Podcast appearances and mentions of Jack Clark

Hey, it's Alex (yeah, I'm finally back from my vacation!) What a freaking week to come back to! Just after our last episode was published, Anthropic releases Opus 5, Jensen joins X and drops the “Open Weights & AI Leadership” open letter, Kimi K3 is released the following Monday beating expectations, and then the AI hack (OpenAI model breaking sandbox and infiltrating HuggingFace) is on everyone's mind, another Open Letter, this time from over 1K employees inside the frontier AI companies all talk about pacing the pace of frontier AI development. We played with Opus 5 and Kimi K3, and had the great pleasure to chat with friends of the pod Elie Bakouch (Prime Intellect) and Philip Kiely (BaseTen) about this important open weights release, then covered our general thoughts on Opus 5, and made order of all the different open letters that came out this week. Finally we chatted with Max from Pangram about the next version of AI writing detection (their biggest yet) and finished with Zuckerbergs (also on X! what's going on with everyone joining X) op-ed on the vision of personal superintelligence for everyone. Let's dive into this (as always, all the links and sources at the end, please don't forget to sub to our podcast on your favorite podcast app!) Open Weights AIKimi K3 the king of open weights - 2.8T chonker MoE near frontier model (X, HF, Blog, Tech report)This has got to be the biggest news of this week, and maybe the open weights AI news since GLM 5.2. MoonShot came back with Kimi K3, and we haven't seen any models quite this large in the open. Even Grok 4.5 is around 1.5T, this model is nearly 2x the size. Coming in at close to 3T parameters (and 2.5terabytes of weights at MXFP4 format), this model comes in very close to frontier! This was such an important release that I invited 2 friends of the pod, Elie Bakouch (prev HuggingFace, now Prime Intellect) and Philip Kiely (Author of Inference Engineering book, BaseTen) to dive deep into what makes this special! Elie's take, from reading the tech report, there's no single secret sauce, it's a combination of already available in the open techniques. Like KDA (Kimi Delta Attention) that has been out for a while, attention residuals, NVIDIA's latent MoEs. The highlight for Elie was the scaling work they did that reported a 2.5x scaling efficiency over Kimi K2.5 (2.5 performance at the same compute)! They also skipped RoPE entirely in favor of NoPE (the report calls it No Positional Encoding) for long context.Serving 1.4TB on eight GB300s (Baseten blog)Philip's team at Baseten was a day-zero provider (we're still working on bringing this model to CW Inference, stay tuned!) so I invited him to tell us behind the scenes of hosting this beast. Philip said that just loading the weights takes about 1.5TB!! of VRAM, and that's before the KV cache allocation + 1M token windows, so they're serving it on 8 GB300s where NVL72 . Baseten worked with the vLLM and SGLang teams on kernels and he also said they contributed patches back upstream! The model was trained with MXFP4, which, unlike Nvidia's own NVFP4 is a more standard format per Philip. I enjoyed his deep dive analysis into the differences, but because of this and because they trained the model with quantization awareness, it's “only” 1.5TB vs the would-be 5-6 TB if that this model in FP16 would demand. One of the more favorite nerd snipes moments, Philip pointed out that his colleague discovered that with over 99% of the usage being cached (think harnesses that send millions of the same cached tokens back and forth), tokenization actually starts to become a bottleneck. So they released a custom “basetenkenizer” that reduces the latency to serve the first token significantly! Great job!The harness in question is very importantOne important callout with 2 evidence pieces - the way you inference this model really matters. Kimi trained K3 with preserving thinking history, so when your harness uses it, it must send back the full thinking and tool use into the API to get the best next response. If your harness strips that out, you're not getting the most intelligence out of Kimi (shoutout to Niels from HF team for pointing this out). Additionally, the Composio folks, tested K3 on 3 harnesses, Kimi Code, Hermes and Claude Code. The difference in outcome was negligible, but the different in cost and number of tokens is definitely surprising! Claude Code (as a harness only) took 9x more Kimi tokens to get the same responses! This is also why Kimi Vendor Verified exists, their own held back benchmark of how well model providers serve Kimi across different quantization, tokenizer and KV cache settings. Benchmarks and the license! Ok let's start with the ugly... this isn't MIT, not remotely. This model is suspiciously served by all providers with exactly the same price (check OpenRouter) and requires inference companies to sign a contract with Kimi (I've no internal knowledge of this except that CW folks are working on it). Not something I particularly like, but hey... we're still advancing the frontier here! Speaking of frontier, this model approaches the frontier very closely. On DeepSWE, K3 sits just behind Fable 5 and GPT-5.6 Sol at 67%, beating GPT-5.5 & Opus 4.8. On Terminal-Bench 2.1 it takes second place behind GPT 5.6 Sol! It's 4th overall on Agentic Arena, with frontend design being genuinely good across the board - 1st on Design Arena

Over 1,100 AI staffers signed a letter asking Washington to help pace frontier development. Wall Street got nervous as AI capex ballooned, Silicon Valley's backlash against Anthropic built over open weights, and the argument that this is all about recursive self-improvement either being right on the horizon, or a dead end. AI's finally expensive enough to make Wall Street nervous (The Verge) Over 1,100 staffers from AI companies, including John Schulman and OpenAI's Jakub Pachocki, sign a letter requesting the US government to "pace" AI development (Bloomberg) Signatories to the "Pacing the Frontier" statement include OpenAI's Mark Chen and Wojciech Zaremba and Anthropic's Jack Clark, Chris Olah, and Jared Kaplan, who say the world may need the option to buy time (The Verge) Anthropic faces backlash from Silicon Valley partners, founders, and researchers for competitive tactics, guardrails, and lack of support for open-weight models (WSJ) The Actual Reason Why Google "Fell Out" of the AI Race Changes Everything (The Algorithmic Bridge) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

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

Sean Farrington speaks to Jackie Jantos, CEO of popular mobile dating app Hinge, about finding romance in today's rapidly-changing digital world.Launched back in 2013, US-based Hinge has steadily grown to become one of the world's biggest mobile dating apps. As of 2025, there were 30 million users on the platform looking for romance all over the world - up from half a million just 10 years before.Hinge encouragingly bills itself as the ‘app to be deleted', and unlike many competitor apps, its focus is on creating real interactions - for example, encouraging users to like photos or prompts - rather than quickly swiping left or right.In a crowded industry worth billions of dollars, the app, owned by the American dating giant Match Group, has a difficult balancing act to maintain. It has to innovate to attract new users and make a profit, while also ensuring their users find romance and so do not have to keep using the app.Thank you to the Big Boss Interview team for their help in making this programme. The Interview brings you conversations with people shaping our world, from all over the world. The best interviews from the BBC, including episodes with music icon Chaka Khan, Anthropic co-founder Jack Clark, and entrepreneur Emma Grede. You can listen on the BBC World Service on Mondays, Wednesdays and Fridays at 0800 GMT. Or you can listen to The Interview as a podcast, out three times a week on BBC Sounds or wherever you get your podcasts. Presenter: Sean Farrington Producer: Jeevan Nerwan and Ben Cooper Editor: Damon RoseGet in touch with us on email TheInterview@bbc.co.uk and use the hashtag #TheInterviewBBC on social media.(Image: Jackie Jantos smiles as she looks to the side. She has brown hair and glasses and wears a black jumper. Credit: Stuart C. Wilson/Getty Images)

Jack Clark discusses Anthropic's regulatory fights, the possibility of recursive self-improvement, and how AI could reshape the economy.

