Podcasts about 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.

As AI's capability grows, what once-human tasks will it be able to do, by when? What will those displaced humans do? We look to the automation revolutions of the past to see what the historical pattern has been, and explore in what ways AI is different that could change the pattern. The pattern will change. The implications are immense. SourcesLeontief's horse. Wassily Leontief, 1983, National Academy of Engineering symposium The Long-Term Impact of Technology on Employment and Unemployment. Quote and horse-population figures via Brynjolfsson & McAfee, "Will Humans Go the Way of Horses?", Foreign Affairs (2015): https://www.foreignaffairs.com/world/will-humans-go-way-horsesSoftware developer pay. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, Software Developers (SOC 15-1252): https://www.bls.gov/oes/2021/may/oes151252.htm · https://www.bls.gov/oes/2022/may/oes151252.htm · https://www.bls.gov/oes/2023/may/oes151252.htm · Occupational Outlook Handbook: https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm · Total-comp figure: Levels.fyi (2026).AI timelines (experts). Katja Grace et al., "Thousands of AI Authors on the Future of AI" (2023 survey, 2,778 researchers): https://arxiv.org/abs/2401.02843 · https://aiimpacts.org/wp-content/uploads/2023/04/Thousands_of_AI_authors_on_the_future_of_AI.pdfAI timelines (forecasters). Metaculus (community forecasts; live figures): "first general AI system" https://www.metaculus.com/questions/5121/ · "weakly general AI" https://www.metaculus.com/questions/3479/Goldman Sachs. "An AI Job Apocalypse?", Goldman Sachs Research, June 25, 2026: https://www.goldmansachs.com/insights/top-of-mind/an-ai-job-apocalypse (Goldman's own view is that the disruption is temporary.)Occupational exposure. Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock, "GPTs are GPTs," Science 384 (2024): https://www.science.org/doi/10.1126/science.adj0998 · working paper: https://arxiv.org/abs/2303.10130Entry-level cracks. Stanford Digital Economy Lab, "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of AI" (Nov 2025): https://digitaleconomy.stanford.edu/app/uploads/2025/11/CanariesintheCoalMine_Nov25.pdfCurrent labor data. Maxim Massenkoff & Peter McCrory, "Labor Market Impacts of AI: A New Measure and Early Evidence," Anthropic (Mar 5, 2026): https://www.anthropic.com/research/labor-market-impacts (Note: Anthropic funds both the models and this research.)Depression unemployment anchor. U.S. unemployment peaked near 25% in 1933 (verify exact figure before citing on air). Hosted on Acast. See acast.com/privacy for more information.

In 1776 — that same year America declared its independence — Adam Smith published the equally revolutionary The Wealth of Nations, his founding explanation of national economic value. Two hundred and fifty years later, Tim O'Reilly argues in the free-market Economist that Elon Musk and his fellow tech barons are building a monarchical form of capitalism that the proto-democratic Smith would have hated. Musk, O'Reilly reports, believes that SpaceX will become “worth more than the rest of Earth”. The merchants are becoming princes, O'Reilly warns. And the rest of us are becoming peasants. Such is the road to serfdom in our AI age. So who should own the AI in our bewildering age of multi-trillion dollar start-ups like SpaceX, Anthropic and OpenAI? Or as That Was The Week publisher Keith Teare asks in his latest editorial, who should own the “intelligence” of our AI age? Keith uses a bottling plant as a metaphor to describe our dilemma. Since no single entity can own this intelligence — the sum total of our common experience — charging us for it would be like seizing the Earth's water supply and selling it back to us, Coca-Cola style, in plastic bottles. Except that the Hayekian Keith approves of the bottling process. Private companies, rather than governments, he argues, are most suited to doing this. For Keith, this dilemma is also an opportunity to redistribute the ownership of intelligence. He argues for a “Human Wealth Fund” into which every consequential AI company should put a slice of its equity. In the manner of Norway's sovereign wealth fund, this fund would be distributed to all citizens. Rather than Denmark, now we should become like Norway, a tiny homogenous nation with a cultural distaste for Muskian individual wealth. Not very realistic, I fear. On top of that, it's hard to imagine our tech princes collaborating on anything. Musk and Altman aren't on speaking terms while Altman and Amodei, who also loathe each other, are focused on their IPOs. Meanwhile, the Trump administration, which presumably would coordinate this fund, is pitching a $100,000-a-month fast feed of the president's posts. Keith's question, “who owns the intelligence”, is the right one. But the answer won't come from trickle-down funds set-up by our tech princes. Such supposed munificence is about as likely as America becoming Norway. Read the fine print of any “Human Wealth Fund” set up by Sam Altman and Elon Musk. As we should know all too well by now, when a “revolutionary” Silicon Valley gives stuff away, it turns out to be exorbitantly expensive. Free plastic bottles of intelligence, anyone? Five Takeaways •       Intelligence, Not AI. The week's framing shift: the word AI is too small, because AI is merely the tool for harvesting and delivering the thing itself — intelligence, the sum total of our common human experience. Keith argues the renaming is not semantic but political: the moment intelligence sits at the center of the discussion, everyone's opinion has to be shaped by what it actually is, and the idea that any single entity could own it starts to look as bizarre as owning the world's water supply. Andrew's rejoinder: they're still just words — though he concedes intelligence is the better one. •       Bottled Intelligence Is Good — The Question Is Who Benefits. Keith refuses the critic's role: bottling intelligence, like Google's bottling of the world's words into search, is a good thing, because only massively capitalized private companies can innovate at that scale — and between private entities and governments as owners of intelligence, he'll take the companies every time. What's wrong is the distribution of the benefits. Even insiders are complaining: Alex Karp is publicly angry at OpenAI and Anthropic's pricing, while China's Kimi K3 — released the day of recording and, Keith claims, better than Claude Fable — signals that very good models are about to get very cheap. •       Capitalism Adam Smith Would Hate. Tim O'Reilly argues in The Economist that Musk and his type are building a capitalism Smith would despise — founders as monarchs, a point Henry Farrell reinforces with a slide from Peter Thiel's startup class placing the king of a monarchy and the founder of a startup side by side. Keith's response is characteristically unsentimental: Smith would have hated everything since the Federal Reserve, and the founder-king structure — Larry and Sergey's voting shares, Zuckerberg's special rights, corporations bigger than countries with user bases bigger than China — is simply the stage of capitalism we're at. The question is whether there's a path from here to somewhere better. •       The Human Wealth Fund. Keith's path comes in two versions: government-down, a sovereign wealth fund holding AI equity for every citizen; or company-up, the AI companies voluntarily endowing a global fund — and it only takes one to move first, because everyone else would have to react. His proxy is Norway, where every citizen benefits from ownership — not payouts, ownership — in the oil fund; AI revenues, unlike Norwegian oil, could eventually drive most of a doubled global GDP. His critique of the Brynjolfsson economists' much-signed statement is that “must act now” is vacuous: he'd have added a point four naming the actual mechanism. •       The Bet. Andrew's counter-case: Musk and Altman loathe each other, the mob hates AI so thoroughly that no pro-AI politician can survive, the states from Newsom's California to Florida are embracing nothing, New York just enacted the first data center moratorium, and the founders — eyes on their IPOs — are in the pockets of the banks. Hence the wager: 5% of the Teare Wealth Fund says no Human Wealth Fund this year, and none in the twenties. Keith declined the bet, on principle: he's an advocate, and only through advocacy does public opinion change. As Andrew put it: keep fighting the good fight — maybe one of the crazy ideas will stick. About the Guest Keith Teare is the founder and editor of the That Was The Week tech newsletter, and Andrew's weekly co-host. A British-born Silicon Valley entrepreneur and investor, he was a co-founder of TechCrunch and runs the Palo Alto–based venture firm SignalRank. He and Andrew have been arguing about technology — productively — every week for years. References: •       That Was The Week — Keith's newsletter; this week's editorial argues that the word AI is too small, and that the central question of the age is who owns intelligence. •       Tim O'Reilly in The Economist — on Elon Musk building a form of capitalism that Adam Smith would hate, quoting Musk's claim that SpaceX will become worth more than the rest of the Earth. •       Henry Farrell — the big tech critic's companion piece, featuring the slide from Peter Thiel's startup class that plac...

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

How much of your life -- and everyone's -- is a compulsive, futile effort to deny your own death? What happens when you see what's really happening?SourcesLeontief's horse. Wassily Leontief, 1983, National Academy of Engineering symposiumHumans and Horses. Brynjolfsson & McAfee, “Will Humans Go the Way of Horses?”, Foreign Affairs (2015): https://www.foreignaffairs.com/world/will-humans-go-way-horsesSoftware developer pay. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, Software Developers (SOC 15-1252): https://www.bls.gov/oes/2021/may/oes151252.htm · https://www.bls.gov/oes/2022/may/oes151252.htm · https://www.bls.gov/oes/2023/may/oes151252.htm · Occupational Outlook Handbook: https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm Total-comp figure: Levels.fyi (2026).AI timelines (experts). Katja Grace et al., “Thousands of AI Authors on the Future of AI” (2023 survey, 2,778 researchers): https://arxiv.org/abs/2401.02843 · https://aiimpacts.org/wp-content/uploads/2023/04/Thousands_of_AI_authors_on_the_future_of_AI.pdfGoldman Sachs. “An AI Job Apocalypse?”, Goldman Sachs Research, June 25, 2026: https://www.goldmansachs.com/insights/top-of-mind/an-ai-job-apocalypse (Goldman's own view is that the disruption is temporary.)Occupational exposure. Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock, “GPTs are GPTs,” *Science* 384 (2024): https://www.science.org/doi/10.1126/science.adj0998 · working paper: https://arxiv.org/abs/2303.10130Stanford Digital Economy Lab, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of AI” (Nov 2025): https://digitaleconomy.stanford.edu/app/uploads/2025/11/CanariesintheCoalMine_Nov25.pdfCurrent labor data. Maxim Massenkoff & Peter McCrory, “Labor Market Impacts of AI: A New Measure and Early Evidence,” Anthropic (Mar 5, 2026): https://www.anthropic.com/research/labor-market-impacts Hosted on Acast. See acast.com/privacy for more information.

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

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/ — More than three years after ChatGPT's release, only 27% of executives say AI has met their ROI expectations. The history of factory electrification explains why — most companies are at the light-bulb stage, adding Copilot licenses rather than reconceptualizing their businesses around AI. In this episode I map the three stages of AI adoption, and show what it actually takes to move from chatbots to the autonomous company — the only stage where the moat becomes real. I covered: (01:40) Ford's electricity playbook: why AI adoption needs a complete rethink (03:51) The congestion problem: why AI gains stall (05:45) Chatbot to autonomous company: your three-stage roadmap (06:40) Why individual productivity gains won't build a moat — and what will (10:17) Which companies are getting AI transformation right (14:12) My 2029 AI adoption forecast — and how to stay ahead Read my essay "Why AI isn't showing up on your bottom line" on Substack: https://www.exponentialview.co/p/why-ai-isnt-showing-up-on-your-bottom-line — Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azeem/ 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.

Quais são as profissões mais ameaçadas pela inteligência artificial? E de que forma a IA pode transformar o ensino? Bernardo Caldas e Hugo van der Ding analisam os sinais da automação no mercado de trabalho e na educação das gerações futuras.Nos últimos três anos, as vagas para juniores em áreas mais expostas à IA caíram 30% a 40%, à medida que tarefas repetitivas, analíticas e administrativas são substituídas por algoritmos. Mas estarão apenas os empregos menos qualificados em risco?Neste episódio, o especialista em IA e o comunicador observam que também as profissões altamente especializadas estão ameaçadas – a começar, ironicamente, pelos engenheiros tecnológicos, mas atingindo, igualmente, advogados, consultores e médicos, sobretudo em especialidades de diagnóstico.Mas nem tudo são más notícias: numa época em que o desemprego se mantém em níveis historicamente baixos, a IA também pode ter impactos positivos na educação, ao democratizar o acesso à informação entre diferentes estratos sociais.A dupla discute ainda os desafios e oportunidades desta revolução — e porque é que o pensamento crítico, uma visão integrada do mundo e a «motivação intrínseca» serão competências decisivas no futuro.Para acompanhar a velocidade das transformações em curso, não perca este episódio do [IN]Pertinente.LINKS E REFERÊNCIAS ÚTEISBASTANI et al., «Generative AI without guardrails can harm learning: Evidence from high school mathematics», (PNAS 122(26), 2025)BRYNJOLFSSON, CHANDAR & CHEN, «Canaries in the Coal Mine?» (Stanford Digital Economy Lab, 2025)DELL'ACQUA, MOLLICK et al., «Navigating the Jagged Technological Frontier» (Harvard/BCG, 2023)KESTIN et al., «AI tutoring outperforms in-class active learning: an RCT», (Scientific Reports, 2025)DE SIMONE et al., «From Chalkboards to Chatbots: Evaluating the Impact of Generative AI on Learning Outcomes in Nigeria», (World Bank WPS 11125, 2025)ACEMOGLU, Autor & JOHNSON, «The Direction of AI», (NBER WP 34854, 2026)GARICANO-RAYO, «AI and the Expertise Leverage Ratio», (CEPR DP 20634, 9/9, 2025)LEE et al. (Microsoft + CMU), «The Impact of Generative AI on Critical Thinking», (CHI 2025)CAPLAN, «The Case Against Education» (Princeton UP, 2018)BJORK & BJORK, «Making things hard on yourself, but in a good way», (Gernsbacher et al., Psychology and the Real World, 2011)RYAN & DECI, «Self-Determination Theory», (American Psychologist, 2000)RISKO & GILBERT, «Cognitive offloading», (Trends in Cognitive Sciences, 2016)MOLLICK & MOLLICK, «Assigning AI: Seven Approaches for Students, with Prompts», (SSRN 4475995, 2023)BIOSBernardo CaldasEspecialista em inteligência artificial e cofundador da associação «Data Science for Social Good Portugal», uma associação que desenvolve projetos de ciência de dados e inteligência artificial com impacto social positivo.Hugo van der Ding Locutor, criativo e desenhador acidental. Criador de personagens digitais de sucesso como a «Criada Malcriada» e «Cavaca a Presidenta», autor de um dos podcasts mais ouvidos em Portugal, «Vamos Todos Morrer», também escreve para teatro e, atualmente, apresenta o programa «Duas Pessoas a Fazer Televisão», na RTP, com Martim Sousa Tavares. 

