a professor of music at UNC Chapel Hill, he left music to help others with ES. Andrew was featured in a 2014 TIME docu-film “Searching for a Golden Cage” about electro-sensitivity. In 2017, he made the leap to become a full-time EMF consultant, channeling his knowledge into creating safer living spaces for all. Utilizing electrical training materials from Mike Holt, Andrew earned a Residential Electrician's Career Diploma in Penn Foster's year-long program in 2019.  In 2020, he wrote 6 short books as a part of his Killing Current series to raise awareness about the dangers of contact current and in 2021 co-created a web course “Staying Healthy in a 5G World.”  Fast forward to today, and Andrew stands as the innovative mind behind the NCB Pro, a groundbreaking solution reshaping the landscape of home grounding safety. As a career diplomat in residential electricity, he brings a unique blend of expertise and passion to the world of EMF protection. He currently works full-time for Bonneville Electric as a project manager and service technician and has earned an OSHA 10 safety certification. He is also the inventor of the ground-breaking NCB, which is available at Shield Your Body.   Connect with Andrew McAfee: Website: https://www.homeemftracing.com/    Find out more about the NCB at: https://shieldyourbody.com/ground      Connect with R Blank and Stephanie Warner:  For more Healthier Tech Podcast episodes, and to download our Healthier Tech Quick Start Guide, visit https://HealthierTech.co and follow https://instagram.com/healthiertech   Additional Links: Shield Your Body website: https://ShieldYourBody.com Shield Your Body Youtube Channel: https://youtube.com/shieldyourbody Host R Blank on LinkedIn: https://www.linkedin.com/in/rblank9/ Shield Your Body on Instagram: https://instagram.com/shieldyourbody

EMF consultant, author and inventor Andrew McAfee joins Keith Cutter to talk about serious, no-nonsense EMF assessment and remediation. We live in a created world. We are created beings -- electromagnetic in nature. For many years now we've been trading the healing and nurturing electromagnetic environment we were given for an electromagnetic environment toxic to life and health. Why? Knowingly or unknowingly we've traded health, life and stewardship of all life on earth  for convenience, amusement and stimulation. Some bargain, huh?You don't have to go along with this. If you want to take a precautionary approach to your family's exposure to harmful man-made electromagnetic radiation or if you MUST, as a life priority, reduce exposure – you're in the right place. Let's bring-back an idea from the past – make your home a castle. One that defends the occupants, in this case, from threats in the electromagnetic realm.Today we're listening to the third and final portion of my recent conversation with Andrew McAfee – Independent EMF Consultant, Author and Inventor. We'll be discussing sleep sanctuaries, inhabitable vs. uninhabitable areas, the evolution of EMF consciousness in personal and family harmony, contact current as ionizing radiation, pristine vs. in-home earthing, two of Andrew's inventions called the NCB and NCB pro.Please, if you have a heart -- to help us in producing and distributing this type of content – consider becoming a financial supporter of the show, link in the description. Writing a review, especially on Apple Podcast, is a help. Most important, please pray that our efforts here would be a blessing to many.Buckle-up! Part three of my recent conversation with Andrew McAfee – here we goAndrew's work is available here:  https://homeemftracing.com/Support the showSupport this podcast here: https://www.emfremedy.com/donate/Keith Cutter is President of EMF Remedy LLChttps://www.emfremedy.com/YouTube Channel: https://www.youtube.com/channel/UCp8jc5qb0kzFhMs4vtgmNlgReversing Electromagnetic Poisoning is a production of EMF Remedy LLCHelping you helping you reduce exposure to harmful man-made electromagnetic radiation in your home.

There's no shortage of books on Silicon Valley, with a quick Amazon search yielding over 40,000 results. Our guest today believes that most, if not all, of these books have overlooked a crucial element of the story: how these high-tech, disruptive, and revolutionary companies are actually run. How they implement and cultivate an organizational culture that is “freewheeling, fast-moving, egalitarian, evidence-driven, argumentative, and autonomous.” Today, we're thrilled to have Andrew McAfee with us. Andrew is a principal research scientist at the MIT Sloan School of Management and the co-founder and co-director of the MIT Initiative on the Digital Economy. His latest book, 'The Geek Way,' is aptly described by Reid Hoffman, the founder of LinkedIn, who wrote the foreword: 'By combining management theory, competitive strategy, the science of evolution, psychology, military history, and cultural anthropology, he has produced a remarkable work of synthesis. This work, which he dubs 'the geek way,' finally explains, with a single unified theory, the reasons why the tech startup approach has taken over so much of the world. This was a great conversation, and we hope you enjoy it as much as we did. With that said, let's get started.

