Podcast appearances and mentions of Erik Brynjolfsson

Erik Brynjolfsson has a challenge for anyone worried about artificial intelligence: Stop asking what AI will do to us, and start asking what we will do with AI. In this episode, the Stanford University economist explains why technology isn't the biggest barrier to progress — people, organizations, and institutions are. Drawing on new research into AI's impact on jobs, productivity, and economic growth, he argues that the future isn't predetermined: It will be shaped by the choices we make today. This is a timely conversation about human agency, shared prosperity, and why the most important AI breakthroughs may have less to do with technology than with how we use it. Read the episode transcript here. Guest bio: Erik 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 Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research, professor by courtesy at the Stanford Graduate School of Business and Stanford Department of Economics, and a research associate at the National Bureau of Economic Research. A best-selling author, Brynjolfsson focuses his research on examining the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets. Me, Myself, and AI is a podcast produced by MIT Sloan Management Review and hosted by Sam Ransbotham. It is engineered by David Lishansky and produced by Allison Ryder. We encourage you to rate and review our show. Your comments may be used in Me, Myself, and AI materials.

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

This week, feuding between some of the biggest tech companies spilled into public view. We discuss Apple's accusation that OpenAI tried to steal secrets about Apple's hardware business, as well as share our reactions about OpenAI's new model, Sol, and Anthropic's decision to extend access to its model Fable. Then, we unpack the loudest warning yet about A.I. and jobs. We talk with Erik Brynjolfsson, a Stanford economist, about a statement he helped organize that implores economists and A.I. researchers to “act now” to steer A.I. in a direction that complements humans. And finally, we play a round of HatGPT.   Guest: Erik Brynjolfsson, senior fellow at the Stanford Institute for Human-Centered A.I., and director of the Stanford Digital Economy Lab.   Additional Reading: Apple Sues OpenAI, Accusing It of Stealing Company Secrets OpenAI's First Device Will Be Movable, Screenless Speaker Built as A.I. Companion Nearly 200 Economists and Tech Leaders Warn of A.I. Threats The loudest warning about A.I. and jobs yet OpenAI Is Showing Kalshi's World Cup Odds in ChatGPT New York Enacts Nation's First Statewide Moratorium on Data Centers Brown Professor Suspects Majority of His Class Used A.I. to Cheat MiniMax CEO Vows to Forgo Salary Until Achieving A.G.I. Lorde Speaks Out — With Expletives — Against A.I. Glasses Nearly 6 in 10 Young Women Get Health and Wellness Information from Influencers Meta Removes A.I. Feature on Instagram After Days of Backlash   We want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, we headed to the heart of Silicon Valley to meet Professor Erik Brynjolfsson, Director of Stanford's Digital Economy Lab and one of the world's leading thinkers on the economics of AI, for a conversation that bridges economics with bold human vision. Erik brings his clarity to some of today's biggest leadership questions: Why are companies spending billions on AI but seeing underwhelming results? What does his famous productivity J curve tell us about where we are right now and how close we are to turning the corner? And what separates the organizations that will thrive from those that won't survive the transition? From the power law of AI adoption and the danger of selection bias in productivity metrics, to the philosophical question of whether AI will one day need to "accept" us humans too, this episode is as thought-provoking as it is practical. Erik also shares why he believes we are not passive bystanders in the AI era, but active agents with more power to shape the world than any generation before us. A must-listen for leaders, innovators, and anyone ready to stop wondering what AI will do to them, and start deciding what they will do with it.

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

What happens if AI delivers major advances in capability and productivity, but also creates significant disruption to jobs, incomes, and public finances? That question sits at the heart of today's episode.Our guest is Adrian Brown, the Founder and Chief Executive of Windfall Trust, a nonprofit focused on helping governments and societies prepare for the economic consequences of advanced AI. Windfall describes itself not as a think tank, but as a “policy accelerator for the age of artificial intelligence”.Their work starts from a simple premise: if AI systems significantly reshape the economy, then the question is not only how we build them, but how we prepare for their impacts, and how the gains are ultimately shared.Before founding Windfall Trust, Adrian was the founding Executive Director of the Centre for Public Impact, worked as a policy advisor in the UK Cabinet Office, and held roles at McKinsey and the Boston Consulting Group.Selected follow-ups:Windfall TrustExercise Cygnus (Wikipedia)Erik Brynjolfsson (personal site)Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence (paper by Erik Brynjolfsson and colleagues)Anton Korinek (personal site)Windfall's UK Scenarios exerciseThe Economic Singularity (recent discussion paper by Calum Chace)Industrial Policy for the Intelligence Age (OpenAI)UK Government announcement of the formation of "The AI and Future of Work Unit"UK Chancellor Rachel Reeve's announcement of a new "AI Economics Institute""AI will kill income tax" - episode of Robert Peston's podcast "The Rest is Money"Windfall Policy AtlasTask-Completion Time Horizons of Frontier AI Models (METR)Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain DeclarationC-Suite PerspectivesElevate how you lead with insight from today's most influential executives.Listen on: Apple Podcasts Spotify

March 5, 2026: The company making AI (Anthropic) just published real data on what AI is actually doing to jobs — and the finding that should concern everyone isn't layoffs. It's that the hiring door for workers aged 22 to 25 has quietly dropped 14% in AI-exposed fields since ChatGPT launched. Today we cover four stories: Stanford's Erik Brynjolfsson on why minimum wage increases are accelerating robot adoption. Anthropic's brand new labor market study — and why you should read it with a critical eye. The February job cut numbers, which look better than January but hide a more troubling signal. And Vinod Khosla predicting today's five-year-olds will never need jobs — a claim we push back on hard. The data is in. It's more complicated than either side wants to admit.   Watch the full episode on YouTube   ----- 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/ Stop patching problems and start designing an intentional workplace. The 8 Laws of Employee Experience gives you the how. Order your copy: 8EXlaws.com

