Podcasts about who owns

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

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

Ben Keneally, BCG's Asia Pacific health care services leader, explains why AI is now many patients' first stop — often before they see a doctor. He argues that pairing AI with personal health records improves outcomes and equity, and that health leaders, not LLMs, must shape how this shift unfolds.You'll Learn:Why AI is becoming the default first step in care, especially in markets with limited access to cliniciansHow pairing AI with personal health records improves treatment compliance and catches errors earlierWhy driving AI into end-to-end care pathways takes leadership, not just frontline experimentationLearn More:BCG's Latest Thinking on the Health Care Industry: https://on.bcg.com/4w2MmPtConsumers Are Ready for AI-Enabled Health Care. Health Systems Need to Be, Too.: https://on.bcg.com/4vo2ZDXChapters (0:00) The Biggest Health Care Shift in Centuries(1:47) How Is AI Being Used in Health Care Today?(2:23) Where Is AI Adoption Highest, and What Does That Tell Us?(3:29) Does AI Create a Two-Tier Health System?(4:55) What's Next for AI Agents?(6:51) Where Are the Biggest Opportunities: Diagnosis, Navigation, or Appointments?(8:13) The Health Care System AI Could Build(9:56) Does Your Health System Need Its Own AI?(11:36) The Danger of Inaccurate Health Advice(12:53) What Role Does Trust Play?(13:52) How Are Health Care Organizations Shaping This Shift Today?(14:47) AI Alone Won't Transform Health Care(15:49) Who Is Responsible When AI Gets Health Care Wrong?(17:22) Who Owns the Patient Relationship?(18:29) Is the Shift to AI in Health Care Inevitable?(19:49) Is It AI Plus Clinicians or AI Replacing Clinicians?Meet the ExpertBen Keneally, BCG Managing Director & Partner: https://on.bcg.com/4woTTbcListen to Other Episodes of The So What from BCG podcastYouTube | https://youtube.com/playlist?list=PLMJgyXjV5gMI9JV-GcF_D1Y6zyf1Eab_0&si=plXqe7-YNzbG56U8Apple | https://podcasts.apple.com/us/podcast/the-so-what-from-bcg/id1591194141Spotify | https://open.spotify.com/show/2NSVR7qrAyZ4CaGsnknbBk?si=1d846c2af8784923Other platforms | https://lnk.to/so-what-general-show12Follow BCGhttps://www.bcg.com/LinkedIn | https://www.linkedin.com/company/boston-consulting-groupThis podcast uses the following third-party services for analysis: Podtrac - https://analytics.podtrac.com/privacy-policy-gdrp

This week on Market Mondays, we tackled the biggest stories shaping the markets, technology, and investing. From the controversy surrounding Trump's investment accounts and crypto allegations to NVIDIA's bold new AI startup strategy, we broke down what matters—and what investors should ignore.We also discussed Michael Saylor's latest Bitcoin sale, the SK Hynix IPO, warnings of a potential AI bubble, Alex Karp's passionate comments on AI spending, TSM's long-term outlook, whether QQQ is still the best ETF choice, lessons from the 2026 market rally, Wall Street's biggest forecasting mistakes, which companies have the strongest competitive moats, and the one private company we'd invest in today. Plus, we answered a practical question: if you started over with $50,000, debt, and a low credit score, how would you rebuild your financial future?Whether you're investing for the long term, trading today's market, or looking to stay ahead of the biggest trends in AI, crypto, and equities, this episode is packed with actionable insights to help you make smarter investment decisions.TIMESTAMPS:00:00 Why Wealth Matters00:33 Show Disclaimer01:08 July Check In01:48 Live Week Schedule02:46 Salon Suite Spotlight04:49 Community Shoutouts05:36 Market Facts Roundup07:17 Semiconductor Volatility09:44 Invest Fest Youth Day11:21 Catering Callout14:21 Relationships Barter Play16:03 Singles Lounge Launch18:00 Trump Accounts Explained19:09 Barriers Trust Education24:17 Compounding Math Examples28:59 ETF Alternatives Plan30:19 Reaching Those In Need33:11 Website Robinhood Details34:20 Culture Responsibility Talk37:38 Spend It Culture38:33 Trump Account Alternatives39:23 Trump Meme Coin Fallout41:12 Rug Pull Mechanics43:59 Crypto Scam Culture46:02 Equities Influence Shift48:30 Presidential Trading Stats52:03 NVIDIA Startup Strategy54:57 Compute for Revenue Share58:30 NVIDIA as Venture Capital01:03:06 Relationship Capital Banter01:05:51 50K Reset Plan01:11:24 Debt Versus Market Returns01:15:37 MicroStrategy Dividend Sales01:22:12 SK Hynix ADR Debut01:23:43 Memory Bottleneck Thesis01:25:15 IPO Signals to Watch01:26:31 Micron vs Hynix Outlook01:31:11 Valuations and Patience01:35:33 AI Bubble Reality Check01:39:41 Alex Karp Safety Rant01:48:23 Who Owns the Stack01:53:01 TSM Earnings Preview01:55:27 Core Four Investing01:57:06 Events and Community01:58:19 World Cup Banter02:01:32 Final Sendoff#MarketMondays #Investing #Stocks #StockMarket #AI #ArtificialIntelligence #NVIDIA #Bitcoin #Crypto #MichaelSaylor #TSMC #QQQ #ETFs #WealthBuilding #Finance #Business #LongTermInvesting #Trading #EarnYourLeisure #MarketAnalysisAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

00:00 She Don't Like How I Make It Jiggle 00:50 Devin Weston's Involvement in The Mile High Club 06:51 Security or Lack Thereof in Countries 08:23 The Kind of Girl a Gamer Wants 10:10 Huge Solar Panel Batteries Installed in My House 11:03 Cutting Costs and Challenges When Building a House 13:20 The Highly Specific Audience for Anime Movies 14:41 My Takes on Project Hail Mary 16:05 Why I Didn't like Watchdogs 2 Compared to the Original 16:56 Channel Dips, Growth and Changes in My Content 18:36 Who Owns the Content being Recorded?

Happy Father's Day! Today we're re-running some of our favorite Star Wars dad interviews — and dedicating this one to my own dad, Kerwin Yarde. Love you, Pop.This compilation brings together dads whose lives have been shaped by this galaxy: the legendary Billy Dee Williams, Pete Fletzer (author of Who Owns the Myth? Star Wars Fandom and the Soul of the Saga), Leland Chee, Todd Hoffman of Big T and Lil T, Raphael Moran from The Geeky Dad Podcast, the crew from The Dad Batch, and autism Dad and advocate Jamiel Owens. Each of them carries their own version of the same story: a movie that became a lifelong bond between father and child.The galaxy has a way in for everyone—and for a lot of us, a parent held the door openWhat's YOUR Star Wars origin story, and who shared it with you? Who got you into this galaxy?

“Leadership that costs you your health, your peace, or your identity is not leadership — it's survival.”Too many leaders don't realize they've stopped leading.They're just surviving.In this episode of the Personalization Outbreak Podcast, Glenn Llopis sits down with Randy, former C-suite executive and author of The Art of Transformation, to explore the hidden cost of success and what happens when achievement comes at the expense of the human being behind the title.Randy shares:⚠️ How high performers can slowly lose themselves inside successful organizations 

Who Owns the Land? That’s the question being asked in homes and on college campuses all over the world. Are the Israelis occupiers or oppressors? We are seeing a rising tide of antisemitism across the globe. What is the history of this conflict? Dr. Charles Dyer, host of The Land and The Book, will join us to provide insight and knowledge behind the Arab-Israeli conflict.Become a Parshall Partner: http://moodyradio.org/donateto/inthemarket/partnersSee omnystudio.com/listener for privacy information.

Industry 4.0 is moving beyond factory walls and into farms, forests, and fields.David Potere, a senior tech leader in BCG's Industrial Goods and Climate Change and Sustainability practices, explores AI's move into the outdoor world. Robotics and connected systems are changing how farming and other outdoor activities get done.You'll Learn:Outdoor automation requires AI systems that can operate with constant uncertainty.Leaders should rethink long-held operating models as AI and robotics reshape how physical work gets done.The most valuable AI systems may be the ones that simplify complexity rather than add more dashboards.Learn More:David Potere: https://www.linkedin.com/in/davidpotere/What 1,000 Farmers Told Us About Tech Adoption: https://on.bcg.com/4euA76VClimate-Smart Agriculture Needs a Better Yardstick: https://on.bcg.com/4ejIfH6David on the Climate Rising Podcast: https://podcasts.apple.com/us/podcast/david-potere-at-bcg-x-using-ai-satellites-in-climate/id1482781075?i=1000767537614AI Foundation Model for Extreme Weather: https://on.bcg.com/4vKiwyzChapters00:00 – How Will AI Impact Outdoor Industries?04:26 –The Challenges of Taking Tech Outside06:11– What Would a Farm That Thinks for Itself Look Like?08:27 – Is AI Rescuing Agriculture?10:55– Will AI Only Help Big Farms?14:39 – Who Owns the Data?16:16 – What Can Leaders Learn from the AI Outdoors?18:51 – Next Steps to Truly Benefit from AIThis podcast uses the following third-party services for analysis: Podtrac - https://analytics.podtrac.com/privacy-policy-gdrp