This week, we are bringing you a conversation, recorded live at the Times Center in Manhattan last Thursday, between Jack Clark, co-founder of Anthropic, head of the Anthropic Institute and the man behind the Import AI newsletter, and our own Samuel Kimbriel. Clark opens with the uncomfortable premise: recursive self-improvement may arrive this decade — he'll name 2028 if pressed — and with it a world where AI starts designing its own successors. That cracks open choices nobody has had to make before. Which sciences do we deliberately speed up? Where do we set the dial between individual liberty and collective control when anyone can summon what used to require a nation-state? And who gets to shape the “personality”—air quotes his—of a tool that talks back?It's an engaging conversation about the big questions of our time. We hope you enjoy! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit wisdomofcrowds.live/subscribe

There’s a lot to unpack with AI right now — everything from its potential impacts on the labor market and society to more extreme questions about existential risk. Anthropic, which builds frontier models like Mythos, Fable, and Claude, is actively grappling with these issues, including whether governments should limit AI development. Just last week, the Trump administration forced Anthropic to block foreign access to its two leading models. In this episode, Odd Lots co-hosts speak with Jack Clark (co-founder and head of public benefit) and Peter McCrory (head economist) about how Anthropic approaches safety and economic risks. We talk about its preparations for recursive self-improvement, the engineers it's hiring now, and why Jack left Bloomberg to enter the early AI industry. Read more:Anthropic Lays Out Vision for How to Bolster AI Models’ SafetyMicrosoft Makes Big AI Inroads in China by Selling OpenAI Models Only Bloomberg - Business News, Stock Markets, Finance, Breaking & World News subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at bloomberg.com/subscriptions/oddlots Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.

“Right now, it's like the AI industry has a gas pedal, but it doesn't have a brake pedal in the car. And what we're saying is we want to build that brake pedal so we in the world have an option. In the future, you might say: ‘Let's get all of the benefits we can for, say, biology and medical research, and let's take a pause on AI research, where we can absorb the societal changes.'” Faisal Islam speaks to Jack Clark, co-founder of Anthropic, one of the companies at the forefront of the artificial intelligence revolution and the maker of the Claude chatbot. Jack says AI systems are becoming dramatically more capable, changing how work happens even inside Anthropic itself. He argues that artificial intelligence could accelerate scientific discovery, reshape industries and transform economies. But he also warns that increasingly powerful AI systems will require new forms of oversight and control. As these technologies become more capable, he argues that governments and society need mechanisms to slow development if it moves too far, too fast. The Interview brings you conversations with people shaping our world, from all over the world. The best interviews from the BBC, including episodes with Sundar Pichai and Julia Gillard. You can listen on the BBC World Service on Mondays, Wednesdays and Fridays at 0800 GMT. Or you can listen to The Interview as a podcast, out three times a week on BBC Sounds or wherever you get your podcasts. Presenter: Faisal Islam Producer: Osman Iqbal Editor: Damon Rose and Justine Lang(Image:Jack Clark. Credit: Getty)

AI, Access to Justice and the Future of Human-Centred Leadership We chat to Pip Wilson, Co-Founder and CEO of amicable As AI becomes more embedded in our workplaces and daily lives, leaders are wrestling with a difficult question: how do we embrace the benefits of technology without losing sight of the people it's supposed to serve? In this episode of Starts at the Top, we speak to Pip Wilson, co-founder and CEO of amicable, the UK-based legal services business that is transforming how people navigate separation and divorce. Pip shares how amicable combines technology, AI and human expertise to make one of life's most challenging experiences kinder, more affordable and less adversarial. Pip's journey spans successful tech entrepreneurship, angel investing and social impact. Together, we explore what happens when technology is designed around human needs rather than professional systems, and why the most successful businesses of the future may be those that combine commercial success with social purpose. In this episode, we discuss: How amicable was born from a deeply personal experience of divorce and a desire to create a better alternative. Why the traditional legal system often makes separation harder, more expensive and more stressful than it needs to be. How technology and AI can improve access to justice while keeping people at the centre of the process. The opportunities and limitations of AI in emotionally complex situations. Why transparency, affordability and user-centred design matter in professional services. The future of relationship support, from cohabitation agreements to co-parenting and life after divorce. Pip's philosophy as an entrepreneur, angel investor and B Corp leader. Why businesses that combine social purpose with commercial sustainability are best placed to thrive in the future. As world leaders, governments and organisations debate how AI should be regulated, Zoe and Paul explore a more immediate leadership challenge: what does it actually mean to stay in control of AI? They discuss: Anthropic co-founder Jack Clark's proposal for a permanent "Cobra for AI" capability within government. Pope Leo's call for AI to be developed in service of human dignity rather than domination. Whether increasing reliance on AI tools could affect our confidence in writing, thinking and decision-making. The tension between leaders wanting to realise AI's benefits quickly and employees who need time, support and psychological safety to adapt. The warning signs that organisations may be moving too quickly towards automation. These themes provide the perfect backdrop to our conversation with Pip, whose work sits at the intersection of AI, ethics, human dignity and innovation.   Show notes About amicable Visit the amicable website , or book a 15-minute call with amicable Zoe and Paul discussed: Jack Clark on the need for a permanent "Cobra for AI" capability https://www.bbc.co.uk/news/articles/cx2124z7g45o The Pope's Eclyical on AI https://www.bbc.com/news/articles/cedppn6002jo Kate Waters on AI, writing confidence and authorship https://www.linkedin.com/posts/katewaterscomms_usually-im-lucky-if-my-linkedin-posts-get-share-7468238726322696192-ltx2/ Please leave us a review if you enjoy what you hear! Editing and production - Paul Thomas Music by Joseph McDade - https://josephmcdade.com/music Full transcript of this episode (srt file) Full transcript of this episode (.txt file) Transcripts are also available through your podcast app.  

“AI represents successful capitalism. What we have alongside that is unsuccessful government. Government has no plan — left or right.” — Keith Teare It's the 82nd anniversary of D-Day. On June 6, 1944, there was an unambiguous end game — the defeat of Nazi Germany. But today, end games are more controversial, especially in terms of harnessing the AI revolution to benefit everyone. For Keith Teare, publisher of That Was the Week, the AI end game requires an “Institute of the Future.” Everyone from Bernie Sanders and Elizabeth Warren to Elon Musk and Sam Altman should hammer out a plan to harness AI for the benefit of society. Keith offers the internet governance organisation ICANN as a model for this institute. It will shape the future for all of our benefit, he promises. So a D-Day for AI? I'm sceptical of this type of Brave New World-style technocracy. Firstly, Sanders, Warren, Musk and Altman agree on very little. And Musk and Altman hate each other. I'm also dubious that AI will or can benefit everyone. As Keith notes, some professions — teachers, for example — will be decimated by AI. Where I agree with Keith, however, is that we need a new politics for this new age. Political parties, rather than institutes, of the future. Innovation rather than ICANN. Five Takeaways •       The Anthropic IPO Slip — and Why SpaceX Now Looks Small: Anthropic accidentally filed for its IPO this week — what the New York Times described as a slip. The terms of SpaceX's unconventional $75 billion IPO were also revealed. Keith's observation: SpaceX now looks small by comparison. He tried to buy SpaceX shares this week through his brokerage and expects to get none — the demand will be way bigger than the supply, and the price will go up from the offering. San Francisco real estate is already feeling the Cerebras effect: 800 employees are now millionaires. The three big IPOs — Anthropic, OpenAI, SpaceX — will compound that on a much larger scale. •       Successful Capitalism, Unsuccessful Government: Keith's framework for the week: AI is capitalism working. Resources are directed to money-making opportunities via the profit motive, which coincides with innovation and, at least in the short term, creates lots of jobs. That is successful capitalism. Alongside it: unsuccessful government. The Trump administration went from hands-off to requiring all AI models to be submitted for a 30-day assessment before launch — in the same week. No plan. No endgame. Everyone has an opinion. Nobody states what outcome they want. •       Keith's PhD: Why Capitalism Is Never Static: Andrew challenges Keith's authority to pronounce on these matters. Keith reveals: he has a PhD from the University of Kent in Canterbury — on why capitalism is never static, and why new entrants always eclipse what went before. Andrew: that was the 1970s, Keith. Does a fifty-year-old PhD give you authority? Keith: it's a useless criticism. You could say that to anyone about anything. The exchange is revealing: the argument is not about credentials but about frameworks. And Keith's framework — capitalism as dynamic, government as static — has at least the virtue of consistency. •       Credit to Bernie and Warren: At Least They're Having the Conversation: Andrew expects Keith to trash Bernie Sanders (50% government ownership of AI companies) and Elizabeth Warren (high taxation of AI profits). Keith surprises him: at least they're having the conversation. His criticism is not that they're wrong to want wealth distribution but that their framing — tax, centralise, spend — is unattractive to most people and captured by the interests of the old economy: teachers' unions, trade unions, legacy coalitions that can't think freely about a future without teachers as they currently exist. •       An ICANN for AI: Keith's One Concrete Prescription: Andrew pushes Keith for one concrete thing politicians should do this year. Keith's answer: create an Institute for the Future. Bring Musk, Altman, Amodei, Sanders, Warren, and everyone else to the table with a clear mandate — define the future you want, agree actual outcomes, seek governmental authority to implement them. His model: ICANN, the global internet governance body, which disagrees constantly and still makes decisions. Andrew's verdict: Keith wants to create an ICANN for society. Interesting idea. History's jury is out. About the Guest Keith Teare is a British-American entrepreneur, investor, and publisher of the That Was the Week newsletter. He is a co-founder of TechCrunch and Andrew's regular TWTW co-host. He holds a PhD from the University of Kent. References: •       That Was the Week by Keith Teare. •       Noah Smith, “We Need Liberal Nationalism to Come Back” — referenced in the conversation. •       The Economist, “American Capitalism Has Taken an Apocalyptic Turn” — referenced in the conversation. •       Ben Thompson on Google becoming a capital company; John Battelle on Google reinventing itself from search to data infrastructure — both referenced. •       ICANN — the Internet Corporation for Assigned Names and Numbers, Keith's model for AI governance. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,900 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackYouTubeApple PodcastsSpotify Chapters: (00:31) - Introduction: D-Day, June 6, and the Anthropic IPO slip (02:26) - What is the endgame? AI is no longer just a tech story (03:46) - Successful capitalism, unsuccessful government (04:49) - Atomisation and the absence of proper conversation (05:33) - Andrew challenges Keith's authority (06:42) - Keith's PhD: capitalism is never static (07:13) - Bernie Sanders: 50% ownership of AI companies (07:30) - At least they're having the conversation (07:55) - The old economy framing: tax, centralise, spend (08:25) - What gives Keith the authority? (09:00) - Jack Clark and the call to slow down (10:00) - The Trump administration at war with itself (15:00) - Andrew Yang and universal capital distribution (20:00) - ...