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ ----- Meet R Mini Arnold - my OpenClaw chief of staff, which manages the equivalent of a ten-person team from a Mac mini in my garden studio. While I slept, that AI team debugged its own code at 3am, researched a trending Substack essay using five parallel investigators, and wrote a 4,600-word script for this very episode in 40 minutes. The gap between people who've started building this way and those who haven't is widening every week.  I covered: 00:51 Introducing my OpenClaw agent “R Mini Arnold” 03:59 What my AI chief of staff actually does 07:58 The hardware and software stack 10:38 A morning brief before you wake up 12:05 Overnight agents: research and code 15:00 How I communicate with my agent 18:56 Example 1: the sovereign wealth fund 22:41 Example 2: how this video was written 26:34 What it costs 29:22 The soul.md personality spec 32:39 Am I losing the judgment muscle? 35:46 Individuals vs. Fortune 500s 38:25 What to try this week ----- Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/ Twitter/X: https://x.com/azeem Production by EPIIPLUS1 Production and research: Baba Films, Chantal Smith, Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI experimentation in the workplace is now showing tangible effects, from productivity gains to layoffs. Erik Brynjolfsson, a professor at Stanford's Institute for Human-Centered AI and cofounder of Workhelix, recently spoke with the WSJ Leadership Institute's Wendy Bounds at the WSJ Technology Council Summit. They discussed AI's influence on the labor market. Plus, WSJ Heard on the Street columnist Jonathan Weil says the AI boom is making it more challenging to analyze tech companies' earnings due to unclear depreciation expenses. Julie Chang hosts. Sign up for the WSJ's free Technology newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this week's episode of WSJ's Take On the Week, co-hosts Telis Demos and Miriam Gottfried are joined by WSJ economics reporter Justin Lahart to discuss why gold has smashed records, and how global instability and the "Sell America" trade has fueled the rally. Next, they look ahead to Amazon's earnings to see if the e-commerce giant can prove AI investments are boosting the bottom line, as Meta did, or if ongoing layoffs signal deeper issues in the labor market. Justin also previews this week's jobs report and explains why an upcoming benchmark revision might rewrite our understanding of the past year's job growth. Then after the break, Telis and Justin are joined by Erik Brynjolfsson, director of the Stanford Digital Economy Lab, to unpack whether AI is actually killing jobs. Brynjolfsson shares his research into how his research has found a decline in entry-level roles, but argues a productivity boom is imminent. Later, we ask him a fun question written by Google's Gemini app.  This is WSJ's Take On the Week where co-hosts Telis Demos, Heard on the Street's banking and money columnist, and Miriam Gottfried, WSJ's private equity reporter, cut through the noise and dive into markets, the economy and finance—the big trades, key players and business news ahead. Have an idea for a future guest or episode? How can we better help you take on the week? We'd love to hear from you. Email the show at takeontheweek@wsj.com. To watch the video version of this episode, visit our WSJ Podcasts YouTube channel or the video page of WSJ.com Further Reading A Weaker Dollar Has Always Been Part of Trump's Plan Dollar Gains, Yen Falls, After Bessent Says Strong Currency Is U.S. Policy Dollar Extends Slide After Trump Says He Isn't Worried About Declines Meta Reports Record Sales, Massive Spending Hike on AI Buildout Amazon to Lay Off Around 16,000 Corporate Employees For more coverage of the markets and your investments, head to WSJ.com, WSJ's Heard on The Street Column, and WSJ's Live Markets blog. Sign up for the WSJ's free Markets A.M. newsletter.  Follow Miriam Gottfried here and Telis Demos here.  Learn more about your ad choices. Visit megaphone.fm/adchoices

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.

This episode explores the evolving impact of AI on the job market, especially its disproportionate effects on younger workers and recent graduates. Dr. Sabba Quidwai and Stefan analyze the recent paper "Canaries in the Coal Mine", revealing how automation and augmentation are reshaping employment trends and urging educational leaders to rethink how students are being prepared for an AI-driven future.Timestamps[00:02:00] Rethinking the AI and Jobs DebateSabba challenges the binary narrative of “AI taking jobs” and advocates for a more nuanced view focused on redesigning existing roles and preparing for emerging ones.[00:05:00] Key Takeaways from ‘Canaries in the Coal Mine'Discussion of six major findings from the Stanford/Hi-Pact paper, highlighting declines in employment for young workers in AI-exposed jobs like coding and entry-level marketing.[00:10:00] Disconnect Between Education and Workforce NeedsReflection on how high schools and colleges must pivot from traditional learning models to design thinking and durable skills to help students remain relevant.[00:26:00] How to Be ‘AI Capable' at WorkBreakdown of Zapier's model distinguishing AI-capable, adaptive, and transformative roles—with implications for what employers now expect from applicants.[00:32:00] Notebook LM and Smarter Learning WorkflowsIntroduction to Google's Notebook LM as a transformative educational tool, enabling students to better engage with readings and improve learning outcomes using AI.Resources Mentioned

This is the single most important paper to come out in tech in recent weeks. Erik Brynjolfsson, Bharat Chandar and Ruyu Chen investigated whether generative AI is leading to job losses in roles most exposed to AI – and how these effects differ by age and the way AI is used. In this episode, I break down these results and their implications. I covered: (01:17) Key finding (03:32) What's going on here? (06:13) A canary in the coal mine? (8:21) The dataset studied and why it matters (10:34) The sectors impacted and why it matters (12:37) Why don't firms just reduce salaries? (14:34) Historical parallels with electricity (17:20) How leadership impacts job losses (20:46) Implications for policy, education, equity (24:53) Outro Where to find me: - Substack: https://www.exponentialview.co/ - Website: https://www.azeemazhar.com/ - LinkedIn: https://www.linkedin.com/in/azhar?originalSubdomain=uk - Twitter/X: https://x.com/azeem ----Production by supermix.io and EPIIPLUS1

A survey of about 1,500 workers showed AI has been a useful tool for repetitive work. But some respondents want more — sometimes, more than the technology is capable of.In this episode, Marketplace's Meghan Mccarty Carino speaks with Stanford economist Erik Brynjolfsson about the disconnect between workers' wants and AI's current role in the workplace.

A survey of about 1,500 workers showed AI has been a useful tool for repetitive work. But some respondents want more — sometimes, more than the technology is capable of.In this episode, Marketplace's Meghan Mccarty Carino speaks with Stanford economist Erik Brynjolfsson about the disconnect between workers' wants and AI's current role in the workplace.

We explore the compelling questions surrounding artificial intelligence. Will AI create more new jobs than it destroys? Is AI already destroying jobs? Are we seeing overinvestment in companies and infrastructure in the AI space? Is there evidence that AI has increased productivity?SponsorsMoney for the Rest of Us PlusAsset CampShow NotesBehind the Curtain: A white-collar bloodbath by Jim VandeHei and Mike Allen—AxiosYuval Noah Harari Statement - Post by Nunki08—RedditChallenger Report June 2025—Challenger, Gray & ChristmasEntry level jobs fall by nearly a third since ChatGPT launch by Karl Matchett—The IndependentStrategic Insights for M&A in the Evolving AI Market—S&P GlobalNvidia Becomes First Public Company Worth $4 Trillion by Tripp Mickle—The New York TimesSilicon Valley is racing to build the first $1trn unicorn—The EconomistHow to use generative AI to augment your workforce by Betsy Vereckey—MIT ManagementHumans must remain at the heart of the AI story by Marc Benioff—The Financial TimesThe AI Industry Is Radicalizing by Matteo Wong—The AtlanticTASKS, AUTOMATION, AND THE RISE IN U.S. WAGE INEQUALITY by DARON ACEMOGLU AND PASCUAL RESTREPO—EconometricaMyPillow CEO's lawyers fined for AI-generated court filing in Denver defamation case by Olivia Prentzel—The Colorado SunWhich Workers Will A.I. Hurt Most: The Young or the Experienced? by Noam Scheiber—The New York TimesGenerative AI at Work by Erik Brynjolfsson, Danielle Li, Lindsey Raymond—Oxford AcademicThe Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity by Parshin Shojaee et al.—AppleRelated Episodes507: Where You Live Matters – How Geography Contributes to Wealth457: AI's Fork in the Road: Societal Bliss or Existential Threat439: How and Why to Invest in AI417: Will Generative AI Replace Your Job?See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Will artificial intelligence help you do your job, or will it just straight-up do your job and leave you unemployable? Or will the future bring something else entirely — either between those two extremes or a world that we simply cannot imagine yet? And are we already starting to see signs of that future emerging? On this episode of The New Bazaar, Cardiff is joined by economist Nathan Goldschlag, Research Director at the Economic Innovation Group. Until recently, Nathan was Principal Economist at the U.S. Census Bureau's Center for Economic Studies, where among other things he led research on the impact of technology, including AI, on the economy. Any worthwhile list of the world's best economists on the subject of AI and work would have to include him. Cardiff and Nathan go through Nathan's own research* and also filter out the megaton of nonsense on the topic and discuss some of the work done by others — research, essays, meanderings — that they think is actually worth sharing with listeners. They discuss, among other things: How many businesses are now using AI to produce goods and servicesHow have things changed since the launch and popularization of large language modelsEconomic growth consequences of AIWhether “learn to code” is still good advice The skills that still matter To steer or not to steer the AI future* Nathan's research on AI was done in collaboration with a large team of researchers at the Center for Economic Studies at the U.S. Census Bureau including Emin Dinlersoz, Lucia Foster, David Beede, John Haltiwanger, Zach Kroff, Nikolas Zolas, Gary Anderson, and Eric Childress, along with program area partners including Kathryn Bonney, Cory Breaux, Cathy Buffington, and Keith Savage, as well as academic partners including Daron Acemoglu, Erik Brynjolfsson, Kristina McElheran, and Pascual Restrepo. Related links:The impact of AI on the workforce: Tasks versus jobs?Tracking Firm Use of AI in Real Time: A Snapshot from the Business Trends and Outlook Survey.The Rapid Adoption of Generative AI | NBERAnswering the Call of AutomationAI-2027.comTyler Cowen - the #1 bottleneck to AI progress is humansDriverless trucks are coming and unions aren't happy about itGenerative AI at Work Hosted on Acast. See acast.com/privacy for more information.

