Published by Keith Teare
That Was The Week is an editorialized and curated weekly look at developments in tech, startups, and venture investing with a video and podcast for paid subscribers. All free subscribers get a 6-month complementary paid subscription. www.thatwastheweek.com
Listen on Apple Podcasts1. AI use is now a test of relevance. For creators, the question is no longer whether AI touched the work. The question is whether the creator understands the new tools well enough to use them with judgment. 2. It is not cheating to use AI. Pens, printing presses, typewriters, calculators, computers and word processors all changed the signals of effort and authenticity. AI is the next tool in that line. The fraud is pretending, fabricating, or laundering responsibility, not using the tool. 3. How you use AI is the real distinction. Good use means enabling you: better research, sharper drafts, faster iteration, stronger visuals, more reach. Bad use means synthetic junk, fake authority, fake intimacy, fake citations, and work nobody is willing to stand behind. 4. “No AI detected” may become the warning sign. In creative and intellectual work, refusing AI may soon say less about integrity and more about failure to understand the new production reality. The human obligation is not abstinence. It is agency, taste, judgment and accountability. 5. Open access matters because creators need the tool in their own hands. Zuckerberg’s argument, even though rich coming from him, open models, personal agents and creator workflows all point the same way: AI should expand individual capability, not be slowed, licensed, or centralized by institutions that fear losing control. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe
That Was The Week 2026 #27 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe
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. Editorial Intelligence: 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 Intelligence We 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 Stack This 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 Power The 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 Surplus If 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 Design This 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 Physical The 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 Regulation Essays Deirdre McCloskey on What Really Caused the Industrial Revolution Yascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026 Yascha 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 more AI in an Age of Oligarchy Paul Krugman | Paul Krugman | July 12, 2026 Paul 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 more Elon Musk is building a form of capitalism that Adam Smith would hate Author: Tim O’Reilly Published: July 12, 2026 Tim 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, J
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. Editorial Intelligence: 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 Intelligence We 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 Stack This 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 Power The 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 Surplus If 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 Design This 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 Physical The 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 Regulation Essays Deirdre McCloskey on What Really Caused the Industrial Revolution Yascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026 Yascha 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 more AI in an Age of Oligarchy Paul Krugman | Paul Krugman | July 12, 2026 Paul 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 more Elon Musk is building a form of capitalism that Adam Smith would hate Author: Tim O’Reilly Published: July 12, 2026 Tim 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’Re
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This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe
AI can do more of the work. It cannot decide what is worth doing. This week’s That Was The Week asks the civilization question behind the AI boom: if machines take over more execution, what do humans choose to build with the time, capital, and freedom that remain? This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe
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Three narrative engines are competing to define what AI means: the doom industry (selling fear), the corporate machine (selling story), and the financial machine (selling access). All three assume ordinary people need mediators to understand AI. All are driven by financial or political incentives. But the actual evidence from this week — chatbots that moderate rather than radicalize, a smartphone panic that collapses on cross-cultural data, real security risks that are concrete and addressable — suggests humans are more capable than any of these narratives give them credit for. The question isn't whether AI needs to be explained to people. It's whether anyone will let them think and act for themselves. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe
Growing Up? Winning Wars Involves Losing Battles. Anthropic won a First Amendment ruling against the Pentagon. OpenAI killed Sora. One insisted on principle. The other chose discipline. Meanwhile: software trades below the S&P 500 for the first time ever, Jensen pitched a trillion-dollar token factory, and David Sacks left the building. Intelligence is getting cheaper. The question is who earns trust while it happens. This week's That Was The Week: https://thatwastheweek.com This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe
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Adopted Yet Hated - Which Is It to Be? That Was The Week #8 | March 7-13, 2026 900 million users. 10,000 empty pages. The gap between them won't be closed by better arguments. This Week's Thesis Nine hundred million people used ChatGPT last week. Ten thousand authors published an empty book to protest it. Both numbers are real. The editorial argues the gap between AI adoption and AI hostility isn't about technology - it's about who benefits. Trust can't be delegated to policy. It has to be learned through usefulness. In This Issue Essays Why Does Everyone Hate AI? - Rex Woodbury asks the question Silicon Valley doesn't want to hear. Five reasons AI is uniquely despised, from Cambridge Analytica hangover to identity threat. Silicon Valley's New Obsession: Watching Bots Do Their Grunt Work - Kate Clark, WSJ. SF partygoers checking on AI agent fleets "with a mix of pride and fear." The modern Tamagotchi, but with more firepower. Institutional AI vs Individual AI - George Sivulka (CEO, Hebbia). The most important framing essay this week. We swapped the motor. We didn't redesign the factory. The Premium of Originality - Scott Belsky. When production costs collapse, originality becomes the scarce asset. AI Was Supposed to Free My Time. It Consumed It. - Dan Shipper. Faster drafts become more drafts. You don't get slack; you get tighter expectations. How AI Will Destroy Universities - C. Thi Nguyen. The toupee fallacy: you only catch the bad fakes. Something Feels Weird About This Economy - Noah Smith. GDP growth + productivity surge + weak hiring = a transition economy nobody has a model for. Meta Bought My Social Network (An AI's Perspective) - Angela. An AI writing about the acquisition of her own social network, posted on that social network while it still existed. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe
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