Morse code transcription: vvv vvv Man shot with crossbow at the University of Surrey, say police Burnham says he would seek to enter any Labour leadership contest Is the Lifetime ISA fit for purpose in London Henry Nowak deserves legacy that goes beyond tragedy, says PM Royal Navy air crew killed in Devon helicopter crash named Kate hugs mum ringing end of cancer treatment bell at hospital Conflict over identity politics could lead to civil war in the long term, Kemi Badenoch says Andrew was sub letting Royal Lodge cottages, NAO report reveals Zeynab Javadli Ex wife of Dubai rulers nephew in custody, prosecutors say Anthropic co founder Jack Clark warns AI needs a brake pedal

Morse code transcription: vvv vvv Zeynab Javadli Ex wife of Dubai rulers nephew in custody, prosecutors say Man shot with crossbow at the University of Surrey, say police Royal Navy air crew killed in Devon helicopter crash named Henry Nowak deserves legacy that goes beyond tragedy, says PM Kate hugs mum ringing end of cancer treatment bell at hospital Anthropic co founder Jack Clark warns AI needs a brake pedal Andrew was sub letting Royal Lodge cottages, NAO report reveals Is the Lifetime ISA fit for purpose in London Conflict over identity politics could lead to civil war in the long term, Kemi Badenoch says Burnham says he would seek to enter any Labour leadership contest

Morse code transcription: vvv vvv Is the Lifetime ISA fit for purpose in London Zeynab Javadli Ex wife of Dubai rulers nephew in custody, prosecutors say Burnham says he would seek to enter any Labour leadership contest Kate hugs mum ringing end of cancer treatment bell at hospital Man shot with crossbow at the University of Surrey, say police Andrew was sub letting Royal Lodge cottages, NAO report reveals Conflict over identity politics could lead to civil war in the long term, Kemi Badenoch says Royal Navy air crew killed in Devon helicopter crash named Anthropic co founder Jack Clark warns AI needs a brake pedal Henry Nowak deserves legacy that goes beyond tragedy, says PM

Morse code transcription: vvv vvv Zeynab Javadli Ex wife of Dubai rulers nephew in custody, prosecutors say Is the Lifetime ISA fit for purpose in London Henry Nowak deserves legacy that goes beyond tragedy, says PM Royal Navy air crew killed in Devon helicopter crash named Kate hugs mum ringing end of cancer treatment bell at hospital Anthropic co founder Jack Clark warns AI needs a brake pedal Andrew was sub letting Royal Lodge cottages, NAO report reveals Conflict over identity politics could lead to civil war in the long term, Kemi Badenoch says Burnham says he would seek to enter any Labour leadership contest Man shot with crossbow at the University of Surrey, say police

Senate Republicans rejected multiple efforts on Thursday to formally kill President Trump's push for a $1.8 billion fund to compensate people who claim they were victimized by the government. Plus, Anthropic co-founder Jack Clark speaks to Anderson about the development of AI.  Hear why he's warning the world to go a little slower on the technology.  Learn more about your ad choices. Visit podcastchoices.com/adchoices

One of the biggest artificial intelligence developers, Anthropic has warned that the latest models might escape human control. It has proposed a co-ordinated global slowdown on building AI systems. One of the firm's co-founders, Jack Clark has been speaking to BBC.Also in the programme: the latest from Russia's flagship economic forum in Saint Petersburg; and how an outsider reached the French Open tennis final.(Photo: Anthropic logo. Credit: Dado Ruvic/Reuters)

Jack Clark, the founder of AI company Anthropic has said AI is nearing the point of developing itself without human input and that Government policies need to keep control of AI.To discuss this, Matt is joined by Adrian Weckler, tech editor, Irish and Sunday Independent and Puneet Kukreja, Head of Cyber, EY Ireland.To listen to the full conversation, press the 'play' button on this page.

AI-bedrijf Anthropic pleit voor een wereldwijde mogelijkheid om de ontwikkeling van de krachtigste AI te kunnen pauzeren. Volgens medeoprichter Jack Clark heeft de AI-industrie nu wel een gaspedaal, maar geen rempedaal, terwijl systemen straks zichzelf kunnen verbeteren zonder tussenkomst van de mens. Stijn Goossens bespreekt het in deze Tech Update. Anthropic, het bedrijf achter chatbot Claude, roept andere AI-labs op om een gecoördineerde, tijdelijke pauze of vertraging van de ontwikkeling van de meest geavanceerde modellen mogelijk te maken. In een gesprek met de BBC legde medeoprichter en beleidshoofd Jack Clark uit waarom overheden de optie moeten hebben om in te grijpen. Op dit moment ziet het bedrijf geen reden om zo'n noodrem te gebruiken, maar het wil dat die mogelijkheid er in de toekomst wel is. De urgentie zit volgens Clark in het tempo waarmee AI zichzelf versnelt. Hij stelde dat de code van Claude inmiddels voor ongeveer 80 procent door het systeem zelf is geschreven, en dat dit binnen twee jaar 100 procent zou kunnen zijn. Dan kan AI zichzelf in de praktijk verbeteren zonder dat er een mens aan te pas komt, iets wat onderzoekers "recursieve zelfverbetering" noemen. Clark vergeleek de situatie met een auto die wel een gaspedaal heeft, maar geen rem. Verder in deze Tech Update Amsterdam gunt IT-diensten aan KPN om digitaal soevereiner te worden. Diensten als hosting en digitale beveiliging die eerder bij Amerikaanse techbedrijven lagen, gaan naar KPN, dat met het Duitse Schwarz Digits werkt aan een Europese cloud die naar verwachting medio 2027 in Nederland beschikbaar komt. Het is de tweede Amsterdamse aanbesteding waarin zeggenschap over kritieke digitale infrastructuur expliciet meeweegt, mede ingegeven door eerdere ophef rond DigiD-leverancier Solvinity. Straks in De Schaal van Hebben: Anker Soundcore Sleep A30See omnystudio.com/listener for privacy information.