Main Topic: Truth-Telling in Fiction and Memoir with Grace Sammon But our conversation spans The Difference Between Telling Your Truth and Owning Your Truth; The Ghostwriter as a Literary Device; and Why Fiction Holds What Memoir Sometimes Can't; Who Owns the Truth and what Silence costs us; invisibility, and relevance. (Really interesting conversation.) PATREON: Thank you to my existing patrons for believing in my work offline and here in the podcast. If you are a patron, in either tier, you get all my content, always. You can support me and my dreams and my writing and my aligned author life for $11.11 USD, and I will be so so grateful. Truly. Heart to heart. Gratitude for your gifts. If you want coaching too (with TWO LIVE CALLS EACH MONTH, you can BACK me at $55.55/mo USD). You will NEVER find coaching sessions for less money than this. If you've ever wanted continued support for your writing and accountability for your projects, this is the way to do it.    Become a patron of the arts and of me at Patreon.com/valerieihsan.    And you can support my friend and colleague and Visiting Co-Host author Erick Mertz at Patreon.com/strangeairstories for short stories in the paranormal mystery genre.      Announcements/Author Updates: designing my next writing retreat in Costa Rica. My first international retreat (even though I live here) so there are a lot more moving pieces than I first imagined. Taking a four-month course to get me through all the legalities and best practices. If you are interested in the updates on the retreat, you can go to valerieihsan.com/retreat. request to bring back a regular patron gathering for all members (paid and unpaid) Mini writing retreat (cozy, candle; oracle card pull to set intention; check-in: 1 struggle, 1 win, what you are working on tonight; guided meditation; writing words; share word count (optional)); PLEASE send me a DM or a comment where you heard this podcast, or in the Patreon community. Let me know if this is something you crave. It's not just shared writing space. It's a retreat from regular life (dishes, dogs, kids, day job) and a safe and sacred space to connect and to write. talking with the architect, nailing down our must-haves half to drive north to sign a document (complications with names and lawyers)   What are you reading? Just finished:   Soul Sourced Entrepreneur (Christine Kane) Mosswood Apothecary (JP Rindfleisch IX) The Reliable Narrator (Grace Sammon) Back-burner Books: (Still on the stack but haven't finished reading them yet...) Creative Act (Rick Rubin)  Four Thousand Weeks: Time Management for Mortals by Oliver Burkeman;  (Main Topic):  Notes: 1. As a novelist, a memoirist, and an author looking into offering ghostwriting services, I was super intrigued by many of your talking points in your media kit. I hope we get to talk about all my favorites. Let's start with Why Fiction Holds What Memoir Sometimes Can't. That's a juicy statement! A place for both; memoir is huge right now; tell our story to ourselves first, and then to others next. Hear a story better as fiction sometimes. Why you are writing the book?   2. I'm a sucker for books about authors, and the last novel that had that hook was also about a ghostwriter, what can you say about using the ghostwriter as a literary device? Fascinated by the job and wanted to dive into that; For instance, what is it like to see your book hit the NYT Bestseller List without your name on it? We see her through the stories she's writing.   3. Difference Between Telling the Truth and Owning Your Truth. Experienced childhood abuse, can you hide behind your own story, what are these effects on me, Invisibility starts when you lose your roles and don't have anything to talk about it. Relevance, meaning, purpose. 4. Who Owns the Truth? And What does silence cost us as women and as authors? (GoShiftKey.com,  Joelle)    And don't forget: Go to valerieihsan.com to schedule a free consultation to see if Aligned Author is right for you.   Find Us:   Valerie's Linktree: https://linktr.ee/valerieihsan  Erick's Linktree link: https://linktr.ee/erickmertzauthor Tools: ProWriting Aid: https://prowritingaid.com/?afid=9378 (affiliate link)

Most people think breast cancer treatment ends when the tumor is gone. Science says that's where the real damage often begins, and the woman making that argument was diagnosed at 28, lost her mother to ovarian cancer the same year, and turned her own dismissal by the medical system into a specialization that now treats women nobody else will touch. In this episode, I sit down with Dr. Corinne Menn Board Certified OBGYN, breast cancer survivor, and one of the few specialists in the world treating menopause in cancer survivors. We break down why 80% of women diagnosed with breast cancer have no strong family history, why tamoxifen and aromatase inhibitors quietly devastate brain, bone, and sexual health, and why telling a woman with severe vaginal atrophy to use coconut oil is not evidence-based medicine. Dr. Corinne also opens up about her own diagnosis, her premature menopause at 28, the truth about hormone replacement therapy after breast cancer, and the BRCA, ApoE4, and surgical menopause snowball nobody is putting together for patients. This conversation will completely change how you think about breast cancer, menopause, and the women's health crisis hiding in plain sight. Reduce your risk of Alzheimer's with my science-backed protocol for women 30+: https://go.neuroathletics.com.au/youtube-sales-page Subscribe to The Neuro Experience for evidence-based conversations at the intersection of brain science, longevity, and performance. _____ TOPICS DISCUSSED 00:00 Intro: Why Nobody Is Coming to Save Breast Cancer Survivors 01:05 Karin's Origin Story: Diagnosed at 28, Losing Her Mom, and Premature Menopause 03:06 The 85% Cure Rate Lie: Why Survival Comes at a Brutal Cost 06:13 Breast Cancer Is Not One Disease: Why 80% Have No Family History 08:14 BRCA1, BRCA2, and the Genetic Mutations Most Women Never Get Tested For 13:14 Karin's Own Genetic Test Story and Why 23andMe Is Not Enough 15:07 BRCA1 vs BRCA2: Age of Onset and When to Remove Ovaries 17:30 The Biology of Estrogen: Why Estrogen Does Not Cause Breast Cancer 23:40 Birth Control, Breastfeeding, and the Real Risk Factors 26:17 Alcohol, Inflammation, and the Toxins Driving Cancer Rates 27:46 Tamoxifen Explained: What It Does to Your Brain, Bones, and Body 34:23 Aromatase Inhibitors: Putting Your Estrogen in the Basement 37:15 Where Women Go When No Doctor Will Help Them 41:17 Oophorectomy, Early Menopause, and the 6 to 12% of Women Affected 44:26 Why Black Women Face the Highest Risk and the Least Care 46:20 The ApoE4, BRCA, and Surgical Menopause Snowball 48:02 Coconut Oil Is Not Medicine: The Vaginal Estrogen Truth 50:35 HRT Denial and the Myths Keeping Women From Treatment 51:13 Who Owns the Breast Cancer Survivor After Treatment Ends 54:18 Why the System Fails: Reimbursement, Resources, and Survivorship Gaps 01:00:07 Can You Be on Tamoxifen and Hormone Replacement Therapy? 01:03:26 Three Neurologists, One Tau Test, and the Dementia Dismissal 01:06:26 Positive Stories: Women Who Took Back Their Health and Won 01:09:16 The One Wish: Valuing Ovarian Function Beyond Reproduction _______ Thank you to our sponsors KetoneIQ: https://ketone.com/NEURO for 30% OFF DailyBasis: https://www.dailybasislife.com/NEURO for 50% off first month IQBARS: https://www.eatiqbar.com/ Biologica: https://biologica.com/NEURO Up to 32% off first subscription order Cure Hydration: https://www.curehydration.com/ Use code NEURO gets 20% off Honey Love: https://www.honeylove.com/NEURO Save 20% Off Honeylove #honeylovepod _______ I'm Louisa Nicola - clinical neurophysiologist - Alzheimer's prevention specialist - founder of Neuro Athletics. My mission is to translate cutting-edge neuroscience into actionable strategies for cognitive longevity, peak performance, and brain disease prevention. If you're committed to optimizing your brain- reducing Alzheimer's risk - and staying mentally sharp for life, you're in the right place. Stay sharp. Stay informed. Join thousands who subscribe to the Neuro Athletics Newsletter → https://bit.ly/3ewI5P0 Instagram: https://www.instagram.com/louisanicola_/ Twitter : https://twitter.com/louisanicola_ Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us Fan Mail0:00:00 Warm-Up Songs, Old Powwow Tapes & “Old Style” vs “Contemp”0:09:00 Sweet Grass, Flying Eagle & The Sweet Spot of 80s–90s Singing0:13:30 Copying Songs, Social Media Beadwork Beef & Who Owns a Melody?0:18:10 “Walking the Red Road”: Black Elk, Recovery Culture & Pan-Indian Buzzwords0:25:00 Medicine Wheel 101: Stone Circles, Four Quadrants & a 1970s Best-Seller0:33:40 Turtle Island Origins: Sky Woman & Jesuit Records0:41:45 Seven Generations: Great Law of Peace, Citation Trails & Slogan Inflation0:51:30 Boozhoo or Bonjour? Ojibwe Greetings, Missionary Dictionaries & Folk Etymology1:03:15 Apsáalooke vs. “Crow”: Large-Beaked Birds & Inside-Language1:22:05 Pan-Indian Gospels: Medicine Wheels, Turtle Island & the Church of ActivismHosts: Aaron Brien (Apsáalooke), Shandin Pete (Salish/Diné). How to cite this episode (apa)Pete, S. H. & Brien, A. (Hosts). (2026, May 11). #71 - Seven Generations Later: ChatGPT Arrives on Turtle Island and Asks, “Where's the Red Road?” [Audio podcast episode]. In Tribal Research Specialist:The Podcast. Tribal Research Specialist, LLC. https://tribalresearchspecialist.buzzsprout.comHow to cite this podcast (apa)Pete, S. H., & Brien, A. (Hosts). (2020–present). Tribal Research Specialist:The Podcast [Audio podcast]. Tribal Research Specialist, LLC. https://tribalresearchspecialist.buzzsprout.com/Podcast Website: tribalresearchspecialist.buzzsprout.comApple Podcast: https://podcasts.apple.com/us/podcast/tribal-research-specialist-the-podcast/id1512551396Spotify: open.spotify.com/show/1H5Y1pWYI8N6SYZAaawwxbX: @tribalresearchspecialistFacebook: www.facebook.com/TribalResearchSpecialistYouTube: www.youtube.com/channel/UCL9HR4B2ubGK_aaQKEt179QSupport the showWant to make a one time donation?https://buymeacoffee.com/tribalresearchSupport the showInterested in some TRS Merch? Click here https://tribal-research-specialist-llc.square.site/Want to make a one time donation?https://buymeacoffee.com/tribalresearch

⁠⁠⁠⁠Subscribe to the Outbound Kitchen newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠---This episode was originally recorded in French for Cognism's Prospect podcast with Laetitia Fall. The English audio you're hearing is an AI-translated dub generated with ElevenLabs. Original French version: https://youtu.be/DMRO_GGp-1A--If you're new here, I'm Elric Legloire, founder of Outbound Kitchen. I help B2B SaaS companies between $2M and $50M ARR boost their outbound results. My view: in 2026, productivity is the multiplier, not headcount. --We discuss:- Why outbound isn't dead, but pipeline got roughly 10x more expensive (1% conversion in 2015 to needing 1,000 emails per opportunity in 2023, source: Winning By Design)- Why global SDR headcount grew over 20% in a year while public layoffs dominated the LinkedIn feed- The Owner.com benchmark: 40 appointments per BDR per month, 25% close rate, $70-80K sourced revenue per BDR per month- When to hire experienced SDRs vs juniors, and the 12-meetings-per-month threshold that signals your playbook is ready- Why ICP work beats tool selection (Tier 1 closes at 25%, Tier 2 at 5%, you need 5x the volume to compensate)- Snowflake's 140 ICP data points and why 10-15 is the realistic target for most teams- Multichannel orchestration when only 30% of your prospects are reachable on LinkedIn- The data-quality test most teams skip: coverage rate per persona, per market- AI in outbound (January 2026): what actually works, why most LinkedIn AI messages fail, and the foundation AI needs (market, CRM, message principles, signals)Referenced:Outbound Kitchen newsletter: https://newsletter.outbound.kitchenElric Legloire on LinkedIn: https://www.linkedin.com/in/elriclegloire/Cognism's original French episode: https://youtu.be/DMRO_GGp-1AHost: Laetitia Fall Sources cited in episode: Winning By Design, Insight Partners (Jeremy Donovan's portfolio data), Owner.com BDR benchmarks, Snowflake ICP methodology----When you're ready⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Want to work with me? Send me a DM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠---Connect with me⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