The Stanford economist unpacks AI's impact on work and productivity, its limitations, and wider implications. He also lays out what organizations can do to get more out of the technology as they invest in the transformation. And he updates his longstanding research into augmenting traditional GDP metrics to capture the value of digital goods and services.

When are you most productive? How do you increase your productivity? And is technology a help or a hindrance?! The amazing Erik Brynjolfsson learns the Chinese word for "productivity" and discusses with ShaoLan the technologies that can cause ripples of productivity growth around the world. ✨ 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.

Impress your Chinese friends with the word for Artificial Intelligence! World-leading economist Erik Brynjolfsson chats with ShaoLan about how smart robots could really get and how that might change the world as we know it. ✨ 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

Stephen Dubner, live on stage, mixes it up with outbound mayor London Breed, and asks economists whether A.I. can be “human-centered” and if Tang is a gateway drug. SOURCES:London Breed, former mayor of San Francisco.Erik Brynjolfsson, professor of economics at Stanford UniversityKoleman Strumpf, professor of economics at Wake Forest University RESOURCES:"SF crime rate at lowest point in more than 20 years, mayor says," by George Kelly (The San Francisco Standard, 2025)"How the Trump Whale and Prediction Markets Beat the Pollsters in 2024," by Niall Ferguson and Manny Rincon-Cruz (Wall Street Journal, 2024)"Artificial Intelligence, Scientific Discovery, and Product Innovation," by Aidan Toner-Rodgers (MIT Department of Economics, 2024) EXTRAS:"Why Are Cities (Still) So Expensive?" by Freakonomics Radio (2020)

Stanford University's Erik Brynjolfsson joins us to share insights on how business can leverage AI to boost productivity, innovation and growth. In this episode, Erik explores the key role of a CFO in the AI era, strategies for staying competitive in today's digital world, and the AI skills professionals need to stay ahead. He also dives into the future of work, the growing importance of intangible assets, and the ethical implications of AI development. Join us to gain valuable insights from a leading AI thought leader and academic. Host: Aidan Ormond, digital content editor, CPA Australia Guest: Erik Brynjolfsson, Senior Fellow at Stanford's Institute for Human-Centered AI and Director of the Stanford Digital Economy Lab. He is also the Ralph Landau Senior Fellow at Stanford's Economic Policy Research Institute, a Professor by Courtesy at the Graduate School of Business and Department of Economics, and a Research Associate at the National Bureau of Economic Research. Known for his groundbreaking research on the economics of information, Erik has written bestsellers and holds a PhD from MIT and degrees from Harvard. For more information on Erik's research work at Stanford University, head to the Stanford Graduate School of Business faculty research page. Erik is also appearing at CPA Australia's Congress 24 in Canberra this October. It's an in-person and virtual event, featuring insightful and inspiring thought leaders who'll share their ideas on a variety of topics to help level up your professional knowledge. Would you like to listen to more INTHEBLACK episodes? Head to CPA Australia's YouTube channel. CPA Australia publishes four podcasts, providing commentary and thought leadership across business, finance, and accounting: With Interest INTHEBLACK INTHEBLACK Out Loud Excel Tips Search for them in your podcast platform. Email the podcast team at podcasts@cpaaustralia.com.au

Erik Brynjolfsson returns for another fascinating episode in which you can learn how to say the word for robot in Chinese. He and ShaoLan also discuss the future of robotic technologies and how they will continue to shape our lives in the coming years. ✨ 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.

Which characters does the Chinese language put together to make the word computer? It will definitely surprise you with its sci-fi nature. In this episode, ShaoLan and leading expert on digital technologies Erik Brynjolfsson share how to construct Chinese words such as computer, TV, movie and telephone. ✨ 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.

In this episode we discuss the prediction by Erik Brynjolfsson in that within five years, human intelligence will be perceived as limited and specialized compared to the expansive capabilities of AI systems. We discuss the latest updates and contextualize as to how this shift will affect and influence the field of education.

Our guest in this episode is David Wakeling, a partner at A&O Shearman, which became the world's third largest law firm in May, thanks to the merger of Allen and Overy, a UK “magic circle” firm, with Shearman & Sterling of New York.David heads up a team within the firm called the Markets Innovation Group (MIG), which consists of lawyers, developers and technologists, and is seeking to disrupt the legal industry. He also leads the firm's AI Advisory practice, through which the firm is currently advising 80 of the largest global businesses on the safe deployment of AI.One of the initiatives David has led is the development and launch of ContractMatrix, in partnership with Microsoft and Harvey, an OpenAI-backed, GPT-4-based large language model that has been fine-tuned for the legal industry. ContractMatrix is a contract drafting and negotiation tool powered by generative AI. It was tested and honed by 1,000 of the firm's lawyers prior to launch, to mitigate against risks like hallucinations. The firm estimates that the tool is saving up to seven hours from the average contract review, which is around a 30% efficiency gain. As well as internal use by 2,000 of its lawyers, it is also licensed to clients.This is the third time we have looked at the legal industry on the podcast. While lawyers no longer use quill pens, they are not exactly famous for their information technology skills, either. But  the legal profession has a couple of characteristics which make it eminently suited to the deployment of advanced AI systems: it generates vast amounts of data and money, and lawyers frequently engage in text-based routine tasks which can be automated by generative AI systems.Previous London Futurists Podcast episodes on the legal industry:Ep 53: The Legal Singularity, with Benjamin AlarieEp 47: AI transforming professional services, with Shamus RaeOther selected follow-ups:David WakelingA&O ShearmanContractMatrixHarvey AIRAG - Retrieval-Augmented GenerationDigital Operational Resilience Act (impacts banking)The Productivity J-Curve (PDF), by Erik Brynjolfsson, Daniel Rock, Chad SyversonAgentic AI: The Next Big Breakthrough That's Transforming Business And Technology, by Bernard MarrMusic: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