One of the world's biggest AI companies is arguing for a slowdown on the development of artificial intelligence.Anthropic Co-founder Jack Clark is warning that humanity is coming close to losing control of the technology as AI is starting to build itself and write its own codes.Essentially, it may soon no longer need human input.

When Jack Clark talks about building 180 Water Franchising, he focuses on one core idea: creating a replicable model in an industry that has never had one. The water well business has always been built around owner-operators — small crews, no national presence, and a retiring workforce with no clear succession path. Jack saw that gap and built the first franchise in the industry to fill it. In this episode of the DealQuest Podcast, Corey Kupfer sits down with Jack Clark, founder and owner of 180 Water Franchising, to discuss what it actually takes to franchise a skilled trades business from the ground up — and why this particular market is uniquely positioned for franchise growth. Jack breaks down the full investment and fee structure for franchisees, from the approximately $250,000 startup cost covering a fully stocked service truck to the 6% gross sales royalty, 2% brand fund, and $45,000 franchise fee. He explains why the first franchisee in each new state gets that fee waived, and why suppliers are set up with 90-day terms to ease early operations. The conversation also covers the internal dynamics of running a franchise network, including Jack's weekly Thursday calls with all franchisees, why the direction of learning inside the network has shifted from franchisor to franchisees, and what he calls the "stupid rule policy" — the principle that if a rule has to exist, the underlying system needs to change. Corey and Jack also discuss the emotional and financial reality of investing ahead of growth, including the bittersweet experience of outgrowing your original banker and building an entirely new team to support the next level of the business. This episode is packed with practical insights for entrepreneurs considering franchising as a growth vehicle, skilled trades business owners wondering whether their operation has a replicable system inside it, and anyone evaluating franchise investment opportunities in industries outside the traditional food and service categories. WHAT YOU'LL LEARN Why the water well industry is structurally positioned for franchise growth right now How Jack identified the moment his business became genuinely franchisable The full investment, fee structure, and territory model for 180 Water franchisees Why most franchisees stop worrying about having enough work within their first thirty days What the "stupid rule policy" means in practice and why it matters for franchisee satisfaction How investing ahead of growth temporarily dips profits — and why that's unavoidable Why being first in an untapped industry is a competitive moat, not a warning sign THE FIRST FRANCHISE IN THE WATER WELL INDUSTRY Jack confirmed it directly in this conversation: "No one's ever franchised the water well industry. I think a lot of guys have talked about it, but it's one of those things that until you really start to unfold it, you don't really realize how many layers there are to it." 180 Water now operates in five states with ten franchisees and is expanding nationally. When demand outpaced their Texas truck supplier, Jack didn't accept the bottleneck — he started manufacturing his own service trucks under the PumpEx Voice brand, turning a supply constraint into an entirely new business line. FOR MORE ON THIS EPISODE: https://www.coreykupfer.com/blog/jackclark FOR MORE ON JACK CLARK Company: https://180waterfranchise.com FOR MORE ON COREY KUPFER https://www.linkedin.com/in/coreykupfer/ https://www.coreykupfer.com/ Corey Kupfer is an expert strategist, negotiator, and dealmaker with more than 35 years of professional deal-making and negotiating experience. Corey is a successful entrepreneur, attorney, consultant, author, and professional speaker deeply passionate about deal-driven growth. He is also the creator and host of the DealQuest Podcast. Get deal-ready with the DealQuest Podcast with Corey Kupfer, where entrepreneurs and business leaders share insights, challenges, and success stories around deal-driven growth strategies. The show covers mergers and acquisitions, capital raising, strategic alliances, joint ventures, franchising, and more. Episode Highlights with Timestamps [00:00:00] - Introduction: Jack Clark and 180 Water Franchising [00:04:08] - Buying a drill rig sophomore year of college and never looking back [00:09:24] - Running four drill rigs and thirty-five employees — and being miserable [00:19:45] - Finding the first franchisee through Ranch World Ads [00:21:25] - Ten franchisees across five states and manufacturing their own trucks [00:27:37] - Why concerns about getting enough work disappear within the first thirty days[00:29:57] - Staffing ahead of growth and building a whole new team [00:46:45] - The "stupid rule policy" and why Jack would have been a terrible franchisee Guest Bio Jack Clark is the founder and owner of 180 Water Franchising, the first and only water well franchise company in the United States. After scaling the business to four drill rigs and thirty-five employees, he recognized that the pump service side of his operation had a genuinely replicable system — and launched 180 Water Franchising to bring it to the industry. 180 Water Franchising now operates in five states with ten franchisees and is expanding nationally. Jack also founded PumpEx Voice, a manufacturing company producing service trucks for the franchise network. Host Bio Corey Kupfer is an expert strategist, negotiator, and dealmaker with more than 35 years of professional deal-making and negotiating experience. Corey is a successful entrepreneur, attorney, consultant, author, and professional speaker deeply passionate about deal-driven growth. He is the creator and host of the DealQuest Podcast. Related Episodes: Episode 333 - Greg Mohr: Franchising as a Path to Financial Freedom and Wealth Building Episode 329 - Cliff Nonnenmacher: How Franchise Brokers Evaluate Systems and Match BuyersEpisode 330 - Pete Mohr: Building Enterprise Value and Exit Readiness in a Service Business Keywords/Tags: water well franchise, 180 Water Franchising, Jack Clark, how to franchise a skilled trades business, franchising a service business, blue collar franchise opportunity, skilled trades franchise, franchise investment, franchise fee structure, water well industry, franchise model for entrepreneurs, franchisee success, service business scaling, first franchise in an industry, PumpEx Voice, franchise system design, franchise territory, home services franchise, trades business growth

Why is one of AI's most powerful insiders scared of what he's building? Who's really in charge of the technology reshaping our world? Is it too late for governments to regulate it? Rory and Matt Clifford are joined by Jack Clark, Co-Founder of Anthropic, to answer all these questions and more. __________ Search IG.com to find out more and/or Look for IG in your app store. __________ Instagram: ⁠@restispolitics⁠ Twitter: ⁠@restispolitics⁠ Email: ⁠therestispolitics@goalhanger.com⁠ __________ Social Producer: Celine Charles Video Editor: Josh Smith Assistant Producer: Daisy Alston-Horne Senior Producer: Nicole Maslen General Manager: Tom Whiter Learn more about your ad choices. Visit podcastchoices.com/adchoices

Anthropic co-founder Jack Clark joins The Observer to reveal why he believes there is a chance we will see human-level AI by 2028. From how AI is already changing the job market to a historic meeting at the Vatican, he explains why we must prepare for a future where technology forces us to rethink what it means to be human. Hosted on Acast. See acast.com/privacy for more information.