// GUEST // Youtube: ⁠https://www.youtube.com/@BeforeSkool⁠   // SPONSORS // Blockware Solutions: ⁠https://mining.blockwaresolutions.com/breedlove⁠ Performance Lab Supplements: ⁠https://www.performancelab.com/breedlove⁠ The Farm at Okefenokee: ⁠https://okefarm.com/⁠   // PRODUCTS I ENDORSE // Protect your mobile phone from SIM swap attacks: ⁠https://www.efani.com/breedlove⁠ Lineage Provisions (use discount code BREEDLOVE): ⁠https://lineageprovisions.com/?ref=breedlove_22⁠ Colorado Craft Beef (use discount code BREEDLOVE): ⁠https://coloradocraftbeef.com/⁠ Salt of the Earth Electrolytes: ⁠http://drinksote.com/breedlove⁠ Jawzrsize (code RobertBreedlove for 20% off): ⁠https://jawzrsize.com⁠   // UNLOCK THE WISDOM OF THE WORLD'S BEST NON-FICTION BOOKS // ⁠https://course.breedlove.io/⁠   // SUBSCRIBE TO THE CLIPS CHANNEL // ⁠https://www.youtube.com/@robertbreedloveclips2996/videos⁠   // TIMESTAMPS // 0:00 – WiM Episode Trailer 1:38 – Podcast Begins 9:00 – What Is Money? Optionality, Debt, and the Nature of Wealth 20:00 – Keynesian vs Austrian Economics: Why One Serves the State 28:00 – Time Preference, Money Printing, and the YOLO Mentality 40:00 – Gold, the Protestant Reformation, and the Birth of Capitalism 51:40 – Mine Bitcoin with Blockware Solutions 52:43 – Bitcoin vs. Shitcoins: Why There Can Only Be One 1:05:00 – The Federal Reserve Is Legalized Counterfeiting 1:14:00 – Who Owns the Fed? 30 Families and the Ultimate Beneficial Owners 1:26:59 – Performance Lab Supplements 1:28:10 – Can Bitcoin Be Stopped? Zero, the Printing Press, and Unstoppable Ideas 1:45:00 – The Sovereignty of Rules: Why No One Controls Bitcoin 2:00:00 – Health, Relationships, and What Money Can't Buy 2:13:22 – The Farm at Okefenokee 2:14:23 – Time, Meaning, and the Five Pillars of a Fulfilled Life 2:44:38 – Protect Yourself From SIM Swaps 2:45:45 – Unlock the Wisdom of the Best Non-Fiction Books   // PODCAST // Podcast Website: ⁠⁠https://whatismoneypodcast.com/⁠⁠ Apple Podcast: ⁠⁠https://podcasts.apple.com/us/podcast/the-what-is-money-show/id1541404400⁠⁠ Spotify: ⁠⁠https://open.spotify.com/show/25LPvm8EewBGyfQQ1abIsE⁠⁠ RSS Feed: ⁠⁠https://feeds.simplecast.com/MLdpYXYI⁠⁠   // SUPPORT THIS CHANNEL // Bitcoin: 3D1gfxKZKMtfWaD1bkwiR6JsDzu6e9bZQ7 Sats via Strike: ⁠⁠https://strike.me/breedlove22⁠⁠ Paypal: ⁠⁠https://www.paypal.com/paypalme/RBreedlove⁠⁠ Venmo: ⁠⁠https://account.venmo.com/u/Robert-Breedlove-2⁠⁠   // SOCIAL // Breedlove X: ⁠⁠https://x.com/Breedlove22⁠⁠ WiM? X: ⁠⁠https://x.com/WhatisMoneyShow⁠⁠ Linkedin: ⁠⁠https://www.linkedin.com/in/breedlove22/⁠⁠ Instagram: ⁠⁠https://www.instagram.com/breedlove_22/⁠⁠ TikTok: ⁠⁠https://www.tiktok.com/@breedlove22⁠⁠ Substack: ⁠⁠https://breedlove22.substack.com/⁠⁠ All My Current Work: ⁠⁠https://linktr.ee/robertbreedlove⁠

✨ Raising a strong-willed child and need support tailored to your specific situation? Join the Amazing Parents Club for weekly live Q&As with Dr. Lindsay → https://www.drlindsayemmerson.com/workshop Strong-willed children who argue about everything aren't being disrespectful — their developing brains are doing exactly what they're supposed to do. In this video, Dr. Lindsay uses Piaget's research on the preoperational stage (ages 2–7) to show you why the arguing is a sign of healthy development, and gives you the exact language to respond in the moment. Once you understand what Dr. Lindsay calls The Preoperational Push, the frustration shifts. Not because the behavior changes immediately — because what you're looking at changes completely. Research shows that children ages two through seven are in a stage of active cognitive development defined by rule-testing and the construction of logical structures (Piaget, 1964). In this video: → What Piaget's preoperational stage tells us about children ages 2–7 → Why "who owns the car?" is a sign of advanced reasoning — not disrespect → How to reframe "she always has to win" as persistence and self-advocacy → The Preoperational Push: why defiance is often problem-solving in disguise → Two language shifts to validate thinking AND hold your boundary

Digital battery passports are set to transform supply chains by driving transparency, interoperability, and circular business models beyond compliance. In this episode, we explore how companies can prepare for 2027 and use compliance as a strategic advantage.Download the ⁠⁠⁠⁠⁠⁠⁠⁠⁠episode transcript⁠⁠===== In this episode, we unpack the EU digital battery passport mandate and what it means for global supply chains. We discuss key requirements, data ownership, interoperability, security, AI, and the business value of digital product passports. The conversation also covers how companies can prepare for 2027 and use compliance as a strategic advantage.===== Guest: Oleksandra Ostapenko, SAP15+ years of international experience in overseeing complex, large-scale programs and products, driving strategic initiatives, and delivering impactful results across cross-functional and cross-cultural teams.• Expertise in product and portfolio management, strategic customer engagements, corporate operations, and mergers and acquisitions.• As Head of Product Management, Industry Standards at SAP, I define the product vision, strategy, and roadmap to embed industry standards such as AAS and regulatory requirements such as ESPR and Digital Product Passport across our portfolio and lead initiatives to drive adoption of both our products and the standards.Host 1: Sin ToSin brings over 15 years of experience in the digital media and technology industry – primarily in marketing, business development, thought leadership, and editorial. At SAP, they ensure that SAP's supply chain solutions are properly visible with a focus on future trends and sustainable innovations as part of the Thought Leadership & Awareness Supply Chain Team.Host 2: Zoriana ZahorodniaZoriana is a Product Marketer specializing in Supply Chain Management. As an engaging content creator, blogger, and podcaster, she explores how supply chain innovations and sustainability shape the future of global business.===== Show Links:SAP Digital Battery Passport Join us at Hannover Messe  SAP Digital Supply ChainFollow Us on Social Media : Oleksandra Ostapenko Sin To Zoriana Zahorodnia SAP Digital Supply Chain Please give us a like, share, and subscribe to stay up-to-date on future episodes!  ===== Chapters: 00:00:00 Beyond Compliance Promise00:00:47 Podcast Intro and Guests00:02:19 What Is Battery Passport00:03:47 2027 Requirements and Scope00:05:50 Who Owns the Data00:08:14 SAP Solution Overview00:11:33 Data Challenges and Standards00:15:18 Global Regulations and Security00:19:13 Value Creation Use Cases00:21:57 Roadmap to 202700:23:51 Blueprint for Other Products00:26:24 Demo Invitation Hannover Messe00:27:10 AI in Battery Passports00:28:14 2030 Outlook and Wrap Up

A tiny group of very wealthy people are burning through carbon like there'll be no tomorrow. Taxcast host Naomi Fowler talks to economist Tasnia Hussain about the most effective ways to tax emissions and address carbon inequality. Plus: we bring you the highlights of the ongoing historic effort at the UN to overturn a century of global tax rule setting largely by former imperial powers at the OECD to suit them and their multinationals. With economist Tasnia Hussain, University of Toronto, also featuring Navid Hanif (UN Assistant Secretary-General), Ryad Selmani from Terre Solidaire, UN representative for Jamaica, UN represntative from the Youth Forum, and private jet and carbon emissions tracker Jack Sweeney and his lawyer (interviewed on TV). Hosted and produced by Naomi Fowler of the Tax Justice Network. Transcript of the show: https://podcasts.taxjustice.net/wp-content/uploads/2026/04/Taxcast_March_26_Transcript.pdf (May not be 100% accurate) Further resources: Want to cut emissions? Young advocate says tax the rich — and what they own https://www.nationalobserver.com/2026/01/19/opinion/carbon-emissions-taxation-billionaires  Optimal Carbon Policy under Carbon Inequality https://tasniahussain.github.io/assets/Optimal_Carbon_Policy.pdf  From Taylor's Jet to Taxation for Climate Rescue: Tasnia Hussain https://newsletter.economics.utoronto.ca/from-taylors-jet-to-taxation-for-climate-rescue-tasnia-hussain/ OECD collapse will lock in countries' tax losses to US firms https://taxjustice.net/press/oecd-collapse-will-lock-in-countries-tax-losses-to-us-firms/ US will be exempt from global tax deal targeting profits of large multinationals https://www.theguardian.com/business/2026/jan/06/us-exemption-oecd-global-tax-deal-multinational-companies  Washington's Proposed Millionaires Tax, FAQs: https://budgetandpolicy.org/resources-tools/2026/03/Updated-3-3-BPC-Millionaires-Tax-FAQ.pdf Washington state's 'historic' millionaire tax takes aim at super-rich – will it succeed? https://www.theguardian.com/us-news/2026/mar/31/washington-state-millionaire-tax-wealth The party of the rich just taxed the rich in WA. Can that hold? https://www.seattletimes.com/seattle-news/politics/the-party-of-the-rich-just-taxed-the-rich-in-wa-can-that-hold/  Previous Taxcasts: https://podcasts.taxjustice.net/episode/134-who-owns-the-climate-crisis/ Who Owns the Climate Crisis? and The Millionaire Exodus Myth https://podcasts.taxjustice.net/episode/155-millionaire-migration-myth/  Our podcast website with all our podcasts is https://podcasts.taxjustice.net/   