In this episode of Cisco Champion Radio, we dive into all the exciting details of the upcoming Webex One 2024 conference, Cisco's flagship event dedicated to hybrid work collaboration and AI innovation. Join us as we explore the key highlights, from inspirational keynote speeches by Fareed Zakaria and Erik Brynjolfsson to hands-on product demos and a bustling expo floor featuring Webex integrations and an AI hub. Whether you're attending in person or virtually, we've got you covered. Learn about the customizable swag options, discover the must-see sessions, and get tips on how to navigate the agenda to maximize your experience. Plus, don't miss the chance to hear about the epic concert party featuring Alter Ego, the Canadian party band known for their electrifying two-hour sets with costume changes! WebexOne 2024 promises an exciting, packed schedule from October 21 to 24, in Fort Lauderdale, FL, offering a unique mix of training programs, keynotes and breakouts, expo and product demos, networking opportunities, and an awards ceremony. Tune in to get all the insights you need to make the most of this transformative event, whether you're attending live or catching up on- demand. As a podcast listener, use this special discount code WX12024 at WebexOne.com to save hundreds on your ticket! Resources https://www.webexone.com/ Cisco guest: Andrew Pearson, Director, Event Marketing, Cisco Cisco Champion hosts: David Macias, Independent Consultant Ruth Duncan, Customer Success Manager, ScanSource Rickey Keith, Consulting Systems Engineer, World Wide Technology Moderator Danielle Carter, Customer Voices and Cisco Champion Program, Cisco

Hey Strangers, #google #ai #art Former Google CEO and chairman Eric Schmidt has made headlines for saying that Google was blindsided by the early the rise of ChatGPT because its employees decided that “working from home was more important than winning.” The comment was made in front of Stanford students during a recent interview, video of which was removed from the university's YouTube channel after Schmidt's gaffe was widely picked up by the press. I managed to watch most of Schmidt's chat with Stanford's Erik Brynjolfsson before it was taken down, however, and something else he said stands out. (You can still read the full transcript here.) While talking about a future world in which AI agents can do complex tasks on behalf of humans, Schmidt says: If TikTok is banned, here's what I propose each and every one of you do: Say to your LLM the following: “Make me a copy of TikTok, steal all the users, steal all the music, put my preferences in it, produce this program in the next 30 seconds, release it, and in one hour, if it's not viral, do something different along the same lines.” That's the command. Boom, boom, boom, boom. A bit later, Schmidt returns to his TikTok example and says: So, in the example that I gave of the TikTok competitor — and by the way, I was not arguing that you should illegally steal everybody's music — what you would do if you're a Silicon Valley entrepreneur, which hopefully all of you will be, is if it took off, then you'd hire a whole bunch of lawyers to go clean the mess up, right? But if nobody uses your product, it doesn't matter that you stole all the content. And do not quote me. ======================================= My other podcast https://www.youtube.com/channel/UCKpvBEElSl1dD72Y5gtepkw ************************************************** Something Strange https://www.youtube.com/watch?v=GRjVc2TZqN4&t=4s ************************************************** article links: https://www.theverge.com/2024/8/14/24220658/google-eric-schmidt-stanford-talk-ai-startups-openai ====================================== Today is for push-ups and Programming and I am all done doing push-ups Discord https://discord.gg/MYvNgYYFxq TikTok https://www.tiktok.com/@strangestcoder Youtube https://www.youtube.com/channel/UCe9xwdRW2D7RYwlp6pRGOvQ?sub_confirmation=1 Twitch https://www.twitch.tv/CodingWithStrangers Twitter https://twitter.com/strangestcoder merch Support CodingWithStrangers IRL by purchasing some merch. All merch purchases include an alert: https://streamlabs.com/codingwithstrangers/merch Github Follow my works of chaos https://github.com/codingwithstrangers Tips https://streamlabs.com/codingwithstrangers/tip Patreon https://www.patreon.com/TheStrangers Webull https://act.webull.com/vi/c8V9LvpDDs6J/uyq/inviteUs/ Join this channel https://www.youtube.com/channel/UCe9xwdRW2D7RYwlp6pRGOvQ/join Timeline 00:00 intro 00:20 What Talking We Talking About 03:16 Article 08:37 Steal it 13:03 My Thoughts 14:09 outro anything else? Take Care --- Send in a voice message: https://podcasters.spotify.com/pod/show/coding-with-strangers/message

While the rapid development of AI technology promises unprecedented productivity gains and innovations, concerns of job displacement and increasing inequality persist. How can we ensure AI complements human labor rather than replacing it? What measures can we take to prevent a dystopian AI future?Erik Brynjolfsson is a professor at Stanford University and has pioneered research on the economics of information technology and AI. In this episode, Erik discusses the potential for AI to enhance job productivity, complement our workforce, and boost economic growth at scales comparable to the Industrial Revolution while also exploring potential negative futures and strategies to avoid them.(03:16) Imagining the future(09:41) Impacts on Productivity and Labor(21:14) GPTs are GPTs!(31:02) Workhelix(32:07) Self Driving Cars(36:25) AI Helping Innovation(38:02) Negative Impacts(38:39) Historical Context and Economic Concerns(42:56) AI's Impact on Job Markets and Productivity(48:22) Shared Benefits(49:46) Pessimism and Belief about AI(01:03:59) Strategies for a Positive AI Future(01:12:17) Last Question

(0:00) Intro.(1:24) About the podcast sponsor: The American College of Governance Counsel.(2:12) Start of interview.(4:04) Sonita's "origin story." (5:45) Her professional career, starting with a startup in the gaming industry.(8:15) Her guiding principles for her career at the intersection of innovation, sustainability and digital transformation.(9:30) Her roles at HP, Siemens and PG&E.(11:00) Her board "portfolio" life starting in 2022: SunRun and TrueBlue. Advisor to Sway Ventures.(14:02) About the NACD Blue Ribbon Commission on Board Culture (where she served as a Commissioner).(17:00) Surprises and takeaways from the report.(22:30) Recommendations for handling the increasing politicization in the boardroom. (26:42) On geopolitics in the boardroom. Supply-chain vs consumer market.(31:30) On the solar and battery industry geopolitical landscape. (38:23) How should directors think about AI in the boardroom. "Everyday AI" vs "Game-changing AI". Use cases: 1) Back-office capabilities, 2) core capabilities, 3) front office, 4) New products and services. AI code of conduct. Use of data. Cybersecurity.(43:51) On the impact of AI in the workplace. *reference to study by Erik Brynjolfsson(47:09) Books that have greatly influenced her life: The Five Levels of Leadership,  by John Maxwell (2011)Venture Mindset, by Ilya Strebulaev and Alex Dang (2024)Last Lecture Series at the Stanford Graduate School of Business (July 2023), by Graham Weaver.(48:06)  Her mentors. (49:22)  Quotes that she thinks of often or lives her life by.(50:44) An unusual habit or absurd thing that she loves.(51:30)  The living person she most admires. Sonita Lontoh is a public company board director, strategic advisor, and former Fortune 100 senior executive who focuses on digital innovation, artificial intelligence (AI), and sustainability — contributing positive impact to businesses, consumers, and society. You can follow Evan on social media at:Twitter: @evanepsteinLinkedIn: https://www.linkedin.com/in/epsteinevan/ Substack: https://evanepstein.substack.com/__You can join as a Patron of the Boardroom Governance Podcast at:Patreon: patreon.com/BoardroomGovernancePod__Music/Soundtrack (found via Free Music Archive): Seeing The Future by Dexter Britain is licensed under a Attribution-Noncommercial-Share Alike 3.0 United States License