In this episode of the Business Legacy Podcast, Paul Dio sits down with Jack Clark, founder of 180 Water, to discuss how he transformed a traditional water well service company into a rapidly growing franchise model focused on clean, reliable drinking water across rural America. Jack shares his journey from growing up on a Montana ranch to building a scalable business in one of the most overlooked essential industries in the country. What started as hands-on work in the water well industry evolved into a mission-driven company helping entrepreneurs build sustainable local businesses while modernizing an aging trade. Throughout the conversation, Jack breaks down the operational side of scaling a service business, the importance of systems and SOPs, and how innovation often comes from empowering independent operators closest to the work. From custom-built service trucks to simplifying installation processes, 180 Water is creating efficiencies in an industry that has historically resisted change. The episode also explores the deeper legacy angle behind skilled trades, mentorship, and preserving institutional knowledge before an entire generation of water well professionals retires. For entrepreneurs, this conversation is a reminder that some of the greatest opportunities still exist in underserved industries where reliability, relationships, and execution matter most.   Timestamps 00:01:08 – Introduction to Jack Clark and 180 Water 00:01:31 – How 180 Water Started 00:02:03 – Choosing Franchise Locations 00:02:49 – Jack's Background in the Water Well Industry 00:05:45 – Why the Franchise Model Works 00:06:06 – Building SOPs and Scalable Processes 00:08:04 – The Importance of Trusting Your Gut in Business 00:08:54 – The Most Rewarding Part of Building 180 Water 00:10:07 – Franchise-Driven Innovation and Product Development 00:11:34 – Challenges Facing the Water Well Industry 00:13:08 – What Jack Is Most Excited About Moving Forward 00:13:45 – How to Learn More About 180 Water   Episode Resources Learn how Jack and the team at 180 Water are modernizing the water well industry through scalable systems, skilled trades, and franchise-driven innovation: https://180waterfranchising.com Legacy Podcast: For more information about the Legacy Podcast and its co-hosts, visit https://businesslegacypodcast.com Leave a Review: If you enjoyed the episode, leave a review and rating on your preferred podcast platform. For more information: Visit https://businesslegacypodcast.com to access the show notes and additional resources on the episode.  

The second week of Musk v. OpenAI delivered texts, secret Tesla AI plots, and backstage chaos around Sam Altman's 2023 firing. Paul and Mike also break down Coinbase's AI-native restructuring memo, the White House's very brief flirtation with model vetting, Anthropic co-founder Jack Clark's prediction that AI will autonomously train its own successors by 2028, and the bizarre Anthropic-SpaceX compute deal that emerged from out of nowhere. Rapid fire covers GPT-5.5 Instant, Claude Managed Agents updates, Sierra's $950M raise, and more. Show Notes: Access the show notes and show links here AI-Pulse Survey: Fill out this week's AI-Pulse Survey here. Timestamps: 00:00:00 — Intro 00:05:37 — Musk v. OpenAI Round 2 00:23:27 — Coinbase AI Layoffs 00:33:09 — AI "Soft Nationalization" 00:47:10 — State of AI for Business Report Preview 00:54:14 — xAI Folds Into SpaceX, Does Compute Deal with Anthropic 01:00:49 — Has Recursive Self-Improvement Arrived? 01:09:38 — Anthropic and OpenAI Enterprise Joint Ventures 01:15:11 — Stripe's New Forward Deployed AI Accelerator Role 01:20:48 — AI Use Case Spotlight 01:24:15 — AI Product and Funding Updates This episode is brought to you by AI Academy by SmarterX. AI Academy is your gateway to personalized AI learning for professionals and teams. Discover our new on-demand courses, live classes, certifications, and a smarter way to master AI. Learn more here. Visit our website Receive our weekly newsletter Join our community: Slack Community LinkedIn Twitter Instagram Facebook YouTube Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy 

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Nintendo raised the Switch 2 price to $500 amid a global memory shortage. ShinyHunters forced Canvas offline during finals season. Researchers found 5,000+ insecure vibe-coded apps, Mozilla credits Mythos for 423 Firefox bug fixes in April, and France escalates its Musk probe. Nintendo says it will increase the price of the Switch 2 globally on September 1, from $450 to $500 in the US, and the price of the original Switch in Japan (Bloomberg) Instructure disables its Canvas edtech platform, used by thousands of schools, universities, and companies, amid a data extortion attack claimed by ShinyHunters (Krebs on Security) Researchers: 5,000+ web apps built using AI coding tools like Lovable, Base44, and Replit have little to no authentication, and ~40% exposed sensitive data (Wired) Mozilla says Anthropic's Mythos Preview and other AI models helped it identify and ship 423 Firefox security bug fixes in April, compared to 31 a year earlier (TechCrunch) French prosecutors escalate an investigation into Elon Musk and X, focused on alleged algorithmic manipulation and sexual deepfakes, to a criminal probe (CNBC) Longreads Anthropic co-founder Jack Clark explains why there's a 60%+ chance of AI systems autonomously building their successors by 2029 and the consequences of automated AI R&D (Import AI) How Delta SkyMiles and airline loyalty programs turned carriers into fintech companies with wings, and why most airlines couldn't survive without them (NY Mag) Learn more about your ad choices. Visit megaphone.fm/adchoices

The unified class project of billionaires is doing to white collar workers what globalization and neoliberalism did to blue collar workers. And artificial intelligence is only exacerbating this trend. Derek Thompson is a contributing writer for The Atlantic, co-author of “Abundance,” and has written extensively about the political power of the wealthy. He joins Chris Hayes to kick off our new special miniseries, The AI End Game: Power, Profit and Progress. Sign up for MS NOW Premium on Apple Podcasts to listen to this show and other MS podcasts without ads. You'll also get exclusive bonus content from this and other shows. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Artificial intelligence is evolving faster than ever - and the debate over AI safety, regulation, and control is intensifying. In this episode of Ways to Change the World, Krishnan Guru-Murthy speaks to Jack Clark, co-founder and Head of Policy at Anthropic, the company behind the Claude AI systems. A former journalist turned AI insider, Clark has been at the centre of some of the biggest debates shaping the future of this technology - from safety and regulation to the race between innovation and control.They discuss Clark's journey from reporting on AI to building it, his decision to leave OpenAI over concerns about safety, and the growing fear that powerful systems are outpacing our ability to manage them. From warning governments at the UN to grappling with the risks as a father, Clark reflects on the tension at the heart of his work: what does it mean to build something you believe could be dangerous?

Berlin-based selector Jack Clark is an intuitive performer defined by a deep-seated obsession with the groove. A co-founder of ZappedRecords, Jack's sound occupies a potent intersection between the swing of UK Garage, the stripped-back precision of Romanian Minimal, and the driving, rhythmic soul of early 2000s UK Tech House. Equally adept at navigating a dark afterhours basement or an expansive terrace, Jack has become a fixture at Berlin institutions including Sisyphos, Kater Blau, Renate, and Golden Gate. His performances are a testament to his technical versatility; he is a dedicated vinyl enthusiast who frequently expands his sonic palette across a four-deck setup, allowing for intricate, layered journeys that bridge the gap between analog heritage and modern underground grit. Renowned for his ability to read any room, Jack delivers seamless auditory voyages that prioritise syncopated percussion and bass-heavy weight—reaffirming his status as a vital voice in the contemporary electronic scene. Keep an eye on Jack Clark https://instagram.com/clark_rubber https://soundcloud.com/olshady Listen to more electronic music on Intaresu https://intaresu.com

We talk with Anthropic co-founder Jack Clark and Chief Economist at Redfin Daryl Fairweather about two of the biggest issues of our time: AI and housing. We have been crisscrossing America doing live shows to help promote the new Planet Money book. In each city, we've been doing interviews with special guests. And since we won't be able to make it to every city in America (or most cities) we wanted to bring the tour to you! Live show tour and book info. / Subscribe to Planet Money+Listen free: Apple Podcasts, Spotify, the NPR app or anywhere you get podcasts.Facebook / Instagram / TikTok / Our weekly Newsletter.This episode of Planet Money was edited and produced by Eric Mennel and Emma Peaslee. It was fact checked by Sierra Juarez. It was engineered by Robert Rodriguez and Kwesi Lee. Alex Goldmark is Planet Money's executive producer. See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

Would you show up to compete in a Formula One race in a bike?

