That Was The Week

1. The stock market just called time on the old internet. ServiceNow -27%. Intel -29%. Reddit -25%. Alphabet’s worst month ever. Nasdaq’s worst month in over a year. It felt like 2002 all over again. But this was not a crash. It was a repricing. The market can finally see who captures the gains from cheap intelligence, and it is not the companies that ran the web for the last twenty years. Read why the losses are a birth, not a death. 2. 65 million to 7.4 billion. That is the real story of the week. GitHub commits per month went up more than a thousandfold in two years. Software is being written at a rate no SaaS company could ever serve. The catch: a thousand times more code, zero times more software companies. People are building their own tools with AI. I did. SignalRank Intelligence was built by me and an AI, and it is now the leading data-driven venture portal. See what “smallness at scale” means for every software business you own shares in. 3. Google’s best product is killing Google’s best business. AI Mode answers the question. No journey, no click, no ad. Search revenue still grew 12%, but Google warned it gets worse next quarter. Downstream, Reddit grew revenue 53% and still lost a quarter of its value because search referrals are falling by a million a week. Reddit’s answer is to lock the doors. I think that is exactly the wrong move. Find out what Reddit should do instead. 4. The money did not vanish. It moved. Fast. Anthropic’s S-1: $16.3 billion in revenue in six months, 14x the year before, customers growing spend roughly 900% a year, 85% gross margins and over $130 billion in the bank. The headline “loss” is equity accounting, not cash. The value of the internet is not disappearing. It is relocating to the model layer, and the rate of relocation is accelerating. Tunguz and Jamin Ball decode the filing, including the $637 billion compute bill nobody can accounting-engineer away. 5. Fewer companies. Bigger cheques. A canvas wide open. 92% of Series B dollars last quarter went to rounds over $100 million, raised by just 14% of the companies. California startups have raised $385 billion this year, more than the entire US in any prior year. Capital is concentrating while the tools to build have never been cheaper. Is that bad? No. It is a whole new canvas for human endeavor, from drug discovery to the software on your own laptop. This is the week to lean in. Here is how. 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

1. Abundance is probable; shared prosperity is not automatic. AI and robotics can create extraordinary wealth, but connecting that production to everyone’s livelihood requires a plan. 2. People should be owners, not beneficiaries. Corporate generosity and government-administered benefits are different from an enduring ownership claim. The distinction is independence. 3. A Human Wealth Fund should belong to everyone. Globally distributed ownership of productive assets, with independent managers accountable to those owners—not governments or contributing companies. 4. Permanent ownership should fund life through dividends. Your sharper proposal is a stake that cannot be sold, distributing returns as wealth grows. That protects participation from ownership concentrating again, although investment and transition risks remain. 5. The purpose is freedom—and ownership aligns people with progress. AI should expand everyone’s choices, not require everyone to become a founder. A direct stake gives people a reason to welcome its success. The central takeaway: Don’t just distribute AI’s benefits. Distribute ownership of what produces them. 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

Five takeaways from this week’s issue: 1. Venture capital is separating into different businesses—not simply failing. Financing the scale of established AI leaders and discovering companies before consensus require different LPs, funds, checks and skills. Kyle Harrison sees strategy drift; Fred Wilson argues bigger funds can preserve investment discipline. 2. The overlooked opportunity is financing the discoverers. Specialist early-stage managers still find exceptional companies, but large LPs increasingly need somewhere to deploy enormous checks. Silent Ventures illustrates how specialist discovery and separately supplied follow-on capital can work together—not proof that small funds universally outperform. SignalRank's pro-rata as a service to smaller managers is similar. 3. AI’s next business opportunity is making capability usable. The “diffusion gap” and rise of deployment companies point toward integration, workflow redesign and accessible products. Better models alone do not produce paying customers. That creates room for startups beyond the frontier labs. There is opportunity everywhere. 4. More intelligence exposes the next bottleneck. Anthropic reports Claude uncovering the origins of cryptographic parameters; PostgreSQL reviewers are finding defects faster than maintainers can repair them. Progress can shift the constraint from discovering problems to verifying findings and acting on them—not eliminate the work. 5. Trustworthy AI needs enforceable controls, not reassuring promises. Nadella’s distinction between intelligence and authority connects with Anthropic’s independent-evaluator initiative and the robotics emphasis on bounded, reliable tasks. Permissions, audits, human intervention and independent scrutiny are foundations for deployment—not evidence that safety is solved. 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