Welcome back to Scaling Theory. In this episode, I speak with Matthew O. Jackson, the William D. Eberle Professor of Economics at Stanford University and an external faculty member at the Santa Fe Institute. Matthew is one of the founders of the modern economics of networks and the author of The Human Network and Social and Economic Networks.We talk about the friendship paradox, why homophily slows how fast a society learns the truth but helps niche ideas catch fire, and the gossip study where villagers in southern India proved remarkably good at naming the most central spreaders in their community. We then turn to AI agents as a different species: Turing tests on LLMs, the steerability of agent personas through system prompts, and what to make of Moltbook, the social network for AI agents.By the end, you will know why telling students how much their peers actually drink reduces binge drinking more than warning them about the dangers of alcohol, why the same network can spread a virus quickly and a belief slowly, and why AI agents change their behavior when asked to explain it.Papers and works referenced in the conversationBooksThe Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors — Matthew O. Jackson (Pantheon, 2019). https://web.stanford.edu/~jacksonm/books.htmlSocial and Economic Networks — Matthew O. Jackson (Princeton University Press, 2008). https://web.stanford.edu/~jacksonm/books.htmlPart I — The scaling of human networks"Diffusion and Contagion in Networks with Heterogeneous Agents and Homophily" — Matthew O. Jackson and Dunia López-Pintado, Network Science 1(1), 2013. https://arxiv.org/abs/1111.0073"How Homophily Affects the Speed of Learning and Best-Response Dynamics" — Benjamin Golub and Matthew O. Jackson, Quarterly Journal of Economics 127(3), 2012. https://web.stanford.edu/~jacksonm/homophily.pdf"Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials" — Abhijit Banerjee, Arun G. Chandrasekhar, Esther Duflo, and Matthew O. Jackson, Review of Economic Studies 86(6), 2019. https://academic.oup.com/restud/article/86/6/2453/5345571"Empathy and Well-Being Correlate with Centrality in Different Social Networks" — Sylvia A. Morelli, Desmond C. Ong, Rucha Makati, Matthew O. Jackson, and Jamil Zaki, PNAS 114(37), 2017. https://www.pnas.org/doi/10.1073/pnas.1702155114Part II — The scaling of AI agents"Inequality's Economic and Social Roots: The Role of Social Networks and Homophily" — Matthew O. Jackson, in Advances in Economics and Econometrics: Twelfth World Congress of the Econometric Society (Cambridge University Press, 2025). https://arxiv.org/abs/2506.13016"AI Behavioral Science" — Jackson, Mei, Wang, Xie, Yuan, Benzell, Brynjolfsson, Camerer, Evans, Jabarian, Kleinberg, Meng, Mullainathan, Ozdaglar, Pfeiffer, Tennenholtz, Willer, Yang, and Ye, arXiv 2509.13323, 2025. https://arxiv.org/abs/2509.13323"A Turing Test of Whether AI Chatbots Are Behaviorally Similar to Humans" — Qiaozhu Mei, Yutong Xie, Walter Yuan, and Matthew O. Jackson, PNAS 121(9), 2024. https://www.pnas.org/doi/10.1073/pnas.2313925121

Does Nick really know what he is talking about? Time to find out. We play a trivia quiz with fifteen questions about information systems research. Nick has an audience joker, a telephone joker, and a 50:50 joker -and he needs all of them to make it through the levels. How well do you know the field? Tune in to find out, or play our game for yourself. The questions are posted below. Play the game for yourself: Round 1 Question: Which three journals were added when the AIS Senior Scholars expanded the old Basket of Eight into the 11-journal premier list in 2023? A. DSS, I&M, and I&O B. DSS, ISJ, and JSIS C. CAIS, I&M, and IT&P D. DSS, JIT, and I&O Round 2 Question: In Fred Davis's 1989 TAM paper, which two beliefs are the famous core constructs? A. Trust and enjoyment B. Performance expectancy and effort expectancy C. Perceived usefulness and perceived ease of use D. Social influence and facilitating conditions Round 3 Question: Which paper introduced UTAUT? A. Venkatesh & Davis, 2000, Management Science B. Davis, 1989, MIS Quarterly C. Venkatesh et al., 2003, MIS Quarterly D. Venkatesh, Thong, & Xu, 2012, MIS Quarterly Round 4 Question: The original DeLone and McLean paper, "Information Systems Success: The Quest for the Dependent Variable," appeared in which year? A. 1988 B. 1990 C. 1992 D. 2003 Round 5 Question: Which paper is generally credited with introducing Action Design Research (ADR) into the IS mainstream? A. Hevner et al. (2004), MISQ B. Sein et al. (2011), MISQ C. Gregor & Hevner (2013), MISQ D. Peffers et al. (2007), JMIS Round 6 Question: Which paper is the 2017 MISQ piece on platform ecosystems with the subtitle-like claim "How Developers Invert the Firm"? A. Parker, Van Alstyne, & Jiang B. Constantinides, Henfridsson, & Parker C. Eisenmann, Parker, & Van Alstyne D. Ghazawneh & Henfridsson Round 7 Question: Which paper is the most impactful technostress article in Information Systems research? A. Tarafdar et al. (2007), JMIS, The impact of technostress on role stress and productivity B. Ragu-Nathan et al. (2008), ISR, The consequences of technostress for end users in organizations C. Tarafdar et al. (2010), JMIS, Impact of technostress on end-user satisfaction and performance D. Tarafdar, Pullins, & Ragu-Nathan (2015), ISJ, Technostress: negative effect on performance and possible mitigations Round 8 Question: As of March 2026, which of the following papers has the highest Google Scholar citation count? A. Venkatesh et al. (2003) UTAUT B. Yoo, Henfridsson, & Lyytinen (2010) The New Organizing Logic C. Hevner et al. (2004) Design Science in Information Systems Research D. Davenport (1993) Process innovation: reengineering work through information technology Round 9 Question: In digital-platform research, the phrase "boundary resources model" is most closely associated with which paper? A. Ghazawneh & Henfridsson (2013), ISJ B. Constantinides, Henfridsson, & Parker (2018), ISR C. Parker, Van Alstyne, & Jiang (2017), MISQ D. Yoo, Henfridsson, & Lyytinen (2010), ISR Round 10 Question: In IS economics / IT business value research, which paper is the classic article on information worker productivity? A. Brynjolfsson & Hitt, 1996, MISQ B. Aral, Brynjolfsson, & Van Alstyne, 2012, ISR C. Aral & Weill, 2007, Org. Science D. Brynjolfsson, Rock, & Syverson, 2017, NBER Level 11 Question: In Feldman and Pentland's routines work, which pairing is correct? A. Ostensive = abstract pattern or idea of the routine; Performative = specific enactments by specific people at specific times and places B. Ostensive = formal SOP; Performative = deviations from the SOP C. Ostensive = managerial intention; Performative = worker resistance D. Ostensive = organizational memory; Performative = organizational forgetting Level 12 Question: Which statement best captures Paul Leonardi's (2013) position on sociomateriality? A. Materiality and human interpretation are always inseparable, so affordances and constraints cannot be analytically distinguished from materiality. B. Materiality exists independently of people, but affordances and constraints do not; they arise in relation to human goals. C. Sociomateriality should only be grounded in agential realism, not critical realism. D. The social and the material are separable in theory, but not in empirical research. Level 13 Question: The 2010 ISR research commentary "Digital Infrastructures: The Missing IS Research Agenda" is associated with which set of authors? A. Yoo, Henfridsson, and Lyytinen B. Tilson, Lyytinen, and Sørensen C. Hanseth, Monteiro, and Hatling D. Eaton, Elaluf-Calderwood, Sorensen, and Yoo. Level 14 Question: Which paper examined whether participation in the gig economy is associated with entrepreneurial activity, and who are its authors? A. Burtch, Carnahan, and Greenwood (2018), Management Science B. Greenwood, Agarwal, Agarwal, and Gopal (2019), Organization ScienceC. Burtch, Ghose, and Wattal (2013), Information Systems Research D. Greenwood and Wattal (2017), MIS Quarterly Level 15 Question: In Kellogg, Valentine, and Christin's "Algorithms at Work: The New Contested Terrain of Control" framework, which set correctly names the six mechanisms of algorithmic control? A. Restricting, recommending, recording, rating, replacing, rewarding B. Ranking, routing, recording, rewarding, reviewing, removing C. Restricting, routing, reviewing, ranking, replacing, rewarding D. Recommending, recording, rating, regulating, replacing, remunerating

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

When we discuss artificial intelligence, what metaphors do we use to illustrate what we mean? Is artificial intelligence some sort of robot—like Ultron—or is it an organism—like a beehive? What happens to our expectations, our thinking, and our conclusions when we change these metaphors, say, from an entitative metaphor (say, an agent) to a relational metaphor (say, belonging to our work network)? We discuss these points with and who wrote a very interesting paper on how management scholars think about artificial intelligence.   Episode reading list Ramaul, L., Ritala, P., Kostis, A., & Aaltonen, P. (2025). Rethinking How We Theorize AI in Organization and Management: A Problematizing Review of Rationality and Anthropomorphism. Journal of Management Studies, . Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing Artificial Intelligence. MIS Quarterly, 45(3), 1433-1450. Alvesson, M., & Sandberg, J. (2020). The Problematizing Review: A Counterpoint to Elsbach and Van Knippenberg's Argument for Integrative Reviews. Journal of Management Studies, 57(6), 1290-1304. Berente, N. (2020). Agile Development as the Root Metaphor for Strategy in Digital Innovation. In S. Nambisan, K. Lyytinen, & Y. Yoo (Eds.), Handbook of Digital Innovation (pp. 83-96). Edward Elgar. Pepper, S. C. (1942). World Hypotheses: A Study in Evidence. University of California Press. Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at Work. The Quarterly Journal of Economics, 140(2), 889-942. Russell, S. J., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. Jarrahi, M. H., & Ritala, P. (2025). Rethinking AI Agents: A Principal-Agent Perspective. California Management Review Insights, . Boxenbaum, E., & Pedersen, J. S. (2009). Scandinavian Institutionalism – a Case of Institutional Work. In T. B. Lawrence, R. Suddaby, & B. Leca (Eds.), Institutional Work: Actors and Agency in Institutional Studies of Organizations (pp. 178-204). Cambridge University Press. Iivari, J., & Lyytinen, K. (1998). Research on Information Systems Development in Scandinavia-Unity in Plurality. Scandinavian Journal of Information Systems, 10(1), 135-186. Alvesson, M., & Sandberg, J. (2024). The Art of Phenomena Construction: A Framework for Coming Up with Research Phenomena beyond ‘the Usual Suspects'. Journal of Management Studies, 61(5), 1737-1765. Brunsson, N. (2003). The Organization of Hypocrisy: Talk, Decisions, and Actions in Organizations. Copenhagen Business School Press. Floyd, C., Mehl, W.-M., Reisin, F.-M., Schmidt, G., & Wolf, G. (1989). Out of Scandinavia: Alternative Approaches to Software Design and System Development. Human-Computer Interaction, 4(4), 253-350. Grisold, T., Berente, N., & Seidel, S. (2025). Guardrails for Human-AI Ecologies: A Design Theory for Managing Norm-Based Coordination. MIS Quarterly, 49, . Forster, E. M. (1909). The Machine Stops. The Oxford and Cambridge Review, November 1909, .   

A new season of podcast episodes is starting and what better place to kick it off as the world's largest business and management conference. We are recording this episode at in beautiful Copenhagen, made possible through a generous invite from who organized a recording studio for us. Being here amid symposia, professional development workshops, panels, and paper presentations makes us wonder: what does it take to produce great, stimulating, and productive academic discourse? Does it depend on the people that get invited to speak, is it about their ideas, or what else? We sit down with our friend with whom we share some stories from the events we've attended at AOM and we distil a few rules that characterize good intellectual debate: let there be cognitive conflict about the merit of ideas, be bold enough to propose new ideas, show humility for the craft and work of others, and be respectful to your colleagues.     Episode reading list Kulkarni, M., Mantere, S., Vaara, E., van den Broek, E., Pachidi, S., Glaser, V. L., Gehman, J., Petriglieri, G., Lindebaum, D., Cameron, L. D., Rahman, H. A., Islam, G., & Greenwood, M. (2024). The Future of Research in an Artificial Intelligence-Driven World. Journal of Management Inquiry, 33(3), 207-229. Brynjolfsson, E., Collis, A., Diewert, W. E., Eggers, F., & Fox, K. J. (2025). GDP-B: Accounting for the Value of New and Free Goods. American Economic Journal: Macroeconomics, . Stelmaszak, M., Wagner, E., & DuPont, N. N. (2024). Recognition in Personal Data: Data Warping, Recognition Concessions, and Social Justice. MIS Quarterly, 48(4), 1611-1636. Habermas, J. (1984). Theory of Communicative Action, Volume 1: Reason and the Rationalization of Society. Heinemann. Lehmann, J., Hukal, P., Recker, J., & Tumbas, S. (2025). Layering the Architecture of Digital Product Innovations: Firmware and Adapter Layers. Journal of the Association for Information Systems, 26, .  

Which research methods are better, quantitative or qualitative? What is more important, getting a richer picture of what goes on in organizations, or seeking generalizable insights about causality? This debate has raged at the very least since Glaser and Strauss popularized the grounded theory method in the mid twentieth century. In 2025, we want to put this debate to rest. We asked one of the best econometric scholars we know () and one of the best qualitative scholars we know () to fight this debate on air and come up with their very own end-of-all arguments. The result? It may surprise you: We all ought to get mad.   Episode reading list Chang, H. (2008). Inventing Temperature: Measurement and Scientific Progress. Oxford University Press. Burtch, G., Carnahan, S., & Greenwood, B. N. (2018). Can You Gig It? An Empirical Examination of the Gig Economy and Entrepreneurial Activity. Management Science, 64(12), 5497-5520. Greenwood, B. N., Kobayashi, B. H., & Starr, E. P. (2025). Can You Keep a Secret? Banning Noncompetes Does Not Increase Trade Secret Litigation. SSRN, . Kraemer, K. L., Dickhoven, S., Tierney, S. F., & King, J. L. (1987). Datawars: The Politics of Modeling in Federal Policymaking. Columbia University Press. Roth, J., Sant'Anna, P. H. C., Bilinski, A., & Poe, J. (2023). What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature. Journal of Econometrics, 235(2), 2218-2244. Matherly, T., & Greenwood, B. N. (2024). No News is Bad News: The Internet, Corruption, and the Decline of the Fourth Estate. MIS Quarterly, 48(2), 699-714. Levitt, S. D., & Dubner, S. J. (2005). Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. William Morrow. Greenwood, B. N., & Wattal, S. (2017). Show Me the Way to Go Home: An Empirical Investigation of Ride-Sharing and Alcohol Related Motor Vehicle Fatalities. MIS Quarterly, 41(1), 163-187. King, A. A. (2025). Does Corporate Social Responsibility Increase Access to Finance? A Commentary on Cheng, Ioannou, and Serafeim (2014). Strategic Management Journal, forthcoming. . Seidel, S., Frick, C. J., & vom Brocke, J. (2025). Regulating Emerging Technologies: Prospective Sensemaking through Abstraction and Elaboration. MIS Quarterly, 49(1), 179-204. Pentland, B. T. (1999). Building Process Theory with Narrative: From Description to Explanation. Academy of Management Review, 24(4), 711-725. Lee, J., & Berente, N. (2013). The Era of Incremental Change in the Technology Innovation Life Cycle: An Analysis of the Automotive Emission Control Industry. Research Policy, 42(8), 1469-1481. Anderson, P., & Tushman, M. L. (1998). Technological Discontinuities and Dominant Designs: A Cyclical Model of Technological Change. Administrative Science Quarterly, 35(4), 604-633. Brynjolfsson, E., & Hitt, L. M. (1996). Paradox Lost? Firm-Level Evidence on the Returns to Information Systems Spending. Management Science, 42(4), 541-558. Noe, R. (2025). Moral Incoherence During Category Emergence: The Contentious Case of Connected Toys. Harvard Business School Working Paper, 24-071, . 

