Podcasts about usage

Jacob takes a deep dive into remaining bell-cow RBs.

Lin Qiao is the Co-Founder and CEO of Fireworks AI, the leading specialized intelligence and AI inference platform that last week raised $1.5BN at a whopping $17BN valuation. With just 200 people, the company has hit $1BN in ARR and expects to hit $2BN before the end of the year. Prior to Fireworks, Lin spent several years at Meta including on the founding team of PyTorch.  AGENDA: 00:07 — Why Did Fireworks Bet on Inference When Everyone Else Was Chasing Training? 00:13 — Can Open-Source Models Turn AI Infrastructure into a Commodity? 00:19 — Should Enterprises Trust Chinese Open Models With Their Most Sensitive Data? 00:25 — Will Model Progress Keep Moving This Fast—or Are We Nearing a Plateau? 00:28 — Will the Multi-Model World Create a $100BN Routing Layer? 00:37 — How Much Will AI Token Usage Explode Over the Next Two Years? 00:43 — Will Token Costs Fall 10x—and Unleash 100x More Demand? 00:49 — Does Fireworks Eventually Have to Build Its Own Data Centres? 01:02 — What Is the Real Bottleneck Holding Back the AI Economy?  

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

This week'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

The 94 WIP Morning Show analyze the impact of Trevor Zegras' extension on the Flyers' future salary cap and team building. They also engage in a spirited debate about LeBron James potentially joining the Sixers, questioning his willingness to take a secondary role alongside Joel Embiid. The conversation also explores the World Cup and listener votes for the best non-human fictional characters and a look ahead to the Phillies' second half of the season. 01:51 - World Cup Final Debate 05:48 - Trevor Zegras Extension Analysis 11:18 - LeBron Sixers Fit Discussion 14:51 - LeBron's Usage and Impact 27:52 - Sixers Defensive Flexibility 33:52 - Best Fictional Characters Poll 43:50 - Phillies Second Half Preview

Zachary Smith discusses all things Pittsburgh Steelers. On today's episode, Nick Farabaugh of Penn Live joins the show. We discuss what a year 2 breakout would look like for Derrick Harmon, the best way for the team to utilize Jalen Ramsey, who the 6th DB on the field in dime looks will be, the ideal 5 on the offensive line and what defensive player is poised for a breakout under new DC Patrick Graham. Let's go for another Steelers Afternoon Drive and discuss all this! Learn more about your ad choices. Visit megaphone.fm/adchoices

Links: Why Amazon Has Dropped its Internal AI Usage Leaderboard   Be sure to follow us on our social media accounts on: LinkedIn: https://www.linkedin.com/company/the-audit-podcast Instagram: https://www.instagram.com/theauditpodcast TikTok: https://www.tiktok.com/@theauditpodcast?lang=en   Also be sure to sign up for The Audit Podcast newsletter and to check the full video interview on The Audit Podcast YouTube channel.

As so many large firms went all-in on AI over the past few years, policies to maximize employee AI adoption ranged from incentives to threats. Now, the Financial Times reports many companies are instead emphasizing quality over quantity, faced with both employee backlash and the rising costs of AI tokens. Calling AI leaderboards and policies tying performance reviews to AI usage "a really stupid way to do anything," a legal AI firm's CTO says staff should be rewarded "for being effective and efficient ... not for necessarily using AI.”

Send us Fan MailPricing AI is the hardest pricing problem in software right now, and most SaaS leaders are getting it wrong on the first try. In this episode of Navigating the Customer Experience, host Yanique Grant sits down with Dan Balcauski, founder of Product Tranquility, to unpack why the first AI price a company sets is always wrong, how to set usage caps when you have no historical data, and what smart B2B SaaS leaders do differently to turn pricing from a liability into a strategic advantage.Dan has spent more than 20 years in software, starting as an engineer before moving into product management and discovering that how a company captures value matters far more than how it builds the product. Today he advises B2B SaaS CEOs on the AI pricing and packaging decisions that keep them up at night, and in this conversation he shares the frameworks, the mistakes to avoid, and the practical playbook he uses with real companies.WHAT YOU WILL LEARN IN THIS EPISODEWhy the first AI price is always wrong, and why that has nothing to do with how smart your team or your consultants are. Dan explains the fundamental economic shift underway in software, where both sides of the pricing equation are moving at once. On the cost side, he points to a benchmark showing the cost per task for a top model dropping by roughly 390 times in a single year, a change no normal business ever absorbs in its cost of goods sold. On the value side, models keep getting more capable, handling this month what they could not handle last month. His research shows that every application layer software company he studied that released AI capabilities revised its pricing and packaging within 18 months. The lesson is not to price perfectly on day one. It is to build for change.How to set usage caps and pricing tiers with zero historical data. Dan frames the real problem plainly. You know what a token costs, but you have no idea what customers will actually do with a new AI feature. Products are full of features that barely got adopted, and AI features do not get to skip that step of the innovation cycle. On top of that, a small group of power users, often around 5 to 10 percent, can drive the overwhelming majority of usage and cost. That makes the tempting shortcuts unreliable. Using dashboard views as a proxy breaks down because good AI gets used far more than the dashboards it replaces, and a beta group rarely matches the usage profile of the full market.The early access playbook that sits between beta and general availability. Dan recommends a stage where companies announce their limits, put a price on the feature, and communicate it clearly, but do not enforce or meter it yet for a defined window that can run anywhere from six weeks to 18 months. This eases customer anxiety about surprise bills, encourages real adoption, and lets the company gather genuine usage patterns instead of guessing from proxies that break down.Why you should separate ordinary plan limits from fair use limits. Even when you are not metering usage, Dan explains, you can reserve the right to throttle or downgrade the rare customer using a capability a hundred or a thousand times more than the average, much like companies already do with API request limits. Those levers let teams keep experimenting during early access without the finance team panicking when the bill arrives.Why communication is where pricing changes succeed or fail. As Dan puts it, most pricing blowups come not from the change itself but from the fact that it was communicated poorly or not at all. Agility beats certainty, and reviewing pricing on a quarterly cadence beats the old annual or five year rhythm.This episode is essential listening for SaaS founders, product leaders, pricing strategists, and customer experience professionals who want to understand how AI is reshaping the economics of software and what to do about it before the market forces the decision for them.ABOUT DAN BALCAUSKIDan Balcauski is the founder of Product Tranquility, where he helps B2B SaaS CEOs turn pricing from a confusing liability into a strategic advantage. With more than 20 years in software, Dan began his career as an engineer before moving into product management and discovering that how companies capture value matters far more than how they build it. His work now centers on one of the most pressing questions in software today: how to price AI. Before founding Product Tranquility, Dan was a principal product strategist at SolarWinds and head of product at LawnStarter. He holds a BSc in computer engineering from Iowa State University and an MBA from the Kellogg School of Management at Northwestern, where he also helps teach executive education courses on product strategy. He is the host of the SaaS Scaling Secrets podcast.QUESTIONS YANIQUE ASKEDCould you share a little about your journey and how you got from where you were to where you are today? You have said the first AI price is always wrong. Why is that, and what should a SaaS company do differently knowing they are going to get it wrong the first time? So many companies are trying to set usage caps and pricing tiers for AI with zero historical data. How would you advise a CEO to make that decision when they are essentially flying blind? What is the one online resource, tool, website, or application that you absolutely cannot live without in your business? Can you share one or two books that have had a positive impact on you, professionally or personally? What is one thing going on in your life right now that you are really excited about? Do you have a quote or saying that keeps you on track during times of adversity? Where can listeners find and connect with you online?KEY TAKEAWAYSThe first AI price is always wrong, and that is not a failure of intelligence. It reflects a fundamental economic shift where both cost and value are moving fast. Every application layer company Dan studied revised its AI pricing and packaging within 18 months. Plan for revision, not perfection. Agility beats certainty. Review pricing on a quarterly cadence rather than annually or every five years. Communication is where pricing changes succeed or fail. Most blowups come from poor communication, not the change itself. Usage proxies break down. Dashboard views and beta groups rarely predict how customers will actually use an AI feature. A small group of power users can drive the majority of usage and cost, so average user assumptions are dangerous. Early access is the smart middle stage. Announce and price the limits, communicate them, but do not meter yet while you gather real data. Separate plan limits from fair use limits. Reserve the right to throttle extreme usage even when you are not metering everyone. Do not borrow problems from the future. Anxiety about what has not happened yet only adds problems to the present. AI is making custom, personal business software economically viable for the first time, opening the door to tools built exactly the way you work.CHAPTERS 00:00 Introduction and Guest Bio 01:51 Dan's Journey: From Engineer to Pricing Strategist 04:03 Learning That Pricing Is Different in Every Industry 04:49 Why the First AI Price Is Always Wrong 05:36 The 390x Cost Shift and the Moving Value Equation 06:49 Agility, Faster Pricing Reviews, and Communication 09:28 Setting Usage Caps With No Historical Data 11:32 Why Dashboard Proxies and Beta Groups Break Down 12:59 The Early Access Playbook Between Beta and GA 13:20 Plan Limits vs. Fair Use Limits 17:04 The One Tool Dan Cannot Live Without: Claude Code 17:38 Book Recommendation: Monetizing Innovation 18:31 Building Custom Business Software With AI 19:55 How to Connect With Dan Online 20:27 Dan's Guiding Quote: Don't Borrow Problems From the FutureFEATURED RESOURCESBook mentioned: Monetizing Innovation by Madhavan Ramanujam and Georg TackeTool mentioned: Claude Code, Dan's work surface and the engine behind his custom business softwareCONNECT WITH DANLinkedIn: Search Dan Balcauski on LinkedIn, and mention that you heard him on the podcast so he can separate you from the spamWebsite: producttranquility.comPodcast: SaaS Scaling Secrets, wherever podcasts are foundDAN'S GUIDING QUOTE"Don't borrow problems from the future." Dan BalcauskiDan explains that most of our anxiety is about things that have not happened yet. Worrying about a future scenario pulls that problem into the present before it ever arrives, giving you more to carry now for no reason. Like debt, it is borrowing against your future self. His practice is to stay focused on what is real and in front of him, which keeps him grounded when challenges or uncertainty threaten to pull him off track.ABOUT N