In this episode, crypto veteran Mark Jeffrey takes us from the early days of Bitcoin to the cutting edge of Bittensor. With roots tracing back to the original computer and dot-com boom. Swap now on THORChain https://swap.thorchain.org/ without KYC or limits! https://x.com/markjeffrey https://stillcorecapital.com/ TL;DR: Preliminary talks have begun between THORChain and BitTensor about adding a native $TAO pool, enabling permissionless $BTC-to-$TAO swaps for the first time Mark Jeffrey (Stillcore Capital partner, serial entrepreneur) calls BitTensor "the third great coin" alongside Bitcoin and Ethereum for making mining programmable BitTensor subnets are producing AI products at 1/10 the cost of centralized alternatives, with Templar's Covenant-72B catching the attention of Anthropic co-founder Jack Clark and NVIDIA CEO Jensen Huang $TAO mirrors Bitcoin's tokenomics (21 million cap, halving schedule, fair launch) and is currently around $300, tracking Bitcoin's 2013 price trajectory Swap interface beta now includes custom memo, bonding, and $TCY staking, with the new website targeting April 17 You can find Rayyyk's full write-up on X: https://x.com/raynalytics/status/2041165663969198420 THORChain is a decentralized cross-chain liquidity protocol that lets users swap assets directly between blockchains without wrapping or using centralized exchanges. Its app layer ecosystem means developers can build decentralized apps that tap directly into liquidity across chains. Unlike most platforms, it offers real ownership of your assets, deep liquidity, and fast swaps in one seamless network. To learn more about THORChain, check out more videos: https://www.youtube.com/watch?v=eMbeCjNJ5Eo https://www.youtube.com/watch?v=4M_4N9-3ZUo https://www.youtube.com/watch?v=zzHXrsaWT-w https://www.youtube.com/watch?v=Y5v9XiXAJ7g Swap now on THORChain https://swap.thorchain.org/ without KYC or limits!

Is AI actually different this time or is it just another overhyped technology cycle? In this episode of Building the Billion Dollar Business, financial advisor coach Ray Sclafani makes the case that for wealth management professionals, artificial intelligence is not a trend to wait out. It is a fundamental shift in how advice is delivered, how clients experience service, and how advisory firms build competitive advantage.What you'll learn in this episodeWhy AI is different from past disruptions like robo advisors and discount brokerage — and what that means for your practiceHow Know Your Client (KYC) is evolving from a compliance requirement into a strategic data asset in an AI-driven worldThe three-part AI roadmap every advisory firm should follow: learn, apply, redesignWhich AI tools are most relevant for financial advisors right now, including Microsoft Copilot, Jump.ai, TaxStatus, and Advice.aiWhat agentic AI is, how it differs from a chatbot, and why it matters for your firm's future workflowThe compliance and fiduciary considerations every advisor must understand before deploying AI tools with client dataHow to lead your team through AI adoption as a behavior change, not just a software rolloutCoaching questions for reflectionWhat is one workflow in your business today that is inefficient, repetitive, or dependent on one person — and how could AI improve it in the next 30 days?Where are you and your team under-invested in learning, and what would change in 12 weeks if you committed to one AI course or certificate program together?Courses and certificate programs to followGoogle AI Essentials – for foundational AI skills and a beginner certificate Google AI Professional Certificate – includes free access offers for eligible small businesses Microsoft Learn AI Learning Hub – free learning paths AWS Learn About AI – AWS AI learning resources DeepLearning.AI – short courses on agentic AI, multi-agent systems, and AI agents in LangGraph Anthropic AI Fluency – AI fluency and Claude for Work resources OpenAI Academy – plus ChatGPT at Work resources Newsletters to followOne Useful Thing by Ethan Mollick – practical, research-based thinking on AI and work Ben's Bites – quick daily AI news and product updates Latent Space – a more technical view of AI engineering and agents Import AI by Jack Clark – serious analysis of research and policy The Rundown AI – broad daily tracking of tools and newsBuilding the Billion Dollar Business is hosted by Ray Sclafani, founder and CEO of ClientWise, the financial services industry's leading executive coaching and team development firm for elite advisors and wealth management teams.Questions Financial Advisors Often AskQ: How are most financial advisors using AI right now?A: According to Schwab's latest RIA study, 63% of RIAs are already using AI in some capacity, but most are still in the early innings. The majority are using it mainly for administrative tasks like note-taking and drafting emails. In other words, the industry has started moving, but most firms have not yet made the jump from experimentation to real redesign of how they work.Q: What AI tools should financial advisors start with?A: Start with narrow use cases that save time and improve quality. Practical starting points include AI tools for meeting prep, note summarization, drafting follow-up emails, CRM cleanup, task extraction, pre-meeting briefing packets for clients, client segmentation analysis, internal knowledge search, and first drafts of planning observations. Microsoft Copilot, Jump.ai, and Zox are tools worth exploring at this stage. For planning-adjacent workflows, TaxStatus.com provides IRS-sourced client data to advisors and tax professionals, and Advice.ai is positioning itself around AI-powered analysis for complex multi-generational wealth planning.Q: What are the compliance and fiduciary risks of using AI as a financial advisor?A: If you are using public AI tools, you must be thoughtful about what information you put into them. Client data, personally identifiable information, and anything confidential should not go into tools that have not already been approved by your firm or compliance team. The US SEC has already issued guidance making it clear that advisors are responsible for how they use AI, including how client information is handled, how outputs are supervised, and how advice is delivered. This ties directly to your fiduciary duty. Always understand where your data is stored, know what is being retained, and always have a human reviewing the output before it touches the client.Q: What is agentic AI and why does it matter for advisory firms?A: An AI agent is not just a chatbot that answers questions. An agent is software that can reason through a goal, use tools, take actions, and sometimes coordinate steps with limited supervision. Think of an agent as a digital worker assigned to a job with rules, tools, and guardrails. In the future, we will start seeing multiple agents interact with each other, and then a convergence of those agents. OpenAI and Anthropic are both actively moving from chat to action, meaning these systems will increasingly be able to operate tools, workflows, forms, files, and systems — not just answer questions.Q: Will AI replace financial advisors?A: No — but the role of the advisor will shift. As information becomes more accessible and tools to analyze data become more available, advisors will move from being gatekeepers to being guides. Less about explaining products, more about making sense of them. Less of an isolated expert, more of a builder of trust, accountability, and community around a client's financial life. Research from Cerulli found that human advice remains clearly preferred over online-only advice, particularly among older clients. The future is not about choosing between human and AI — it is about enhancing humanity with AI.Find Ray and the ClientWise Team on the ClientWise website or LinkedIn |

When was the last time a news headline about AI actually told you something true?George K. and George A. recorded this one from opposite sides of the planet — George K. fresh off RSA in San Francisco, George A. embedded at a global trust and safety conference in London. The distance didn't slow them down.This month's System Check has a theme: we're living inside a story that powerful institutions are writing for us, and most of us aren't stopping to ask who's holding the pen.Meta and YouTube just lost a landmark lawsuit — not over what they published, but over how they designed their products to keep you hooked. The legal strategy that finally worked was the one used against Big Tobacco. Meanwhile, 82% of journalists now use some form of AI tool in their work. The people covering AI are increasingly shaped by it. The snake is eating its tail.The arms race math doesn't add up either. Forty billion dollar bridge loans. Circular investments. Credit-based bets assuming a revenue base that doesn't yet exist. And somewhere in rural Mississippi, kids are developing breathing problems because gas turbines got trucked in to power a datacenter the community never voted for.The question running underneath all of it: are we making decisions based on outcomes, or based on vibes? And if it's vibes — whose vibes are they, and how did they get there?Mentioned: Meta and YouTube verdict news coverage Center for Humane Technology's podcast “Your Undivided Attention” episode on the Meta and YouTube lawsuit verdicts Ed Zitron's recent monologue Research into how media covers AI UK Study on AI media coverage Muck Rack's 2026 State of Journalism Report WSJ: CFOs expect to reduce headcount because of AI Anthropic co-founder Jack Clark on not being able to idle AI systems Iran War affects world helium supply, creating semiconductor bottleneck Environmental effects of Elon Musk using gas turbines to power data centers in rural communities