Is it okay to use large language models in the research process? For what task, exactly, and to automate the task or to augment the researcher? In this episode, we try to explore whether and how LLMs could be used in five aspects of the research process - for paper writing, reviewing, data analysis, as a subject of research, or as a surrogate for research subjects. We also discuss whether they should be used at all, and what some long-term consequences could be of such a choice, and we develop a number of heuristic rules to help researcher make decisions about using LLMs for research. Episode reading list Kankanhalli, A. (2024). Peer Review in the Age of Generative AI. Journal of the Association for Information Systems, 25(1), 76-84. Yang, Y., Duan, H., Liu, J., & Tam, K. Y. (2024). LLM-Measure: Generating Valid, Consistent, and Reproducible Text-Based Measures for Social Science Research. arXiv preprint, . Li, J., Larsen, K. R. T., & Abbasi, A. (2020). TheoryOn: A Design Framework and System for Unlocking Behavioral Knowledge Through Ontology Learning. MIS Quarterly, 44(4), 1733-1772. Larsen, K. R., Yan, S., & Lukyanenko, R. (2024). LLMs and Psychometrics: Global Construct Validity Integrating LLMs and Psychometrics. 45th International Conference on Information Systems, Bangkok, Thailand. Anthis, J. R., Liu, R., Richardson, S. M., Kozlowski, A. C., Koch, B., Evans, J., Brynjolfsson, E., & Bernstein, M. (2025). LLM Social Simulations Are a Promising Research Method. arXiv preprint, . Abbasi, A., Somanchi, S., & Kelley, K. (2025). The Critical Challenge of using Large-scale Digital Experiment Platforms for Scientific Discovery. MIS Quarterly, 49(1), 1-28.

AI has been dominating investing headlines for almost two years, but it's not just a buzzword. It's a powerful technology that's poised to revolutionize industries and economies on a global scale. Investors are asking how will AI reshape job markets, productivity and economic growth? Nicholas Fawcett, a senior economist in the BlackRock Investment Institute joins Oscar to explore what AI means for the broad economy and the different stages of AI's evolution.Sources: Productivity estimates based on Brynjolfsson, Li, and Raymond (2023), Dell'Acqua et al. (2023) , Cui et al. (2024); Capex spend from BlackRock Investment Institute, Reuters, October 2024; Agricultural data based on IPUMS USA, October 2024This content is for informational purposes only and is not an offer or a solicitation. Reliance upon information in this material is at the sole discretion of the listener. In the UK and Non-European Economic Area countries, this is authorized and regulated by the Financial Conduct Authority. In the European Economic Area, this is authorized and regulated by the Netherlands Authority for the Financial Markets. Reference to the names of each company mentioned in this communication is merely for explaining the investment strategy and should not be construed as investment advice or investment recommendation of those companies. For full disclosures go to Blackrock.com/corporate/compliance/bid-disclosuresSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Erik Brynjolfsson, un importante economista specializzato in tecnologia dell'Università di Stanford, ha recentemente pubblicato un articolo in cui illustra i potenziali effetti economici dei nuovi sistemi di intelligenza artificiale.Brynjolfsson e i suoi colleghi hanno pubblicato quello che, per quanto ne sappiamo, è il primo studio empirico sugli effetti economici reali dei nuovi sistemi di intelligenza artificiale. Hanno analizzato cosa è successo a un'azienda e ai suoi lavoratori dopo aver incorporato una versione di ChatGPT nei flussi di lavoro.Tutti i miei link: https://linktr.ee/br1brownFonti:https://www.npr.org/sections/money/2023/05/02/1172791281/this-company-adopted-ai-heres-what-happened-to-its-human-workersTELEGRAM - INSTAGRAMSe ti va supportami https://it.tipeee.com/br1brown/

Erik Brynjolfsson is a researcher, author, senior fellow at the Stanford Institute for Human-Centered AI, and director of the Stanford Digital Economy Lab. He joined WorkLab to offer business leaders an overview of how AI will transform productivity. Brynjolfsson is the first guest of season 4 of the WorkLab podcast, in which hosts Elise Hu and Tonya Mosley have conversations with economists, designers, psychologists, and technologists who explore the data and insights into why and how work is changing.   WorkLab 

From entering the tech space in his early teens with a keen interest in all things IT, to becoming the Global Head of Engineering and Transformation at Vodafone, Felipe Canedo joins Gareth to recall his journey to success in one of the biggest telecom companies in the world. On this week's episode, Felipe discusses his role in transforming Vodafone into a fully-fledged ‘tech company', as well as promoting an effective leadership style that centres around ‘moving people together to achieve a common goal'. The conversation shifts between candid reflections on Felipe's former life in Brazil, to a discussion on the world of AI and how software such as Chat GPT should be considered ‘a partner, not a replacement' to workers. When reminiscing over his career journey, Felipe concludes by offering sagely guidance to his younger self, advising young people to ‘believe in your dreams and chase them as soon as you get the chance'.   Time Stamps What good leadership means to Felipe (01:33) Felipe's tech origins (02:40) Moving to the UK (04:17) What is a Global Head of Engineering? (05:10) Creating a global community of developers (06:00) What Felipe misses about Brazil (09:30) How does the British tech industry compare to the Brazilian tech industry? (11:05) How to answer your own questions and find your own solutions (13:49) Becoming a senior manager (15:09) Coding at home (16:23) How ChatGPT is changing the game (17:01) The ways that AI will change telecoms (20:10) The ‘gloomy side' of AI (25:00) What is 5G? (26:06) How Vodafone has changed as a result of the pandemic (34:21) Navigating online meetings (38:37) Felipe's advice to his 21-year-old self (40:20) Outside of work interests (43:40) *Book recommendation- ‘Machine, Platform, Crowd' Andrew McAfee and Erik Brynjolfsson- Machine, Platform, Crowd – Harnessing Our Digital Future: Amazon.co.uk: Mcafee, Andrew, Brynjolfsson, Erik: 9780393356069: Books* *Disclaimer: the views expressed in this episode are Felipe's own personal views and not necessarily those of Vodafone* 

Erik Brynjolfsson's paper “The Turing Trap: The Promise and Peril of Human-Like Artificial Intelligence” argues that the “imitation game” of creating tech that mimics humans has increased productivity and living standards, but does not exist without costs. Those costs make up “The Turing Trap” which happens when humans not involved in creating AI cannot compete with the productivity and efficiency of the robots designed to do their jobs, and lose control of their economic and political futures. The Turing Trap sits at the center of contemporary labor force struggles, including the Great Resignation, the fight for “good jobs” and cratering male labor force participation. Michael Strain, who directs AEI's Economic Policy Studies, joins Dr. Brynjolfsson and I to discuss what economic policy can do to encourage more innovators aim higher and create machines that augment rather than replace human labor, and how that effort is crucial to the American Dream. Mentioned in the episode https://www.brynjolfsson.com/ (Erik Brynjolfsson) https://www.amazon.com/Utopia-Thomas-More/dp/1512093386 (Utopia Paperback by Thomas More) https://www.amazon.com/Foundation-Isaac-Asimov/dp/0553293354 (Foundation Mass Market Paperback by Isaac Asimov) https://www.amazon.com/Worldly-Philosophers-Economic-Thinkers-Library/dp/1441743669 (Heilbronner's Worldly Philosophers) https://newsinfo.iu.edu/news/page/normal/5075.html (Doug Hofstadter) https://digitaleconomy.stanford.edu/news/the-turing-trap-the-promise-peril-of-human-like-artificial-intelligence/ (The Turing Trap by Erik Brynjolfsson) https://www.aei.org/profile/michael-r-strain/ (Michael R. Strain) https://www.amazon.com/American-Dream-Not-Dead-Populism/dp/159947557X (The American Dream is Not Dead) https://www.city-journal.org/html/when-high-schools-shaped-americas-destiny-15254.html (The High School Movement) https://taxfoundation.org/tax-basics/pigouvian-tax/#:~:text=A%20Pigouvian%20tax%2C%20named%20after,sugar%20taxes%2C%20and%20carbon%20taxes. (Pigouvian Tax) https://taxfoundation.org/tax-basics/consumption-tax/ (Consumption Tax) https://www.investopedia.com/terms/t/taxreformact1986.asp (Tax Reform Act of 1986) https://scholar.harvard.edu/files/mankiw/files/smart_taxes.pdf (Greg Mankiw Pigou Club)

Lexman interviews Erik Brynjolfsson, the CEO of Mosaic and co-author of "The Second Machine Age: Work, Progress, and Prosperity in a Time of New Technologies." Brynjolfsson discusses the future of jobs and how new technologies are poised to change them.