What is a geek? What geek norms are associated with success?  Andrew McAfee will answer these questions and more. He's a principal research scientist at the MIT Sloan School of Management and author of the new book, The Geek Way: The Radical Mindset that Drives Extraordinary Results. This is a guest episode from McKinsey's Forward Thinking podcast, with co-host and McKinsey partner Michael Chui.See www.mckinsey.com/privacy-policy for privacy information

Organisational psychologist Adam Grant and IT research scientist Andrew McAfee discuss how we can all learn to unlock a bit more of our own potential with Intelligence Squared's executive producer, Hannah Kaye. Grant is the author of recent book Hidden Potential: The Science of Achieving Greater Things. McAfee recently published The Geek Way, which charts the rise of what he terms geeks in running some of the most successful organisations on the planet over the past two decades.  You can get even more of Adam Grant in the new year when he returns for a rare London appearance for Intelligence Squared's live event: Achieving Greatness. This time Grant will be teaming up with FT columnist, author and economist Tim Harford at Cadogan Hall on 18 January 2024. Visit the link below to get tickets. https://www.intelligencesquared.com/events/achieving-greatness-with-adam-grant-and-tim-harford/ ... If you'd like to get access to all of our longer form interviews and members-only content, just visit intelligencesquared.com/membership to find out more.  For £4.99 per month you'll also receive: - Full-length and ad-free Intelligence Squared episodes, wherever you get your podcasts - Bonus Intelligence Squared podcasts, curated feeds and members exclusive series - 15% discount on livestreams and in-person tickets for all Intelligence Squared events - Our member-only newsletter The Monthly Read, sent straight to your inbox ... Or Subscribe on Apple for £4.99: - Full-length and ad-free Intelligence Squared podcasts - Bonus Intelligence Squared podcasts, curated feeds and members exclusive series ... Already a subscriber? Thank you for supporting our mission to foster honest debate and compelling conversations! Visit intelligencesquared.com to explore all your benefits including ad-free podcasts, exclusive bonus content, early access and much more ... Subscribe to our newsletter here to hear about our latest events, discounts and much more. https://www.intelligencesquared.com/newsletter-signup/ Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode, Andrew McAfee shares his insights on "The Geek Way", a new book that explores a different approach to work and collaboration. Andrew McAfee is a Principal Research Scientist at the MIT Sloan School of Management and co-founder of MIT's Initiative on the Digital Economy. He has written several books, including "More from Less" and "The Second Machine Age". During the conversation, McAfee discusses the importance of embracing a geek culture that revolves around science, ownership, speed, and openness. He explains how this culture can lead to freewheeling, evidence-driven, and autonomous organizations. McAfee highlights the discomfort and challenges that come with implementing the geek way, but emphasizes that it is far more rewarding than working in stifling bureaucracies. Some highlights we explore: The benefits of iterating and taking action before feeling fully prepared The power of pointed conversations and learning from constructive criticism The contrast between bureaucratic environments and geek culture

The rules of business are changing. And those rules are being written by some … unlikely characters. Andrew McAfee is a Principal Research Scientist at the MIT Sloan School of Management and author of a number of books, including The Geek Way. Mary Long caught up with McAfee to discuss how culture shapes companies – and brings about impressive returns along the way. They discuss: The power of “geekiness” How Satya Nadella turned Microsoft around And why Amazon *wants* to see billion-dollar failures. Tickers discussed: AMZN, MSFT, AAPL, GOOG, META Host: Mary Long Guest: Andrew McAfee Producer: Ricky Mulvey Engineers: Dan Boyd, Rick Engdahl Learn more about your ad choices. Visit megaphone.fm/adchoices