Sendil Nellaiyapen, Engineering Manager at Uber, has built systems that scale to millions of users. In this episode he shares what most engineers get wrong about both system design and the move into engineering managementIn this episode, we cover:Ingredients for designing systems that scale to millions of usersHow to know when to compromise on architectureThe trade-offs of going from IC to engineering manager and why the role is harder than it looksHow to handle opinionated engineers, set team guardrails, and build high-performing engineering cultureWhether you're a senior engineer weighing the move into management, or already leading teams and looking to sharpen your system design thinking, this one's for you.OUTLINE:00:00:00 - Intro00:01:05 - The Ingredients for Building Systems at Scale00:02:23 - When to Compromise on Your Foundation00:03:42 - Scaling from 2,000 to 5 Million Users00:06:37 - Why Clarity Beats Seniority Every Time00:08:27 - The Danger of Muscle Memory in Engineering00:10:25 - MVP Mindset: What You Can and Can't Compromise00:13:22 - How High-Performing Teams Handle Growing Complexity00:15:04 - Who Owns the Assumptions? Shared Team Responsibility00:17:04 - Building Open Frameworks Instead of Closed Rules00:19:53 - Latency Is Overrated (Here's Why)00:22:52 - Recipes for Disaster: The Biggest System Design Pitfalls00:24:17 - The Scala Horror Story: When Elegance Kills Velocity00:26:52 - How to Handle Opinionated Engineers on Your Team00:29:03 - Setting Guardrails: The Manager's Design Responsibility00:32:01 - The Hardest Trade-Off Going from IC to Engineering Manager00:34:35 - Should Great Engineers Stay IC or Go into Management?00:37:11 - BFS vs DFS Engineers: Which Type Makes a Better Manager?00:39:05 - The Real Cost of Becoming a Manager (And Why It's Worth It)00:41:52 - Outro#systemdesign #engineeringmanager #softwareengineering

Just who do you think you are?! When Jesus dared to do some much-needed housekeeping in the Temple, the religious elite were furious. They challenged His authority. He challenged their authority... and identity... and destiny. Before the day was over Christ would checkmate the scribes, the elders, the Pharisees, the Sadducees and even the Herodians. It was a bad day to be a hypocrite. Here's Jim to open a sermon from Mark 12 called, Who Owns the Vineyard? Listen to Right Start Radio every Monday through Friday on WCVX 1160AM (Cincinnati, OH) at 9:30am, WHKC 91.5FM (Columbus, OH) at 5:00pm, WRFD 880AM (Columbus, OH) at 9:00am. Right Start can also be heard on One Christian Radio 107.7FM & 87.6FM in New Plymouth, New Zealand. You can purchase a copy of this message, unsegmented for broadcasting and in its entirety, for $7 on a single CD by calling +1 (800) 984-2313, and of course you can always listen online or download the message for free. RS02182026_0.mp3Scripture References: Mark 12:1-12

It's a fun one! We're talking with Jessamyn about the public domain, new and old technology, blogging at the DNC, and lots of inside baseball. Media mentioned https://en.wikipedia.org/wiki/Jessamyn_West_(librarian) https://www.librarian.net/stax/5566/the-mining-of-the-public-domain/ https://jessamyn.com/tweets/  https://tararobertson.ca/2016/oob/  Request to Verify Eligibility for Free Ebooks for the Print Disabled https://docs.google.com/forms/d/e/1FAIpQLScSBbT17HSQywTm-fQawOK7G4dN-QPbDWNstdfvysoKTXCjKA/viewform  Veii's music: https://veii.bandcamp.com/ Who Owns this Sentence?: A History of Copyrights and Wrongs https://fit.princeton.edu/publications/who-owns-sentence-history-copyrights-and-wrongs  Free Whistles https://linktr.ee/3Dwhistles My Justice of the Peace tumblr https://vermontjp.tumblr.com/ Logan Airport chapel https://discovermass.com/church/our-lady-of-the-airways-east-boston-ma/ Soul of a New Machine https://www.tracykidder.com/the-soul-of-a-new-machine.html Matteo Lane https://matteolanecomedy.com/ My DNC blog https://www.librarian.net/dnc/ Librarian at Burning Man https://www.jessamyn.com/journal/02/burnlib.html Flickr Commons Explorer https://commons.flickr.org/Without a Net, Librarians Bridging the Digital Divide https://www.librarian.net/digitaldivide/ Marrakesh Treaty https://www.wipo.int/en/web/treaties/ip/marrakesh/index Flickypedia https://commons.wikimedia.org/wiki/Commons:Flickypedia That's My Grandma Flickr Gallery https://flickr.com/photos/flickrfoundation/galleries/72157722979767596/ Murkutu https://mukurtu.org/ Barnard Zine Library https://zines.barnard.edu/ Cleaning out mom's house https://www.flickr.com/photos/iamthebestartist/albums/72157719713137030 Transcript: https://pastecode.io/s/684xqjj9  Join the Discord: https://discord.gg/qWPTurTnkT

Jaron Lanier, E. Glen Weyl, and Taylor Black join Beauty at Work for a wide-ranging conversation on artificial intelligence, innovation, and the deeper questions of meaning, faith, and human flourishing that surround emerging technologies.Jaron Lanier coined the terms Virtual Reality and Mixed Reality and is widely regarded as a founding figure of the field. He has served as a leading critic of digital culture and social media, and his books include You Are Not a Gadget and Who Owns the Future? In 2018, Wired Magazine named him one of the 25 most influential people in technology of the previous 25 years. Time Magazine named him one of the 100 most influential people in the world. Jaron is currently the Prime Unifying Scientist at Microsoft's Office of the Chief Technology Officer, which spells out “Octopus”, in reference to his fascination with cephalopod neurology. He is also a musician and composer who has recently performed or recorded with Sara Bareilles, T Bone Burnett, Jon Batiste, Philip Glass, and many others.E. Glen Weyl is Founder and Research Lead at Microsoft Research's Plural Technology Collaboratory and Co-Founder of the Plurality Institute and RadicalxChange Foundation. He is the co-author of Radical Markets and Plurality and works at the intersection of economics, technology, democracy, and social institutions.Taylor Black is Director of AI & Venture Ecosystems in the Office of the Chief Technology Officer at Microsoft and the founding director of the Leonum Institute on Emerging Technologies and AI at The Catholic University of America. His background spans philosophy, law, and technology leadership.In this second part of our conversation, we talk about:1. The idea that modern technology and AI, in particular, have taken on religious or idolatrous qualities2. Why the Talmud offers a powerful model for collective intelligence without erasing individual voices3. The dangers of excessive anonymity in digital systems and AI training4. The idea of “superintelligences” as collective human systems like corporations, democracies, and religions5. Vatican-led efforts toward algorithmic ethics and the protection of human dignity6. Where Glen and Jaron disagree about human-centered AI7. AI as a tool for metacognition8. How imagination, storytelling, and shared meaning can shape the future of innovationTo learn more about Jaron, Glen and Taylor's work, you can find them at: Jaron Lanier - https://www.jaronlanier.com/ Glen Weyl - https://glenweyl.com/ Taylor Black - https://www.linkedin.com/in/blacktaylor/ Books and Resources mentioned:You Are Not a Gadget (Jaron Lanier)Who Owns the Future? (Jaron Lanier)Radical Markets (Eric Posner & E. Glen Weyl)Plurality (Audrey Tang & E. Glen Weyl)The Human Use of Human Beings (Norbert Wiener)The Fellowship of the Ring (J.R.R. Tolkien)This season of the podcast is sponsored by Templeton Religion Trust.Support the show

Jaron Lanier, E. Glen Weyl, and Taylor Black join Beauty at Work for a wide-ranging conversation on artificial intelligence, innovation, and the deeper questions of meaning, faith, and human flourishing that surround emerging technologies.Jaron Lanier coined the terms Virtual Reality and Mixed Reality and is widely regarded as a founding figure of the field. He has served as a leading critic of digital culture and social media, and his books include You Are Not a Gadget and Who Owns the Future? In 2018, Wired Magazine named him one of the 25 most influential people in technology of the previous 25 years. Time Magazine named him one of the 100 most influential people in the world. Jaron is currently the Prime Unifying Scientist at Microsoft's Office of the Chief Technology Officer, which spells out “Octopus”, in reference to his fascination with cephalopod neurology. He is also a musician and composer who has recently performed or recorded with Sara Bareilles, T Bone Burnett, Jon Batiste, Philip Glass, and many others.E. Glen Weyl is Founder and Research Lead at Microsoft Research's Plural Technology Collaboratory and Co-Founder of the Plurality Institute and RadicalxChange Foundation. He is the co-author of Radical Markets and Plurality and works at the intersection of economics, technology, democracy, and social institutions.Taylor Black is Director of AI & Venture Ecosystems in the Office of the Chief Technology Officer at Microsoft and the founding director of the Leonum Institute on Emerging Technologies and AI at The Catholic University of America. His background spans philosophy, law, and technology leadership.In this first part of our conversation, we discuss:1. How aesthetic experience shapes worldview, imagination, and intellectual vocation2. The historical rivalry between artificial intelligence and cybernetics3. The danger of treating AI as an object of faith or a replacement for human meaning4. The psychological and spiritual costs of assuming people will become obsolete5. A tension between two different modalities of beautyTo learn more about Jaron, Glen and Taylor's work, you can find them at: Jaron Lanier - https://www.jaronlanier.com/ Glen Weyl - https://glenweyl.com/ Taylor Black - https://www.linkedin.com/in/blacktaylor/ Books and Resources mentioned:You Are Not a Gadget (Jaron Lanier)Who Owns the Future? (Jaron Lanier)Radical Markets (Eric Posner & E. Glen Weyl)Plurality (Audrey Tang & E. Glen Weyl)The Human Use of Human Beings (Norbert Wiener)The Fellowship of the Ring (J.R.R. Tolkien)This season of the podcast is sponsored by Templeton Religion Trust.Support the show

Welcome to Entertainment Law Update, your monthly rundown of the most important legal developments in the entertainment, media, and technology industries. As 2025 comes to a close, the legal fault lines of the entertainment industry are impossible to ignore.… Read the rest The post Who Owns the Stage, the Script, and the Algorithm? Entertainment Law's Wild Year-End appeared first on Entertainment Law Update.