The global economy is entering a new era, but what does that mean for interest rates, inflation and long-term growth? Host Mandy Drury speaks with CommBank Head of Market Strategy & Rates Research Adam Donaldson about the long-term forces reshaping interest rates. They discuss the rising neutral interest rate, the impact of AI investment, defence spending and the energy transition, and why central banks are facing a very different environment than they did over the past three decades. Mandy also speaks with CommBank Senior Geoeconomics Analyst Dr Madison Cartwright about the changing global order. They explore the end of the globalisation era, why governments are prioritising security over efficiency, and what a more fragmented world could mean for inflation, investment and economic growth. Plus, CommBank International & Sustainable Economist John Oh shares the key focuses for markets in the week ahead. Important Information This podcast is approved and distributed by Global Economic & Markets Research (“GEMR”), a business division of the Commonwealth Bank of Australia ABN 48 123 123 124 AFSL 234945 (“the Bank”). Before listening to this podcast, you are advised to read the full GEMR disclaimers, which can be found at www.commbankresearch.com.au. No Reliance This podcast is not investment research and nor does it purport to make any recommendations. Rather, this podcast is for informational purposes only and is not to be relied upon for any investment purposes. This podcast does not take into account your objectives, financial situation or needs. It is not to be construed as a solicitation or an offer to buy or sell any securities or other financial products, or as a recommendation, and/or investment advice. You should not act on the information in this podcast. The Bank believes that the information in this podcast is correct and any opinions, conclusions or recommendations made are reasonably held at the time given, and are based on the information available at the time of its compilation. No representation or warranty, either expressed or implied, is made or provided as to accuracy, reliability or completeness of any statement made. Liability Disclaimer The Bank does not accept any liability for any loss or damage arising out of any error or omission in or from the information provided or arising out of the use of all or part of the podcast. Usage of Artificial Intelligence To enhance efficiency, GEMR may use the Bank approved artificial intelligence (AI) tools to assist in preparing content for this podcast. These tools are used solely for drafting and structuring purposes and do not replace human judgment or oversight. All final content is reviewed and approved by GEMR analysts for accuracy and independence.See omnystudio.com/listener for privacy information.

MUKTZAH 6 USAGE BASED

Laurence & Carmen explain how Ben Johnson's tight end usage is a key aspect to his unpredictability as a play-caller.

A structural shift is occurring as employees and customers increasingly bypass sanctioned IT systems in favor of faster, unsanctioned "shadow" tools that offer comparable or "good enough" functionality with less friction. This shift is highlighted through evidence from Gartner, SparkToro, Microsoft, and reports from Altran Digital Business, which collectively show sanctioned internal and customer-facing systems losing relevance as users opt for alternative solutions that optimize convenience and efficiency over formal governance. The most consequential development referenced is Microsoft's move to replace premium OpenAI and Anthropic models in core applications like Excel and Outlook with lower-cost in-house models, as reported by Bloomberg and Channel Insider. Microsoft claims these new models offer similar accuracy with increased efficiency, reflecting a broader market trend toward solutions that meet minimal functional thresholds at drastically reduced costs. This mirrors broader enterprise behavior, where cost and sufficiency now outweigh premium features, driving a reconsideration of value in AI provisioning. Supporting developments include a Gartner survey showing consumers are about three times more likely to use general AI tools like ChatGPT than corporate chatbots, and a report from Altran Digital Business revealing that over half of employees rely on personal devices or unauthorized tools for work, with nearly a third ceasing to report IT problems entirely. Clickstream data shows that more than two-thirds of Google searches end without a click as users accept AI summary answers, bypassing source links altogether. Vendors such as N-Able and Okta are responding with new products aimed at identifying and gating shadow tool usage, but these approaches often add operational friction without actually closing governance gaps, as Kaseya data indicates most SaaS accounts remain unmanaged despite existing controls. For MSPs and IT leaders, the key implication is that additional controls and "lockdown" measures are likely to increase friction without effectively steering users back to sanctioned processes. Current market tools that focus on visibility and gating of shadow IT may exacerbate the problem by making official workflows less attractive. The practical recommendation is to map where users have already abandoned sanctioned paths and focus on improving those official workflows until they are easily usable and competitive with shadow alternatives. The effectiveness of service delivery should be measured not by control metrics, but by whether users actively choose sanctioned systems for their work.   00:00 The quiet walkout  03:49 Even Microsoft picked good-enough 06:22 Why more control backfires 09:00 Why Do We Care?  Supported by:  Pax8   

This week in search, we covered how Google's Nick Fox announced Google Search broke all usage records when Argentina scored its winning goal in the World Cup. Google Search Console now shows third-party platform content performance data from Instagram, TikTok...