What can we learn about success from the phenomenal growth of digital technology and the extraordinary achievements of Silicon Valley teams?And almost more importantly, what can we learn from the shell-shocking failures of some other seemingly great businesses?Today's guest has been researching the digital transformation of businesses for three decades, and he's landed on some fascinating— and extremely practical— conclusions.In this episode, I'm thrilled to have Andrew McAfee join us for a deep dive into cultures of innovation— environments where risks are not just taken but embraced, where failures are seen as pivotal learning opportunities, and where the status quo is openly challenged.Andrew is not only a principal research scientist at MIT but also a visionary thinker and a best-selling author. His latest work, 'The Geek Way,' is providing a framework to help teams work well in our rapidly changing business landscape – where success means more than profits and market share.Andrew has identified four pillars of ‘the geek way': science, speed, ownership, and openness. The beauty of the geek way is it's not confined to the boardroom or the tech lab. This approach can have profound implications for all of us, in every aspect of our lives. Whether you're in business, sports, arts, or really any field, Andrew has some fascinating insights, frameworks, and tools that I think you'll find helpful on your personal and professional journeys. I'm excited for what we can learn together as we dive into this week's conversation with Andrew McAfee.___Connect with us on our Instagram.For more information and shownotes from every episode, head to findingmastery.com.To check out our exclusive sponsor deals and discounts CLICK HERESee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

My guest today is Andrew McAfee, a principal research scientist at MIT and co-founder and co-director of the MIT Initiative on the Digital Economy at the MIT Sloan School of Management. He studies how digital technologies are changing the world. Throughout his career, McAfee has written and co-written several books on digital technology and related topics. He speaks frequently to both academic and industry audiences. The topic is The Geek Way: The Radical Mindset that Drives Extraordinary Results. In this episode of Trend Following Radio we discuss: Geek way mindset Evolution of technology Successful geek-driven companies Challenges faced by geeks in different industries Netflix and its success Quality vs quantity in content production Opportunity and challenges faced by streaming platforms Jump in! --- I'm MICHAEL COVEL, the host of TREND FOLLOWING RADIO, and I'm proud to have delivered 10+ million podcast listens since 2012. Investments, economics, psychology, politics, decision-making, human behavior, entrepreneurship and trend following are all passionately explored and debated on my show. To start? I'd like to give you a great piece of advice you can use in your life and trading journey… cut your losses! You will find much more about that philosophy here: https://www.trendfollowing.com/trend/ You can watch a free video here: https://www.trendfollowing.com/video/ Can't get enough of this episode? You can choose from my thousand plus episodes here: https://www.trendfollowing.com/podcast My social media platforms: Twitter: @covel Facebook: @trendfollowing LinkedIn: @covel Instagram: @mikecovel Hope you enjoy my never-ending podcast conversation!

This EXCLUSIVE episode is only available to Great Leadership+ Subscribers. Dr. Andy McAfee is the best-selling author of “The Geek Way" and research scientist at MIT. Today, he explains how to implement the “geek way” inside of your organization which is based on: science, ownership, speed, and openness. But how do you make these things actually come to life and what's an example of a company that has done this well? __________________ Start your day with the world's top leaders by joining thousands of others at Great Leadership on Substack. Just enter your email: ⁠⁠https://greatleadership.substack.com/

Andrew McAfee, Principal Research Scientist at MIT and author of The Geek Way, explains how the OpenAI saga could impact future development of artificial intelligence. Mary Lou Gardner, Associate Partner for CPG, Retail and Logistics at Infosys Consulting, discusses retail earnings and consumer outlook for the holidays. Dan Morgan, Senior Portfolio Manager at Synovus Trust, breaks down Nvidia earnings. Blink CEO Brendan Jones discusses the global EV charging market.Hosts: Carol Massar and Tim Stenovec. Producer: Paul Brennan. See omnystudio.com/listener for privacy information.

What's different about companies that accomplish amazing things? Perhaps surprisingly, says Andrew McAfee of MIT, it has nothing to do with being agile or with better technology. Instead, they've developed what he calls "geek" cultures, which emphasize intense cooperation, rapid learning curves, and a lack of hierarchy. Listen as McAfee talks about his book The Geek Way with EconTalk's Russ Roberts and how focusing on company norms, as opposed to organizational charts and structure, is a key to realizing big ambitions. They also discuss the role that data and evidence play in geek companies' decision-making and why the willingness to embrace failure is a winning strategy. 