Brazil is one of the most promising ride-hailing markets in the world — but it's also one of the toughest to win. In this episode, Ana Paula Picasso sits down with Luiz Fittipaldi, former global strategist at Bolt, to unpack why global players like Uber, Didi, and others face unique hurdles when entering the Brazilian market.From regulation gaps to payment challenges, Luiz shares first-hand insights on what works, what doesn't, and where the real opportunities lie — especially in tier-2 and tier-3 cities and frontier markets.For more stories on innovation and emerging markets, visit www.emergingmarkets.today and subscribe to the Substack newsletter: https://emergingmarketstoday.substack.comYou can contact Luiz Fittipaldi here https://luizfittipaldi.com/00:00:00 — New Season Kickoff: guest Luiz Fittipaldi, former global strategist at Bolt.00:02:14 — Who Owns the Road in Brazil: A deep dive into the ride-hailing landscape: Uber, 99 (Didi), and the smaller challengers trying to break in.00:05:11 — Why Brazil Attracts Global Players: Luiz explains why Brazil's size, infrastructure gaps, and urban mobility issues make it a strategic market.00:08:02 — Cowboy Style vs Regulation: How Uber and other players grew fast in Latin America with little pushback, and why that doesn't work in Europe.00:12:30 — Cash, Cards, and PixBrazil's payments puzzle: from cash-based habits to leapfrogging with Pix and digital wallets.00:16:13 — Cracking the Market (or Not): Luiz lays out hard truths and smart strategies for anyone trying to enter Brazil's ride-hailing market.00: 21:45 — Beyond Bolt: Frontier Markets - From Bolt to mentoring startups, Luiz shares why frontier markets like Kazakhstan and Tajikistan are the next big play.

Israel and Gaza continue to make headlines. As followers of Jesus, how should we respond to what we see and hear in the media? Should Christians still support Israel? On the next Equipped, guest host Collin Lambert welcomes Dr. Charlie Dyer to discuss the latest developments in Israel and Gaza—and how we can respond with truth, compassion, and biblical wisdom. Join the conversation on the next Equipped. Featured resource:Who Owns the Land? An In-Depth Look at the Truth Behind the Middle East Conflict by Stanley Ellisen, updated and revised by Charles H. Dyer August thank you gift:The Quiet Time Kickstart by Rachel Jones Equipped with Chris Brooks is made possible through your support. To donate now, click here.

In this episode of Building Better Developers with AI, Rob Broadhead and Michael Meloche revisit their earlier discussion on defining ‘done' in Agile – how to stay on Track and Avoid Scope Creep. They explain why “done” must mean more than “I finished coding,” and they show how a shared Definition of Done (DoD) keeps teams aligned and projects on schedule. What Does “Done” Really Mean? In Agile, “Done” extends beyond writing code. It often includes: Passing unit and integration tests Receiving QA approval Deploying to staging or production Updating documentation Securing acceptance sign-off Without a clear, documented DoD, each team member may interpret “done” differently. As a result, projects risk rework, delays, and frustration. “If we ask, ‘Is it done?' we should get a clear yes or no—no ‘sort of' or ‘almost.'” – Rob Broadhead Why Ambiguity Leads to Trouble Michael points out a common problem: a developer finishes their code, marks the ticket as done, and passes it to QA—only for testers to find gaps in the requirements. A login screen ticket might say “Allow users to log in with username and password.” But does that mean: Username is case-insensitive? Special characters are allowed? Do error messages display on failure? If these details aren't defined, both the developer and tester may interpret “done” differently, leading to frustration on all sides. The Link Between “Done” and Scope Creep Rob and Michael agree: unclear definitions open the door to scope creep. Without a firm DoD, features get stuck in an endless loop of revisions: Developers feel QA keeps moving the goalposts. QA feels developers aren't meeting the requirements. Clients think the delivered feature isn't what they expected. Over time, this erodes trust and pushes delivery dates further into the future. Lessons from the Field Michael contrasts two scenarios from his career that highlight the power of a strong Definition of Done. Before an acquisition, his team worked with a crystal-clear DoD. Every ticket had precise requirements, clear acceptance criteria, and well-defined testing steps. As a result, tasks finished on time, testing followed a predictable pattern, and rework was rare. The team knew exactly when work met the agreed standards, and stakeholders trusted that “done” truly meant done. After the acquisition, the situation changed dramatically. Tickets became vague and massive in scope, often resembling open-ended “make it work” directives. Multiple teams modified the same code simultaneously, resulting in merge conflicts, inconsistent results, and unpredictable delivery schedules. Without a clear DoD, developers, testers, and stakeholders all had different ideas of what completion looked like, and work frequently circled back for revisions. The difference between the two environments came down to one factor: a clear and enforceable Definition of done. In the first scenario, it acted as a shared contract for quality and completion. In the second, the lack of it created confusion, wasted effort, and missed deadlines. Building a Strong Definition of Done The hosts outline key components every DoD should include: Code complete and reviewed – Ensures quality and shared understanding. Automated tests passing – Reduces regressions. Documentation updated – Prevents future confusion. Deployment verified – Proves it works in the target environment. Acceptance criteria signed off – Confirms alignment with the original requirements. Pro Tip: Keep your tests fresh—don't just update them to pass without meeting the real requirement. Who Owns the DoD? One person doesn't own the DoD—it's a team responsibility. Product owners, Scrum Masters, and developers should collaborate to create and update it, reviewing it regularly to adapt to evolving project needs. Making “Done” Part of the Process Once defined, your DoD should be visible and integrated into your workflow: Add it to user stories during sprint planning. Track it in tools like Jira, Trello, or GitHub. Use workflow stages that match your DoD steps—coding, testing, review, deployment, and sign-off. Michael emphasizes that personal accountability matters just as much as team accountability. Great developers hold themselves to the DoD without needing reminders. Your Challenge: Define “Done” This Week If your team doesn't have a documented Definition of Done—or if it's been more than three months since you reviewed it—set aside time this week to: Write down your current DoD. Identify where ambiguity still exists. Get agreement from the entire team. Update your workflow so that every ticket must meet the DoD before it is closed. This single step can prevent months of wasted effort and ensure your work delivers exactly what's intended. The Bigger Picture A well-defined DoD is more than a checklist—it's your guardrail against wasted effort and shifting goals. It ensures the final product matches what the client truly needs, not just what was coded. Your Definition of Done is your “why” for each task—it keeps your work focused, aligned, and valuable. Stay Connected: Join the Developreneur Community We invite you to join our community and share your coding journey with us. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at info@develpreneur.com with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development. Additional Resources Getting It Right: How Effective Requirements Gathering Leads to Successful Software Projects The Importance of Properly Defining Requirements Changing Requirements – Welcome Them For Competitive Advantage Creating Use Cases and Gathering Requirements The Developer Journey Videos – With Bonus Content Building Better Developers With AI Podcast Videos – With Bonus Content

In this episode, Jon Charbonneau of DBA joined us at Permissionless to discuss regulatory arbitrage in crypto, challenges around decentralization, and the evolving role of L2s. We also explore opportunities in prediction markets, memecoins, and modular lending. Finally, we touch on investment trends, talent pools, and institutional adoption across ecosystems like Solana, Base, Monad, and Hyperliquid.Thanks for tuning in! As always, remember this podcast is for informational purposes only, and any views expressed by anyone on the show are solely their opinions, not financial advice. -- Katana is a DeFi-first chain built for deep liquidity and high yield. No empty emissions, just real yield and sequencer fees routed back to DeFi users.  Pre-deposit now: Earn high APRs with Turtle Club https://app.turtle.club/campaigns/katana or spin the wheel with Katana Krates https://app.katana.network/krates -- Ledger, the global leader in digital asset security, proudly sponsors 0xResearch! As Ledger celebrates 10 years of securing 20% of global crypto, it remains the top choice for securing your assets. Buy a LEDGER™ device now and build confidently, knowing your precious tokens are safe. Buy now on https://shop.ledger.com/?r=1da180a5de00. -- Marinade is the premier staking delegation platform on Solana, bringing billions in liquidity and security to the Solana network, and connecting SOL holders to the best staking rates.  Since launching in 2021, Marinade has expanded their suite of products to provide solutions for both DeFi users and TradFi, including liquid and native staking, as well as direct enterprise integrations.  To learn more about Marinade, follow the link below: https://marinade.finance/?utm_source=blockworks&utm_medium=partnerships&utm_campaign=podcast -- Follow Jon: https://x.com/jon_charb Follow Carlos: https://x.com/0xcarlosg Follow Danny: https://x.com/defi_kay_ Follow Boccaccio: https://x.com/salveboccaccio Follow Blockworks Research: https://x.com/blockworksres Subscribe on YouTube: https://bit.ly/3foDS38 Subscribe on Apple: https://apple.co/3SNhUEt Subscribe on Spotify: https://spoti.fi/3NlP1hA Get top market insights and the latest in crypto news. Subscribe to Blockworks Daily Newsletter: https://blockworks.co/newsletter/ Join the 0xResearch Telegram group: https://t.me/+z0H6y2bS-dllODVh -- Timestamps: (0:00) Introduction (1:48) Regulatory Arbitrage In Crypto (7:09) Degrees of Decentralization (12:32) Ads (Katana & Ledger) (13:09) Switching Costs (16:02) Who Owns the User? (19:18) Monad vs MegaETH (22:50) Crypto's Next Breakout App (30:09) Ads (Katana & Ledger) (31:14) Crypto's Talent Pool (35:09) Navigating the Liquid Markets (40:53) Marinade Ad (41:25) Who Benefits From TradFi Entering Crypto? (47:46) Has Base Been Successful? (50:22) Where Are New Apps Deploying? -- Check out Blockworks Research today! Research, data, governance, tokenomics, and models – now, all in one place Blockworks Research: https://www.blockworksresearch.com/ Free Daily Newsletter: https://blockworks.co/newsletter -- Disclaimer: Nothing said on 0xResearch is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Boccaccio, Danny, and our guests may hold positions in the companies, funds, or projects discussed.