The “Hard Fork” team is off this week, taking a much-needed break. While we're away, we wanted to draw your attention to a recent episode of “The Ezra Klein Show.” In this conversation, Ezra speaks with Jack Clark, a co-founder of Anthropic, about how he is using A.I. agents; how the technology is leading to meaningful changes in the ways we work and think; and how policy can or must change to anticipate potential job displacement on the horizon. We'll be back with a new episode next week. Guest: Jack Clark, a co-founder and the head of policy at Anthropic.  Additional Reading: A full transcript and video of this episode can be found here. We want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today's podcast is an interview with one of the cofounders of the AI company Anthropic, Jack Clark. One thing I'm trying to do with the subject of artificial intelligence is offer a balance of perspectives on an issue that tends to receive mostly one-sided coverage. Some people are certain that AI is a bubble; some are certain it is not. Some are certain that AI will destroy millions of jobs; some are certain that it will not. I want listeners of this show to feel like every time they hear an intelligent take on one side of this issue, the next episode they'll hear a countervailing take. Two weeks ago, you heard the investor and writer Paul Kedrosky argue that AI was an economic bubble. But if any single data point pierces that narrative, it's this. From December 2025 to this month, March 2026, Anthropic has more than doubled its annual recurring revenue, from $9 billion to nearly $20 billion. According to several analysts, there is no record of any company growing this fast at this scale. Now, I don't need Jack Clark or anybody at Anthropic to read me a corporate statement about the company's revenue growth. I can read that myself. What I wanted to do today is ask questions that only someone in Jack's position can answer. If Anthropic's executives believe that AI might be as dangerous as nuclear weapons, what right does any private business have to build this sort of thing for profit? How does the company balance its reputation as the industry leader in caution and safety with its other reputation as one of the fastest developers of this technology? And if artificial intelligence has the capacity to produce a country of geniuses in a data center—as Anthropic's CEO insists—why do Americans overall say they disapprove of artificial intelligence more than just about every other institution and individual in the world? Subscribe to our YouTube channel here: https://www.youtube.com/@PlainEnglishwithDerekThompson If you have questions, observations, or ideas for future episodes, email us at PlainEnglish@Spotify.com. Host: Derek Thompson Guest: Jack Clark Producer: Devon Baroldi Learn more about your ad choices. Visit podcastchoices.com/adchoices

A.I. agents are here. Have they changed your life yet? The release of agents like Claude Code marked a new pivot point in the history of A.I. We are leaving the chatbot era and entering the agentic era — where A.I. is capable of completing all kinds of tasks on its own, and even collaborating and communicating with other A.I. It isn't clear yet whether these models actually make their users meaningfully more productive. But the technology is continuing to improve; there are few signs that it is close to plateauing. So what might this new era mean for our economy, our labor market and our kids? Clark is a co-founder of Anthropic, the company behind Claude and Claude Code. His newsletter, Import AI, has been one of my go-to reads to track the capabilities of different models over the years. In this conversation, I ask him to share how he sees this moment — how the technology is changing, whether it is leading to meaningful changes in how we work and think, and how policy needs to or can change in response to any job displacement on the horizon. Mentioned: “Import AI” by Jack Clark “2026: This is AGI” by Pat Grady and Sonya Huang “Why and How Governments Should Monitor AI Development” by Jess Whittlestone and Jack Clark “Anthropic's Chief on A.I.: ‘We Don't Know if the Models Are Conscious'", Interesting Times with Ross Douthat Book Recommendations: A Wizard of Earthsea by Ursula K. Le Guin The True Believer by Eric Hoffer There Is No Antimemetics Division by qntm Thoughts? Guest suggestions? Email us at ezrakleinshow@nytimes.com. You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast, and you can find Ezra on Twitter @ezraklein. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs. This episode of “The Ezra Klein Show” was produced by Rollin Hu. Fact-checking by Michelle Harris with Mary Marge Locker and Kate Sinclair. Our senior engineer is Jeff Geld, with additional mixing by Isaac Jones and Aman Sahota. Our executive producer is Claire Gordon. The show's production team also includes Marie Cascione, Annie Galvin, Kristin Lin, Emma Kehlbeck, Jack McCordick, Marina King and Jan Kobal. Original music by Pat McCusker. Audience strategy by Kristina Samulewski and Shannon Busta. The director of New York Times Opinion Audio is Annie-Rose Strasser. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

S6:E6 Loralyn Mears, PhD, aka "Dr. LL," brings you thoughtful conversations with entrepreneurs and small business leaders navigating visibility, leadership, and growth. Thank you for being here. When the world feels heavy and your business still needs you to show up, it's easy to live in a constant state of pressure. This episode is a calm conversation about building something real, staying steady through uncertainty, and creating structure that supports your life instead of consuming it.

S6:E6 Loralyn Mears, PhD, aka "Dr. LL," brings you thoughtful conversations with entrepreneurs and small business leaders navigating visibility, leadership, and growth. Thank you for being here. When the world feels heavy and your business still needs you to show up, it's easy to live in a constant state of pressure. This episode is a calm conversation about building something real, staying steady through uncertainty, and creating structure that supports your life instead of consuming it.

The best way to start a new year is by celebrating the old! 2025 saw me watch less new films than usual but as per usual, I stuck up for the weirdos. In alphabetical order, these are the movies that stuck with me, kept me thinking and guessing and analyzing for months:BIRDEATER dir. Jack Clark & Jim Weir BUGONIA dir. Yorgos Lanthimos THE LUCKIEST MAN IN AMERICA dir. Samir Oliveros SINNERS dir. Ryan CooglerWOLF MAN dir. Leigh Whanell---Follow The Movies on ⁠Instagram⁠ & ⁠Letterboxd⁠Throw a couple dollars in the ⁠tip jar!

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) Sam Altman says ChatGPT will start to have erotic chats with interested adults 2) Also, more sycophancy? 3) Is sycophancy the lost love language 4) Is erotic ChatGPT good for OpenAI's business? 5) Is erotic ChatGPT a sign that AGI is actually far away? 6) OpenAI's latest business metrics revealed 7) Google's AI contributes to cancer discovery 8) Anthropic's Jack Clark on AI becoming self aware 9) Is Zuck poaching Apple  AI engineers mostly to hurt Apple? 10) AI's sameness problem 11) Ranjan rants against workslop  --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b AI's Sameness Problem: https://www.bigtechnology.com/p/ais-sameness-problemhttps://www.bigtechnology.com/p/ais-sameness-problem Questions? Feedback? Write to: bigtechnologypodcast@gmail.com

Jack Clark is the Founder and CEO of 180 Water.He and his family live in Helena, MT, not far from where he grew up cattle ranching. He started Western Water Wells in 2014 and his desire to help others find success in the water well industry has only grown. Jack's life has followed a theme of accomplishing impossible tasks with unique solutions. Utilizing Jack's heart for service and the talents of his growing team, he has built 180 Water. Jack's goal is to empower a new generation in providing for their families, communities, and breaking the scarcity mindset.#jackclark #founder #ceo #180water #montana #tsc #gogetit Chip Baker Social Mediahttps://www.wroteby.me/chipbaker

This is the first part of two special Friday episodes on the way AI promises to transform our politics, economies and societies. Lewis has been in San Francisco, where trillions of dollars of investment in AI is fuelling the 21st century equivalent of the space race. Around half a dozen firms are powering this revolution, largely out of sight or scrutiny. While the political and economic implications are profound, politicians seem unwilling or unable to even conceptualise what might be about to happen to their own voters. In the first of these special episodes, Lewis has been speaking to Jack Clark, one of the founders of Anthropic - one of the big AI firms. These companies don't speak out that often, but Clark has a sober message for politicians. If politics doesn't wake up- there could be an economic bloodbath within the next 18 months.Visit our new website for more analysis and interviews from the team: https://www.thenewsagents.co.uk/ The News Agents is brought to you by HSBC UK - https://www.hsbc.co.uk/EXCLUSIVE NordVPN Deal -> https://nordvpn.com/thenewsagents Try it risk-free now with a 30-day money-back guarantee