Microsoft saw a significant surge in its carbon emissions last year due to AI infrastructure. For more, KCBS's Margie Shafer spoke with Bloomberg's Matt Day. Getty Images // Petmal

Aujourd'hui, Bruno Poncet, cheminot, Barbara Lefebvre, professeur d'histoire-géographie, et Charles Consigny, avocat, débattent de l'actualité autour d'Alain Marschall et Olivier Truchot.

Kevin and Cory analyze Jake Ferguson's target rate and read progression within the Cowboys' offense. They debate whether George Pickens, CeeDee Lamb, or Javonte Williams will lead the team in touchdowns while identifying training camp stock risers like Ryan Flournoy. The conversation also features a critical look at Jonathan Mingo's production and an injury update on Pirates rookie Konnor Griffin.

Mike & Tommy tackle the surprisingly tricky problem of tracking Power BI app usage at scale, exploring why app-level telemetry is harder to surface than report-level metrics and how teams can map usage back to districts using Entra ID, Admin APIs, and audit logs.They break down which telemetry sources are actually viable, how to avoid common pitfalls like audience filters hiding true reach and shared devices skewing counts, and lay out a scalable architecture for ~9,000 users across 70 districts built around a centralized semantic model with incremental refresh.Resources mentioned: Chicagoland Power BI Meetup, Fabric Runtime Release Channels, Deep Dive into Tooltip Options in Power BI VisualsGet in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

AI transformation doesn't fail because the technology isn't good enough. It fails because organizations try to layer it on top of cultures that were already struggling with trust, learning, experimentation, and leadership. In this conversation, David Rice sits down with Meagan Bond, Founder and CEO of The Human Method, to unpack why psychological readiness—not technical readiness—is the real foundation of successful AI adoption.Together they explore the hidden costs of dysfunctional culture, why managers play an outsized role in determining whether AI succeeds or fuels burnout, and why organizations chasing quick AI wins often undermine their long-term competitive advantage. If culture is treated as an afterthought instead of infrastructure, AI simply accelerates the problems that were already there.Related Links:Join the People Managing People CommunitySubscribe to the newsletter to get our latest articles and podcastsConnect with Meagan on LinkedInVisit The Human MethodSupport the show

New figures from the CSO shows data centres account for nearly a-quarter of electricity usage in Ireland, while electricity consumption rose by ten per cent last year alone…Leader of the Green Party, Roderic O'Gorman, joins Ciara to discuss.

Australia’s economy is slowing, but what does the global outlook mean for the path ahead? Host Mandy Drury speaks with CommBank Head of Australian Economics Belinda Allen about why housing has become the key headwind for the Australian economy. They discuss weaker market momentum, consumer spending, the outlook for interest rates and whether AI investment can help offset softer domestic demand. Mandy also speaks with CommBank Head of FX, International and Geoeconomics Joseph Capurso about the global forces shaping the economic outlook. They discuss lower oil prices, maritime trade risks, the US AI boom, China's two-speed economy and Europe's ongoing challenges. Plus, CommBank’s Associate Economist Lucinda Jerogin shares the key focuses for markets in the week ahead. Important Information This podcast is approved and distributed by Global Economic & Markets Research (“GEMR”), a business division of the Commonwealth Bank of Australia ABN 48 123 123 124 AFSL 234945 (“the Bank”). Before listening to this podcast, you are advised to read the full GEMR disclaimers, which can be found at www.commbankresearch.com.au. No Reliance This podcast is not investment research and nor does it purport to make any recommendations. Rather, this podcast is for informational purposes only and is not to be relied upon for any investment purposes. This podcast does not take into account your objectives, financial situation or needs. It is not to be construed as a solicitation or an offer to buy or sell any securities or other financial products, or as a recommendation, and/or investment advice. You should not act on the information in this podcast. The Bank believes that the information in this podcast is correct and any opinions, conclusions or recommendations made are reasonably held at the time given, and are based on the information available at the time of its compilation. No representation or warranty, either expressed or implied, is made or provided as to accuracy, reliability or completeness of any statement made. Liability Disclaimer The Bank does not accept any liability for any loss or damage arising out of any error or omission in or from the information provided or arising out of the use of all or part of the podcast. Usage of Artificial Intelligence To enhance efficiency, GEMR may use the Bank approved artificial intelligence (AI) tools to assist in preparing content for this podcast. These tools are used solely for drafting and structuring purposes and do not replace human judgment or oversight. All final content is reviewed and approved by GEMR analysts for accuracy and independence.See omnystudio.com/listener for privacy information.

As part of an ongoing legal dispute with three Hollywood studios, Midjourney is seeking to compel those studios to reveal how they use AI themselves. Also, Selby's VC firm Copper Sky Capital is currently raising a $300 million second fund, according to a regulatory filing. Learn more about your ad choices. Visit podcastchoices.com/adchoices

KJ Carson & Jon Lyons, filling in on a holiday Friday | July 3, 2026

KJ Carson & Jon Lyons, filling in on a holiday Friday | July 3, 2026

Tu veux que je te raconte l'histoire des meilleures inventions? Alors attrape ta brosse à dents, ton dentifrice, et c'est parti!

Let me walk you through a scenario. A voice actor gets an offer. Major food delivery brand. Session fee is $500. Buyout is $10,000. Usage is worldwide, all media, in perpetuity. Broadcast TV, streaming, social media, paid and organic, radio, in stores, stadium, cinema, email marketing, every platform, every country, forever. Is that a good deal? Not even close. Today I'm going to give you the math, the framework, and the language you need to have the buyout conversation without feeling like you're making up numbers or asking too much or too little. Session Fee vs Usage Fee These two things are important to distinguish because a lot of voice actors, especially newer ones, bundle them incorrectly. The session fee is what you get paid for your time in the booth. It compensates you for the recording session itself, your preparation, your studio, your performance. For a typical commercial session, session fees range from a couple hundred dollars to a couple thousand depending on the scope. For a major national brand, being at the low end of that range is usually a red flag. The usage fee, the buyout in a flat fee situation, is something completely different. This is not paying for your time. It's paying for access to your voice, your identity, your performance across platforms and time. It's the price of a license. And the value of that license scales with how broadly and for how long the client intends to use it. When a client asks for perpetual worldwide all media rights, they are not just buying the recording. They are locking your voice into their brand identity indefinitely. You can't relicense that usage. You can't adjust the price if they want to run it on the Super Bowl. You cannot renegotiate when the campaign runs for three years instead of six months. So the buyout price has to account for all that upside they're capturing. $10,000 for a Fortune 500 brand running a perpetual worldwide all media campaign is not accounting for it. How to Actually Value Usage Here is a framework that will give you a defensible starting point. It's not a substitute for a rate sheet or scale calculator, but it will get you in the right conversation. Step one is identify the scope. What media, what geography, what duration. Each of those variables multiplies the value. Local, three months, one platform is very different from global, perpetual, all platforms. Step two is consider the brand scale. A Fortune 500 company running a perpetual campaign is not the same as a small regional business running something for six months on local radio. The larger the brand and the broader the reach, the higher the floor. Step three is use the session fee as your anchor and multiply for usage. For local, limited use, maybe one to two times the session fee. For regional, one year, limited platforms, three to five times. For national, multi-platform, one year, eight to fifteen times. For global, all media, perpetual, you are in the twenty to sixty times range minimum for a major brand. So in the scenario I opened with, a $500 session fee for worldwide perpetual all media rights for a major brand, the usage fee should be somewhere in the $50,000 to $85,000 range. Not $10,000. They'll Just Go Hire Someone Else I know that's what's happening in your head right now. And yeah, sometimes they will. But when a major brand is running a perpetual worldwide campaign, they have a budget. They have an agency. The agency has rate cards. The $10,000 buyout they offered you is almost certainly not their max. It is their opening number. It's what they offer when they think they can get away with it. When you counter calmly and professionally with a number that reflects actual market value, one of a few things happens. They come up. Or they negotiate to limit the scope, maybe it's two years instead of perpetual. Or yes, in some cases they walk. And if they walk because you asked to be paid appropriately for a perpetual worldwide all media license, they were never a client you could build a sustainable business on. The voice actors who have long healthy careers are the ones who train themselves early to understand what their work is worth and how to ask for it. Not aggressive, not apologetic. Matter of fact, the way any other professional would quote a rate. The Language to Use Knowing the number is only half the battle. Here is what to say when you get an offer that doesn't match the scope of usage. Not "that's way too low." Not "whatever works for you." Something like: thank you so much for sending this over. I want to make sure we're aligned on the usage scope. For worldwide all media in perpetuity rights, my rate is X. If the scope is more limited I'm happy to adjust the quote accordingly. What flexibility is there on either the budget or the usage terms? That does three things. It treats the rate as a natural consequence of the scope, not a personal ask. It opens the door to negotiating the scope if the budget is fixed. And it invites a conversation instead of creating a standoff. You can also offer tiered options. For a two-year term with an option to renew I can come down to Y. For perpetual rights it's X. Giving them choices makes it easier to say yes to something. And if they say this is our standard rate, that is a negotiating position, not a fact. Standard rates are what gets offered. They're not what gets paid when the talent knows the market. The Bottom Line The buyout conversation is not confrontation. It's calibration. You're not asking for more than you deserve. You're asking for what a license of this scope is actually worth based on the market. Perpetuity. Worldwide. All media. Those are not boilerplate. Those are the most expensive words in the industry. Price them accordingly. You worked really hard to build a voice people want to use. Make sure you're getting paid for how much they want to use it. Want to Keep the Conversation Going? If you have questions about rates, contracts, negotiation, or your marketing strategy, reach out at mandy@actingbusinessbootcamp.com. I can't wait to hear what you're working on.