Are public relations and marketing two distinct disciplines—or is PR simply one piece of the broader marketing puzzle? It's a question that stirs up strong opinions in boardrooms, agencies, and comms teams alike. Some argue PR is strategic storytelling aimed at building relationships, while marketing is focused on driving sales. Others say that in today's world of integrated messaging, the separation is outdated.In this episode, we take on the debate head-on, exploring how the roles overlap, where they diverge, and whether the modern media landscape has blurred the lines beyond repair. Is PR still its own function—or has it become just another lane on marketing's highway?Listen For::10 Fyre Festival The Luxury Mirage 4:02 Influence vs Image Showdown 6:25 PR Is the Pie 10:14 The Sad Fate of Local Media 13:02 Metrics KPIs and the Dashboard Wars 18:54 Who Owns the Message 25:10 The Final Prediction Marketers Report to PRRate this podcast with just one click Stories and Strategies WebsiteCurzon Public Relations WebsiteAre you a brand with a podcast that needs support? Book a meeting with Doug Downs to talk about it.Apply to be a guest on the podcastConnect with usLinkedIn | X | Instagram | You Tube | Facebook | Threads | Bluesky | PinterestRequest a transcript of this episodeSupport the show

In 1710, the British Parliament passed a piece of legislation entitled An Act for the Encouragement of Learning. It became known as the Statute of Anne, and it was the world's first copyright law. Copyright protects and regulates a piece of work - whether that's a book, a painting, a piece of music or a software programme. It emerged as a way of balancing the interests of authors, artists, publishers, and the public in the context of evolving technologies and the rise of mechanical reproduction. Writers and artists such as Alexander Pope, William Hogarth and Charles Dickens became involved in heated debates about ownership and originality that continue to this day - especially with the emergence of artificial intelligence. With:Lionel Bently, Herchel Smith Professor of Intellectual Property Law at the University of CambridgeWill Slauter, Professor of History at Sorbonne University, ParisKatie McGettigan, Senior Lecturer in American Literature at Royal Holloway, University of London. Producer: Eliane GlaserReading list:Isabella Alexander, Copyright Law and the Public Interest in the Nineteenth Century (Hart Publishing, 2010)Isabella Alexander and H. Tomás Gómez-Arostegui (eds), Research Handbook on the History of Copyright Law (Edward Elgar Publishing, 2016)David Bellos and Alexandre Montagu, Who Owns this Sentence? A History of Copyrights and Wrongs (Mountain Leopard Press, 2024)Oren Bracha, Owning Ideas: The Intellectual Origins of American Intellectual Property, 1790-1909 (Cambridge University Press, 2016)Elena Cooper, Art and Modern Copyright: The Contested Image (Cambridge University Press, 2018)Ronan Deazley, On the Origin of the Right to Copy: Charting the Movement of Copyright Law in Eighteenth Century Britain, 1695–1775 (Hart Publishing, 2004)Ronan Deazley, Rethinking Copyright: History, Theory, Language (Edward Elgar Publishing, 2006)Ronan Deazley, Martin Kretschmer and Lionel Bently (eds.), Privilege and Property: Essays on the History of Copyright (Open Book Publishers, 2010)Marie-Stéphanie Delamaire and Will Slauter (eds.), Circulation and Control: Artistic Culture and Intellectual Property in the Nineteenth Century (Open Book Publishers, 2021) Melissa Homestead, American Women Authors and Literary Property, 1822-1869 (Cambridge University Press, 2005)Adrian Johns, Piracy: The Intellectual Property Wars from Gutenberg to Gates (University of Chicago Press, 2009)Meredith L. McGill, American Literature and the Culture of Reprinting, 1834-1853 (University of Pennsylvania Press, 2002)Mark Rose, Authors and Owners: The Invention of Copyright (Harvard University Press, 1993)Mark Rose, Authors in Court: Scenes from the Theater of Copyright (Harvard University Press, 2018)Catherine Seville, Internationalisation of Copyright: Books, Buccaneers and the Black Flag in the Nineteenth Century (Cambridge University Press, 2006)Brad Sherman and Lionel Bently, The Making of Modern Intellectual Property Law (Cambridge University Press, 1999)Will Slauter, Who Owns the News? A History of Copyright (Stanford University Press, 2019)Robert Spoo, Without Copyrights: Piracy, Publishing and the Public Domain (Oxford University Press, 2013)In Our Time is a BBC Studios Audio production

Simplify, Document, Scale: The 3-Step System for Process Excellence  EP302 Profit Withe a Plan Podcast Released May 13, 2025 Guest: Errol Allen, Process Expert, Founder of Errol Allen Consulting Host: Marcia Riner, Business Growth Strategist, CEO of Infinite Profit®

In this episode, Ericka Andersen joins Rusty Reno at The Editor's Desk to talk about her recent essay, “Who Owns the Embryos?” from the April 2025 issue of the magazine. Please subscribe at www.firstthings.com/subscribe in order to access this and many other great pieces!

In this episode, Ericka Andersen joins Rusty Reno at The Editor's Desk to talk about her recent essay, “Who Owns the Embryos?” from the April 2025 issue of the magazine. Please subscribe at www.firstthings.com/subscribe in order to access this and many other great pieces!

In this episode, we explore PumpFun's competition with Raydium, frontend design as a competitive moat, Solana's share of network REV and application revenue, Ethereum's ongoing scaling and governance challenges, and Bitcoin's correlation to the stock market. Thanks for tuning in! As always, remember this podcast is for informational purposes only, and any views expressed by anyone on the show are solely their opinions, not financial advice. -- Resources PumpSwap Data: https://x.com/blockworksres/status/1905691039912087660 Fluid Dashboard: https://x.com/blockworksres/status/1908165062650675637 PumpFun Dashboard: https://app.blockworksresearch.com/analytics/pumpfun Bitcoin March 2025 Update: https://app.blockworksresearch.com/flashnotes/bitcoin-march-2025-update Solana March 2025 Update: https://app.blockworksresearch.com/flashnotes/solana-march-2025-update -- Ledger, the global leader in digital asset security, proudly sponsors 0xResearch! As Ledger celebrates 10 years of securing 20% of global crypto, it remains the top choice for securing your assets. Buy a LEDGER™ device now and build confidently, knowing your precious tokens are safe. Buy now on https://shop.ledger.com/?r=1da180a5de00. -- Missed DAS? Join us from June 24th-June 26th at Permissionless IV! Use Code 0x10 at checkout for 10% off! Tickets: https://blockworks.co/event/permissionless-iv -- Follow Carlos: https://x.com/0xcarlosg Follow Jacob: https://x.com/0xSharples Follow Marc: https://x.com/marcarjoon Follow Danny: https://x.com/defi_kay_ Follow Blockworks Research: https://x.com/blockworksres Subscribe on YouTube: https://bit.ly/3foDS38 Subscribe on Apple: https://apple.co/3SNhUEt Subscribe on Spotify: https://spoti.fi/3NlP1hA Get top market insights and the latest in crypto news. Subscribe to Blockworks Daily Newsletter: https://blockworks.co/newsletter/ Join the 0xResearch Telegram group: https://t.me/+z0H6y2bS-dllODVh -- Timestamps: (0:00) Introduction (1:34) The Decoupling Narrative (6:43) Outlook For Crypto Majors (10:57) Ethereum's Struggles (15:27) Ledger Ad (15:43) Solana's REV Share (21:21) Diving into the PumpFun Data (30:31) Ledger Ad (31:04) Who Owns the End User? (37:34) Incentivizing User Behavior (47:15) PumpFun vs Raydium: What's Next? (52:01) New Opportunities Going Forward -- Check out Blockworks Research today! Research, data, governance, tokenomics, and models – now, all in one place Blockworks Research: https://www.blockworksresearch.com/ Free Daily Newsletter: https://blockworks.co/newsletter -- Disclaimer: Nothing said on 0xResearch is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Boccaccio, Danny, and our guests may hold positions in the companies, funds, or projects discussed.

“What I meant when I said there is no AI is that I don't think we serve ourselves well when we put our own technology up as if it were a new God that we created. I think we confuse ourselves too easily. This goes back to Alan Turing, the main founder of computer science, who had this idea of the Turing test. In the test, you can't tell whether the computer has gotten more human-like or the human has gotten more computer-like. People are very prone to becoming more computer-like. When we're on social media, we let ourselves be guided by the algorithms, so we start to become dumb in the way the algorithms want us to. You see that all the time. It's really degraded our psychologies and our society.”Jaron Lanier is a pioneering technologist, writer, and musician, best known for coining the term “Virtual Reality” and founding VPL Research, the first company to sell VR products. He led early breakthroughs in virtual worlds, avatars, and VR applications in fields like surgery and media. Lanier writes on the philosophy and economics of technology in his bestselling book Who Owns the Future? and You Are Not a Gadget. His book Dawn of the New Everything: Encounters with Reality and Virtual Reality is an inventive blend of autobiography, science writing, and philosophy. Lanier has been named one of TIME's 100 most influential people and serves as Prime Unifying Scientist at Microsoft's Office of the CTO—aka “Octopus.” As a musician, he's performed with Sara Bareilles, Philip Glass, T Bone Burnett, Laurie Anderson, Jon Batiste, and others.Episode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcastEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

“What I meant when I said there is no AI is that I don't think we serve ourselves well when we put our own technology up as if it were a new God that we created. I think we confuse ourselves too easily. This goes back to Alan Turing, the main founder of computer science, who had this idea of the Turing test. In the test, you can't tell whether the computer has gotten more human-like or the human has gotten more computer-like. People are very prone to becoming more computer-like. When we're on social media, we let ourselves be guided by the algorithms, so we start to become dumb in the way the algorithms want us to. You see that all the time. It's really degraded our psychologies and our society.”Jaron Lanier is a pioneering technologist, writer, and musician, best known for coining the term “Virtual Reality” and founding VPL Research, the first company to sell VR products. He led early breakthroughs in virtual worlds, avatars, and VR applications in fields like surgery and media. Lanier writes on the philosophy and economics of technology in his bestselling book Who Owns the Future? and You Are Not a Gadget. His book Dawn of the New Everything: Encounters with Reality and Virtual Reality is an inventive blend of autobiography, science writing, and philosophy. Lanier has been named one of TIME's 100 most influential people and serves as Prime Unifying Scientist at Microsoft's Office of the CTO—aka “Octopus.” As a musician, he's performed with Sara Bareilles, Philip Glass, T Bone Burnett, Laurie Anderson, Jon Batiste, and others.Episode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcastEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