In this episode 25, Teodora Groza & Thibault Schrepel talk with Erik Brynjolfsson (Stanford University) about how antitrust agencies can document market dynamism, gain a better understanding of the digital economy using the GDP-B measure, track AI dynamics, and more. Follow Stanford Computational Antitrust at https://law.stanford.edu/computationalantitrust

World leading economist Erik Brynjolfsson visits the Talk Chineasy studio in London to learn a hugely important word for our lives in Chinese, “work”! Also, find out how to say “I want to work!” and “I don't want to work”!

The economy affects almost every area of our lives and who better to learn the Chinese word for the economy than world leading economist and author of “The second machine age” Erik Brynjolfsson.

When are you most productive? How do you increase your productivity? And is technology a help or a hindrance?! The amazing Erik Brynjolfsson learns the Chinese word for "productivity" and discusses with ShaoLan the technologies that can cause ripples of productivity growth around the world.

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

Impress your Chinese friends with the word for Artificial Intelligence! World-leading economist Erik Brynjolfsson chats with ShaoLan about how smart robots could really get and how that might change the world as we know it.

Erik Brynjolfsson returns for another fascinating episode in which you can learn how to say the word for robot in Chinese. He and ShaoLan also discuss the future of robotic technologies and how they will continue to shape our lives in the coming years.

Which characters does the Chinese language put together to make the word computer? It will definitely surprise you with its sci-fi nature. In this episode, ShaoLan and leading expert on digital technologies Erik Brynjolfsson share how to construct Chinese words such as computer, TV, movie and telephone.

In policing, as in most vocations, the best employees are often promoted into leadership without much training. One economist thinks he can address this problem — and, with it, America's gun violence. SOURCESKenneth Corey, director of outreach and engagement for the Policing Leadership Academy at the University of Chicago and retired chief of department for the New York Police Department.Stephanie Drescher, operations captain in the City of Madison Police Department.Max Kapustin, assistant professor of economics and public policy at Cornell University.Jens Ludwig, economist and director of the Crime Lab at the University of Chicago.Sandy Jo MacArthur, curriculum design director for the Policing Leadership Academy at the University of Chicago.Sean Malinowski, D.O.J. strategic site liaison for the Philadelphia Police Department and retired chief of detectives from the Los Angeles Police Department.Sindyanna Paul-Noel, lieutenant with the City of Miami Police Department.Michael Wolley, deputy chief of operations with the Indianapolis Metropolitan Police Department. RESOURCES:"Policing Leadership Academy (PLA) Graduation of Inaugural Cohort," by the University of Chicago Crime Lab (2023)."Policing and Management," by Max Kapustin, Terrence Neumann, and Jens Ludwig (NBER Working Paper, 2022)."Getting More Out of Policing in the U.S.," by Jens Ludwig, Terrence Neumann, and Max Kapustin (VoxEU, 2022)."What Drives Differences in Management?" by Nicholas Bloom, Erik Brynjolfsson, Lucia Foster, Ron S. Jarmin, Megha Patnaik, Itay Saporta-Eksten, and John Van Reenen (NBER Working Paper, 2017)."Management as a Technology?" by Nicholas Bloom, Raffaella Sadun, and John Van Reenen (NBER Working Paper, 2017)."Measuring and Explaining Management Practices Across Firms and Countries," by Nick Bloom and John Van Reenen (NBER Working Paper, 2006)."Crime, Urban Flight, and the Consequences for Cities," by Julie Berry Cullen and Steven D. Levitt (SSRN, 1997). EXTRAS:"Why Are There So Many Bad Bosses?" by Freakonomics Radio (2022)."What Are the Police for, Anyway?" by Freakonomics Radio (2021).