Predictable revenue creates predictable freedom. In this episode of The Level Up Podcast, Paul Alex breaks down why recurring revenue is one of the strongest business models for building stability, valuation, and long-term wealth. Let's be real… If every month starts at zero… And you have to chase every dollar all over again… You are not building peace of mind. You are building pressure. In this episode, you'll learn: Why one-time sales can create unstable cash flow How recurring revenue turns clients into long-term value Why subscriptions, retainers, and residual systems increase business stability How predictable income can raise your company's valuation and reduce financial anxiety The truth is simple: The goal is not just to make a sale. The goal is to build continuity. Monthly retainers. Subscription access. Usage-based billing. Maintenance packages. Residual income streams. Systems that create value every month and get paid every month. High-level operators do not want to restart from zero every thirty days. They engineer recurring revenue. They build retention. They automate billing. They make their service so valuable that clients cannot afford to cancel. Because when the baseline is secure… The business breathes easier. The founder thinks clearer. And the company becomes more valuable. Stop starting over every month. Build the recurring model. Lock in the clients. Secure the baseline. And keep leveling up. Your Network is your NETWORTH! Make sure to add me on all SOCIAL MEDIA PLATFORMS: Instagram: https://jo.my/paulalex2024Facebook: https://jo.my/fbpaulalex2024YouTube: https://www.youtube.com/channel/UCGhDAD1JyGGzSQUPD9lc9HQLinkedIn: https://jo.my/inpaulalex2024 Looking for a secondary source of income or want to become an entrepreneur? Check out one of my companies below to see if we can help you: www.CashSwipe.com FREE Copy of my book “Blue to Digital Gold - The New American Dream”www.officialPaulAlex.com Learn more about your ad choices. Visit megaphone.fm/adchoices

On this special solo episode, Jeff Mains sets a new agenda for the founder-led audience: futureproofing your company in an AI-driven, fast-changing landscape. This episode dives deep on why traditional business moats—complex code bases, gorgeous interfaces, and integrations—no longer hold up, and what unbreakable moats have emerged: Data, Trust, and Gravity.With frameworks straight from the fire and battle-tested pricing guidance, Jeff Mains shows how to build a company that outlasts market shifts—one that doesn't just survive, but leads. Packed with practical tools and candid stories, this episode is founder intelligence you can actually use.Key Takeaways07:52 Explaining outdated software protections10:11 AI agents and API focus19:49 Community loyalty and cultural moat21:57 Assessing customer reliance and trust31:04 Usage-based billing challenges33:13 Importance of Sales Strategy Adjustment40:33 Discussing pricing strategy questions47:29 Helping Customers Achieve Their Goals50:04 Evaluating customer impact without the company53:29 Letting go and gaining controlTweetable QuotesViral Topic: "Could Your Biggest Customer Rebuild You?": If the smartest team inside, your biggest customer, armed with Claude retool, maybe lovable and and a week of uninterrupted time, decided to rebuild your product or replicate your service, what would they realize? They don't have that you do. — Jeff Mains Slowing Down to Accelerate: "She also had something really counterintuitive to say about when slowing down is actually the most aggressive move you can make, that episode is worth your time as well." — Jeff Mains AI Will Make Beautiful Dashboards Obsolete: "your beautiful interface is invisible functionality, invisible. All that matters is whether your API can do the thing. Your gorgeous dashboard is wallpaper that a machine never looks at." — Jeff Mains Viral Topic: The Power of Community Ecosystems: "When you have a community, your customers aren't just using your product, they're embedded in an ecosystem." — Jeff Mains The Power of Cultural Moats: "That's not a product moat, that is a cultural moat." — Jeff Mains The Chaos of Usage-Based Pricing: "For a lot of SaaS or service companies, jumping straight to usage base without some sort of bridge can be a chaos generator. And chaos, that ain't good, especially when it comes to money." — Jeff Mains Pricing Time Bomb: "If it's the second one, then you're sitting on a pricing time bomb and that's got to be something. Defuse it before, before it's too late." — Jeff Mains SaaS Leadership LessonsDon't Confuse Activity for DefensibilityIf your moat is complex code or UI, you're exposed. Invest in what gets stronger as AI accelerates.Ask What Would Be Gone If You DisappearedIf the only answer is “inconvenience,” you're a vendor, not a partner.Build Moats That CompoundData gets better over time, trust deepens with high-stakes moments, gravity multiplies as processes and identity grow.Own the Transition to Outcome-Focused PricingPer-seat/effort-based pricing punishes efficiency. Build a bridge to usage and outcome models—don't force it overnight.Evolve Faster Than the MarketYour team's learning speed and your own growth determine survivability, not your initial playbook.Recurring Relevance Is the Real MetricAre you needed, or just hard to replace? Build relationships and deliver business outcomes that outpace any AI copycat.Guest Resourceshttps://www.facebook.com/jeffkmains/https://www.linkedin.com/in/jeffkmains/https://x.com/jeffkmainshttps://www.youtube.com/@championleadershiphttps://jeffmains.com/books/https://drive.google.com/file/d/1CPrpxILI2vi_YYJMdv5cwYo-bMlauaLH/view?usp=sharing Episode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