Are you a geek? A curious person, one who's not afraid to tackle hard problems and embrace unconventional solutions? Geeks in business have created a new culture based around four norms: science, ownership, speed, and openness. Today I sit down with Principal Research Scientist at the MIT Sloan School of Management, Andrew McAffee, who defines what it means to lead with a geek mindset and the unconventional approach it takes towards success. By following Andrew's ‘Geek Way' you will find yourself equipped with a willingness to be unconventional in order to push the boundaries towards success!   __________________ Start your day with the world's top leaders by joining thousands of others at Great Leadership on Substack. Just enter your email: ⁠⁠https://greatleadership.substack.com/

Andrew McAfee (@amcafee) stops by The Business Brew to discuss his new book The Geek Wayhttps://www.amazon.com/Geek-Way-Radical-Mindset-Extraordinary/dp/B0C1DQW5FC/ref=sr_1_1?keywords=Andrew+McAfee&qid=1700075448&s=audible&sr=1-1Andrew is a Principal Research Scientist at the MIT Sloan School of Management, co-founder and co-director of MIT's Initiative on the Digital Economy, and the inaugural Visiting Fellow at the Technology and Society organization at Google. He studies how technological progress changes the world. His previous books includeMore from Less and, with Erik Brynjolfsson, The Second Machine Age.McAfee has written for publications including Foreign Affairs, Harvard Business Review, The Economist, The Wall Street Journal, and The New York Times. He's talked about his work on CNN and 60 Minutes, at the World Economic Forum, TED, and the Aspen Ideas Festival, with Tom Friedman and Fareed Zakaria, and in front of many international and domestic audiences. He's also advised many of the world's largest corporations and organizations ranging from the IMF to the Boston Red Sox to the US Intelligence Community.McAfee and his frequent coauthor Erik Brynjolfsson are only people named to both the Thinkers50 list of the world's top management thinkers and the Politico 50 group of people transforming American politics.

Andrew McAfee, Principal Research Scientist at MIT, discusses his book The Geek Way: The Radical Mindset that Drives Extraordinary Results. Hosts: Carol Massar and Tim Stenovec. Producer: Paul Brennan.See omnystudio.com/listener for privacy information.

Here's a question: who walked into work recently and said, "I hope I suck at my job today?" I'm hoping the answer is NONE of our Stacker community. And yet, many of us underperform every day. Why? There are so many ways that we can make ourselves smarter, faster, better at our jobs. We shouldn't leave whether we're excellent at our jobs to our boss, and yet, there ARE some things that we and our boss can both do to transform our workplace and make our company better. Today we're joined by the guy who the world's wisest bosses turn to when they're wondering how to get their workforce to work better -- Andrew McAfee. Andrew has picked the lock on why some companies stink when it comes to empowering their team to rock at their goals and others wallow in mediocrity. He tells us the meaningful stories of how some leaders have transformed their workplace on today's show.But of course, that's not all. In our headline segment, there's good news for people saving for college. The rules have gotten easier? Unfortunately, we don't mean "easier to understand," but just "you can now save more money that helps you also not screw yourself over for financial aid." We'll discuss the secrets you need to know on today's show. You think transforming the workplace is all you need? Now you can also save money on your college costs? Double-whammy!And yet, we still bring you more. We also throw out the Haven Life Line to a guy wanting to "learn something" from the show (he must not know that Andrew McAfee is bringing it this episode!)...and asks us about putting money into a "donor advised fund." What is that exactly, how does it work, and will it transform your workplace? The answers are: "It's complicated, so you should listen," "It helps you avoid LOTS of taxes," and "No."There's still SO MUCH MORE! Come join the fun.FULL SHOW NOTES: https://www.stackingbenjamins.com/find-bigger-opportunities-andrew-mcafee-1431Deeper dives with curated links, topics, and discussions are in our newsletter, The 201, available at https://www.stackingbenjamins.com/201Enjoy! Learn more about your ad choices. Visit podcastchoices.com/adchoicesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Here's a question: who walked into work recently and said, "I hope I suck at my job today?" I'm hoping the answer is NONE of our Stacker community. And yet, many of us underperform every day. Why? There are so many ways that we can make ourselves smarter, faster, better at our jobs. We shouldn't leave whether we're excellent at our jobs to our boss, and yet, there ARE some things that we and our boss can both do to transform our workplace and make our company better. Today we're joined by the guy who the world's wisest bosses turn to when they're wondering how to get their workforce to work better -- Andrew McAfee. Andrew has picked the lock on why some companies stink when it comes to empowering their team to rock at their goals and others wallow in mediocrity. He tells us the meaningful stories of how some leaders have transformed their workplace on today's show. But of course, that's not all. In our headline segment, there's good news for people saving for college. The rules have gotten easier? Unfortunately, we don't mean "easier to understand," but just "you can now save more money that helps you also not screw yourself over for financial aid." We'll discuss the secrets you need to know on today's show. You think transforming the workplace is all you need? Now you can also save money on your college costs? Double-whammy! And yet, we still bring you more. We also throw out the Haven Life Line to a guy wanting to "learn something" from the show (he must not know that Andrew McAfee is bringing it this episode!)...and asks us about putting money into a "donor advised fund." What is that exactly, how does it work, and will it transform your workplace? The answers are: "It's complicated, so you should listen," "It helps you avoid LOTS of taxes," and "No." There's still SO MUCH MORE! Come join the fun. FULL SHOW NOTES: https://www.stackingbenjamins.com/find-bigger-opportunities-andrew-mcafee-1431 Deeper dives with curated links, topics, and discussions are in our newsletter, The 201, available at https://www.stackingbenjamins.com/201 Enjoy! Learn more about your ad choices. Visit podcastchoices.com/adchoices