“What I meant when I said there is no AI is that I don't think we serve ourselves well when we put our own technology up as if it were a new God that we created. I think we confuse ourselves too easily. This goes back to Alan Turing, the main founder of computer science, who had this idea of the Turing test. In the test, you can't tell whether the computer has gotten more human-like or the human has gotten more computer-like. People are very prone to becoming more computer-like. When we're on social media, we let ourselves be guided by the algorithms, so we start to become dumb in the way the algorithms want us to. You see that all the time. It's really degraded our psychologies and our society.”Jaron Lanier is a pioneering technologist, writer, and musician, best known for coining the term “Virtual Reality” and founding VPL Research, the first company to sell VR products. He led early breakthroughs in virtual worlds, avatars, and VR applications in fields like surgery and media. Lanier writes on the philosophy and economics of technology in his bestselling book Who Owns the Future? and You Are Not a Gadget. His book Dawn of the New Everything: Encounters with Reality and Virtual Reality is an inventive blend of autobiography, science writing, and philosophy. Lanier has been named one of TIME's 100 most influential people and serves as Prime Unifying Scientist at Microsoft's Office of the CTO—aka “Octopus.” As a musician, he's performed with Sara Bareilles, Philip Glass, T Bone Burnett, Laurie Anderson, Jon Batiste, and others.Episode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcastEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

“What I meant when I said there is no AI is that I don't think we serve ourselves well when we put our own technology up as if it were a new God that we created. I think we confuse ourselves too easily. This goes back to Alan Turing, the main founder of computer science, who had this idea of the Turing test. In the test, you can't tell whether the computer has gotten more human-like or the human has gotten more computer-like. People are very prone to becoming more computer-like. When we're on social media, we let ourselves be guided by the algorithms, so we start to become dumb in the way the algorithms want us to. You see that all the time. It's really degraded our psychologies and our society.”Jaron Lanier is a pioneering technologist, writer, and musician, best known for coining the term “Virtual Reality” and founding VPL Research, the first company to sell VR products. He led early breakthroughs in virtual worlds, avatars, and VR applications in fields like surgery and media. Lanier writes on the philosophy and economics of technology in his bestselling book Who Owns the Future? and You Are Not a Gadget. His book Dawn of the New Everything: Encounters with Reality and Virtual Reality is an inventive blend of autobiography, science writing, and philosophy. Lanier has been named one of TIME's 100 most influential people and serves as Prime Unifying Scientist at Microsoft's Office of the CTO—aka “Octopus.” As a musician, he's performed with Sara Bareilles, Philip Glass, T Bone Burnett, Laurie Anderson, Jon Batiste, and others.Episode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcastEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

“What I meant when I said there is no AI is that I don't think we serve ourselves well when we put our own technology up as if it were a new God that we created. I think we confuse ourselves too easily. This goes back to Alan Turing, the main founder of computer science, who had this idea of the Turing test. In the test, you can't tell whether the computer has gotten more human-like or the human has gotten more computer-like. People are very prone to becoming more computer-like. When we're on social media, we let ourselves be guided by the algorithms, so we start to become dumb in the way the algorithms want us to. You see that all the time. It's really degraded our psychologies and our society.”Jaron Lanier is a pioneering technologist, writer, and musician, best known for coining the term “Virtual Reality” and founding VPL Research, the first company to sell VR products. He led early breakthroughs in virtual worlds, avatars, and VR applications in fields like surgery and media. Lanier writes on the philosophy and economics of technology in his bestselling book Who Owns the Future? and You Are Not a Gadget. His book Dawn of the New Everything: Encounters with Reality and Virtual Reality is an inventive blend of autobiography, science writing, and philosophy. Lanier has been named one of TIME's 100 most influential people and serves as Prime Unifying Scientist at Microsoft's Office of the CTO—aka “Octopus.” As a musician, he's performed with Sara Bareilles, Philip Glass, T Bone Burnett, Laurie Anderson, Jon Batiste, and others.Episode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcastEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

“What I meant when I said there is no AI is that I don't think we serve ourselves well when we put our own technology up as if it were a new God that we created. I think we confuse ourselves too easily. This goes back to Alan Turing, the main founder of computer science, who had this idea of the Turing test. In the test, you can't tell whether the computer has gotten more human-like or the human has gotten more computer-like. People are very prone to becoming more computer-like. When we're on social media, we let ourselves be guided by the algorithms, so we start to become dumb in the way the algorithms want us to. You see that all the time. It's really degraded our psychologies and our society.”Jaron Lanier is a pioneering technologist, writer, and musician, best known for coining the term “Virtual Reality” and founding VPL Research, the first company to sell VR products. He led early breakthroughs in virtual worlds, avatars, and VR applications in fields like surgery and media. Lanier writes on the philosophy and economics of technology in his bestselling book Who Owns the Future? and You Are Not a Gadget. His book Dawn of the New Everything: Encounters with Reality and Virtual Reality is an inventive blend of autobiography, science writing, and philosophy. Lanier has been named one of TIME's 100 most influential people and serves as Prime Unifying Scientist at Microsoft's Office of the CTO—aka “Octopus.” As a musician, he's performed with Sara Bareilles, Philip Glass, T Bone Burnett, Laurie Anderson, Jon Batiste, and others.Episode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcastEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

“AI is obviously the dominant topic in tech lately, and I think occasionally there's AI that's nonsense, and occasionally there's AI that's great. I love finding new proteins for medicine and so on. I don't think we serve ourselves well when we put our own technology up as if it were a new God that we created. I think we're really getting a little too full of ourselves to think that. This goes back to Alan Turing, the main founder of computer science, who had this idea of the Turing test. In the test, you can't tell whether the computer has gotten more human-like or the human has gotten more computer-like. People are very prone to becoming more computer-like. When we're on social media, we let ourselves be guided by the algorithms, so we start to become dumb in the way the algorithms want us to. You see that all the time. It's really degraded our psychologies and our society.”Jaron Lanier is a pioneering technologist, writer, and musician, best known for coining the term “Virtual Reality” and founding VPL Research, the first company to sell VR products. He led early breakthroughs in virtual worlds, avatars, and VR applications in fields like surgery and media. Lanier writes on the philosophy and economics of technology in his bestselling book Who Owns the Future? and You Are Not a Gadget. His book Dawn of the New Everything: Encounters with Reality and Virtual Reality is an inventive blend of autobiography, science writing, and philosophy. Lanier has been named one of TIME's 100 most influential people and serves as Prime Unifying Scientist at Microsoft's Office of the CTO—aka “Octopus.” As a musician, he's performed with Sara Bareilles, Philip Glass, T Bone Burnett, Laurie Anderson, Jon Batiste, and others.Episode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcastPhoto credit: Michael Springer

“AI is obviously the dominant topic in tech lately, and I think occasionally there's AI that's nonsense, and occasionally there's AI that's great. I love finding new proteins for medicine and so on. I don't think we serve ourselves well when we put our own technology up as if it were a new God that we created. I think we're really getting a little too full of ourselves to think that. This goes back to Alan Turing, the main founder of computer science, who had this idea of the Turing test. In the test, you can't tell whether the computer has gotten more human-like or the human has gotten more computer-like. People are very prone to becoming more computer-like. When we're on social media, we let ourselves be guided by the algorithms, so we start to become dumb in the way the algorithms want us to. You see that all the time. It's really degraded our psychologies and our society.”Jaron Lanier is a pioneering technologist, writer, and musician, best known for coining the term “Virtual Reality” and founding VPL Research, the first company to sell VR products. He led early breakthroughs in virtual worlds, avatars, and VR applications in fields like surgery and media. Lanier writes on the philosophy and economics of technology in his bestselling book Who Owns the Future? and You Are Not a Gadget. His book Dawn of the New Everything: Encounters with Reality and Virtual Reality is an inventive blend of autobiography, science writing, and philosophy. Lanier has been named one of TIME's 100 most influential people and serves as Prime Unifying Scientist at Microsoft's Office of the CTO—aka “Octopus.” As a musician, he's performed with Sara Bareilles, Philip Glass, T Bone Burnett, Laurie Anderson, Jon Batiste, and others.Episode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcastPhoto credit: Michael Springer

“AI is obviously the dominant topic in tech lately, and I think occasionally there's AI that's nonsense, and occasionally there's AI that's great. I love finding new proteins for medicine and so on. I don't think we serve ourselves well when we put our own technology up as if it were a new God that we created. I think we're really getting a little too full of ourselves to think that. This goes back to Alan Turing, the main founder of computer science, who had this idea of the Turing test. In the test, you can't tell whether the computer has gotten more human-like or the human has gotten more computer-like. People are very prone to becoming more computer-like. When we're on social media, we let ourselves be guided by the algorithms, so we start to become dumb in the way the algorithms want us to. You see that all the time. It's really degraded our psychologies and our society.”Jaron Lanier is a pioneering technologist, writer, and musician, best known for coining the term “Virtual Reality” and founding VPL Research, the first company to sell VR products. He led early breakthroughs in virtual worlds, avatars, and VR applications in fields like surgery and media. Lanier writes on the philosophy and economics of technology in his bestselling book Who Owns the Future? and You Are Not a Gadget. His book Dawn of the New Everything: Encounters with Reality and Virtual Reality is an inventive blend of autobiography, science writing, and philosophy. Lanier has been named one of TIME's 100 most influential people and serves as Prime Unifying Scientist at Microsoft's Office of the CTO—aka “Octopus.” As a musician, he's performed with Sara Bareilles, Philip Glass, T Bone Burnett, Laurie Anderson, Jon Batiste, and others.Episode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcastPhoto credit: Michael Springer

“AI is obviously the dominant topic in tech lately, and I think occasionally there's AI that's nonsense, and occasionally there's AI that's great. I love finding new proteins for medicine and so on. I don't think we serve ourselves well when we put our own technology up as if it were a new God that we created. I think we're really getting a little too full of ourselves to think that. This goes back to Alan Turing, the main founder of computer science, who had this idea of the Turing test. In the test, you can't tell whether the computer has gotten more human-like or the human has gotten more computer-like. People are very prone to becoming more computer-like. When we're on social media, we let ourselves be guided by the algorithms, so we start to become dumb in the way the algorithms want us to. You see that all the time. It's really degraded our psychologies and our society.”Jaron Lanier is a pioneering technologist, writer, and musician, best known for coining the term “Virtual Reality” and founding VPL Research, the first company to sell VR products. He led early breakthroughs in virtual worlds, avatars, and VR applications in fields like surgery and media. Lanier writes on the philosophy and economics of technology in his bestselling book Who Owns the Future? and You Are Not a Gadget. His book Dawn of the New Everything: Encounters with Reality and Virtual Reality is an inventive blend of autobiography, science writing, and philosophy. Lanier has been named one of TIME's 100 most influential people and serves as Prime Unifying Scientist at Microsoft's Office of the CTO—aka “Octopus.” As a musician, he's performed with Sara Bareilles, Philip Glass, T Bone Burnett, Laurie Anderson, Jon Batiste, and others.Episode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcastPhoto credit: Michael Springer