Science. Ownership. Speed. Openness.These are the four pillars of Andrew McAfee's observed structure for successful companies. It is the “geeks,” the leaders at the forefront of cross-industry innovation, who embrace these norms and have the potential to redefine business as we know it. In order to break ground and create the kind of future we dream of, organizational leaders need to banish the fear of failure, embrace mistakes, and accept hard feedback with open arms.Andrew is a best-selling author, Principal Research Scientist at the MIT Sloan School of Management, and co-founder of MIT's Initiative on the Digital Economy. His books include More from Less and The Second Machine Age, co-authored with Erik Brynjolfsson. Today on the podcast, we discuss the ideas captured in his most recent book, The Geek Way: The Radical Mindset that Drives Extraordinary Results. In This Episode* The universal geek (1:35)* The four geek norms (8:29)* Tales of geeks and non-geeks (15:19)* Can big companies go geek? (18:33)* The geek way beyond tech (26:32)Below is a lightly edited transcript of our conversation.The universal geek (1:35)Pethokoukis: Is The Geek Way really the Silicon Valley Way? Is this book saying, “Here's how to turn your company into a tech startup”?McAfee: You mentioned both Silicon Valley and tech, and this book is not about either of those—it's not about a region and it's not about an industry, it's about a set of practices. And I think a lot of the confusion comes because those practices were incubated and largely formulated in this region called “Silicon Valley” in this industry that we call “tech”. So I understand the confusion, but I'm not writing about the Valley. Plenty of people do that. I'm not writing about the tech industry. Plenty of people do that. The phenomenon that I don't think we are paying enough attention to is this set of practices and philosophies that, I believe, when bundled correctly, amounts to a flat old upgrade to the company, just a better way to do the thing a company is supposed to do. That needed a label, because it's new. “Geek” is the label that I latched onto.But there's a universal aspect to this, then.Yeah, I believe there is. I understand this sounds arrogant—I believe it's a flat better way to run a company. I don't care where in the world you are, I don't care what industry you are in, if you're making decisions based on evidence, if you're iterating more and planning less, if you're building a modular organization that really does give people authority and responsibility, and if you build an organization where people are actually comfortable speaking truth to power, I think you're going to do better.One reason I'm excited about this book is because, you as well, we think about technological progress, we think about economic growth and productivity and part of that is science and coming up with new ideas and a new technology, but all that stuff has to actually be turned into a commercial enterprise and there has to be well-run companies that take that idea and sell it. Maybe the economist's word might be “diffusion” or something like that, but that's a pretty big part of the story, which I think maybe economists tend not to focus as much on, or policy people, but it's pretty darn important and that's what I think is so exciting about your book is that it addresses that: How to create companies that can do that process—invention-to-product—better. So how can they do it better?Let me quibble with you just a little bit. There are alternatives to this method of getting goods and services to people, called “the company.” That's what we do in capitalist societies. Jim, like you know all too well, over the course of the 20th century, we ran a couple of experiments trying it a different way: These collectivist, command-and-control, centrally planned economies, those were horrible failures! Let's just establish that right off the bat.So in most of the parts of the world—I think in all the parts of the world where you and I would actually want to live—I agree with you, we've settled on this method of getting most goods and services to people, most of what they consume, via these entities called companies, and I don't care if you're in a Nordic social democracy, or in the US of A, or in Southeast Asia, companies are the things getting you most of what you consume. I think in the United States, about 85 percent of what you and I consume, by some estimates, comes from companies. So, like them or hate them, they're incredibly important, and if a doohickey comes along that lets them their work X percent better, we should applaud that like crazy because that's an X percent increase in our affluence, our standard of living, the things that we care about, and the reason I got excited and decided to write this book is I think there's an upgrade to the company going on that's at the same level as the stuff that [Alfred] Chandler wrote about a century ago when we invented the large, professionally managed, pretty big company. Those dominated the corporate landscape throughout the 20th century. I think that model is being upgraded by the geeks.It's funny because, I suppose maybe the geeks 50 years ago, maybe a lot of them worked at IBM. And your sort-of geek norms are not what I think of the old Big Blue from IBM in the 1960s. That has changed. Before we get into the norms, how did they develop? Why do we even have examples of this working in the real corporate world?The short answer is, I don't know exactly. That's a pretty detailed piece of corporate history and economic history to work on. The longer answer is, what I think happened is, a lot of computer nerds, who had spent a lot of time at universities and were pretty steeped in that style of learning things and building things, went off and started companies and, in lots of cases, they ran into the classic difficulties that occur to companies and the dysfunctions that creep in as companies grow and age and scale. And instead of accepting them, my definition of a geek is somebody who's tenacious about a problem and is willing to embrace unconventional solutions. I think a lot of these geeks—and I'm talking about people like Reed Hastings, who's really articulate about what he did at Netflix and at his previous company, which he says he ran into mediocrity—a lot of these geeks like Hastings sat around and said, “Wait a minute, if I wanted to not repeat these mistakes, what would I do differently?” They noodled that hard problem for a long time, and I think via some conversation among the geeks, but via these fairly independent vectors in a lot of cases, they have settled on these practices, these norms that they believe—and I believe—help them get past the classic dysfunctions of the Industrial Era that you and I know all too well: their bureaucratization, their sclerosis, their cultures of silence. They are just endless stifling meetings and turf wars and factions and things like that. We know those things exist. What I think is interesting is that the geeks are aware of them and I think they've come up with ways to do better.The four geek norms (8:29)It's funny that once you've looked at your book, it is impossible to read any other sort of business biography of a company or a CEO and not keep these ideas in your head because I just finished up the Elon Musk biography by Walter Isaacson, and boy, I just kept on thinking of speed and science and the questioning of everything: Why are we doing this? Why are we building this rocket engine like this? Who told us to do that? Somebody in legal told us to do that?Exactly.So certainly those two pop to mind: the speed and the constant iteration. But rather than have me describe them, why don't you describe those norms in probably a much better way than I can.There's a deep part of the Isaacson Musk biography that made my geek eyes light up, and it's when Isaacson describes Musk's Algorithm—I think it's capitalized, too, it's capital “The,” capital “Algorithm,”—which is all about taking stuff out. I think that is profound because we humans have a very strong status quo bias. We're reluctant to take things out. It's one of the best-documented human biases. So we just add stuff, we just layer stuff on, and before you know it, for a couple different flavors of reason, you wind up with this kind of overbuilt, encrusted, process-heavy, bureaucracy-heavy, can't get anything done [corporation]. You feel like you're pushing on a giant piece of Jell-O or something to try to get any work done. And I think part of Musk's brilliance as a builder and an organization designer is to come up with The Algorithm that says, “No, no, a big part of your job is to figure out what doesn't need to be there and make it go away.” I adore that. It's closest to my great geek norm of ownership, which is really the opposite of this processification of the enterprise of the company that we were super fond of starting in the '90s and going forward.So now to answer your question, my four great geek norms, which are epitomized by Musk in a lot of ways, but not always, are:Science. Just make decisions based on evidence and argue a lot about that evidence. Science is an argument with a ground rule. Evidence rules.Ownership. We were just talking about this. Devolve authority downward, stop all the cross-communication, coordination, collaboration, process, all that. Build a modular organization.Speed. Do the minimum amount of planning and then start iterating. You learn, you get feedback, you see where you're keeping up to schedule and where you're not by doing stuff and getting feedback, not by sitting around asking everybody if they're on schedule and doing a lot of upfront planning.Finally, openness, this willingness to speak truth to power. In some ways, a good synonym for it is “psychological safety” and a good antonym for it is “defensiveness.”If anything, from what I understand about Musk, the last one is where he might run into challenges.That's what I was going to say. The ownership and the speed and the science struck me and then I'm like… the openness? Well, you have to be willing to take some abuse to be open in that environment.There are these stories about him firing people on the spot and making these kind of peremptory decisions—all of that is a violation, in my eyes, of the great geek norm of openness. It might be the most common violation that I see classic Silicon Valley techies engage in. They fall victim to overconfidence like the rest of us do, and they're not careful enough about designing their companies to be a check on their own overconfidence. This is something Hastings is very humble and very articulate about in No Rules Rules, the book that he co-wrote with Erin Meyer about Netflix and he highlights all these big calls that he was dead-flat wrong about, and he eventually realized that he had to build Netflix into a place that would tell him he was wrong when he was wrong, and he does all these really nice jobs of highlighting areas where he was wrong and then some relatively low-level person in the organization says, “No, that doesn't make sense. I'm going to go gather evidence and I'm going to challenge the CEO of the company with it.” And to his eternal credit, Hastings goes, “It's pretty compelling evidence. I guess I was wrong about that.” So that, to me, is actually practicing the great geek norm of openness.So someone reading this book is thinking that this book is wrong. Where would that come from? Would that come from overconfidence? Would it come from arrogance? Would it come from the idea that if I am in the C-suite, that obviously I have it figured out and I can probably do all your jobs better than you can, so why are you challenging me? Why are you challenging the status quo? “Hey, that's how we got here was through a process, so trust the process!”It's one of the main flavors of pushback that I hear, and it's very often not as naked as you just made it, but it is, “Hey, the reason I'm sitting in this executive education classroom with you is because I'm fairly good at my job. I made some big calls right, and my job is to provide vision to my team and to direct them not to be this kind of lead-from-behind more coach-y kind of leader.” That's one flavor of pushback I get. Another one is a very pervasive tendency, when we come across some challenging information, to come up with reasons why this doesn't apply to us and why we're going to be just fine. It's some combination of the status quo bias and the overconfidence bias which, again, two of the most common human biases. So very often when I'm talking about this, I get the idea that people in the room are going, “Yeah, okay, wow, I really wouldn't want to complete with SpaceX, but this doesn't apply to me or to my industry.” And then finally, look, I'm clearly wrong about some things. I don't know exactly what they are. Maybe the incumbents of the Enterprise Era are going to mount a surprising comeback by falling back on their 20th-century playbook as opposed to adopting the geek way. I will be very surprised if that happens and I'm taking bets like, “Let's go, let's figure out a bet based on that,” but maybe it'll happen. I'm definitely wrong about some things.Tales of geeks and non-geeks (15:19)Given what you've said, I would certainly think that it would be easier to apply these norms at a newer company, a younger company, a smaller company, rather than a company with a hundred thousand employees that's been around for 30 years. But it's possible to do the second one, right?It is possible. Let me violently agree with you, Jim. You and I are of a vintage and we're both Midwesterners. We both remember Arthur Andersen, right? And what an iconic American Midwestern symbol of rectitude and reliability and a healthy culture that kept the business world honest by auditing their books. Remember all that? Remember how it fell apart?I knew people, and if you got an interview with Arthur Andersen, they're like, “Wow, you are with the Cadillac of accounting consulting firms.”But beyond that, you were doing a valuable thing for society, right? These people had status in the community because they kind of kept companies honest for a living.That's right. That's right. You were true of the truth tellers.Yeah. It was a big deal and a lot of your listeners, I think, are going to be too young to remember it firsthand, but that company became a dysfunctional, unethical, ongoing, miserable train wreck of an organization in its final years before it finally fell apart. It could not have been more surprising to people of our vintage and where we came from. I tell the story of how that happened a little bit in the book to drive home that cultures can go off track in profound ways and in AA's late years, if someone had teleported The Geek Way and waved it around, would it have made any difference? I'd like to hope so, but I kind of don't think so.However, to tell a more optimistic story, I had the chance to interview Satya Nadella about his turnaround at Microsoft, which I think is at a level maybe even above the turnaround that [Steve] Jobs executed when he came back to Apple. The amount of value that Nadella has created at Microsoft in nine years now is staggering, and Microsoft is back. Microsoft has mojo again in the tech industry. But when he took over, Microsoft was still a large profitable company, but it was dead in the water. It wasn't innovating. The geek elite didn't want to go work there. The stock price was flat as a highway for a decade. It was absolutely an afterthought in anything that we care about. And so I use Nadella and I learned from him, and I try to tell the story about how he executed this comeback, and, to my eyes, he did it in a very, very geek way kind of a way.Can you give me an example?My point in telling that story is: I do think it's possible for organizations that find themselves in a bad spot—Established organizations.Established. Large, established organizations find themselves in a bad spot. Those kinds of leopards can change their spots. I firmly believe that.Can big companies go geek? (18:33)What are the first steps to change the corporate culture of a big company?That's why I'm so blown away by what Nadella and his team were able to do. Let me pick out a couple things that seem particularly geeky to me that he did. One was to say that—it doesn't matter if you develop them or not—you do not own code or data at Microsoft. What he meant by that was, subject to legal requirements and safety and some guardrails, if you want to grab some of the code repository at Microsoft to go try something or some data and go try something, you have the right to do that. That just eliminates huge amounts of gatekeeping and hard and soft bureaucracy and all of that inside the company. And that led to things like Copilot. It's a very, very smart way to start dealing with bureaucracy: just saying, “No, you don't get to gatekeep anymore.”He also did fairly obvious things like make sure that their really dysfunctional evaluation system was over. He also emphasized this thing that he called “One Microsoft,” which at first sounded like just CEO rah-rah talk. And it is to some extent, but it's also incredibly clever because we humans are so tribal. In addition to the status quo bias and the overconfidence bias, the third easy, easy bias to elicit is “myside” bias. We are tribal. We want our tribe to win. I think part of Nadella's brilliance was to say, “The tribe that you belong to is not Office versus Windows versus Bing versus… the tribe you belong to is Microsoft.”And he changed compensation, so that it also worked that way. He worked with incentives—he took an Econ 101 class—but he also kept emphasizing that “we are one tribe,” and that makes a difference if the leader at the top keeps saying it and if they behave that way. I think one of the deepest things that he did was act in an open way and demonstrate the norm of openness that he wanted to see all over the place. He got a ton of help with it, but if you talk to him, you immediately realize that he's not this table-pounding, my-way-or-the-highway kind of a guy. He's somebody that wants to get it right, and if you have an idea, you might get a fair erring for that idea. He also embraced agile methods and started to move away from the old ways that Microsoft had to write software, which were out of date, and they were yielding some really unimpressive projects.So as he and I were talking, I was doing my internal checklist and I kept on saying, “Yep, that's speed. That is science. That is ownership. That is openness,” and just emphasizing, as I listened to him, I just kept hearing these norms come up over and over. But one thing that he clearly knows is that this ain't easy and it ain't fast, and cultural change is a long, slow, grinding process, and you've got to keep saying the same thing over and over. And then I think, especially as a leader, you've got to keep living it because people will immediately sense if what you're doing is not lining up with what you're saying.One bit that popped out, because obviously I'm in Washington and I see a government that doesn't work very efficiently, and you wrote, “To accelerate learning and progress, plan less and iterate more,” and to iterate means to experiment, it means you're going to fail. And boy, oh boy, failure-averse organizations, you can find that in government, you can find it in corporate America, that acceptance of: try something and if it fails, it's a learning experience. It's not a black mark on your career forever. Now let's go try the next thing.Exactly. To me, it's the most obvious thing that the geeks do that's starkly different from Industrial Era organizations, “plan less, iterate more.” The great geek norm of speed, and there are a bunch of exemplars of that. The clearest one to me is SpaceX, where they blow up a rocket and that is a win for them, not a loss. And even if it gets written up in the press as, “Oh, Starship blew up, or whatever”—they don't care, right? They'd rather that it didn't blow up or that it stayed together longer, but if they got the learning that they were looking for, then they're like, “Great, we're going to incorporate that, we're going to build another rocket, we're not going to put any people on until we're very, very, very sure, but we're going to blow up a bunch of rockets.” From the start of the company, that has been an okay thing to do.They also are willing to embrace pretty big pivots. The first plan for Starship was that it was going to be a carbon fiber rocket because carbon fiber is so strong and lightweight, but their method for making it was too slow, too expensive, and had a reject rate that was too high. The thing's now made out of stainless steel! It's the opposite kind of material! But they said, “Look, the goal is the goal, and the goal is not to stick to the original plan, the goal is to build a great big rocket that can do all kinds of things. The way we get there is by trying—legitimately trying—a bunch of stuff and failing at it with the eyes of the world upon us.”I want to draw a really sharp distinction between the process and the product, and what I mean by that is a failure-tolerant process can yield an incredibly robust, safe product. We don't need to look any farther for that than the Dragon Capsule that SpaceX makes, which is the only capsule currently made in America that is certified by NASA to take human beings into space. It's how all Americans these days get back and forth to the ISS. NASA doesn't have one. NASA gave a contract to Boeing at the same time it gave one to SpaceX. Boeing still has not had the first crude test of its capsule. This geek way of speed, it's uncomfortable, and you got to be willing to fail publicly and own it, but it works better.Is the geek way, to some degree, an American phenomenon?So far.I was going to say, can the geek way be implemented in other countries? Is there something special about American culture that allows the geek way to work and to be adopted—I said universal earlier, maybe I meant, is it truly universal? Can it be implemented in other places?Jim, you and I, as proud Americans, like to believe that we're an exceptional country, and I do believe that. I don't believe the geek way only works with a bunch of Americans trying it. I travel lots of different places, and especially the energy that I see among younger people to be part of this transformation of the world that's happening (that you and I are lucky enough to get to observe and try to think about), this transformation of the world in the 21st century because of the technological toolkit that we have, because of the amount of innovation out there, the thirst to be part of that is very, very, very widespread. And I don't think there's anything in the drinking water in Munich or Kyoto or Lima that makes this stuff impossible at all. It is true, we're an individualistic culture, we're kind of mouthy, we celebrate these iconoclastic people, but I don't think any of those are absolutely necessary in order to start following norms of science, ownership, speed and openness. I hope those are universal.The geek way beyond tech (26:32)We've been talking a lot about tech companies. Are there companies which really don't seem particularly techie (even though obviously all companies use technology) that you could see the geek way working currently?I haven't gone off and looked outside the tech industry for great exemplars of the geek way, so I have trouble answering this question. But think about Bridgewater, which is really one of the weirdest corporate cultures ever invented, and I haven't read the new biography of Ray Dalio yet, but it appears that all might not be exactly as it appears. But one thing that Bridgewater has been adamant about from the get-go, and Dalio has been passionate about, is this idea of radical transparency, is the idea of openness. Your reputation is not private from anybody else in the company at any point in time. So they've taken this norm of openness and they've really ran with it in some fascinating directions. In most organizations, there's a lot of information that's private, and your reputation is spread by gossip. Literally, that's how it works. Bridgewater said, “Nope. We really believe in openness and everything that's important about your performance as a professional in this company, you're going to get rated on it by your colleagues, and you're going to have these visible to everybody all the time inside the company so that if you start espousing how important it is to be ethical, but your score as an ethical leader is really low, nobody's going to listen to you.” I think that's fascinating, and I think as time goes by, we're going to come across these very, very geeky norms and practices being implemented in all kinds of weird corners of the global economy. I can't wait to learn about it.I would think that, given how every country would like to be more productive, every country's having a white paper on how to improve their productivity, and this, to me, is maybe something that policymakers don't think about, and I'm not sure if there's a policy aspect to this, but I hope a lot of corporate leaders and aspiring corporate leaders at least read your book.Well, the one policy implication that might come up is, what happens when the geeks start unignorably beating up the incumbents in your favorite industry. When I look at what's happening in the global auto industry right now, I see some of that going on, and my prediction is that it's going to get worse instead of better. Okay, then what happens?Save us! Save us from this upstart!Exactly, but then there could be some really interesting policy choices being made about protecting dinosaur incumbents in the face of geek competitors. I hope we don't retreat into nationalism and protectionism and that kind of stuff. What I hope happens instead is that the world learns how to get geeky relatively quickly and that this upgrade to the company spreads.The only thing I would add here is I would also urge business journalists to read the book so you understand how companies work and how these new companies that work, companies that look like they are—and not to keep harping on SpaceX, but so many people who I think should know better, will look at SpaceX and think, “Oh, they're failing. Oh, that rocket, as you said earlier, the rocket blew up! Apollo had a couple of problems, they're blowing up a rocket every six weeks!” And they just simply do not understand how this kind of company works. So I don't know. So I guess I would recommend my business journalists to read it, and I imagine you would think the same.That recommendation makes a ton of sense to me. Jim. I'm all on board with that.Andrew. This is an outstanding book and a wonderful companion piece to your other work which is very pro-progress, and pro-growth. I absolutely loved it, and thanks so much for coming on the podcast today,Jim, thanks for being part of the Up Wing Party with me. Let's make it happen.Absolutely. Thank you.Thank you, sir. 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