Anyone who's ever been called a geek probably wouldn't say it's a compliment, but today's guest would beg to differ. Andrew McAfee's book The Geek Way: The Radical Mindset that Drives Extraordinary Results dissects the mind of the tech startup founder to understand the traits that built some of the most impactful companies in the world. In this episode of Hello Monday, Andrew sits down with Jessi to discuss what he calls the “four norms” of geek behavior and how embracing them can make us better thinkers and innovators. Follow Andrew McAfee on LinkedIn and check out his book here.  Follow Jessi Hempel on LinkedIn and order her debut memoir, now available in paperback!  Join the Hello Monday community: Subscribe to the Hello Monday newsletter, and join us on the LinkedIn News page for Hello Monday Office Hours, Wednesdays at 3p ET.  To continue the conversation this week and every week, join our free LinkedIn group for Hello Monday listeners https://lnkd.in/hellomondaygroup

Dive into the mind of Andrew McAfee, the visionary behind 'The Geek Way,' as he discusses with John R. Miles the profound impact geek culture has on innovation and societal progress, challenging the status quo with a unique set of cultural norms. Full show notes and resources can be found here: https://passionstruck.com/andrew-mcafee-the-geek-way-builds-smarter-world/  Passion Struck is Now Available for Pre-Order Want to learn the 12 philosophies that the most successful people use to create a limitless life? Pre-order John R. Miles's new book, Passion Struck, which will be released on February 6, 2024. Sponsors Brought to you by OneSkin. Get 15% off your order using code Passionstruck at https://www.oneskin.co/#oneskinpod. Brought to you by Indeed: Claim your SEVENTY-FIVE DOLLAR CREDIT now at Indeed dot com slash PASSIONSTRUCK. Brought to you by Lifeforce: Join me and thousands of others who have transformed their lives through Lifeforce's proactive and personalized approach to healthcare. Visit MyLifeforce.com today to start your membership and receive an exclusive $200 off. Brought to you by Hello Fresh. Use code passion 50 to get 50% off plus free shipping!  --► For information about advertisers and promo codes, go to: https://passionstruck.com/deals/ The Power of Geek: Andrew McAfee on Cultivating Innovation Join host John R. Miles on Passion Struck as he welcomes Andrew McAfee, the influential Co-Director of the MIT Initiative on the Digital Economy and Principal Research Scientist at the MIT Sloan School of Management. Today's episode dives into Andrew's latest book, 'The Geek Way,' a visionary take on what it truly means to be geeky.   Like this show? Please leave us a review here -- even one sentence helps! Consider including your Twitter or Instagram handle so we can thank you personally! How to Connect with John Connect with John on Twitter at @John_RMiles and on Instagram at @john_R_Miles. Subscribe to our main YouTube Channel Here: https://www.youtube.com/c/JohnRMiles Subscribe to our YouTube Clips Channel: https://www.youtube.com/@passionstruckclips Want to uncover your profound sense of Mattering? I provide my master class on five simple steps to achieving it. Want to hear my best interviews? Check out my starter packs on intentional behavior change, women at the top of their game, longevity, and well-being, and overcoming adversity. Learn more about John: https://johnrmiles.com/ 