Markets began the year expecting US interest rate cuts, so why are they now preparing for hikes instead? Host Mandy Drury speaks with CommBank Senior Economist & Currency Strategist Kristina Clifton about the changing outlook for US monetary policy and what it means for global markets. They discuss why expectations for the Federal Reserve have shifted so dramatically, how stronger US growth and AI investment are supporting the economy, and why CommBank continues to expect a Fed hiking cycle. They also explore Kevin Warsh's first meeting as Fed Chair, the importance of central bank independence, the outlook for inflation and interest rates, and why developments in the US continue to shape financial markets around the world. Plus, CommBank Senior Associate in Market Strategy & Rates Research Michael Tang shares the key focuses for markets in the week ahead. Important Information This podcast is approved and distributed by Global Economic & Markets Research (“GEMR”), a business division of the Commonwealth Bank of Australia ABN 48 123 123 124 AFSL 234945 (“the Bank”). Before listening to this podcast, you are advised to read the full GEMR disclaimers, which can be found at www.commbankresearch.com.au. No Reliance This podcast is not investment research and nor does it purport to make any recommendations. Rather, this podcast is for informational purposes only and is not to be relied upon for any investment purposes. This podcast does not take into account your objectives, financial situation or needs. It is not to be construed as a solicitation or an offer to buy or sell any securities or other financial products, or as a recommendation, and/or investment advice. You should not act on the information in this podcast. The Bank believes that the information in this podcast is correct and any opinions, conclusions or recommendations made are reasonably held at the time given, and are based on the information available at the time of its compilation. No representation or warranty, either expressed or implied, is made or provided as to accuracy, reliability or completeness of any statement made. Liability Disclaimer The Bank does not accept any liability for any loss or damage arising out of any error or omission in or from the information provided or arising out of the use of all or part of the podcast. Usage of Artificial Intelligence To enhance efficiency, GEMR may use the Bank approved artificial intelligence (AI) tools to assist in preparing content for this podcast. These tools are used solely for drafting and structuring purposes and do not replace human judgment or oversight. All final content is reviewed and approved by GEMR analysts for accuracy and independence.See omnystudio.com/listener for privacy information.

Today we are talking about Marketing, AI, and Drupal with guest Paul Johnson. We'll also cover Curated Colors as our module of the week. For show notes visit: https://www.talkingDrupal.com/559 Topics Paul's Current Projects Enterprise AI Summit Details Marketing the AI Initiative Partnering on Event Booths Drupal's Outside Perception What's Working Now Growing the Marketing Team How to Contribute Outside In Storytelling Case Study Examples AI Initiative Impact Roadmap and Launch Planning Finding New Adopters Where Pros Research Conference Pitch Story Local Event Playbook Funnel and Webinars Industry Guides and Demos SEO and AI Search Why Agents Avoid Drupal High Leverage Contributions Measuring AI Mentions Vibe Coders to Governance Fixing Misconceptions Resources Drupal AI Initiative home page Slack #ai-initiative-marketing Enterprise AI Summit Rotterdam AI Dev Summit Rotterdam Drupal AI TV We've curated a selection of the best presentations, workshops and demonstrations freely available to provide a practical way to stay informed about the latest innovations in Drupal AI. Drupal AI Webinars playlist Demos Ryan Whitcombe 1xINTERNET S1xSignals free AIO GEO assessment All things open World cancer day Guests Paul Johnson - pdjohnson Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Scott Falconer - managing-ai.com scott-falconer MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Have you ever wanted to allow editors on your Drupal site to choose styling from a brand-approved color palette? There's a module for that. Module name/project name: Curated Colors Brief history How old: created in Apr 2026 by Kyle Einecker (ctrladel) of True Summit Versions available: 1.0.0 which works with Drupal 10.3, 11, and 12 Maintainership Actively maintained Security coverage Test coverage Documentation - in-depth README Number of open issues: 2 open issues, neither of which are bugs Usage stats: 27 sites Module features and usage Curated Colors enforces brand consistency by replacing generic color text inputs or wide-open color pickers with a curated, visual swatch popover containing only pre-approved, named options It streamlines rebranding by storing abstract keys (such as brand-primary) instead of raw hex values (e.g., #0678be) in the database. That means updating a brand color in the future only requires a CSS or configuration change rather than a massive data migration Curated Colors is also extensible beyond colors. It functions as a generic visual variant selector. Site builders can repurpose it to let editors pick card layouts, button styles (like primary, outline, or danger), hero text alignments, or icon themes Editors can pick from neatly organized groups with human-readable labels and see a live preview swatch of their selection before saving Palettes are managed as exportable Drupal configuration. Each entry maps a machine key to a label, administrative hex preview, and optional custom CSS The module provides a curated_color field type and an accompanying swatch-based popover widget that can be restricted to specific palette groups. It also features a native curated_color_picker Form API element and integrates with the Canvas module via SDC annotations The field exposes properties like value, hex, style, and css, making it simple to output selections as classes, inline styles, or raw codes in Twig templates Finally, Curated Colors includes an example submodule providing a working SDC component and sample palette templates so you can see exactly how it's meant to be used

Petros Papadakis in Los Angeles joins Dave Softy Mahler to talk about drugs, the lack of drug use, and their history with drugs, then they discuss the Brendan Sorsby situation and what will happen next now that the NFL is not having a Supplemental Draft coming up this year, plus the new NCAA five in five rule and how it relates to the ages of teams.See omnystudio.com/listener for privacy information.

Pitts sets the market Detroit must face The Atlanta Falcons just changed the tight end economy. They signed Kyle Pitts to a three-year, $54 million extension with $36 million guaranteed. It is the richest three-year deal ever for an NFL tight end. That number immediately matters to the Detroit Lions and Sam LaPorta. Recent comps drive negotiations. The Detroit Lions Podcast digs into what this means. By annual average value, George Kittle and McBride sit at the top tier. Pitts now lands at $16 million per year. The next band is where Detroit will hunt comps for LaPorta: Isaiah Likely at three years and $40 million with $26 million guaranteed, Mark Andrews at roughly $13.9 million per year, Dalton Schultz at $12.6 million, and Cole Kmet at $12.5 million. As much as Detroit likes LaPorta, he has been roughly in that neighborhood with Kmet. Will he take that number to stay in Detroit? Expect him to aim higher after the Pitts deal. Usage and value in Detroit's offense Context matters. McBride earned heavy usage in Drew Petzing's system in Arizona. That led many to assume a similar spike for LaPorta under Petzing in Detroit. It could happen, but the situations are different. McBride was the best player on that offense. In Detroit, LaPorta is not even the third-best offensive piece. Jahmyr Gibbs and Penei Sewell are central pillars. Jameson Williams offers higher peak plays even if the week-to-week is still building. That distribution of talent can cap volume and, in turn, price. LaPorta brings real value beyond catches. His blocking stacks up well, better than Pitts in this discussion. Pitts also aligns outside as a receiver often, while LaPorta plays a more traditional tight end role. Those distinctions will surface in negotiations as both sides frame what they are paying for. Numbers, guarantees, and timing A practical floor sits around Likely's deal: three years, $40 million, $26 million guaranteed. A target from the player side could be three years, $50 million with $35 million guaranteed. A logical counter from the team lands near three years, $48 million at $16 million per year. With the Lions, guarantees are the meat. Expect creative structure with void years to spread cap hits. That is how Detroit handles these mid-length veteran deals. Health will guide the calendar. LaPorta is working back from the back injury that ended last season. He was on the field last week but not yet full go. The staff also wants Brian Branch healthy and contributing. If that holds, do not expect an immediate extension. Training camp will be the first checkpoint. A more natural window sits near the bye or toward the end of summer. September 21 feels like a soft boundary. By then, Detroit should know LaPorta's role and output in Petzing's offense. The hard choice no one wants One prevailing viewpoint around the league is that if Detroit must let someone walk among pending extension candidates, tight end is the easiest to replace. That argument has merit on roster-building grounds. Even so, the intent is to keep LaPorta. Pitts' new deal just sharpened the pencil. Now the Lions must decide how far they will go to match it. #detroitlions #lions #detroitlionspodcast #samlaportacontract #kylepittsextension #tightendmarket #lionscontracts #overthecap #treymcbride Learn more about your ad choices. Visit megaphone.fm/adchoices