In CXOTalk episode number 812, Michael Krigsman speaks with Andrew McAfee, a principal research scientist at MIT, for a detailed discussion on creating a business culture that supports AI. As the author of 'The Geek Way, McAfee shares lessons drawn from his extensive research on how technological advancements impact business operations and organizational culture.This conversation is particularly valuable for business leaders interested in learning how to create culture that supports the strategic role of AI in their organization.Key highlights from this episode include:►*The Intersection of AI and Business Culture:* Insight into how AI is reshaping business strategies and influencing organizational dynamics.►*'Geek Culture' in Organizations:* Exploration of the concept of 'geek culture' within enterprises and its significance in fostering innovation.►*Ethical and Strategic Implications:* Discussion on the ethical aspects of AI integration and strategies for effective implementation in corporate settings.►*Adapting to Technological Change:* Guidance on how businesses can evolve to embrace technological advancements and the future of work.*Andrew McAfee* 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 St. 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.*Michael Krigsman* is an industry analyst and publisher of CXOTalk. For three decades, he has advised enterprise technology companies on market messaging and positioning strategy. He has written over 1,000 blogs on leadership and digital transformation and created almost 1,000 video interviews with the world's top business leaders on these topics. His work has been referenced in the media over 1,000 times and in over 50 books. He has presented and moderated panels at numerous industry events around the world.#cxotalk #enterpriseai #culture #culturetransformation