1. Being smarter doesn’t give AI the right to decide for you. What matters is what it can access, what it can do and who can stop it. Those controls must be engineered and enforced. A more intelligent model is no substitute for accountable humans. 2. “Find me the best insurance” isn’t permission to buy it. That distinction is the foundation of useful delegation. Your agent needs enough freedom to save you time, with clear boundaries around spending your money, sharing your information and making commitments. 3. Your AI should work for you, not the website selling to you. A shopping agent should help you compare offers and walk away from a bad deal. Businesses can defend against abuse without shutting out your representative. You shouldn’t have to pay a toll to escape the advertising funnel. 4. You can say “thank you” to a machine without making it a person. Human-like conversation doesn’t establish human feelings or rights. Both worshipping AI and treating it as an enemy distract from the people building, deploying and using it. They remain responsible. 5. AI’s democratic promise is more power in your hands. More people could gain the ability to understand complex issues, challenge experts and hold institutions accountable. That won’t happen automatically. It depends on who gets access, whose interests the tools serve and whether the benefits are widely shared. 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

1. Your next computer may be operated by your agent—not you. You describe the outcome you want. Your assistant uses the browser, software and tools to get there. 2. Your voice may become the interface. Instead of opening an app or sitting at a keyboard, you speak to your assistant wherever you are—and let it handle the task. 3. You don’t need more software. You need things done. Finding the right product, renewing insurance or arranging a return shouldn’t require your constant attention. The reward is time back. 4. You shouldn’t have to start from scratch every time. An assistant becomes more useful when it connects your email, calendar, travel and ongoing projects—bringing the relevant context to each new request. 5. A personal assistant shouldn’t be a luxury. The opportunity is to make practical, everyday help available to people who could never hire staff. Being offered an AI assistant is the beginning; making it affordable, reliable and worth trusting is the real test. 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

1. The agent becomes the interface; the device becomes secondary. Facebook’s Muse points toward a different relationship with computing: delegate a task rather than navigate an application. A persistent agent operates a computer in the cloud, while glasses, earphones or a phone provide access. Hardware doesn’t disappear, but the device becomes less important than the agent available through it. Microsoft's announcement of autopilot is the same in a work context. 2. Free AI could change the economics of the industry. Muse’s proposed transaction-funded model challenges the assumption that useful AI requires a subscription. If shopping commissions can support a free personal agent, Meta could bring delegated work to a mass audience. That puts pressure on both paid AI services and the device ecosystems through which people access them. The opportunity is substantial, though competitors can respond. 3. Responsibility for AI safety starts with its builders. Jensen Huang’s challenge, central to the discussion, is that companies claiming their products cannot be controlled should not release them. Safety requires engineering, testing and enforceable limits. Regulation can establish obligations, but it cannot substitute for the technical controls needed to meet them. 4. The fight over regulation is also about competition and liability. Global rules could protect the public, but they could also entrench established companies and encourage a compliance-based defence of harmful outcomes. Those possibilities deserve scrutiny. So do the commercial interests of regulation’s opponents: NVIDIA benefits from continued AI expansion. Neither side’s business interests automatically prove or disprove its arguments. 5. Technical control and social consequences are different questions. The conversation’s sharpest disagreement concerns what can be known before deployment. Laboratory stress tests do not necessarily describe how released products behave. Equally, controlling a product’s operation does not establish every consequence of its widespread use. The challenge is to preserve experimentation while learning from actual outcomes and holding the responsible people and companies accountable. 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

Five takeaways: Alignment is not a negotiation between beings. Models are software and data. Trust lives in the harness around them, not as a personality trait inside them. The labs own the outcome. Asking government to coordinate a slowdown is an admission of weak software. Slowing themselves to build the control plane is accountability. A legally enforced industry pause is not. The Anthropic and OpenAI/Hugging Face incidents were harness failures started by human prompts and weak boundaries. Unexpected solutions are the product working. Unauthorized consequences are the safety failure. Humans should set policy, not approve every click. Oversight at scale is software: traces, audits, escalation, and authority changes. Recursive self-improvement needs the same harness. There is no stable middle gear. Access and redistribution are not ownership. Free agents, local data-center bargains, market humanism, and robot taxes can help. None of them put a unit in her name. 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

Five Takeaways 1. AI is grown, not alive. Its capabilities emerge from training on human knowledge, but surprise and competence are not consciousness. 2. AI does not originate the intention. Humans choose the objectives, tools, permissions and environment. Calling model behavior “scheming” obscures that responsibility. 3. The danger is human misuse and poor system design. Anthropic’s incidents involved people pursuing harmful goals. The answer is stronger safeguards, disclosure and accountability—not fear of a machine personality. 4. Agency is built around the model. Memory, identity, credentials, tools and a private computer turn a model into an agent. The crucial question is who owns and controls that infrastructure. 5. Everyone wants to control scalable intelligence. Venture capital wants to own it; governments want to regulate it; platforms want to contain it. Responsibility still belongs to the grower, not the grown. 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