Productivity growth has slowed across Australia and much of the developed world, but what will it take to turn the trend around? CommBank Chief Economist Luke Yeaman sits down with Productivity Commission Chair Danielle Wood about the forces shaping productivity growth in Australia and globally. They discuss the causes of the productivity slowdown, the opportunities and challenges presented by artificial intelligence, and what Australia must do to improve business investment, innovation and technology adoption. They also explore the role of tax reform, regulation, housing, migration and public sector productivity in supporting long-term economic growth, as well as why lifting productivity remains critical to improving living standards in the years ahead. Plus, CommBank Economist & Currency Strategist Carol Kong shares the key focuses for markets in the week ahead. Important Information This podcast is approved and distributed by Global Economic & Markets Research (“GEMR”), a business division of the Commonwealth Bank of Australia ABN 48 123 123 124 AFSL 234945 (“the Bank”). Before listening to this podcast, you are advised to read the full GEMR disclaimers, which can be found at www.commbankresearch.com.au. No Reliance This podcast is not investment research and nor does it purport to make any recommendations. Rather, this podcast is for informational purposes only and is not to be relied upon for any investment purposes. This podcast does not take into account your objectives, financial situation or needs. It is not to be construed as a solicitation or an offer to buy or sell any securities or other financial products, or as a recommendation, and/or investment advice. You should not act on the information in this podcast. The Bank believes that the information in this podcast is correct and any opinions, conclusions or recommendations made are reasonably held at the time given, and are based on the information available at the time of its compilation. No representation or warranty, either expressed or implied, is made or provided as to accuracy, reliability or completeness of any statement made. Liability Disclaimer The Bank does not accept any liability for any loss or damage arising out of any error or omission in or from the information provided or arising out of the use of all or part of the podcast. Usage of Artificial Intelligence To enhance efficiency, GEMR may use the Bank approved artificial intelligence (AI) tools to assist in preparing content for this podcast. These tools are used solely for drafting and structuring purposes and do not replace human judgment or oversight. All final content is reviewed and approved by GEMR analysts for accuracy and independence.See omnystudio.com/listener for privacy information.

Today we are talking about AI, Agents, and A System to manage them with guest Luke McCormick. We'll also cover AI Auto-reference as our module of the week. For show notes visit: https://www.talkingDrupal.com/558 Topics Introducing Agent Management Origin Story Claude Credits Scrum Meets AI Retention Handoff Protocol Filesystem Why Handoffs Work So Well Examples and Human Loop Agent Roles and Model Costs Choosing Models by Task Not Drupal Specific Works With Any Model Scrum Sprints For Agents Human Cognitive Overload Tuning Autonomy Levels Setup And Handoff File Updating Customized AMS Persistent Memory Artifacts Demand Better Summaries Solo Power With Agents Roadmap And AMS Trio Resources Stanford Web Camp - Agile for Agents – Managing Robots The Way We Manage Humans. AMS Robert Douglas spec kitty xdebug tui ams-trio Guests Luke McCormick - cellear Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Have you ever wanted to use AI to suggest related content on your Drupal site? There's a module for that. Module name/project name: AI Auto-reference Brief history How old: created in June 2023 by Scott Euser (scott_euser) or Soapbox Versions available: 1.0.0-rc4 Maintainership Actively maintained Security coverage - opted in, needs stable release Test coverage Number of open issues: 4 open issues, 1 of which is a bug Usage stats: 19 sites Module features and usage AI Auto-reference works with any reference fields, so it could find suitable taxonomy terms, nodes, etc It does that by rendering a specified view mode, so it should with any kind of complex layout approach you may have implemented on your site It will also automatically shorten your content to fit within your AI model's token window, which you can also configure The module extends Drupal's main AI module, which means you can select which model to use, and probably means you can also use guardrails, and all the other powerful features that come with that ecosystem Ai Auto-reference comes with default prompts, but you can also edit those if you really want to make sure you're squeezing out every drop of relevance You can also choose for which fields in each content type you want to generate suggestions, as well as whether you want the suggestions should be automatically applied, or whether you want them manually reviewed As mentioned on the project page, you can already have AI suggest things like tags using the AI module without this project, but this may be a better choice if you want to make sure the recommendations stick to an existing set

Aujourd'hui, Emmanuel de Villiers, entrepreneur, Charles Consigny, avocat, et Joëlle Dago-Serry, coach de vie, débattent de l'actualité autour d'Alain Marschall et Olivier Truchot.

Send us Fan MailIn this episode of The Alternative Dog Moms, we're joined by Sean to learn the origin story behind Tvati and why this topical product has become part of so many pet wellness conversations. We discuss what animals have taught him about Tvati's topical-systemic results, how pet parents can decide where to invest in their dog's wellness, and why traditional use matters when looking at long-term support.We also dig into the big questions: Can't you just buy hibiscus anywhere? Does Tvati really produce cartilage and heal bone? Is it appropriate for pets with cancer? And how can it support quality of life in senior pets?Plus, we talk about Tvati's entrance into the integrative veterinary world with Dr. Katie Kangas and Jessica, and how this product is being used by both people and pets.Chapters:Tvati's origin story (0:55)What animals have taught Sean about Tvati's topical-systemic results (10:21)Choosing Where to Invest in Your Dog's Wellness (18:13)How Long Should You Use Tvati? A Look at Its Traditional Origins (28:41)Can't You Just Buy Hibiscus Anywhere? (31:56)Does Tvati really produce cartilage and heal bone? (34:26)Entering the Integrative Veterinary World with Dr. Katie Kangas (and Jessica!) (37:46)Is Tvati appropriate for pets with cancer? (41:14)Usage in people and pets (44:10)How Tvati improves quality of life in senior pets (52:14)Links Discussed:https://tvati.com/Social Media:Kimberly: Raw Feeder Life, RawFeederLife.comErin Scott: Believe in Dog podcast, BelieveInDogPodcast.comRaw Feeder Life, Instagram.com/RawFeederLifeBelieve in Dog Podcast, Instagram.com/Erin_The_Dog_MomThanks for listening to our podcast.  You can learn more about Erin Scott's first podcast at BelieveInDogPodcast.com.  And you can learn more about raw feeding, raising dogs naturally, and Kimberly's dogs at KeepTheTailWagging.com.  And don't forget to subscribe to The Alternative Dog Moms.