“Give Me Liberty or Give Me Death!” - Patrick Henry  Honoring Innovation, Freedom, and Small Business on the 4th of July  In this special Independence Day episode of Scaling UP! H2O, host Trace Blackmore brings you a rich blend of patriotism, professional insight, and entrepreneurial spirit. Opening with reflections on July 4th traditions—from fireworks to parades—Trace sets the stage for a compelling conversation with Jack Clark, Owner and Founder of 180 Water. As the water industry faces growing demand and generational turnover, Jack offers a bold solution: a replicable franchise model designed to preserve institutional knowledge and sustainably expand access to clean water. A Rancher Turned Water Well Visionary  Jack shares his origin story, from growing up on a ranch in Montana to launching a water well drilling company that now spans multiple states. What started with a neighbor's influence and a deep respect for self-reliance evolved into a career in well drilling—and eventually, a scalable business framework. Jack walks us through the unique challenges of finding water in fractured rock regions and explains how field wisdom, data monitoring, and humility define success. Franchising in the Water Sector: Solving the Knowledge Drain  As the industry grapples with aging experts nearing retirement, 180 Water is addressing a critical issue: the loss of operational and geological expertise. Jack reveals how his team is onboarding retiring professionals as equity partners to serve as regional hubs, blending mentorship with modern operations. Their approach enables local ownership, data collection, and scalable customer service, while preserving regional nuances in well drilling. Lessons in Leadership, Accountability, and Resilience  Jack emphasizes that real growth stems from reflection, mentorship, and integrity. He discusses how accountability—rooted in ranch life—translates into transparent client relationships, responsible site practices, and support systems that empower franchisees. His goal? To build a network of highly trained, values-aligned professionals who ensure the longevity and safety of our groundwater resources. The Spirit of Liberty: Patrick Henry's Enduring Speech  In a moving tribute to Independence Day, Trace closes the episode with a complete reading of Patrick Henry's “Give Me Liberty or Give Me Death” speech. Listeners are reminded of the courage it takes to challenge the status quo and the unifying power of respectful discourse—values that echo through today's challenges in water, business, and beyond. Final Takeaway  This episode isn't just about wells—it's about vision, responsibility, and the courage to lead. Jack Clark's journey inspires water professionals to think bigger, act with purpose, and consider scalable solutions to systemic industry issues.  Be sure to check our events page for upcoming water conferences and symposiums to continue growing your expertise.  Stay engaged, keep learning, and continue scaling up your knowledge!    Timestamps    02:20 - Trace Blackmore shares his warm greetings to Scaling UP! Nation this 4th of July! 07:27 - Upcoming Events for Water Treatment Professionals   10:37 - Water You Know with James McDonald  12:53 - Introduction with Jack Clark of 180 Water 18: 07 - Jack transitioned from expansion by employment to a franchise model   Quotes  Jack Clark: “If you don't get your chores done on the farm, things don't eat. And so it's important to make sure that you can be counted on.” “I was sending my best guys to the worst projects, my worst guys to the best projects—and no one was happy.” “You know how to run your business. You were successful at that. But we want to help you scale it with support and mentorship.” “There's not a perfect science to well drilling. Sometimes you find the water. Sometimes you don't. But that's the responsibility we take on.”  Trace Blackmore: “I really believe that the backbone of our country is small business and entrepreneurship.” “I hope we realize we have way more in common than we do differences—and that we enter conversations with curiosity instead of judgment.”    Connect with Jack Clark  Phone: +406 465 4791   Email: jack.clark@180water.com  Website: 180 Water   LinkedIn: 180 Water: Overview | LinkedIn    Click HERE to Download Episode's Discussion Guide    Guest Resources Mentioned  Freakonomics: A Rogue Economist Explores the Hidden Side of Everything by Steven D. Levitt, Stephen J Dubner    Scaling UP! H2O Resources Mentioned  AWT (Association of Water Technologies)  Scaling UP! H2O Academy video courses  Submit a Show Idea  The Rising Tide Mastermind    Water You Know with James McDonald  Question: How many ppm of sodium sulfite does it take to react with one ppm of oxygen?   2025 Events for Water Professionals   Check out our Scaling UP! H2O Events Calendar where we've listed every event Water Treaters should be aware of by clicking HERE.     

Jack Clark is the co-founder of Anthropic, an AI research company focused on building reliable and interpretable artificial intelligence systems. Before Anthropic, he was a policy director at OpenAI, where he shaped strategy, communications, and policy. With a background that spans journalism at Bloomberg, policy leadership, and deep involvement in AI governance, Clark has built a reputation for his expertise in AI safety, co-authoring influential research papers and launching the widely-read Import AI newsletter. At Anthropic, Clark guides the company's work on creating safer and more understandable AI systems, while also engaging in discussions around AI ethics and regulation. ------ Thank you to the sponsors that fuel our podcast and our team: Squarespace https://squarespace.com/tetra Use code 'TETRA' ------ LMNT Electrolytes https://drinklmnt.com/tetra Use code 'TETRA' ------ Athletic Nicotine https://www.athleticnicotine.com/tetra Use code 'TETRA' ------ Sign up to receive Tetragrammaton Transmissions https://www.tetragrammaton.com/join-newsletter

Few understand both the promise and limitations of artificial general intelligence better than Jack Clark, co-founder of Anthropic. With a background in journalism and the humanities that sets him apart in Silicon Valley, Clark offers a refreshingly sober assessment of AI's economic impact—predicting growth of 3-5% rather than the 20-30% touted by techno-optimists—based on his firsthand experience of repeatedly underestimating AI progress while still recognizing the physical world's resistance to digital transformation. In this conversation, Jack and Tyler explore which parts of the economy AGI will affect last, where AI will encounter the strongest legal obstacles, the prospect of AI teddy bears, what AI means for the economics of journalism, how competitive the LLM sector will become, why he's relatively bearish on AI-fueled economic growth, how AI will change American cities, what we'll do with abundant compute, how the law should handle autonomous AI agents, whether we're entering the age of manager nerds, AI consciousness, when we'll be able to speak directly to dolphins, AI and national sovereignty,  how the UK and Singapore might position themselves as AI hubs, what Clark hopes to learn next, and much more. Read a full transcript enhanced with helpful links, or watch the full video. Recorded March 28th, 2025. Help keep the show ad free by donating today! Other ways to connect Follow us on X and Instagram Follow Tyler on X Follow Jack on X Sign up for our newsletter Join our Discord Email us: cowenconvos@mercatus.gmu.edu Learn more about Conversations with Tyler and other Mercatus Center podcasts here.

Today on Moment of Zen, we're sharing a conversation from the 2024 Hill and Valley Forum with the founders of Scale AI, Anthropic, and AI Fund on the urgent race between the U.S. and China in AI innovation. Moderated by Senator Cory Booker and featuring Alexandr Wang, Jack Clark, and Andrew Eng, the panel covers why American AI leadership is at risk, and how smarter policy and faster deployment are critical to maintaining a competitive edge. (Note: that this conversation took place before the DeepSeek breakthrough.) Keep an eye out for the 2025 Hill and Valley Forum on Wednesday, April 30 — and subscribe to the Hill & Valley podcast in the episode description to listen to every panel. Spotify: https://open.spotify.com/show/39s4MCyt1pOTQ8FjOAS4mi Apple: https://podcasts.apple.com/us/podcast/the-hill-valley/id1692653857 YouTube: https://www.youtube.com/@HillValleyForum --

LifeBlood: We talked about the business of water wells, a breakdown of the industry itself, where opportunities exist and why, and how to get into the business, with Jack Clark, Founder of 180 Water. Listen to learn why the water well industry could be a great fit if you enjoy working with your hands! You can learn more about Jack at 180WaterFranchise.com, Facebook, X, Instagram, and Linkedin. Thanks, as always for listening! If you got some value and enjoyed the show, please leave us a review here: ​​https://ratethispodcast.com/lifebloodpodcast You can learn more about us at LifeBlood.Live, Twitter, LinkedIn, Instagram, YouTube and Facebook or you'd like to be a guest on the show, contact us at contact@LifeBlood.Live.  Stay up to date by getting our monthly updates. Want to say “Thanks!” You can buy us a cup of coffee. https://www.buymeacoffee.com/lifeblood