Andrew McAfee is joining us for DREAM THINK DO! Andrew is a Principal Research Scientist at the MIT Sloan School of Management.  He's also the co-founder and co-director of MIT's Initiative on the Digital Economy. Oh… and he's the inaugural Visiting Fellow at the Technology and Society organization at Google.  Andrew studies how technological progress changes the world and his next book “The Geek Way” he explores what he's calling the most important technological revolution of our time!  So… yes… let's talk about THAT! GET YOUR SCORE! Have YOU ever thought about becoming a Life Coach, Success Coach or Business Coach… but you've wondered whether it would be a good fit for YOU? Let's get YOUR predictability of success score: www.mitchmatthews.com/cpsa  That's right!  Download the Coaching Predictability of Success Assessment to see if becoming a successful and profitable Coach is right for YOU! MORE ON ANDREW McAFEE: Andrew's new book: THE GEEK WAY: Click here Andrew McAfee on X/Twitter: @amcafee MIT's Initiative on the Digital Economy.  Check out Mitch's NEW Daily PODCAST: ENCOURAGING THE ENCOURAGERS You can now check out Mitch's new DAILY podcast called “ENCOURAGING THE ENCOURAGERS” anywhere you listen to podcasts.   It's specifically designed for coaches, speakers and content creators and provides quick doses of inspiration, strategy AND… of course… encouragement! Check out: www.encouragingtheencouragers.com!  Find it on Apple Podcasts:  Click here Find it on Spotify: Click here Find it on Anchor: Click here Find it on Google: Click here RELATED DREAM THINK DO EPISODES: Things are changing fast BUT don't miss out!  YOU have a role to play!  Listen to THIS convo with Astronaut Dr. Shawna Pandya: mitchmatthews.com/382/  Let's talk about another secret weapon: YOUR CREATIVITY! Listen to THIS convo with award-winning photographer Chase Jarvis: mitchmatthews.com/236  Let's talk about getting MORE DONE in LESS TIME!  Yup… let's talk about TIME BLOCKING! mitchmatthews.com/257  MINUTE BY MINUTE: 00:00 Introduction to the episode and our conversation with Andrew McAfee 01:40 Andrew McAfee studies how technological progress changes the world  02:25 Andrew dives into the meaning behind his book, “The Geek Way”  04:34 What Andrew saw emerging that led him to write this book  06:21 How the geek way is duplicated and implemented into any business  07:53 The four norms that all successful organizations have  13:07 Examples of non-tech organizations that are implementing this method well 15:14 The importance of being open and transparent as possible as a business  17:06 Andrew talks about the cultural evolution and how it plays into the geek way  21:14 The behavior of senior leaders matters when it comes to creating culture  24:07 How to follow Andrew and where to find his book, “The Geek Way”  25:00 One thing that Andrew would equip somebody with to pursue the geek way 28:52 Mitch's Minute! Biggest takeaways from our conversation with Andrew McAfee I WANT TO HEAR FROM YOU: How about YOU!?  What stood out to you from this convo and the strategies and observations that Andy mentioned? Plus… I'm curious. (Like always!)  What part of the idea of the TRUE GEEK CULTURE that resonated with you?  Whether you lead a software company, run a restaurant or teach in a classroom… we can ALL lean into the concepts of SCIENCE, OWNERSHIP, SPEED and OPENNESS more.  But what specifically stood out to YOU!? Lastly… PLEASE share this powerful episode with someone who YOU think would appreciate it and/or might be needing it… right now!  They'll thank you! Know I'm rooting for you! Mitch