AI pricing is changing fast. OpenAI, Anthropic, and Microsoft's GitHub are all moving away from flat-rate subscriptions toward usage-based billing, and the shift is going to hit anyone whose business runs heavily on AI tools. Anthropic has already shifted some business customers to actual-usage billing. GitHub launched a new usage-based system that kicks in after monthly allotments run out. OpenAI executives have publicly floated pricing AI more like electricity or water, where heavier users pay more for slide decks, longer agent runs, code debugging, and email drafting.This episode breaks down the AI pricing shock hitting OpenAI, Anthropic, and Microsoft, what it means for businesses already building on these tools, and which alternatives are starting to look attractive. The driver is straightforward. AI labs are burning cash on chips, data centers, and talent at a rate that flat-rate subscriptions can't support. OpenAI reported a $14 billion projected loss for 2026. Anthropic just filed for IPO at a $965 billion valuation. Microsoft is spending tens of billions on AI infrastructure. The math on a $20-a-month subscription that produces unlimited GPT-5 output doesn't work anymore.The corporate response is already visible. Walmart capped staff use of its in-house AI agent. Uber is limiting monthly employee spending to $1,500 per AI coding tool. Companies that rolled out generative AI broadly in 2024 and 2025 are now reading the meters, because the same prompt that cost $0.02 in 2024 can cost $2 today on a reasoning model.The lower-cost alternatives are gaining real attention. Alibaba's Qwen and DeepSeek both run at a fraction of OpenAI and Anthropic pricing, and both have closed the quality gap enough that routing simpler tasks to a cheaper model is a defensible engineering decision. The question for every business spending on AI is which tasks need a frontier model and which can run on a model that costs 90% less for the same output.What this means for AI strategy in 2026. Flat-rate pricing was a customer acquisition tactic that worked when the labs were trying to win mindshare. Usage-based pricing reflects what AI actually costs to deliver, and it's the model the industry will settle on. For developers, freelancers, and small businesses using ChatGPT, Claude, GitHub Copilot, and Cursor every day, the bill is about to look different. For agencies and consultants billing clients for AI work, the margin model needs a rebuild.We cover the OpenAI, Anthropic, and GitHub pricing changes in detail, how Walmart and Uber are responding, why Qwen and DeepSeek matter more this quarter than they did last quarter, and what the shift to electricity-style AI pricing means for the cost of doing business in the AI economy.Keywords: AI pricing, OpenAI pricing, Anthropic billing, GitHub Copilot pricing, usage-based AI, token pricing, AI subscription, ChatGPT pricing, Claude pricing, Qwen, DeepSeek, Walmart AI, Uber AI, GPT-5 cost, AI ROI, AI infrastructure cost.

On this week’s edition of Inside the (Rob) Parker, Rob discusses the streaking Chicago White Sox, Byron Buxton's All-Star starter candidacy, and the greatest father-son duos in MLB history. Plus, World Series champion Barry Larkin swings by, and MLB Network's Brian Kenny and Ron Darling debate Shohei Ohtani's usage rate with Professor Parker. Finally, we drop Rob's latest appearance on MLB Network, and reveal the eleventh installment of Rob’s Memory Lane series. Subscribe and download all of the latest Inside the Parker podcasts and follow Rob on Twitter!! #OddCoupleSee omnystudio.com/listener for privacy information.

Seema Shah and Stephen Sopko discuss the state of the AI trade and continuing impacts infrastructure buildout will have on markets. Seema explains the trends on user consumption in generative AI apps, which she sees increasing. Stephen talks about the timeline to AI driving growth and how adoption will broaden into the wider economy.======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Seamus asked members of the public about doom scrolling off the back of the under 16 social media ban in the UK.

Summary: In this episode, Adrian and Roshan explorethe rapidly growing world of AI tokens, their impact on costs, efficiency, and investment opportunities. They discuss how tokenization affects AI performance, energy consumption, and global investment prospects.Hashtags: AI tokens, tokenization, AI investment,energy consumption, data centers, memory chips, AI growth, investmentopportunitiesSocial Media Description: New Episode Out Now: AItokens—what they are, why they matter, and how they could shape the future of technology, efficiency, and investment opportunities. Listen now!Chapters00:00 Introduction and Episode Overview00:15 Guest Introduction and Personal News00:38 Understanding AI Tokens and Their Significance02:27 How Tokenization Affects AI Products and Costs04:47 Global Investment Opportunities with EfficientTokenization06:46 Growth Projections and Usage of AI Tokens10:19 Memory and Conversation Length in AI11:36 Investment Opportunities in AI Infrastructure andSupply Chain15:47 Power and Energy Bottlenecks in Data Centers20:01 Human-AI Interaction and Token Efficiency24:34 Supply Chain Bottlenecks: Memory and Data Centers30:04 Future Outlook and Investment Strategies in AI32:05 Risks, Challenges, and the Future of AI Investmenthttps://retirementlifestyleshow.com/⁠⁠⁠⁠ ⁠⁠⁠⁠https://www.retirewithroshan.com⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@retirementlifestyleshow⁠⁠⁠⁠ ⁠⁠⁠⁠https://twitter.com/RoshanLoungani⁠⁠⁠⁠ ⁠⁠⁠⁠https://www.linkedin.com/in/roshanloungani⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/retirewithroshan⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/adrian-nicholson-74b82b13b⁠⁠⁠⁠  All opinions expressed by podcast hosts and guests are solely their own. While based on information they believe is reliable, neither Arete Wealth nor its affiliates warrant its completeness or accuracy, nor do their opinions reflect the opinion of Arete Wealth. This podcast is for general informational purposes only and should not be regarded as specific advice or recommendations for any individual. Before making any decisions, consult a professional

HPE President & CEO Antonio Neri talks with Bloomberg's Paul Sweeney and Scarlet Fu on Bloomberg Intelligence about how AI usage is helping grow the company and how important it is as a tech company to see the acceleration of AISee omnystudio.com/listener for privacy information.

Tuesday, June 16, 2026 The Dominant Duo – Total Dominance Hour -Berry Tramel for Jim, OU Baseball has a chance, College World Series, Thunder plans, technology usage and more. Follow the Sports Animal on Facebook, Instagram and X PLUS Jim Traber on Instagram, Berry Tramel on X and Dean Blevins on X Follow Tony Z on Instagram and Facebook Listen to past episodes HERE! Follow Total Dominance Podcasts on Apple, Google and SpotifySee omnystudio.com/listener for privacy information.

Plus: The U.K. moves to ban minors under 16 from major social media platforms next year. And Fox Corp to buy streaming service Roku for $22 billion. Imani Moise hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

Today we are talking about Test Driven Development, ebooks, and Drupal with guest Oliver Davies. We'll also cover Juicer Social Feed as our module of the week. For show notes visit: https://www.talkingDrupal.com/557 Topics What Is Test Driven Drupal Why Automated Tests Matter How TDD Works AI and Test Quality Balancing Test Coverage When to Write Tests Why Write the Book Why Write an Ebook From Email Course to Ebook Ebook vs Print Tradeoffs Who the Book Helps What You Will Learn Keeping Content Updated Publishing Tools Workflow Lessons and Drupal Changes Podcast and Future Books Mob Programming Explained Free Ebook and Wrap Up Resources Juicer io Drupal 11: The Upgrade Experience I've Been Waiting For codethatships Test-Driven Drupal Sculpin Guests Oliver Davies - oliverdavies.uk opdavies Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Scott Falconer - managing-ai.com scott-falconer MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Have you ever wanted to embed social feeds into your Drupal website? There's a module for that. Module name/project name: Juicer Social Feed Brief history How old: created in Mar 2026 by Denis Omerović (drupalchille) Versions available: 1.0.2, that works with Drupal 10.3 or 11 Maintainership Actively maintained (version released today!) No open issues Usage stats: 4 sites Module features and usage This module embeds an aggregated social media feed from Juicer.io directly into Drupal as a configurable block. It natively supports content from Instagram, LinkedIn, Facebook, X (Twitter), TikTok, Bluesky, YouTube, and more. Traditionally, displaying feeds from platforms like Facebook, X, or Instagram requires creating developer accounts, managing rotating OAuth tokens, and keeping up with constantly shifting API restrictions. Juicer handles all API authentication on its platform, shielding your website from sudden breaking changes by individual social networks. To use this module, you will need an active account on Juicer.io. They offer both free and paid tiers depending on how many sources you want to aggregate and how frequently you need the feed to sync. The module is created and maintained by the official Juicer.io team. That should ensure that the module is closely aligned with the product's features and any potential API changes over time. The embedded feed is made available as a Drupal block, to make it easy to control where it should appear on your site. When placing the Juicer block, the UI exposes several user-friendly settings: Feed Slug: Just paste your unique Juicer feed ID to establish the connection. Post Limit: Control exactly how many items populate initially. Source Filtering: If your Juicer account aggregates five networks, but you only want to show LinkedIn posts on a specific page, you can filter down to a single network right inside the block settings. SEO/Semantic Control: You can set titles/subtitles and choose the exact heading level hierarchy ( through ) to ensure your pages remain semantically correct and accessible. I did get a chance to test out the module and the service today, and I can tell you from experience, it's a huge improvement on having to create and pull in feeds directly. I did notice that the block didn't show up in the Drupal Canvas component library, but I was able to determine that two lines of code to declare the block as FullyValidatable were all that was needed. So I opened a Feature Request to add that, and it was merged in and a new release cut in less than an hour. So it's now Drupal Canvas compatible too! It's worth pointing out that the standard Juicer's embed script loads HTMX, which conflicts with the version of HTMX included in Drupal 11 core. As a result, the module fetches feed HTML directly from the Juicer API and includes a minimal HTMX shim to prevent errors. John, you nominated this module, why don't you start us off by telling us about how you got started using it?