Few economists think more creatively and also more rigorously about the future than Robin Hanson, my guest on this episode of Faster, Please! — The Podcast. So when he says a future of radical scientific and economic progress is still possible, you should take the claim seriously. Robin is a professor of economics at George Mason University and author of the Overcoming Bias blog. His books include The Age of Em: Work, Love and Life when Robots Rule the Earth and The Elephant in the Brain: Hidden Motives in Everyday Life.In This Episode:* Economic growth over the very long run (1:20)* The signs of an approaching acceleration (7:08)* Global governance and risk aversion (12:19)* Thinking about the future like an economist (17:32)* The stories we tell ourselves about the future (20:57)* Longtermism and innovation (23:20)Next week, I'll feature part two of my conversation with Robin, where we discuss whether we are alone in the universe and what alien life means for humanity's long-term potential.Below is an edited transcript of our conversation.Economic growth over the very long runJames Pethokoukis: Way back in 2000, you wrote a paper called “Long-Term Growth as a Sequence of Exponential Modes.” You wrote, “If one takes seriously the model of economic growth as a series of exponential … [modes], then it seems hard to escape the conclusion that the world economy will likely see a very dramatic change within the next century, to a new economic growth mode with a doubling time perhaps as short as two weeks.” Is that still your expectation for the 21st century?Robin Hanson: It's my expectation for the next couple of centuries. Whether it's the 21st isn't quite so clear.Has anything happened in the intervening two decades to make you think that something might happen sooner rather than later … or rather, just later?Just later, I'm afraid. I mean, we have a lot of people hyping AI at the moment, right?Sure, I may be one of them on occasion.There are a lot of people expecting rapid progress soon. And so, I think I've had a long enough baseline there to think, "No, maybe not.” But let's go with the priors.Is it a technological mechanism that will cause this? Is it AI? Is it that we find the right general-purpose technology, and then that will launch us into very, very rapid growth?That would be my best guess. But just to be clear for our listeners, we just look at history, we seem to see these exponential modes. There are, say, four of them so far (if we go pre-human). And then the modes are relatively steady and then have pretty sharp transitions. That is, the transition to a growth rate of 50 or 200 times faster happens within less than a doubling time.So what was the last mode?We're in industry at the moment: doubles roughly every 15 years, started around 1800 or 1700. The previous mode was farming, doubled every thousand years. And so, in roughly less than a thousand years, we saw this rapid transition to our current thing, less than the doubling time. The previous mode before that was foraging, where humans doubled roughly every quarter million years. And in definitely less than a quarter million years, we saw a transition there. So then the prediction is that we will see another transition, and it will happen in less than 15 years, to a faster growth mode. And then if you look at the previous increases in growth rates, they were, again, a factor of 60 to 200. And so, that's what you'd be looking for in the next mode. Now, obviously, I want to say you're just looking at a low data set here. Four events. You can't be too confident. But, come on, you've got to guess that maybe a next one would happen.If you go back to that late ‘90s period, there was a lot of optimism. If you pick up Wired magazine back then, [there was] plenty of optimism that something was happening, that we were on the verge of something. One of my favorite examples — and a sort of non-technologist example, was a report from Lehman Brothers from December 1999. It was called “Beyond 2000.” And it was full of predictions, maybe not talking about exponential growth, but how we were in for a period of very fast growth, like 1960s-style growth. It was a very bullish prediction for the next two decades. Now Lehman did not make it another decade itself. These predictions don't seem to have panned out — maybe you think I'm being overly pessimistic on what's happened over the past 20 years — but do you think it was because we didn't understand the technology that was supposedly going to drive these changes? Did we do something wrong? Or is it just a lot of people who love tech love the idea of growth, and we all just got too excited?I think it's just a really hard problem. We're in this world. We're living with it. It's growing really fast. Again, doubling every 15 years. And we've long had this sense that it's possible for something much bigger. So automation, the possibility of robots, AI: It sat in the background for a long time. And people have been wondering, “Is that coming? And if it's coming, it looks like a really big deal.” And roughly every 30 years, I'd say, we've seen these bursts of interest in AI and public concern, like media articles, you know…We had the ‘60s. Now we have the ‘90s…The ‘60s, ‘90s, and now again, 2020. Every 30 years, a burst of interest and concern about something that's not crazy. Like, it might well happen. And if it was going to happen, then the kind of precursor you might expect to see is investors realizing it's about to happen and bidding up assets that were going to be important for that to really high levels. And that's what you did see around ‘99. A lot of people thought, “Well, this might be it.”Right. The market test for the singularity seemed to be passing.A test that is not actually being passed quite so much at the moment.Right.So, in some sense, you had a better story then in terms of, look, the investors seem to believe in this.You could also look at harder economic numbers, productivity numbers, and so on.Right. And we've had a steady increase in automation over, you know, centuries. But people keep wondering, “We're about to have a new kind of automation. And if we are, will we see that in new kinds of demos or new kinds of jobs?” And people have been looking out for these signs of, “Are we about to enter a new era?” And that's been the big issue. It's like, “Will this time be different?” And so, I've got to say this time, at the moment, doesn't look different. But eventually, there will be a “this time” that'll be different. And then it'll be really different. So it's not crazy to be watching out for this and maybe taking some chances betting on it.The signs of an approaching accelerationIf we were approaching a kind of acceleration, a leap forward, what would be the signs? Would it just be kind of what we saw in the ‘90s?So the scenario is, within a 15-year period, maybe a five-year period, we go from a current 4 percent growth rate, doubling every 15 years, to maybe doubling every month. A crazy-high doubling rate. And that would have to be on the basis of some new technology, and therefore, investment. So you'd have to see a new promising technology that a lot of people think could potentially be big. And then a lot of investment going into that, a lot of investors saying, “Yeah, there's a pretty big chance this will be it.” And not just financial investors. You would expect to see people — like college students deciding to major in that, people moving to wherever it is. That would be the big sign: investment moving toward anything. And the key thing is, you would see actual big, fast productivity increases. There'd be some companies in cities who were just booming. You were talking about stagnation recently: The ‘60s were faster than now, but that's within a factor of two. Well, we're talking about a factor of 60 to 200.So we don't need to spend a lot of time on the data measurement issues. Like, “Is productivity up 1.7 percent, 2.1?”If you're a greedy investor and you want to be really in on this early so you buy it cheap before everybody else, then you've got to be looking at those early indicators. But if you're like the rest of us wondering, “Do I change my job? Do I change my career?” then you might as well wait and wait till you see something really big. So even at the moment, we've got a lot of exciting demos: DALL-E, GPT-3, things like that. But if you ask for commercial impact and ask them, “How much money are people making?” they shrug their shoulders and they say “Soon, maybe.” But that's what I would be looking for in those things. When people are generating a lot of revenue — so it's a lot of customers making a lot of money — then that's the sort of thing to maybe consider.Something I've written about, probably too often, is the Long Bets website. And two economists, Robert Gordon and Erik Brynjolfsson, have made a long bet. Gordon takes the role of techno-pessimist, Brynjolfsson techno-optimist. Let me just briefly read the bet in case you don't happen to have it memorized: “Private Nonfarm business productivity growth will average over 1.8 percent per year from the first quarter of 2020 to the last quarter of 2029.” Now, if it does that, that's an acceleration. Brynjolfsson says yes. Gordon says no…But you want to pick a bigger cutoff. Productivity growth in the last decade is maybe half that, right? So they're looking at a doubling. And a doubling is news, right? But, honestly, a doubling is within the usual fluctuation. If you look over, say, the last 200 years, and we say sometimes some cities grow faster, some industries grow faster. You know, we have this steady growth rate, but it contains fluctuations. I think the key thing, as always, when you're looking for a regime change, is you're looking at — there's an average and a fluctuation — when is a new fluctuation out of the range of the previous ones? And that's when I would start to really pay attention, when it's not just the typical magnitude. So honestly, that's within the range of the typical magnitudes you might expect if we just had an unusually productive new technology, even if we stay in the same mode for another century.When you look at the enthusiasm we had at the turn of this century, do you think we did the things that would encourage rapid growth? Did we create a better ecosystem of growth over the past 20 years or a worse one?I don't think the past 20 years have been especially a deviation. But I think slowly since around 1970, we have seen a decline in our support for innovation. I think increasing regulations, increasing size of organizations in response to regulation, and just a lot of barriers. And even more disturbingly, I think it's worth noting, we've seen a convergence of regulation around the world. If there were 150 countries, each of which had different independent regulatory regimes, I would be less concerned. Because if one nation messes it up and doesn't allow things, some other nation might pick up the slack. But we've actually seen pretty strong convergence, even in this global pandemic. So, for example, challenge trials were an idea early voiced, but no nation allowed them. Anywhere. And even now, hardly they've been tried. And if you look at nuclear energy, electric magnetic spectrum, organ sales, medical experimentation — just look at a lot of different regulatory areas, even airplanes — you just see an enormous convergence worldwide. And that's a problem because it means we're blocking innovation the same everywhere. And so there's just no place to go to try something new.Global governance and risk aversionThere's always concern in Europe about their own productivity, about their technological growth. And they're always putting out white papers in Europe about what [they] can do. And I remember reading that somebody decided that Europe's comparative advantage was in regulation. Like that was Europe's superpower: regulation.Yeah, sure.And speaking of convergence, a lot of people who want to regulate the tech industry here have been looking to what Europe is doing. But Europe has not shown a lot of tech progress. They don't generate the big technology companies. So that, to me, is unsettling. Not only are we converging, but we're converging sometimes toward the least productive areas of the advanced world.In a lot of people's minds, the key thing is the unsafe dangers that tech might provide. And they look to Europe and they say, “Look how they're providing security there. Look at all the protections they're offering against the various kinds of insecurity we could have. Surely, we want to copy them for that.”I don't want to copy them for that. I'm willing to take a few risks.But many people want that level of security. So I'm actually concerned about this over the coming centuries. I think this trend is actually a trend toward not just stronger global governance, but stronger global community or even mobs, if we call it that. That is the reason why nuclear energy is regulated the same everywhere: the regulators in each place are part of a world community, and they each want to be respected in that community. And in order to be respected, they need to conform to what the rest of the community thinks. And that's going to just keep happening more over the coming centuries, I fear.One of my favorite shows, more realistic science-fiction shows and book series, is The Expanse, which takes place a couple hundred years in the future where there's a global government — which seems to be a democratic global government. I'm not sure how efficient it is. I'm not sure how entrepreneurial it is. Certainly the evidence seems to be that global governance does not lead to a vibrant, trial-and-error, experimenting kind of ecology. But just the opposite: one that focuses on safety and caution and risk aversion.And it's going to get a lot worse. I have a book called The Age of Em: Work, Love, and Life when Robots Rule the Earth, and it's about very radical changes in technology. And most people who read about that, they go, “Oh, that's terrible. We need more regulations to stop that.” I think if you just look toward the longer run of changes, most people, when they start to imagine the large changes that will be possible, they want to stop that and put limits and control it somehow. And that's going to give even more of an impetus to global governance. That is, once you realize how our children might become radically different from us, then that scares people. And they really, then, want global governance to limit that.I fear this is going to be the biggest choice humanity ever makes, which is, in the next few centuries we will probably have stronger global governance, stronger global community, and we will credit it for solving many problems, including war and global warming and inequality and things like that. We will like the sense that we've all come together and we get to decide what changes are allowed and what aren't. And we limit how strange our children can be. And even though we will have given up on some things, we will just enjoy … because that's a very ancient human sense, to want to be part of a community and decide together. And then a few centuries from now, there will come this day when it's possible for a colony ship to leave the solar system to go elsewhere. And we will know by then that if we allow that to happen, that's the end of the era of shared governance. From that point on, competition reaffirms itself, war reaffirms itself. The descendants who come out there will then compete with each other and come back here and impose their will here, probably. And that scares the hell out of people.Indeed, that's the point of [The Expanse]. It's kind of a mixed bag with how successful Earth's been. They didn't kill themselves in nuclear war, at least. But the geopolitics just continues and that doesn't change. We're still human beings, even if we happen to be living on Mars or Europa. All that conflict will just reemerge.Although, I think it gets the scale wrong there. I think as long as we stay in the solar system, a central government will be able to impose its rule on outlying colonies. The solar system is pretty transparent. Anywhere in the solar system you are, if you're doing something somebody doesn't like, they can see you and they can throw something at you and hit you. And so I think a central government will be feasible within the solar system for quite some time. But once you get to other star systems, that ends. It's not feasible to punish colonies 20 light-years away when you don't get the message of what they did [until] 20 years later. That just becomes infeasible then. I would think The Expanse is telling a more human story because it's happening within this solar system. But I think, in fact, this world government becomes a solar system government, and it allows expansion to the solar system on its terms. But it would then be even stronger as a centralized governance community which prevents change.Thinking about the future like an economistIn a recent blog post, you wrote that when you think about the future, you try to think about it as an economist. You use economic analysis “to predict the social consequences of a particular envisioned future technology.” Have futurists not done that? Futurism has changed. I've written a lot about the classic 1960s futurists who were these very big, imaginative thinkers. They tended to be pretty optimistic. And then they tended to get pessimistic. And then futurism became kind of like marketing, like these were brand awareness people, not really big thinkers. When they approached it, did they approach it as technologists? Did they approach it as sociologists? Are economists just not interested in this subject?Good question. So I'd say there are three standard kinds of futurists. One kind of futurist is a short-term marketing consultant who's basically telling you which way the colors will go or the market demand will go in the short term.Is neon green in or lime green in, or something.And that's economically valuable. Those people should definitely exist. Then there's a more aspirational, inspirational kind of futurist. And that's changed over the decades, depending on what people want to be inspired by or afraid of. In the ‘50s, ‘60s, it might be about America going out and becoming powerful. Or later it's about the environment, and then it's about inequality and gender relations. In some sense, science fiction is another kind of futurism. And these two tend to be related in the sense that science fiction mainly focuses on an indirect way to tell metaphorical stories about us. Because we're not so interested in the future, really, we're interested in us. Those are futures serving various kinds of communities, but neither of them are that realistically oriented. They're not focused on what's likely to actually happen. They're focused on what will inspire people or entertain people or make people afraid or tell a morality tale.But if you're interested in what's actually going to happen, then my claim is you want to just take our standard best theories and just straightforwardly apply them in a thoughtful way. So many people, when they talk about the future, they say, “It's just impossible to say anything about the future. No one could possibly know; therefore, science fiction speculations are the best we can possibly do. You might as well go with that.” And I think that's just wrong. My demonstration in The Age of Em is to say, if you take a very specific technology scenario, you can just turn the crank with Econ 101, Sociology 101, Electrical Engineering 101, all the standard things, and just apply it to that scenario. And you can just say a lot. But what you will find out is that it's weird. It's not very inspiring, and it doesn't tell the perfect horror story of what you should avoid. It's just a complicated mess. And that's what you should expect, because that's what we would seem to our ancestors. [For] somebody 200 or 2000 years ago, our world doesn't make a good morality tale for them. First of all, they would just have trouble getting their head around it. Why did that happen? And [what] does that even mean? And then they're not so sure what to like or dislike about it, because it's just too weird. If you're trying to tell a nice morality tale [you have] simple heroes and villains, right? And this is too messy. The real futures you should just predict are going to be too messy to be a simple morality tale. They're going to be weird, and that's going to make them hard to deal with.The stories we tell ourselves about the futureDo you think it matters, the kinds of stories we tell ourselves about what the future could hold? My bias is, I think it does. I think it matters if all we paint for people is a really gloomy one, then not only is it depressing, then it's like, “What are we even doing here?” Because if we're going to move forward, if we're going to take risks with technology, there needs to be some sort of payoff. But yet, it seems like a lot of the culture continues. We mentioned The Expanse, which by the modern standard of a lot of science fiction, I find to be pretty optimistic. Some people say, "Well, it's not optimistic because half the population is on a basic income and there's war.” But, hey, there are people. Global warming didn't kill everybody. Nuclear war didn't kill everybody. We continued. We advanced. Not perfect, but society seems to be progressing. Has that mattered, do you think, the fact that we've been telling ourselves such terrible stories about the future? We used to tell much better ones.The first-order theory about change is that change doesn't really happen because people anticipated or planned for it or voted on it. Mostly this world has been changing as a side effect of lots of local economic interests and technological interests and pursuits. The world is just on this train with nobody driving, and that's scary and should be scary, I guess. So to the first order, it doesn't really matter what stories we tell or how we think about the future, because we haven't actually been planning for the future. We haven't actually been choosing the future.It kind of happens while we're doing something else.The side effect of other things. But that's the first order, that's the zeroth-order effect. The next-order effect might be … look, places in the world will vary in to what extent they win or lose over the long run. And there are things that can radically influence that. So being too cautious and playing it safe too much and being comfortable, predictably, will probably lead you to not win the future. If you're interested in having us — whoever us is — win the future or have a bright, dynamic future, then you'd like “us” to be a little more ambitious about such things. I would think it is a complement: The more we are excited about the future, and the future requires changes, the more we are telling ourselves, “Well, yeah, this change is painful, but that's the kind of thing you have to do if you want to get where we're going.”Long-term thinking and innovationIf you've been reading the New York Times lately or the New Yorker, the average is related to something called “effective altruism,” is the idea that there are big, existential problems facing the world, and we should be thinking a lot harder about them because people in the future matter too, not just us. And we should be spending money on these problems. We should be doing more research on these problems. What do you think about this movement? It sounds logical.Well, if you just compare it to all the other movements out there and their priorities, I've got to give this one credit. Obviously, the future is important.They are thinking directly about it. And they have ideas.They are trying to be conscious about that and proactive and altruistic about that. And that's certainly great compared to the vast majority of other activity. Now, I have some complaints, but overall, I'm happy to praise this sort of thing. The risk is, as with most futurism, that even though we're not conscious of it, what we're really doing is sort of projecting our issues now into the future and sort of arguing about future stuff by talking about our stuff. So you might say people seem to be really concerned about the future of global warming in two centuries, but all the other stuff that might happen in two centuries, they're not at all interested. It's like, what's the difference there? They might say global warming lets them tell this anti-materialist story that they'd want to tell anyway, tell why it's bad to be materialist and so to cut back on material stuff is good. And it's sort of a pro-environment story. I fear that that's also happening to some degree in effective altruism. But that's just what you should expect for humans in general. Effective altruists, in terms of their focus on the future, are overwhelmingly focused as far as I can tell on artificial intelligence risk. And I think that's a bit misdirected. In a big world I don't mind it …My concern is that we'll be super cautious and before we have developed anything that could really create existential risk … we will never get to the point where it's so powerful because, like the Luddites, we'll have quashed it early on out of fear.A friend of mine is Eric Drexler, who years ago was known as talking about nanotechnology. Nanotechnology is still a technology in the future. And he experienced something that made him a little unsure whether he should have said all these things, he said, which is that once you can describe a vivid future, the first thing everybody focuses on is almost all the things that can go wrong. Then they set up policy to try to focus on preventing the things that can go wrong. That's where the whole conversation goes. And then people are distancing themselves from it. He found that many people distanced themselves from nanotechnology until they could take over the word, because in their minds it reflected these terrible risks. So people wanted to not even talk about that. But you could ask, if he had just inspired people to make the technology but not talked about the larger policy risks, maybe that would be better? It might be in fact true that the world today is broken so much that if ordinary people and policymakers don't know about a future risk, the world's better off, because at least they won't mess it up by trying to limit it and control it too early and too crudely.Then the challenge is, maybe you want the technologists who might make it to hear about it and get inspired, but you don't want everybody else to be inspired to control it and correct it and channel it and prepare for it. Because honestly, that seems to go pretty bad. I guess the question is, what technology that people did see well ahead of time, did they not come up with terrible scenarios to worry about? For example, television: People didn't think about television very much ahead of time. And when it came, a lot of people watched it. And a lot of people complained about that. But if you could imagine ahead of time that in 20 years people are going to spend five hours a day watching this thing. If that's an accurate prediction, people would've freaked out.Or cars: As you may know, in the late 1800s, people just did not envision the future of cars. When they envisioned the future of transportation, they saw dirigibles and trains and submarines, even, but not cars. Because cars were these individual things. And if they had envisioned the actual future of cars — automobile accidents, individual people controlling a thing going down the street at 80 miles an hour — they might have thought, “That's terrible. We can't allow that.” And you have to wonder… It was only in the United States, really, that cars took off. There's a sense in which the world had rapid technological progress around 1900 or so because the US was an exception worldwide. A lot of technologies were only really tried in the US, like even radio, and then the rest of the world copied and followed because the US had so much success with them.I think if you want to pick a point where that optimistic ‘90s came to an end, it might have been, speaking of Wired magazine, the Bill Joy article … “Why the Future Doesn't Need Us.” Talking about nanotech and gray goo… Since you brought up nanotech and Eric Drexler, do you know what the state of that technology is? We had this nanotechnology initiative, but I don't think it was working on that kind of nanotech.No, it wasn't.It was more like a materials science. But as far as creating these replicating tiny machines…The federal government had a nanotechnology initiative, where they basically took all the stuff they were doing that was dealing with small stuff and they relabeled it. They didn't really add more money. They just put it under a new initiative. And then they made sure nobody was doing anything like this sort of dangerous stuff that could cause what Eric was talking about.Stuff you'd put in sunscreen…Exactly. So there was still never much funding there. There's a sense in which, in many kinds of technology areas, somebody can envision ahead of time a new technology that was possible if a concentrated effort goes into a certain area in a certain way. And they're trying to inspire that. But absent that focused effort, you might not see it for a long time. That would be the simplest story about nanotech: We haven't seen the focused effort and resources that he had proposed. Now, that doesn't mean had we had those efforts he would've succeeded. He could just be wrong about what was feasible and how soon. But nevertheless, that still seemed to be an exciting, promising technology that would've been worth the investment to try. And still is, I would say.One concern I have about the notion of longtermism, is that it seems to place a lot of emphasis on our ability to rally people, get them thinking long term, taking preparatory steps. And we've just gone through a pandemic which showed that we don't do that very well. And the way we dealt with it was not through preparation, but by being a rich, technologically advanced society that could come up with a vaccine. That's my kind of longtermism, in a way: being rich and technologically capable so you can react to the unexpected.And that's because we allowed an exception in how vaccines were developed in that case. Had we gone with the usual way vaccines had been developed before, it would've taken a lot longer. So the problem is that when we make too many structures that restrain things, then we aren't able to quickly react to new circumstances. You probably know that most companies, they might have a forecasting department, but they don't fund it very much. They don't actually care that much. Almost everything they do is reactive in most organizations. That's just the fact of how most organizations work. Because, in fact, it is hard to prepare. It's hard to anticipate things.I'm not saying we shouldn't try to figure out ways to deflect asteroids. We should. To have this notion of longtermism over a broad scope of issues … that's fine. But I hope we don't forget the other part, which is making sure that we do the right things to create those innovative ecosystems where we do increase wealth, we do increase our technological capabilities to not be totally dependent on our best guesses right now.Here's a scary example of how this thinking can go wrong, in my mind. In the longtermism community, there's this serious proposal that many people like, which is called the Long Reflection.The Long Reflection, which is, we've solved all the problems and then we take a time out.We stop allowing change for a while. And for a good long time, maybe a thousand years or even longer, we're in this period where no change substantially happens. Then we talk a lot about what we could do to deal with things when things are allowed to change again. And we work it all out, and then we turn it back on and allow change. That's giving a lot of credit to this system of talking.Who's talking? Are these post-humans talking? Or is it people like us?It would be before the change, remember. So it would be people like us. I actually think this is this ancient human intuition from the forger world, before the farming era, where in the small band the way we made most important decisions was to sit down around the campfire and discuss it and then decide together and then do something. And that's, in some sense, how everybody wants to make all the big decisions. That's why they like a world government and a world community, because it goes back to that. But I honestly think we have to admit that just doesn't go very well lately. We're not actually very capable of having a discussion together and feeling all the options and making choices and then deciding together to do it. That's how we want to be able to work. And that's how we maybe should, but it's not how we are. I feel, with the Long Reflection, once we institutionalize a world where change isn't allowed, we would get pretty used to that world.It seems very comfortable, and we'd start voting for security.And then we wouldn't really allow the Great Reflection to end, because that would be this risky, into the strange world. We would like the stable world we were in. And that would be the end of that.I should say that I very much like Toby Ord's book, The Precipice. He's also one of my all-time favorite guests. He's really been a fantastic guest. Though, the Long Reflection, I do have concerns about.Come back next Thursday for part two of my conversation with Robin Hanson. 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