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.

We often speak about the AI revolution as though it were something that might happen in the far future. But, what about today, when AI is modifying our culture, society, and the economy as we speak? In this episode, we consider how AI is currently creating massive shifts, and the potential benefits and challenges we face right now. Is AI revitalizing the economy, or is it on track to displace countless workers? Will AI render filmmakers and writers obsolete, or are we at the beginning of a modern-day cultural renaissance? And is AI being used to help preserve Indigenous cultures, or is it a tool to harvest their data for corporate gain? To address these questions, host Raffi Krikorian speaks with Erik Brynjolfsson, Director of the Digital Economy Lab; Justine Bateman, director, writer, producer and author; Ari Melenciano, artist and creative technologist; and Keolu Fox, assistant professor at the University of California, San Diego and co-founder and co-director of the Indigenous Futures Institute. Together, they chart a path to help us navigate AI in the current moment. To learn more about Technically Optimistic and to read the transcript for this episode: emersoncollective.com/technically-optimistic-podcast For more on Emerson Collective: emersoncollective.com Learn more about our host, Raffi Krikorian: emersoncollective.com/persons/raffi-krikorian Technically Optimistic is produced by Emerson Collective with music by Mattie Safer. Email us with questions and feedback at technicallyoptimistic@emersoncollective.com. Subscribe to Emerson Collective's newsletter: emersoncollective.com To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Erik Brynjolfson joins host Jeanne Meserve to discuss the timelines of the AI industrial revolution, the future of generative AI, and democratic values. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit scsp222.substack.com

World leading economist Erik Brynjolfsson visits the Talk Chineasy studio in London to learn a hugely important word for our lives in Chinese, “work”! Also, find out how to say “I want to work!” and “I don't want to work”!

The economy affects almost every area of our lives and who better to learn the Chinese word for the economy than world leading economist and author of “The second machine age” Erik Brynjolfsson.

When are you most productive? How do you increase your productivity? And is technology a help or a hindrance?! The amazing Erik Brynjolfsson learns the Chinese word for "productivity" and discusses with ShaoLan the technologies that can cause ripples of productivity growth around the world.

Impress your Chinese friends with the word for Artificial Intelligence! World-leading economist Erik Brynjolfsson chats with ShaoLan about how smart robots could really get and how that might change the world as we know it.