Is now a good time for pilots to buy a home, or does it make more sense to wait?In this episode of The Pilot's Portfolio, Timothy P. Pope, CFP® welcomes back Kevin Walker, and Jade Barnett, respectively CEO and COO of Beacon Relocation, for a mid-year housing market check-in.Tim, Kevin, and Jade revisit earlier real estate forecasts and discuss how today's market is actually playing out, from higher mortgage rates and shifting buyer behavior to softer pricing in select markets like Florida.The conversation also covers why waiting for lower rates or prices may not always pay off, what “marry the property, date the rate” really means, and how pilots should think about buying a home within the bigger picture of cash flow, family needs, retirement savings, and long-term financial planning.What You'll Learn from This EpisodeThe market is not behaving the same everywhere. Some regions are seeing softening, but buyers should not assume every seller is willing or able to take a major discount.Florida is one of the clearest examples of market pressure because insurance costs have changed the affordability picture for many buyers.Waiting can be expensive. If a buyer is paying rent while waiting for a major home price drop, they need to compare the potential savings against the actual cost of delaying.Mortgage rates may not return to the unusually low levels buyers remember from recent years. Pilots should build their plan around today's numbers first, then look for opportunities to refinance later if rates improve.The purchase price still matters most. A refinance may change the rate later, but the buyer still needs to buy a home that fits their budget, cash flow, and long-term plan.New construction can offer real opportunities, especially when builders provide incentives or rate buy-downs. But buyers need to look closely at future property taxes, HOA costs, and lender requirements.Representation matters. Even with new construction, the builder's agent usually represents the builder, not the buyer.In multiple-offer situations, buyers should know their number before emotions take over. The goal is to make an offer they can live with whether they win or lose.Family support is becoming more common as the average first-time homebuyer age rises. But gifted funds, inherited assets, and crypto proceeds need to be coordinated with the lender early.Real estate can be a powerful wealth-building tool, but the timeline matters. If a buyer does not expect to stay in the home for at least several years, the numbers deserve extra scrutiny.Resources:Visit https://www.beaconrelocation.com/Schedule An AppointmentOur Practice's WebsiteSend Us Your Questions: info@pilotsportfolio.comThis episode is sponsored by: Beacon RelocationBeacon Relocation is a real estate firm helping pilots and air traffic controllers save money on their real estate transactions. By tapping into their network of over 1500 real estate agents across the country, pilots can save 20% of the real estate agent's commission towards your closing cost on the sale or purchase of your home. Visit https://www.beaconrelocation.com/ to learn more. Timothy P. Pope is a Certified Financial Planner™and principal owner of 360 Aviation Advisors, LLC (“360 Aviation Advisors”), a registered investment advisory firm. Investment advisory services are provided through 360 Aviation Advisors, in its separate and individual capacity as a registered investment adviser. Podcast episodes are provided through Pilot's Portfolio, in its separate and individual capacity.We try to provide content that is true and accurate as of the date of publishing; however, we give no assurance or warranty regarding the accuracy, timeliness, or applicability of any of the contents. We assume no responsibility for information contained on this website and disclaim all liability in respect of such information, including but not limited to any liability for errors, inaccuracies, omissions, or misleading or defamatory statements.Links to external websites are provided solely for your convenience. We accept no liability for any linked sites or their content and remind you that we have no control over their content. When visiting external web sites, users should review those websites' privacy policies and other terms of use to learn more about, what, why and how they collect and use any personally identifiable information.Usage of this content constitutes an explicit understanding and acceptance of the terms of this disclaimer. 

Between the down payment, the mortgage, and the repairs, owning a home is never as simple as getting the keys. The government is proposing broader use of housing provident fund. On the show: Steve, Fei Fei & Yushun

Today we are talking about Drush, Core Contributions, and Drupal's Past with guest Moshe Weitzman. We'll also cover Cache Metrics as our module of the week. For show notes visit: https://www.talkingDrupal.com/556 Topics Moshe Updates and Clients Maintaining Drush Long Term Locale Performance Overhaul CLI in Core Initiative Which Commands Make the Cut Roadmap Contrib Commands Moving Commands Technical Hurdles How to Help From AI Initiative DDEV Add-ons for Local CI MySQL Toolkit Database Images Testing With Real Databases Devel Module Status Organic Groups Origins Where Ideas Come From Finding Drupal Early Days Release Cadence And Backward Compatibility Avoiding Maintainer Burnout Maintaining With AI And Xdebug Resources Drush's Final Act Drupal cli issue DDEV addons https://github.com/ddev/ddev-drupal-contrib https://github.com/weitzman/ddev-mtk https://www.drupal.org/project/dtt Guests Moshe Weitzman - weitzman.github.io moshe-weitzman Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Scott Falconer - managing-ai.com scott-falconer MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Have you ever wanted insights into how cache is working on your Drupal site? There's a module for that. Module name/project name: Cache Metrics Brief history How old: created in Oct 2019 by Moshe Weitzman (moshe weitzman), today's guest, a consistent core contributor, a member of the security team, and one of the rare few with a two-digit user id on drupal.org Versions available: 2.0.3, 2.1.0, and 2.2.0, the last of which works with Drupal 8.7.7, 9, 10, and 11 Maintainership Actively maintained Security and test coverage Documentation - in depth README Number of open issues: 2 open issues, 1 of which is a bug, but is marked fixed Usage stats: 37 sites Module features and usage With this module enabled, your Drupal site will log all cache tag invalidations Additionally, cache tag invalidations will be sent to New Relic as custom events, where you can use the rich reporting tools available to mine for further insights. Many Drupal hosting options include New Relic out-of-the-box, and there's a free tier you can use if you're self-hosting, so this a reporting tool lots of Drupal sites can use Cache hits and misses are also sent to New Relic, so you can investigate things like cache misses as a percentage by cache bin Finally, the aforementioned README also includes information about how to use a different analytics provider, in case New Relic doesn't meet your specific needs Drupal sites probably don't need this kind of visibility on a regular basis, but if you're troubleshooting any kind of cache-related issue, this could be really useful

Carl and Mike close out the show with more thoughts on the NBA Finals, Quin Snyder getting his contract extension and Alex Van Pelt saying there currently is not a QB competition due to the fact MP9 is not able to fully participate.