Erik Brynjolfsson, a professor at MIT and a principal research scientist at the Institute for Quantitative Social Sciences, talks about his new book, "The Second Machine Age: Work, Progress and Prosperity in a Time of Brilliant Technologies." In it, he argues that technological change is transforming the way work is done and will continue to do so through the next century. Brynjolfsson discusses the impact of new digital technologies on the economy, including 3D printing, artificial intelligence and the sharing economy. He also discusses how the changing nature of work affects society as a whole.

Erik Brynjolfsson is the director of the Stanford Digital Economy Lab and professor at the Stanford Institute for Human-Centered AI. He joins Big Technology Podcast for a discussion of why our fears that artificial intelligence would take human jobs haven't yet come to fruition. We also cover how humans and AI can work together and how AI is changing work already. Stay tuned for the second half where we discuss the latest on robotic process automation and address why we're working at all in the age of machines. Check out Prof. Brynjolfsson's paper: The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence

Tar maskinerna våra jobb? I samhällsdebatten har det blivit ett faktum att jobben försvinner på grund av automatiseringen. Men nu kommer kritiken. Aaron Benanav och Jason E Smith hävdar i sina böcker att vi lever i en produktivitetsparadox: trots automatiseringen ökar inte produktiviteten i produktionen. Avindustrialiseringen och jobben som försvinner gör det av andra skäl än ny teknik. Hur förändrar det en radikal samhällsstrategi för att ge ett trovärdigt framtidsprojekt? Vad innebär det för accelerationismen, den helautomatiska lyxkommunismen, ekomodernismen, teknopopulismen och Green new deal-politiken som vänstern lyft fram? Vilka subjekt kan genomföra en förändring om industrijobben minskar? Dave från Centrum för marxistiska samhällsstudier Stockholm gästar podden och diskuterar dessa frågor. Läs mer: Red may: Smart machines and service work med Jason E Smith: https://www.youtube.com/watch?v=Jb8I1E0VKpo The Dig: Don't blame the robots - med Aaron Benanav https://poddtoppen.se/podcast/1043245989/the-dig/dont-blame-robots-with-aaron-benanav Aufhebunga bunga: It's Not Robots, It's Capitalism med Aaron Benanav och Liz Pancotti https://podcasts.apple.com/in/podcast/149-its-not-robots-its-capitalism-ft-aaron-benanav/id1229278776?i=1000492333411 Scocconomics, Avsnitt: "Trickle down" & "Trickle up" https://poddtoppen.se/podcast/935202361/scocconomics/trickle-up E. Brynjolfsson & A. McAfee, The Second Machine Age (2014), W. W. Norton & Company. M. Ford, Rise of the Robots (2016), Basic Books. A. Benenav, Automation and the Future of Work (2020), Verso. J. Smith, Smart Machines and Service Work (2020), Reaktion Books. P. Cockshott & D. Zachariah, Conservation Laws, Financial Entropy and the Eurozone Crisis (2014), E-conomics https://www.degruyter.com/document/doi/10.5018/economics-ejournal.ja.2014-5/html

I denne episoden av LØRN bokbad snakker, Silvija Seres, med forfatter, Tarjei Heggernes. Tarjei jobber på Høgskulen på Vestlandet og BI, og er opptatt av anvendelse av teknologi i forretningsdrift og pedagogikk. Heving av digital kompetanse har blitt en hjertesak for Heggernes, som den siste tiden har skrevet tredje utgave av boka Digital Forretningsforståelse som handler om å utvikle et program i digital kompetanse for renovasjons- og gjenvinningsbransjen.— Eg meiner vi har eit kjempestort ansvar overfor både dei unge som skal ut og endre arbeidslivet, utan at dei heilt veit kva det skal endrast til, og for dei vaksne som er i jobb, for at dei skal forstå korleis det digitale vil endre måten å jobbe på, og ikkje minst til å hjelpe dei så sjå sin eigen rolle i framtidas arbeidsliv, forteller han i episoden. Dette lørner du:Faglitteratur Digitalisering på arbeidsplassenStyreverden Økonomi Lederrollen Anbefalt litteratur:Bøkene til Brynjolfsson og McAfee (Second Machine Age, Machine Platform Crowd) See acast.com/privacy for privacy and opt-out information.

SOLO POD - Prominent venture capitalist, Kai-Fu Lee, estimates that automation and AI will bring about at least 25% NET job losses over the next two decades. While predicting *exactly* how this would translate across job types in the future is next to impossible—might there be a “mindset” we can all adopt to approach it?   In this episode, Julian turns to an unlikely source for inspiration… ~ YouTube FULL EPISODES: https://www.youtube.com/channel/UC0A-v_DL-h76F75xik8h03Q  YouTube CLIPS: https://www.youtube.com/channel/UChs-BsSX71a_leuqUk7vtDg  ~ Show Notes: https://www.trendifier.com/podcastnotes  TRENDIFIER Website: https://www.trendifier.com  Julian's Instagram: https://www.instagram.com/julianddorey  ~ Beat provided by: https://freebeats.io  Music Produced by White Hot

In this episode I argue that we need clear terminology for thinking about the future. I use McAfee & Brynjolfsson's Machine, Platform, Crowd to tease out the difference between prediction and thinking about the future. Clarifying our terms could actually help (which I will attempt in a future episode).