“AI is obviously the dominant topic in tech lately, and I think occasionally there's AI that's nonsense, and occasionally there's AI that's great. I love finding new proteins for medicine and so on. I don't think we serve ourselves well when we put our own technology up as if it were a new God that we created. I think we're really getting a little too full of ourselves to think that. This goes back to Alan Turing, the main founder of computer science, who had this idea of the Turing test. In the test, you can't tell whether the computer has gotten more human-like or the human has gotten more computer-like. People are very prone to becoming more computer-like. When we're on social media, we let ourselves be guided by the algorithms, so we start to become dumb in the way the algorithms want us to. You see that all the time. It's really degraded our psychologies and our society.”Jaron Lanier is a pioneering technologist, writer, and musician, best known for coining the term “Virtual Reality” and founding VPL Research, the first company to sell VR products. He led early breakthroughs in virtual worlds, avatars, and VR applications in fields like surgery and media. Lanier writes on the philosophy and economics of technology in his bestselling book Who Owns the Future? and You Are Not a Gadget. His book Dawn of the New Everything: Encounters with Reality and Virtual Reality is an inventive blend of autobiography, science writing, and philosophy. Lanier has been named one of TIME's 100 most influential people and serves as Prime Unifying Scientist at Microsoft's Office of the CTO—aka “Octopus.” As a musician, he's performed with Sara Bareilles, Philip Glass, T Bone Burnett, Laurie Anderson, Jon Batiste, and others.Episode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcastPhoto credit: Michael Springer

Clifton-Taulbert He is best known for his books Once Upon a Time When We Were Colored and Eight Habits of the Heart: Embracing the Values that Build Strong Communities.According to Clifton L. Taulbert, noted author and entrepreneur businessman, he could have failed had he not encountered community builders and entrepreneurial thinkers early on in his life. Taulbert was born on the Mississippi Delta during the era of legal segregation where he completed his secondary education. Though opportunities were few and barriers were plentiful, Taulbert managed to dream of being successful, not knowing the shape that success would take. Today Taulbert is the President and CEO of the Freemount Corporation (a human capital development company) serving clients nationally and internationally-Fortune 500 Companies, small businesses, federal agencies, professional organizations, community colleges and K-12 leadership. Additionally, entrepreneur Taulbert is the President and CEO of Roots Java Coffee-an African-American owned national coffee brand, importing coffee from Africa. To pass his life lessons along, Taulbert shares his entrepreneurial journey with others as a Thrive15.com mentor.He is a Generational Bridge of Segregated Times to Integrated Times Today.In "The Invitation," Clifton Taulbert returns to the themes of "Once Upon a Time When We Were Colored," his award-winning book and the source of a major motion picture. This new memoir chronicles Taulbert's transformative experience of a supper invitation to a former plantation house in Allendale, South Carolina, where the successful adult confronts his childhood memories and wrestles with the legacies of slavery and segregation that demand to be acknowledged in his present circumstances.Taulbert has authored thirteen books, several of which are foundational to his consulting philosophy: Eight Habits of the Heart and Who Owns the Ice House-Eight Life Lessons from an Unlikely Entrepreneur [Who Owns the Ice House is part of a Kauffman Foundation sponsored education initiative to expose the impact of the entrepreneurial mindset at all levels] and more recently, Shift Your Thinking: Win Where You Stand and The Invitation-living beyond the lingering lessons of race and place. Taulbert's Eight Habits has become foundational to his work on leveraging community as an asset in the workplace, and garnered him an invitation to address members of the United States Supreme Court as a personal guest of former Supreme Court Justice, Sandra Day O'Connor.Clifton L. Taulbert is a trustee of the University of Tulsa has been recognized international by the Sales and Marketing Academy of Achievement, the Library of Congress, the NAACP, Rotary International as a Paul Harris Fellow and has been a recipient of the Jewish Humanitarian of the Year Award and the Richard Wright Literary Award. The Freemount Corporation is located in Tulsa, Oklahoma.© 2025 All Rights Reserved© 2025 Building Abundant Success!!Join Me on ~ iHeart Media @ https://tinyurl.com/iHeartBASSpot Me on Spotify: https://tinyurl.com/yxuy23baAmazon ~ https://tinyurl.com/AmzBASAudacy:  https://tinyurl.com/BASAud

“Your will to succeed remains one of your greatest assets.”Clifton-Taulbert He is best known for his books Once Upon a Time When We Were Colored and Eight Habits of the Heart: Embracing the Values that Build Strong Communities.According to Clifton L. Taulbert, noted author and entrepreneur businessman, he could have failed had he not encountered community builders and entrepreneurial thinkers early on in his life. Taulbert was born on the Mississippi Delta during the era of legal segregation where he completed his secondary education. Though opportunities were few and barriers were plentiful, Taulbert managed to dream of being successful, not knowing the shape that success would take. Today Taulbert is the President and CEO of the Freemount Corporation (a human capital development company) serving clients nationally and internationally-Fortune 500 Companies, small businesses, federal agencies, professional organizations, community colleges and K-12 leadership. Additionally, entrepreneur Taulbert is the President and CEO of Roots Java Coffee-an African-American owned national coffee brand, importing coffee from Africa. To pass his life lessons along, Taulbert shares his entrepreneurial journey with others as a Thrive15.com mentor.He is a Generational Bridge of Segregated Times to Integrated Times Today.In "The Invitation," Clifton Taulbert returns to the themes of "Once Upon a Time When We Were Colored," his award-winning book and the source of a major motion picture. This new memoir chronicles Taulbert's transformative experience of a supper invitation to a former plantation house in Allendale, South Carolina, where the successful adult confronts his childhood memories and wrestles with the legacies of slavery and segregation that demand to be acknowledged in his present circumstances.Taulbert has authored thirteen books, several of which are foundational to his consulting philosophy: Eight Habits of the Heart and Who Owns the Ice House-Eight Life Lessons from an Unlikely Entrepreneur [Who Owns the Ice House is part of a Kauffman Foundation sponsored education initiative to expose the impact of the entrepreneurial mindset at all levels] and more recently, Shift Your Thinking: Win Where You Stand and The Invitation-living beyond the lingering lessons of race and place. Taulbert's Eight Habits has become foundational to his work on leveraging community as an asset in the workplace, and garnered him an invitation to address members of the United States Supreme Court as a personal guest of former Supreme Court Justice, Sandra Day O'Connor.Clifton L. Taulbert is a trustee of the University of Tulsa has been recognized international by the Sales and Marketing Academy of Achievement, the Library of Congress, the NAACP, Rotary International as a Paul Harris Fellow and has been a recipient of the Jewish Humanitarian of the Year Award and the Richard Wright Literary Award. The Freemount Corporation is located in Tulsa, Oklahoma.© 2024 All Rights Reserved© 2024 Building Abundant Success!!Join Me on ~ iHeart Media @ https://tinyurl.com/iHeartBASSpot Me on Spotify: https://tinyurl.com/yxuy23baAmazon ~ https://tinyurl.com/AmzBASAudacy:  https://tinyurl.com/BASAud

On Culture Friday, John Stonestreet reflects on the harms of social media, Collin Garbarino reviews a Lego documentary about Pharrell Williams, and George Grant highlights a wordsmith president on Word Play. Plus, the Friday morning newsSupport The World and Everything in It today at wng.org/donateAdditional support comes from The Master's University. The Master's University offers over 150 programs, all designed to disciple the next generation toward lives of faithfulness to The Master, Jesus Christ. We equip students with the knowledge, skills, and attitudes they need for their careers, while cultivating their spiritual growth, moral character, and leadership skills as they seek to glorify the Lord. If you're looking for an education uncompromisingly rooted in Christ and Scripture, we want to meet you. Learn more at https://www.masters.edu/.And from Moody Publishers and the book Who Owns the Land? Biblical insight and historical context to the conflict in the Middle East. moodypublishers.com

On Washington Wednesday, chasing after voters in Michigan; on World Tour, news from Mozambique, Kazakhstan, the UK, and the Philippines; and sheepdog trials in Australia. Plus, a beaver gets tenure, Joe Rigney with questions about political candidates, and the Wednesday morning newsSupport The World and Everything in It today at wng.org/donate.Additional support comes from The Master's University. The Master's University offers over 150 programs, all designed to disciple the next generation toward lives of faithfulness to The Master, Jesus Christ. We equip students with the knowledge, skills, and attitudes they need for their careers, while cultivating their spiritual growth, moral character, and leadership skills as they seek to glorify the Lord. If you're looking for an education uncompromisingly rooted in Christ and Scripture, we want to meet you. Learn more at https://www.masters.edu/.And from Moody Publishers and the book Who Owns the Land? Biblical insight and historical context to the conflict in the Middle East. moodypublishers.com

The Legal Docket team previews the upcoming Supreme Court term, David Bahnsen digs into the September jobs report on Moneybeat, and the Hamas October 7th attack on the World History Book. Plus, the Monday morning newsSupport The World and Everything in It today at wng.org/donate.Additional support comes from The Master's University. The Master's University offers over 150 programs, all designed to disciple the next generation toward lives of faithfulness to The Master, Jesus Christ. We equip students with the knowledge, skills, and attitudes they need for their careers, while cultivating their spiritual growth, moral character, and leadership skills as they seek to glorify the Lord. If you're looking for an education uncompromisingly rooted in Christ and Scripture, we want to meet you. Learn more at https://www.masters.edu/.And from Moody Publishers and the book Who Owns the Land? Biblical insight and historical context to the conflict in the Middle East. moodypublishers.com

The Legal Docket team previews the upcoming Supreme Court term, David Bahnsen digs into the September jobs report on Moneybeat, and the Hamas October 7th attack on the World History Book. Plus, the Monday morning newsSupport The World and Everything in It today at wng.org/donate.Additional support comes from The Master's University. The Master's University offers over 150 programs, all designed to disciple the next generation toward lives of faithfulness to The Master, Jesus Christ. We equip students with the knowledge, skills, and attitudes they need for their careers, while cultivating their spiritual growth, moral character, and leadership skills as they seek to glorify the Lord. If you're looking for an education uncompromisingly rooted in Christ and Scripture, we want to meet you. Learn more at https://www.masters.edu/.And from Moody Publishers and the book Who Owns the Land? Biblical insight and historical context to the conflict in the Middle East. moodypublishers.com