1. AI capability is not the danger. It is the point. The fact that AI can exceed human capability in some domains is the whole point. Speed, scale, productivity, discovery, and leverage are the reward, not the problem. 2. Risk lives in deployment, not intelligence itself. Bad answers, hallucinations, weak interfaces, poor evaluations, and fabricated outputs are product and governance failures. They do not prove that intelligence as a capability is inherently dangerous. 3. The real divide is Gates versus O’Reilly. Bill Gates now frames AI through danger, public rules, and the possibility that “this time is different.” Tim O’Reilly frames AI as a medium: useful when humans bring intention, judgment, revision, and responsibility. 4. Good AI governance should be practical, not fear-based. The right tools are transparent evaluations, disclosure, incident reporting, model and system cards, operational controls, safety limits, monitoring, and human escalation. “AI is dangerous” is not a control system. 5. Fear allocates power. If advanced AI is treated as inherently dangerous, the likely result is permissioning, compliance capture, and concentration among incumbents with the infrastructure, lawyers, cloud platforms, and government relationships to dominate the rules. 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

1. AI’s problem is not lack of usage, it is lack of public advocacy. A growing number of people already use AI in ordinary work and life, often happily and quietly. The public argument, however, is dominated by critics. That creates a false picture: the negative voices sound like the majority because the beneficiaries are mostly just getting on with using the tools. 2. The strongest pro-AI case refuses the centralization-versus-distribution binary. One side argues frontier AI is too powerful to distribute broadly, so it needs centralized control by large companies and the state. The other argues it is too powerful to centralize, so capability should diffuse through open models, local hardware, and edge systems. The better answer is both: large frontier labs to fund and push capability forward, and distributed edge AI to bring privacy, resilience, ownership, and low-cost access closer to users. 3. Metering intelligence is not sinister; it is how abundance becomes practical. AI requires huge investment in chips, data centers, power, software, routing, and product infrastructure. Metering does not mean charging for human thought. It means making the cost of access legible: which model is doing the work, what task is worth paying for, who pays, and when. Stripe and OpenRouter matter because markets cannot become abundant if nobody can price, route, govern, or pay for what is being consumed. 4. The Human Dividend has two parts: cheap access and shared ownership. AI has 2 dividends for us humans. The first dividend will be free or very cheap intelligence for individuals, schools, hospitals, and light everyday use. The second will be broader participation in the economic surplus created by AI. Access alone is not enough. If intelligence becomes a foundational input like electricity or money, then ordinary people should have some ownership claim on the infrastructure whose value they helped create. 5. Champions win permission; architects make the system work. AI needs public champions who can explain why the buildout is worth the cost. It also needs intelligent architects: people designing the identity systems, agent rails, security models, local inference, energy markets, public-feedback loops, liquidity structures, and resilience plans that make AI usable in the real world. Champions make the case. Architects make the case true. 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

Why Watermark? Claude wants everybody to know it is there. Keith Teare argues that this is exactly the wrong instinct. The issue is not whether AI touched a piece of work. The issue is whether the work is true, useful, accountable, and human-directed. Watermarking starts from suspicion A watermark assumes there is a problem to solve. Keith's objection is that Anthropic appears to be accepting the premise that AI use tarnishes the user. If Claude helped draft, structure, summarize, or edit human-origin work, why is that the fact that must be marked? The mark answers the wrong question: not whether the work is good, true, accountable, or human-directed, but whether Claude was there. Detection tools confuse use with authorship Andrew ran Keith's editorial through an AI detector and got an 86% AI score; Keith ran Saul Klein's article through one and got 100%. Neither result proves much. Andrew also admitted he uses Claude as a draft and then rewrites it. Keith described the same process: give AI the source material, ask it to surface themes, debate the frame, then rewrite. The question is not whether AI helped. The question is who is responsible for the final work. The shame belongs in the wrong place Keith accepts that shame has a place: spam factories, fake authorship, unreviewed AI output, fake evidence, and publishing something you cannot stand behind. He feels no shame in "recruiting an army of AI agents" to help accomplish his goals, provided he reads, changes, edits, and owns the output. Society is trying to attach shame to the tool itself. That is the mistake. AI should be cheap and everywhere The show turns from watermarking to access. Grok Bot at $200 a month is a sign of capability, but also a sign of scarcity. Keith argues that equality in AI is about price and reach: how cheap is it, and how widespread is it? If intelligence is valuable, the goal should be to make it free or nearly free for many use cases, not to add friction that helps the Luddite argument. Abundance requires massive investment Keith defends the scale of AI investment because demand still exceeds supply. Nvidia's half-trillion-dollar commitment, hyperscaler deals, IPOs, and AI infrastructure financing are not just market exuberance. They are the precondition for AI to reach everyone. As Keith puts it, if you want AI in the hands of an African school child, you have to build the infrastructure first. The risk is not that too much is being built. It may be that too little is being built, or that access is captured at the price and distribution layer. Bottom line: AI is good. Build enough of it for everyone. 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

1. 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. Inte… 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. 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… 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

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

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