I started these conversation series to further the underlying philosophy of this publication - helping the spread of good rival ideas. I want to speak to brilliant people with experience, who also have interesting things to say about Nigeria's political economy and society in general. In the first episode, I spoke with Affiong Williams - Founder/CEO of Reelfruit. I learned a lot from Affi and it was hard to condense our conversation into the bits that made the cut. Her take on economic complexity, and how social systems and expectations can change culture are quite refreshing takes. She is an advocate of epistemic humility, and she practices what she preaches. But make no mistake, she is a deep and profound thinker.You can listen to our conversation above or read the transcript(it’s looong) below. You can also listen on Stitcher here. I owe an immeasurable amount of gratitude to Quadbee, who is the producer and editor of these series.TRANSCRIPTTobi: Affiong, it's nice to have you.Affiong: Thank you.Tobi: So I want to start with human capital...because, there was something you said on Twitter a while back and I think I ran a poll on that idea, that some African countries do have a lot more human capital than Nigeria. So can you try to unpack what you mean by that because, obviously, when you look at the data you can see it differently sometimes but as an entrepreneur, you experience human capital daily - so can you unpack a bit of that?Affiong: This tweet you're referring to was borne out of my visit to Zimbabwe a country that was once known for quite a superior educational system and maybe good leadership under the early days of Robert Mugabe but also has been known in the last twenty years as a place where the economy has been completely topsy-turvy and they face one crisis of hyperinflation after the other etcetera. But in my visit to Harare, what I noticed was... (And I'll use the specific example), was that in the lodge we were staying where we were about thirty people. There were only three people were managing it, only three people managing in terms of cleaning, the lodge manager, maybe four or five and the cooks who would come in to make breakfast etcetera. But in terms of facilitating the entire lodge which was like ten room lodge with about like thirty guests, I would say, I've realized in Nigeria there is absolutely no way three people could manage complexities of giving people stellar, sort of warm service and as well as keep the grounds as pristine as it were and it got me thinking about this learning that I've uncovered in my years of just operating as an entrepreneur but also reading about development that a lot of education is in firms not in schools. And even though Zimbabwe has had a history of good education like Nigeria...we want to brag has had in the past, the idea is that the economy was way more diversified and specialized in things like tourism so you found that people are much more equipped to handle...to be more productive than in countries where there's not been any specialization or the economies are less diversified.So, that, for me was sort of the link that the diversification of an economy makes human beings more productive, much more than 1) the educational sector, and 2) that the inverse is true. The less diversified a country is in terms of its learnings, what it can do, what it's good at, the less productive people are. If you look at Zimbabwe, a hotel manager there could probably manage the equivalent of a mid-sized hotel in Nigeria or a large hotel in Nigeria whereas the reverse will not be true. A hotel manager in Nigeria is just not quipped to do the same in another country and offer the same type of service and Zim is known for its stellar...the development of its tourist industry and actually it receives tourists from across the world. So the industry has had to become more competitive and be able to sell tourism from across the world; whereas Nigeria has not, so you can see the quality of our hotel etcetera just being under par.People may dispute this but I posit that the more complex and the more diversified your economy is, the more people learn and get to know. - AWAnd it is easy to make this comparisons with countries like America which is a first world nation with all the resources in the world but if you look at more comparable countries like Kenya or Ghana, when you compare sectors like...for instance, and I'll use Ghana as an example - food processing - because Ghana has learnt to build this industry for export and self-diversified companies, you find that a factory manager in Ghana will be better on average than a factory manager in Nigeria. And people may dispute this but I posit that the more complex and the more diversified your economy is, the more people learn and get to know. You look at the banks in Nigeria and I’ll use India as an example, a bank manager in India, a middle manager can do mergers and acquisitions. That's because the economy allows for it. The economy is diversified enough where companies are buying each other up, and that skill and that learning is necessary for the competitiveness and growth of that bank. In Nigeria, people who've been in banking for ten years still push paper and I know that as an entrepreneur who have bank relationship managers working for me, and they haven't even learnt how to do that seamlessly. Every time you want to do some complex different deal, there's reporting to head office, there's all these issues that even a middle manager someone with ten years [of] experience in the bank cannot solve at that level. And that speaks to the diversification and complexity of the economy, we're simply not doing mergers and acquisition at a rate in Nigeria that can forces that learning on everybody. So those people are less productive than if you compare them with an Indian banker or... not to talk of an American banker or even a South African bank where you have such a diversified and sophisticated economy. So, for me, human capital is very much driven by the firms in the country and the make-up of firms in the country and you find that in Nigeria, it stagnates because our companies were not getting new industries really coming in to improve and increase that learning.And Nigerians love to talk up how smart and how they do well abroad and how given the right tools etc., and that's true. But that's also a factor of what those countries offer. it's not really inherent to being Nigerian or being here because you find that that logic almost collapses...Tobi: There's a place premium to it.Affiong: Exactly. So it's where you are that unearths that potential versus inherently in you. And you see that with human capital deficiencies in Nigeria; and I didn't have that small country, smaller GDP perspective until I went to Zim and I was like, “wow!”, these people by virtue of having a more diversified economy know more and could produce more in their industries and businesses. Somebody told me something that in India, even on the factory floor, what one person does in India you need three people to do it here and I think that's telling. That the capacity of one person to solve and make decisions around three processes increased over there just because the economy demands that.Tobi: It's an interesting point you're making because a lot of the problems we talk about in development centers on productivity. And firms have to be productive for us to be able to call an economy developing so to speak. A lot of the effect, the aggregate effects that gets measured transmits out of firms...but you made two points that I want us to speculate on a bit.Affiong: Okay.Tobi: One is economic complexity. And from I think Ricardo Hausmann and co., they actually say that economic complexity is a better predictor...Affiong: Yeah.Tobi: Of development...Affiong: Yeah.Tobi: But there is this other school of development that says you have to specialize. Oh, you have to...Affiong: Competitive advantage, yes.Tobi: Yeah, you have to find your comparative advantage, you have to, oh, do agriculture or its manufacturing or whatever [else]. But you are saying from experience that complexity actually works. So how do we get policymakers to absorb that message? How do you craft policy for complexity, really?Affiong: That's a great question. I feel like I should preface by saying I'm by no means an economist, I just enjoy talking about and thinking about it. This I could do for free. I geek out on development especially and firm productivity and things like that. But the schools of thought are... from what I've read and understand, I think...the way I see it and please for the audience this is a very layman’s way of thinking...that when you look at something like economic complexity and doing different things and countries that are successful do things they're not previously good at. It's somewhat walking backwards, somewhat looking at developed economies and saying, "well, they're very diverse in their output", but they also started somewhere and built up. So I do think there is ...comparative advantage is almost like a first-level sort of step for policymakers to begin to say "well, where should we deploy resources and deploy thinking and deploy attention to". It's easier to sort of start with what you have and maybe the comparison to countries that have really been good at starting with what they have, that have been able to create what they didn't have is the wrong comparison in terms of policy because if you look at America or Asia or all these countries that are highly specialized, are highly diverse, they all started somewhere. Compare that to a country like Nigeria where we haven't yet really kicked off industrialization in anywhere. It may be too big a job to say start crafting policy for diversity. I would say that the negatives of focusing on comparative advantage is what you see in Nigeria - ban, protect a small pie, we talked about, oh, agriculture is our thing so let's ban import because that's what we have comparative advantage in production in.So it is a dangerous hill to die on as it were if you are not able to really multiply your comparative advantage. What you end up doing is saying, “oh this is a small pie let's protect it”, and all the policies are somewhat skewed [against] actually widening the pie. So my thinking is that there's still room for countries like Nigeria to focus on their comparative advantage. So if I'm looking at, like, an example is petroleum or oil and gas. There is still billions of dollars in unlocked investment in oil and gas that is trapped by bad policy. So it's saying…well, we produce oil and gas but we can still derive a lot more value from that for long as shale and all these renewables don't stand in the way. Although I think despite that, there's still room for investment. On agriculture - if you're saying you're the largest grower of cassava in the world. There's still room to be hyper productive at growing cassava which will create jobs. We're not even talking about processing, just producing cassava that can be used for different resources, producing enough high-quality cassava to export, all sorts of things. So there is room for policy attention to go to actually developing your comparative advantage and I think that then spurs you to start getting into more complex policy around say wanting to be the financial services capital of Africa, which you don't have skills to do that yet. But yes, you can sort of engineer that through policy. That's probably a harder reach for government and people. We're still stuck on what we're doing but I don't think that it's bad to focus on what you're good at. But I do think that there is room to unearth a lot of value in that. So yeah.Tobi: Okay. So, I worry about timing though, because obviously when we talk about comparative advantages, the go-to example is always Asia. East Asia. South Korea, Taiwan, Japan, Vietnam now. But what I mean about timing, which to me is a bit of an argument in favour of freer markets…(I don't want to say free markets because that attracts a lot of angry letters)... is that the rules of global trade has changed a lot. Asia industrialized at the time when there were a lot of permissiveness in trade and trade regimes around the world today has changed. And also if we want to do comparative advantage now, however bad it is [the economy], some companies here have built-in competencies, learning over years, you will be sort of redirecting resources and human capital away from those people to few chosen sectors. So I'm wondering will that be a way to go. Can we really do comparative advantage? Has the time not passed?Affiong: From my understanding or from my perception is that the things we're talking about in terms of comparative advantage, the sectors or industries, (they) are still very largely agrarian sector. They're sectors that one would assume human capital at the lowest level can absorb [a lot more capacity] versus a large redirection of specialized skills to actually play in the sector. So if you look at agriculture for instance, I'll take agric because we are largely an agrarian society and that's where fifty percent of jobs are created. Increasing agriculture input is not necessarily... yes there is huge technological, in terms of machinery component to it but a lot of it is fertilizer and if you look at Asia, a lot of it was people who were not doing anything getting on the farm. We have a lot of people who are not doing anything who can get on the farms and be a little bit more productive. We're not talking about people who are going to go on to start agribusinesses and do those kinds of things. But if we focus on that comparative advantage, I think we can drive up human capital that would otherwise not be doing anything and I think that that might be a benefit. I read the book how Asia works and sort of household farming, going into smallholder farming with people farming on their own, in their homes, outside their lots and then moving them into some sort of small holder...being a little bit more productive than what they were doing...which puts them into jobs and make them employed. I think that opportunity may still exist and I think when you start driving up that kind of productivity you hopefully can attract more human capital from outside to sort of buildup on that. Then start the processing for factories, do more complex manufacturing and things like that. I think there is still a window of time and maybe Nigeria's population has a lot to do with it as well that for domestic consumption there's huge opportunity to make the price of every commodity cheaper by growing more so that more people consume it and increase consumption. That could be some sort of out [for] job creation. I'm not a big fan of produce what you consume but I think in my view, consumption of almost every sort of product is low compared to even African averages and that's because the price is high and bringing these prices down by increasing productivity would help drive local demand of them and hopefully that will then bring more people into employment, make them more productive that way. I hope that theory is... sort of makes sense in a way.LaughsYeah, I'm making a case for increased…focusing on comparative advantage and the impact of that on really using a level of docile, docile is not the right word, but stagnated human capital which are people who are not employed, can't do much but if you've got them farming more, they're generally becoming more productive. Yeah, that's my thinking around that.Tobi: Alright, let's talk about perception a bit because you mentioned earlier that I don't know maybe because of our size we think “oh, Nigeria is awesome, we have this huge stock of human capital and any day now we are going to zoom ahead”...Affiong: Yeah, unleash it and take our rightful place on the African continent.Tobi: What's the biggest perception difference you've observed in the business landscape in Nigeria. What do you think Nigerian business owners, entrepreneurs or professionals are mostly wrong about that they think they are right about?Affiong: That's a very very good question. I think its human nature for everybody to overrate our capacity, that is what we do. We are not logical about what we can realistically achieve. Studies, everything, proves this. But in Nigeria, what I think a lot of entrepreneurs are wrong about is that idea that there's something innate in the capacity of Nigerians to sort of make a plan or make things happen. I think we are very wrong about a lot of times why firms or businesses fail in Nigeria and a lot of it is around lack of structure. I mean, yes, there are market issues and there are lots of issues but I'm going to focus on this - the need to sort of build in systems and processes vis-a-vis firm failure and I think a lot of people, a lot of entrepreneurs think that kind of disorder or not building that order within companies is not necessary to their success because Nigeria is a very disorderly place and things don't happen. But I think a lot of firms damn it and die when there are no systems built-in businesses were basically - capacity and decision-making (etc.) is filtered down and processes are filtered down from the top to the bottom. So you find in a lot of businesses [that] the CEO knows a lot and owns a lot but there is no(t) emphasis on management and middle management being equally (skilled up) upskilled, being able to make decision, being able to do stuff… so there's literally only very few resources being aimed at growing a business - which is the CEO, usually his or her networks and things like that versus upskilling the management team to be growth led.There is this idea that systems and processes don't matter but i think they contribute. I don't have empirical evidence to prove it but I think they contribute more to the failure of businesses than we give credit to. Yes, market realities are real but it's a slower way to grow a business if you start looking at...There [are] different stages of growth in a business. You don't need a full management team in day one but when you need that and that system is not put in play, I think you're already regressing as a business and you just don't know it. Then things start to unravel and you point fingers at the market or the economy or you point fingers at competition or import etc. whereas there wasn't that sort of universal firm learning that comes from everybody - [having] more people being involved in the growth and capacity of the business. And you see businesses at all sizes where there's a disproportionate amount of the responsibility of the business to grow on one person versus the entire firm. That is also [a] lack of systems and processes (and making everybody's job at a particular level to see the business grow from their departmental perspective) that's missing and I think it leads to regression of businesses. Some entrepreneurs don't [consider it important] because it's not a cost per say that come out of the business, and in fact the cost is hiring (these) people to do it, [so] it's not measured as a real or a lack thereof of a strong team and processes in the business. It’s not measured as importantly as it should in a business. That’s something I think we are all still very wrong about. Yeah.Tobi: I like where you went with that. So let's talk about management a bit.Affiong: Yeah.Tobi: There was this study I read, and I'll try and put up links for context, by I think John Van Reneen and Nicholas Bloom. Their argument is that management also determines the wealth of nations, that is, how firms are managed is an underrated factor. In fact, there was another study where they found that management is a better predictor of a firm's success than every other factor, even technology. But obviously here, management is something we don't really talk about as such. It is endogenous. So what's your experience been like with other business owners, how do we improve management and how do we increase our awareness in that area?Affiong: That's very very interesting, and I think quite a study to bring up because I tend to agree with that. People will say "well, if you're in a great industry and have terrible management you'll still be successful" and I think that might be true. But it's true for very few. It’s true when you are maybe the only player, there's two of you and there is not much competition for the market. But when there's a lot more competition for a market like we're seeing in a more globalized world, the quality of the people who are in firms matter and accelerate or decelerate the growth of the company. Now, I'll use myself as a case study, I have a really strong management team, I have managerial heads in all facets of the business and all departments of the business and that has helped us grow tremendously - from a sales perspective, from being invested ready, from being able to handle numerous things at a go, from our ability to launch new products (be)cause there are just people who are responsible for getting these things done and understand it and the collective output of that means that things are done better. If we want to enter a new territory, for instance, it's not one person doing it, I have somebody from finance looking at the numbers, somebody from admin calling agents to figure of the space, my production team figuring out product integrity, my sales manager finding the stores that we're going to go in to and we can execute that in two to three months - of people putting their collective outputs together to get that done. Now, if that management team was not as strong or not as defined, it will be one person trying to be all those things and we will invariably make a poorer decision; and if we make a poorer decision, you'll either fail at that or you're operating sub-optimally, which then impacts, for me, revenue, which then impacts the signal of whether the business as legs or not.So might find that there is market, but because there is less execution capacity in your firm, you can't actually achieve that market. But what does it look like? It looks like your firm is a non-growing firm, and not doing well primarily because you alone cannot do everything and the people around the table are not equipped to actually access the market available - and then as you learn within the firm to do these things, you can replicate them much easier. So I kind of find that, tied my previous point, that decentralization of output and expectation of output and productivity obviously helps the firm grow faster, make better decisions; and those businesses are more likely to demonstrate investment readiness, demonstrate that they can scale, demonstrate better unit economics (etc.) because there's just a talent or pool of people who are equipped in each of their departments to make and optimize the decision making - make the best decision etc.Tobi: That's interesting. You talked about processes, but I want to push on the personnel aspect of it. Obviously you have a great management team but what do you have to get right to build a great management team because, well, when you talk to people, what they say is that "oh, managerial talent is actually scarce and that it's our culture to be sloppy management wise". We're going to talk about culture in a bit, but what do you have to get right to build a great management team?Affiong: That's a fantastic question because I also struggle with that. I say “do I have a good management team because I have sort them out, and can that be devoid of a growing company?” This is, for me, one of the big things. I also find that a lot of Nigerian companies are not growing and they are sort of stagnant, sort of peaked in terms of learning. So sometimes the need or the ability for the firm to attract people and keep them and sustain them and even pay them is limited when the company is not growing. So if you're not growing as a company, can you attract people to grow the company with you which is how they learn and get better at doing well? If you're a company that's not doing a lot of things and doing more things, people's learning pretty much stagnates, right? It's like government agency…LaughsSo I'll say what are the inputs in a growing company? In a growing company people are learning more, people are learning things they've not done before, so there's a lot of external learning coming in, there's a lot of self-learning; which then becomes process because we've learned what to do. We didn't know what to do before, we've learnt it and now we’ve agreed that’s the right thing to do - sort of install it as a process and people repeat it. For me, a growing company is necessary to [not only] keep attracting really good pool of management, but also the pool of people who are curious and who are excited by growing a company. It's somewhat a chicken and egg thing. (People who are curious about doing things they've not done before.) Because that's what a growing firm is, you are learning to do stuff you've never done before. That's how you grow, and then being able to apply that curiosity in a company that is growing - so that they are learning to do things they've not done before - is my idea of how you get a good managerial team or how you can bring in the talent. It is not just for today but for tomorrow when you're not doing [well]. When we started for instance, we were selling 2 products in Lagos. We're now selling 6, actually our SKU is about 16 right now in Lagos and 12 other states… Tobi: Wow.Affiong: and in over 300 stores. So the learning to be able to scale up and do that comes from the fact that we're growing but also comes from the fact that there are people who can actually achieve that successfully and those people started up not working in 12 States but have now grown to the capacity to be able do that. So, it's a chicken and egg thing.LaughsI don't know where I fit in. Can you grow without people who are not curious about growth and cannot execute things they didn't know? That's a tough one. I don't know all the right answer.LaughsTobi: It's interesting you mentioned growth…Affiong: Yeah.I think that people don't know - and even I myself irrespective of - how much having money to survive and stay in business is what ultimately leads to growth of companies. - AWTobi: Because we're zooming out a bit. Now, people would say "well, it's tough to build a growing business in Nigeria". It's almost a cliché hearing that. So what I want to know is...okay, we hear that X or Y isn't helping. As a business owner, what does it take to build a growing company? Does government have to get out of the way or help in a certain way? What are the things that has to go right to have a growing company that then attracts quality managerial talent?Affiong: That's a great question and I have a very loaded answer. I'm not going to focus on the banal part of government policy and initiatives. I think businesses are very limited in how much they can grow outside of government policies but let's sort of control for that. I think we underrate the need for capital. I think that people don't know - and even I myself irrespective of - how much having money to survive and stay in business is what ultimately leads to growth of companies. People will tell you - and I find a lot of the advice to entrepreneurs quite asinine and platitudinal…Tobi: That money doesn't matter.Affiong: "Oh, you don't need money at every level"...oh, yeah, I do. I always need money because money is one resource that can unlock others. I say that not facetiously in the sense that money does not solve all problems. But when you start a business, in my opinion, you're assuming a product-market fit. You're making a lot of assumptions around your customer wants, around your product's ability to solve that customer's problems or service, and you may not get it right in the first time. It doesn't mean that you cannot solve that customer’s problem, where you can then generate the value that we see in [a] growing company. But those pivots, those mini pivots that happen in companies, they are not free. Innovation is not free. Changing of business model is not cheap. And that's where capital comes in. Capital helps you overcome some of those mistakes you make or right those wrong assumptions you made that ultimately lead to growth. When I started my business, I'll use myself again as an example, and I'm very wary of using anecdotes but this is an opinion conversation…Tobi: Please go ahead.LaughsAffiong: it's not one that requires all that fact. But the first two products we launched are not our best selling products today. Our best-selling product is a product that we got out of putting two products in the market, the customer rejecting one and telling us what they wanted. Now launching that next product requires significant investment for me to get new packaging, get new suppliers, get new staff, get all these things and had I not had the capital to do that, you would have said "well, there's no market for dried fruits because these two product, out of them, one was rejected". But really, what capital helped me do is bridge the gap to learn more about what the customer wanted and be able to provide for them and then scale that and then launch new products. So money is important. Capital is important. And I think that...People always say "oh don't give an entrepreneur too much capital in the beginning". I'm sorry, very few entrepreneurs get too much capital in the beginning. And, even if you give an entrepreneur a lot more capital than they are able to absorb in the beginning, you're increasing their chances of actually figuring out where the value is, where the customer”s need really is so that they can solve that problem and that's how firms grow. So, for me, we talked a lot about training, and we talk a lot about product-market fit and understanding your customer; that's not free, that's not cheap. It takes money to survive to do it. You have to be in existence, ou have to have enough working capital, you have to serve them different iterations of this product. And in my view, capital is, as simple and basic as it is, one of the big things that would help firms grow.Tobi: It's a very great point. It's sad some of our capital control measures, because...again, talking about Asia, take a country like China. When China started industrializing, a lot of the capital came from Chinese overseas. In Taiwan, in Korea, in Japan... but here, you find that even government is fighting against remittances and stuffs like that and we don’t have enough capital.Affiong: No, we don't. We absolutely do not and a lot of the capital that's coming in is quite conditioned and I think sometimes also... (complex). We simply do not have enough capital that is needed to spur any sort of growth in enterprise, at even a modest rate I would say.Tobi: What other things do you need to build a growing [business]? I read a blog of yours, once, where you mentioned networking. What role does that play?Affiong: I think it plays a huge role especially in a country like Nigeria where access to opportunity is not democratized (anywhere), but it certainly is more unequal here than in other parts of the world. So in a country where a lot of the resources and opportunities are in the hands of a few, to sort of gain access to that where you are not from that class or cadre, I think networking is absolutely vital. And I think the power of networking is proven globally, it is not just about Nigeria. Take any example, you can't just walk into anywhere and say you have a product/service or whatever and say “you to want to sell it” where you don't need some network, some sort of reputational stamp of approval or something like that to get you through those doors. So networking is simply trying to to do that, and, say leapfrog some of the challenges you face in running a business where the decision rests in the hands of a few people. For me, expanding your network and going outside of your traditional network of family and friends and knowing people who know people, and knowing people who are not in your circle increases your chances of success as a company to raise funding, raise awareness, raise purchases etc.It's sort of very difficult to measure in terms of firm success but I think one of those underrated things that really really help businesses, and people in general, the world over at that.Tobi: So networking in this case functions as a bit of a social capital?Affiong: Yes, it's leveraging your social capital to sort of grow your business directly through getting financing, through getting buyers. Yes, it's sort of a resource. A standalone resource that adds to the growth of your business is how I look at it.Tobi: But how does this square with merit? What you hear is that there has to be a level playing field for businesses to compete fairly. But if successful businesses are better networked, isn't that problematic for, let me say, our idea of merit?Affiong: I think that idea is unfounded. I think the idea of equality is a nice virtue that people think is what leads to the success of societies and I don't think that that's true. I don't think it's played out in history. I think there's a lot of circumstance that leads people down the path that they get on to and if you trace back people's history especially successful people. I know you have this very interesting comment which I leverage (a lot) on privilege analysis. And what people do is, if you see people doing successful things, you go backwards and say they are privileged…Tobi: Yes.I think that if people are allowed to thrive individually as humans are meant to do, using all the resources at their arsenal, society wins more than it loses. -AWAffiong: And I think that’s true. You find that success is certainly not equally distributed, and I think you find successful people are more likely to be successful. You find that people who are better networked and experienced tend to build better businesses. It's an amalgamation of people's histories and where they are coming from. It's not that you just said, "Everybody run", and you open the playing field and everybody has an equal footing in terms of what they can do. And, I don't think that's necessarily a bad thing. I think that... especially for entrepreneurial success, you don't need to so many entrepreneurs to be successful to build a successful economy. I look at America, the most successful economy in the world, with three hundred million people who are all most(ly) working in private institutions, very small percentage are successful. So for me, equality in terms of entrepreneurial success is not something you can control for and I don't think it is fair to punish people who have the means to build bigger businesses - that would absorb more people, create more income and productivity for the country - to try and taper that or to try temper that because you want to build an equal playing field. It is like dragging people down to the mean and I don't think that creates the kind of success that benefits society as a whole. So I say "everybody, work with the privilege you have" because that is not something you can easily trade. People don't give up their privilege or people cannot automatically say because I have this kind of access I going to give it away and expunge it [or] I will exchange it for somebody else to have. It’s a very difficult thing to do and you cannot erase centuries of history that has led people to where they are today to try control for equality. I don't think it makes much sense. I think that if people are allowed to thrive individually as humans are meant to do, using all the resources at their arsenal, society wins more than it loses. It’s not a perfect system but I think it's the best.Tobi: Interesting point on priviledge. My favourite example of that point is Bill Gates. You hear today how his middle school was selected in his district for a pilot computer program and how that is some kind of privilege and what you don't hear is, he was not the only kid in that school… Affiong: Yeah, in that school, exactly...every other kid had access to that computer…LaughsTobi: And people just sort of discount the hours he had to work, all the nights of misery, of uncertainty and hard work that people put into this. Another thing I discovered and I would like to know your insight on that is, on this privilege issue, people think that group traits transmits individually like “oh, if you're born into a certain income class (X percent of the income distribution) it means you enjoy privileges that come with that regardless of whatever you do” and often I find that that's not true. There are lots of rich dumb kids who are failures. Absolute failures, who I wouldn't want to be in a second regardless of their privilege. In our social discuss, we're importing a lot of that and maybe my experience is limited to social media, so I want us to lean into that here a bit.Affiong: I think there is certainly and I would say at this point, also an attack of individual virtue and individualism in a way. Where it seems, like you say, people discount the individual components that make people successful and they want to tag it as a privilege of wealth or a privilege of going to Ivy League schools or one privilege or another which is a very simplistic way to look at it. You find that everybody's route to success has a mixture of lottery of birth, economy you're born in, the time in which government policy favoured you or your parents or not. There is a huge very complex mix of things that make people - that lead and support people's success.But there is also, like you mentioned, thousands other people who grew up in those similar conditions. The individual trait of wanting to, of striving or building or doing something is one that is discounted. And I think that there is a general attack on individualism, free thought and individual thought. Even in societies where there is a lot of success, people want to demerit their own individual input to their success and sort of collectivize it as a way of diminishing that. In those societies where everybody else had access, there are also failures. So it's not just that the society was the reason you did well, it's that there is something about you as individual who has been able to build whatever success that you have. So this social discourse around collectivism and collectivizing people's successes and things like that is a worrying kind of phenomenon happening because I think it is us going against who people are as individuals. It is going against the way human beings are made and are built and I think it's worrisome to start punishing and tagging people who are individually successful has being [this] sort of greedy barons (as it were) for being successful or feeling bad for being successful, feeling bad for building a lot of wealth, because somehow they are meant to collectivize their wealth, or somehow they also stepped on other people to be able to get wealthy. So there's sort of very dangerous and insidious ideas around individual productivity and individuals building that I think are being disproportionately spread in a way where they are being accepted much more than I think they should be, and not being critiqued.I certainly don't subscribe to privilege identification of any kind. I think it's actually an insidious thing to do to point at people and say “declare your privilege, declare your privilege”. It is tagging people in a negative way that I don't subscribe to. I think does not do anything for anyone really. So, I say, as individuals we'll always look for all the resources at our disposal to get to whatever level of self actualization we're trying get on and that should not be diminished in any way. I don't care if you're a billionaire or you're having 100 Naira in your pocket. That is human, that's what we do and shouldn't be discouraged.Tobi: So, now, how much do you think categorical disadvantages like gender or social class matter? If we're talking about individuals like you said, what you find is that individual still fits, however defined - however poorly defined sometimes - into certain categories. Of course, you're a woman in business, you hear things like X is difficult for a woman in business whereas for a man they don't face the same barriers or challenges or maybe things around sexual solicitation. So, how much do you think that matters in terms of striving and success?Affiong: Like you said, equality in general doesn't exist and I think that that's the general premise. That even if you control for gender and look at men, there is huge inequalities in men. (In one gender where you sort of look at different strata, for instance.) So, for me, this idea of gender equality is....let me be careful here...I am not dismissing at all there are categorical disadvantages for women in business and women as a whole. But, again, what do we compare it to? We often compare it to men. We don't compare it to categorical differences that men face as a gender, that there are differences between men. So, the men versus women view is the most obvious way to look at the disadvantages for women. But I generally think that we need to look at that from the perspective of the history of women and [ask] when did women start moving out of the home as a primary occupation into business and does that explain why there are differences in the number of women represented in the industry versus not? Because if you take a different look at it, I think you'll find huge successes, I think you'll find that in a generation of women or someone like my grandmother not being educated, in only 200 years her granddaughter can become the CEO of a business. But if you look at it from the perspective of “well, let's try equal the playing field where we have 50/50 men and women”, that's not really solving the problem in my opinion. The problem is wanting to encourage more women - that women are moving into formal careers and rising to the top - versus saying that the dearth of women in top management is because of some patriarchy or some system that is blocking women. It's that just from the get-go men got into those roles more and more women are getting into those roles [too]. It depends on how you look at it, you might see that what we're actually seeing is an advancement of women versus women [in] the world today still being disadvantaged. And that's true. I think trying to uncover the root cause is very complex and not based on one thing and certainly not based on the fact that men are advancing at the expense of women is a hundred percent true. And that is the simplistic discuss that you're hearing that “okay, men are patriarchal, they are all-boys club etc.” Well then, how do you explain for women being more university graduates than men in developed economies? Women are getting more degrees, women are advancing, more women in management. The way to look at it to solve the problem matters and I think it's more sensationalist to look at it one way versus another.Now, bringing it back to myself as a woman in business, I can't categorically say and I'm not going to speak for every woman because that's silly to do and my experience is very unique. But I can't categorically say that my gender has hindered my progress as an entrepreneur. I've never gotten a "No, you are a woman so therefore you cannot get progress". I don't know that there are many women starting businesses who have heard that. Now, are there other challenges women face, maybe the impact of motherhood, the impact of social conditioning to make women maybe not...? Social conditioning is even a stretch for me to actually apply to this scenario but whatever experience women face as a collective (their collective experiences growing up which is actually very individualistic [because] it is not all collective) matter but they matter for men too. Men success is not equal. It's...you know, two men that grow up in the same household don't automatically become successful because they grow up in the same household. Two women who grow up in the same household don't automatically just ascend to the same levels because they grow up in the same household. There is something that we're trying to, I think, aggregate which should be disaggregated to actually find ways to advance with it. Now again back to me, no one has ever said "you're a woman so you will not succeed because you're a woman and I don't do business with women". That never happened to me. I don't know how many women that's happened to. And when I look at the impact of challenges that affected me as a woman, say sexual misconduct - Yes, I've had people say inappropriate things to me as a woman and I take that as a challenge that is it different from another challenge that a man faces where... that is a hindrance to his success in business? We have to weigh those up. There are those challenges that affect women but do they disproportionately affect them to the challenges affecting men's success? I don't know that that's the case. I can't say, I can't say that...Tobi: What about the implicit bias? Like maybe there might not be explicit stuff [and] nobody is going to tell you because you are a woman. But some think that there are implicit biases against women that, oh, because you're a woman I can implicitly conclude that I'm not going to offer you that promotion because I imagine that five years from now you're going to get married and have a child. What about implicit bias, do you think that plays a huge role and can it really be corrected? Is it a problem we can solve, really?Affiong: I've read some studies around this and I'm not going to say that my reading is up-to-date on it but if you look at even the studies around senior management and why women are dropping out of management, a lot of it has to do with motherhood - becoming mothers, suspending their career advancement. But a lot of it is...if a woman who has an MBA and is a mother, was working and halfway becomes a mother and decides to quit and become a mother full-time, it's a choice. And it's her choice because she wants to really be [a mother]. There are various reasons people make those kinds choices but I guess the point I'm trying to drive at is that the dearth of the female management is that women at the stage of becoming mothers don't go back into work or take part time jobs etc. So for me, if you look at that you're saying "are women being punished for being mothers or are women choosing - that they enjoy motherhood so they want to do that?" The narrative is that "no, women cannot go back to work because they're mothers". That's not true or that what should be an ideal solution to allow women to have children and are not overburdened by childcare to make that choice and women who choose to actually stay and not want to go back to work are doing so because it is their own desirous outcome. For me, that's something to unpack and I think we're seeing more flexibility around women being able reintegrate in the workplace versus their careers ending because of having children.Again, I can't say how much that bias of “let me not give a woman a chance because in five years she'll be a mother” is? Maybe it's a bias that is probably more widely held in a different age or category of person. It's hard to test. Does it exist? Probably. Does it exist to the extent that people think that it does? I'm not sure, I'm not sure. I think you're finding a lot of self-selection out of [going up]. I think women are self-selecting out of going up because of motherhood and the demands of motherhood. And if that is to change, we should unpack that as the issue, not necessarily say that, it's because men are stopping women from going up in their careers. So that makes sense. I mean, where would you pinpoint the solution? Where would you pinpoint where to investigate…Tobi: Exactly.Affiong: And how to solve the problem? I think it's looking at that time where you become a mother and your career takes second place. And again, we assume that there is no payoff for people who are deciding to choose that path. The narrative is that it is completely "oh, a do-or-die affair"Tobi: You ought to find satisfaction in work.Affiong: Exactly...and because every woman is made to be this career sort of [person]. Every woman who is working in career is meant to get up to the total ranks and become the boss therefore having a child is not [or] choosing "motherhood" as it were is not success. For some people it is, absolutely. And some people the payoff and trade-off of being a mother is absolutely a form success. I recently become a mother and while I'm an absolute workaholic, I can appreciate why women would want to focus on motherhood. And it's not a failure that they want to do that, and it's not patriarchy or misogyny or a man stopping them from doing that. It's a choice. It's a valid choice for a woman to do. So where women don't want to make that choice and want to advance is where I say let's find solutions for. But not automatically paint it as a huge deal in the world that women choose motherhood. They love being mothers, if they didn't we would not exist as a race, right? Like if women didn't actually enjoy the journey of motherhood. So I think that that's more nuanced than people like to understand.Tobi: Yeah, this is an interesting area, so I'm just going to ask you straight up. Are you a feminist?Affiong: Ah, am I a feminist? Yes I am. I would say that. Do I believe in the equality of sexes which is the traditional definition of feminism? I say I am. I do believe in that. What I don't believe is that equality is that anything a man can do, a woman can do. There is not equality amongst the female gender, there is not equality amongst the male gender in terms of the playing field or in terms of people's access, networks, everything. So this view of feminism that anything a man can do a woman [can] that defines equality as like "oh, I must pick myself up and get what a man does, has, says" (etc.) is equality... is not...it fails to me. It's not real. And I can barely stand by that. Now, what I believe should be equal is access. Whatever a woman wants to do, her gender should not prevent her from doing as a man.But I don't believe that the male standard is what "success" looks like so if women are not equal, (if) they're not exactly on the level as men. Men are not on the level as men; women are not on the same level. So that sort of generic ideology of equality falls flat for me when you think about it logically and it's not a measure that you can even attain because you cannot control everybody's individual experiences.What you can try and democratize is access - to education. I don't believe no woman should not have access to education, and neither should any man. Access to working, access to things, access to people making their lives better. I believe equality of access is what I'll say I understand feminism to be and I'll consider myself a feminist.Now, new age feminism.LaughsNo. If that is what the decision is, I'll hundred percent say "No, I'm not". I don't even call it feminism, I think it's more...let's not even go into what I think it is because I have strong views about that.Tobi: Laughs...Affiong: Don't get me in trouble. But, yeah…Equality of access is, for me, the best chance...even that is hard to assimilate. it's really hard when you think about it...Tobi: Yeah.Affiong: you can't control for all the things that makes a person's experience in this world.Tobi: You see a lot of, of course, discuss around gender issues these days and I've been trying to really tease out what it is that we as a society are going for actually. But you see a lot of anger. I don't know how much that helps, but...Affiong: Laughs...yeah.Tobi: certainly there's a lot to be angry about. But I don't know how much anger helps in that area. But one idea I want to put forward and I would like to hear your views here is gender issues and economic development generally. Like, when I see people, protesting or campaigning to end rape for example. Of course rape is a huge huge problem that no society should tolerate but I also wonder on the other side about state capacity…Affiong: Uh hmm.Tobi: You know?Affiong: I doTobi: How does a police force that cannot stop petty crimes investigator rape? how can a police force that can not prevent murders or investigate... properly investigate and prosecute rape? People talk about passing laws where we have laws that are not enforced and where you can easily wiggle your way out of [crime].So shouldn't a lot of this activism be focused on development generally because I think a lot of social progress is positively correlated with income growth?Affiong: Yeah, exactly.Tobi: You'll be surprised.Affiong: And that is a hill I'll die on because I talked today about a lot of lending a voice to causes and social media making it easy for people to join causes and say they support things on a very very superficial level. It's easy to say I support no sexual abuse to women and I completely agree. Only a deviant will support sexual abuse or rape of women or any other cause or out of school kids. But the reality is that a lot of these things are a byproducts of poor societies. You find these ills are more common in societies that are not economically advancing (that are getting poorer). And like you said, for me, in the history and development of a country, the way you solve problem is not...let me take this back to the size of government.When you have a society where people are too poor to send their kids to school, it doesn't matter what you legalize around the need for education. People simply can't afford it. - AWThe size of government for Nigeria relative to its GDP and the complexity of the economy is completely inflated. You have over-regulation where you should have less government and let industries thrive. Look at China. Like you said, a lot of things where allowed that will not be allowed today in China but those things that were allowed, allowed economic progress and allowed for the wealth of that nation to be possible, right? And it's the same thing when you take it to any social cause. A lot of it is rooted...you call it culture, you call it tribalism, but it is really a competition for resources. Who wants to say that children should stay out of school? But when you don't have a country that is rich enough to build schools (good schools for people), are they better off in schools where they're not learning anything? It's easy to say "well, everybody should be in school". Yes it is, but when you're churning out kids that cannot do anything or cannot learn anything or when you have a society where people are too poor to send their kids to school, it doesn't matter what you legalize around the need for education. People simply can't afford it. So I think with all these causes, there is a very crucial stage of economic development that actually solves a lot of the problems, and it's not more laws. Right now you'll say "well, they're signing a law against this sort of victory". It's not a victory. If you go to the police station and somebody who perpetrates [a crime] can simply pay off his way or somebody who doesn't have access to a lawyer cannot get justice or the fact that the police do not even have the capacity or, Jesus Christ, the petrol to drive to come and see what you're talking about...Tobi: Or even basic investigation… Affiong: Yes, exactly. So for me, a lot of focus should really be on how do we get Nigeria richer? A lot of gender issues are byproducts of poverty. It's not a byproduct of patriarchy or people's cultures. It's that if women got richer…there's a stat that says if women go to school till...(we talk about Nigeria's population or overpopulation, for instance), if women go to school up till high school they have like two or three fewer kids. If women get better education they are likelier to choose who they marry and get in better domestic situation. All these things that are underpinned on just income, education. Those two simple things that are as basic as having more money in your pocket to give you the agency to make more decisions and being literate that improve the outcomes of women and protect them from all these social issues that they're exposed to. But we make a lot of fuss around the symptoms and not the underlying issues. You talked about rape being a huge problem. It is a problem but what would stem that problem is, I think, often invariably linked to increased wealth.Tobi: Yeah.Affiong: If you punish more people who rape, probably less people would rape. But to punish more people who rape, you need a whole host of things to happen.Tobi: You need to be able to catch rapist for one.Affiong: So I do agree that a lot more focus should be on those basic foundations. I believe I live that through my values in terms of being an entrepreneur. My thesis is that if you give people, especially women... I'll shamelessly plug that my company is over 65% female employees and my management team is a 100% women. And my thesis is if you give women opportunity to earn their own money they have more agency with their lives. I'm not going to sit here and say I support a million causes, I support that cause, I believe in that. I believe that if women get better educated and work for themselves and earn their own money, their outcomes in life are better. And that's my thesis and I'm going to stick to that for as long as I can. I may be criticized for not saying "oh, I don't support this kind of mission or social"... I believe human beings are limited in what they can support and I am somebody who believes in depth over breadth anyway. So I want my life's work to unearth that and multiply that as much as possible. I'm not going to say I can be all things to all people. I don't think people can and I think if you focus on core issues, your outcomes are better. So that's my view. That's my hypothesis and I am walking that talk as an entrepreneur in business, and in person.Tobi: That's interesting. Another point that I want to mention is, of course, we see a lot of funding into social impact programs for women. I was reading a post lately (again, I'm going to put up links for listeners). It was a field study on fertility which is a huge problem in Africa. Nigeria is about five children per woman which we don't have enough income to cover that much. So what they found from the experiment was that fertility interventions and education were more effective within groups where the men were targeted as opposed to the women. What that tells me is that fertility decisions are not made individually. Couples make that decision. But what you see is that a lot of the social intervention programs target women specifically. They don't target families or couples and that's usually celebrated. You think that's a problem? Or how can we resolve that tension?Affiong: I'm not going to say that I know too much about it. I remember reading one study or was it a summary of the interventions of women in the North, who are getting injections and how they have to lie about where they're going because they can't take pills because if their husbands sees them taking their pills, that is a problem and they have to make an excuse to go get injections. Now, where they can't do that, then the intervention completely fails. Because they will get pregnant if you don't take these injections very regularly. So, what's the better solution if you want family planning to be truly adopted and not done in secret? (This is obviously a must on the most vulnerable way women have no rights in these domestic situations.) I would say it makes sense to get men on board and to get them to see the value in family planning because there [in the North], women barely have rights and women disproportionately suffer from having the burden of having too many children. Not just health-wise, income [wise], in most outcomes. A woman who has more children is worse off than a woman who had less where resources are scarce is my view. And to target the man means to help to ease the burden of women. Maybe it feels like more resources are going towards men than women but if the beneficiary is a woman, I don't care how you achieve it. If you really care about women's progress (however you achieve it, if that is the outcome and the aim), that should be what counts not necessarily that the interventions must directly benefit them because sometimes it's counter-intuitive but they [women] are more likely to be benefited if you take an intervention that targets the man. And this might be the case in this situation.Tobi: Let's talk about poverty a bit...Affiong: Okay.Tobi: One of your, should I say mentors or people you admire, Esther Duflo, recently won…Affiong: Yes, I stan.Tobi: [Esther Duflo] recently won the Nobel prize and there's been a lot of coverage around that area. Earlier we talked about employment and things like that. But what do you think is the best way to address the poverty issue? I know you do a lot of work in that area.Affiong: I think the best way is not to assume that there is a best way. And if I leverage the work of Esther Duflo, the biggest take for me from her book, Poor Economics (which was my 2017 book of the year), [is] it opened my mind to how hard poverty is to solve. The idea that (even economic growth) capitalism has solved poverty more than any other system, it still has its shortcomings in terms of its ability to actually rid the world of poverty. Poverty will always exist and that's the first thing but, for me, all the interventions and the mini studies showed that it's very difficult to isolate a driver of poverty and think that when you eradicate that driver or when you add an intervention, you're going to see outcomes that you can replicate. The biggest for me was microfinance. So everybody touts microfinance...Tobi: Massive failure... Affiong: ...has it helps the poor come out of it. No, the studies have shown that what it does is, it just gives people more trading capital. It doesn't make people richer. They just have more money to trade and…Tobi: And more debts in some cases.Affiong: And yes, in many ways, it really burdens people with a lot of debts. So if you look up microfinance and say you're giving finance to the poorest most risky customers at the most exorbitant interest rate. That hasn't changed their lives. Giving them more cash doesn't necessarily mean that they have amassed or built more wealth. That just, to me, shows how hard it is to solve poverty. If we say "well, we have scarce resources, gun to head, what would I pick?" I will pick economic growth. I will say do that,&#

If one question has driven mankind’s quest for innovation, it very well might be this: How can we get more from less?For most of our time on this planet, the answer was simple: We couldn’t. As my guest Andrew McAfee points out, for just about all of human history – particularly the Industrial Era – our prosperity has been tightly coupled to our ability to take resources from the earth. We got more from more.That tradeoff yielded incredible positive contributions in nearly every field: Technology, industry, medicine. But there’s one glaring area – one of those “aside from that, Mrs. Lincoln, how was the play” areas – where the trade wasn’t so incredibly positive. Of course, that’s the environment.As global industry rode the combination of human’s infinite ingenuity and Mother Nature’s finite resources – we all reaped the benefits. But we also saw the costs: Exponential global warming. Perhaps it’s not an exact straight line, but the connection is clear to all but a few climate deniers.Luckily, we know the solutions: Consume less; Recycle; Impose limits; Live more closely to the land.Or do we? What if, instead, these central truths of environmentalism haven’t been the force behind whatever improvements we’ve made and, more importantly, aren’t the drivers that will solve the existential task at hand: Saving the planet?Instead, as McAfee argues in his new book, the answer is dematerialization – we’re getting more output while using fewer resources. We’re getting, as his title suggests: “More from Less: The Surprising Story of How We Learned to Prosper Using Fewer Resources – and What Happens Next.”McAfee argues that the two most important forces responsible for the change are capitalism and technological progress, the exact two forces “that came together to cause the massive increases in resource use of the Industrial Era.” Combined with two other key attributes – public awareness and responsive government – we can and do “tread ever more lightly on our planet.”Some background: Put simply, Andrew McAfee studies how digital technologies are changing the world. He is Co-Founder and Co-Director of “The MIT Initiative on the Digital Economy” and a Principal Research Scientist at the MIT Sloan School of Management. One of his previous books, with MIT colleague and sometime co-author Erik Brynjolfsson was a New York Times and Wall Street Journal top ten bestseller; his books in total have been translated into more than 15 languages; and he and Brynjolfsson are the 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.McAfee knows his prescription to save the planet is controversial. He knows it will frustrate – if not outrage – most of his friends… assuming they’re still willing to call him friend. But as us non-academics say about people like McAfee: He’s done the math. He’s researched the data. And like it or not, he’s ready for the conversation.

Erik Brynjolfsson, MIT Sloan School professor, explains how rapid advances in machine learning are presenting new opportunities for businesses. He breaks down how the technology works and what it can and can’t do (yet). He also discusses the potential impact of AI on the economy, how workforces will interact with it in the future, and suggests managers start experimenting now. Brynjolfsson is the co-author, with Andrew McAfee, of the HBR Big Idea article, “The Business of Artificial Intelligence.” They’re also the co-authors of the new book, “Machine, Platform, Crowd: Harnessing Our Digital Future.

Is a network -- whether a crowd or blockchain-based entity -- going to replace the firm anytime soon? Not yet, argue Andrew McAfee and Erik Brynjolfsson in the new book Machine, Platform, Crowd. But that title is a bit misleading, because the real questions most companies and people wrestle with are more "machine vs. mind", "platform vs. product", and "crowd vs. core". They're really a set of dichotomies. Yet the most successful systems are rarely all one or all the other. So how then do companies make choices, tradeoffs in designing products between humans and machines, whether it's sales people vs. chatbots, or doctors vs. AIs? How can companies combine the fundamental building blocks of businesses -- such as network effects, platforms, crowds, and more -- in a way that lets them get ahead on the chessboard against the Red Queen? And then finally, at a macro level, how do we plan for the future without falling for the "fatal conceit" (which has now, arguably flipped from radical centralization to radical decentralization) ... and just run a ton of experiments to get there? We (Frank Chen and Sonal Chokshi) discuss all this and more with Brynjolfsson and McAfee, who also founded MIT's Initiative on the Global Economy -- and previously wrote the popular The Second Machine Age and Race Against the Machine. Maybe there's a better way to stay ahead without having to run faster and faster just to stay in place like Alice in a tech Wonderland.

Profs. Kochan and Brynjolfsson discuss the modern technological wave of innovations and how to potentially shape and design the technologies for future shared prosperity.

More than a decade after the first Internet boom, U.S. productivity growth has stagnated and the economy has been unable to break out of 2 percent expansion. This situation is testing even the most optimistic of forecasters, but in contrast to our recent guest Robert Gordon, MIT professor Erik Brynjolfsson is unbowed. Brynjolfsson -- who's also director of MIT's Initiative on the Digital Economy, and co-author of the book "The Second Machine Age" -- joins Daniel Moss and Scott Lanman to explain why he thinks the current wave of advances in technology means we don't have to worry about secular stagnation after all.

In this extra-long podcast, we review the important new book from MIT’s Andrew McAfee and Erik Brynjolfsson, THE SECOND MACHINE AGE: Work, Progress, and Prosperity in a Time of Brilliant Technologies. The first half is a detailed, Cliffs-Notes version of the book’s arguments for those that have not read it; others may want to skip to […]

Erik Brynjolfsson of MIT and co-author of The Second Machine Age talks with EconTalk host Russ Roberts about the ideas in the book, co-authored with Andrew McAfee. He argues we are entering a new age of economic activity dominated by smart machines and computers. Neither dystopian or utopian, Brynjolfsson sees this new age as one of possibility and challenge. He is optimistic that with the right choices and policy responses, the future will have much to celebrate.

Erik Brynjolfsson of MIT and co-author of The Second Machine Age talks with EconTalk host Russ Roberts about the ideas in the book, co-authored with Andrew McAfee. He argues we are entering a new age of economic activity dominated by smart machines and computers. Neither dystopian or utopian, Brynjolfsson sees this new age as one of possibility and challenge. He is optimistic that with the right choices and policy responses, the future will have much to celebrate.