Podcasts about innovation labs

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

How do you create the runway for innovation inside one of the world's largest airlines? Jorie Sax, Head of Airshop Innovation Lab at United Airlines, shares how her team explores emerging technologies years before they're ready for scale while balancing safety, trust, and operational excellence. From why the best innovations are often invisible to how AI can create more human experiences, and why startups win through relentless focus, Jorie offers a masterclass in moving bold ideas beyond the pilot phase and into enterprise impact. Whether you're leading innovation, transformation, or venture partnerships, this episode is full of practical lessons for scaling what matters.

This week on Cooking Issues, Dave and the crew are joined by Alex Hawk, Manager of Culinary Innovation at Burger King, and Zach Young, Director of Culinary and Commercialization at Burger King, for a rare look inside the food science, logistics, and product development behind one of the largest restaurant systems in the world.They get into what it actually takes to create food for millions of people: bakery supply chains, fresh bread logistics, Whopper improvements, tomato specs, lettuce formats, clean-label constraints, regional menu development, and the challenge of making recipes work across thousands of restaurants and multiple generations of equipment.The conversation also covers home frying versus commercial frying, fryer oil, Popeyes chicken, Burger King's relationship to Puerto Rico and the Whopper Jr., artificial sweeteners, frozen carbonated beverages, CVAP ovens, water fryers, potato freezers measured in hundreds of millions of pounds, and why even a seemingly simple menu change can become a massive operational question.Plus: Diet Coke loyalty, green ketchup, edible Tide Pods that mercifully never happened, the original chicken sandwich, secret-menu chaos, and the realities of innovating inside a global fast-food system. Hosted on Acast. See acast.com/privacy for more information.

Emma RobotsSee omnystudio.com/listener for privacy information.

Neste episódio, Leandro Costa conversa com Guga Peccicacco, executivo de Comunicação e Relações Públicas da Infor, sobre um tema que ganhou urgência na era da IA: reputação como infraestrutura de negócio.A conversa passa por comunicação corporativa, vendas, branding, liderança, IA generativa e construção de narrativa pública. Guga mostra por que reputação não deveria ser tratada como consequência, como a percepção do mercado já é formada antes da primeira reunião comercial e por que comunicação precisa se aproximar cada vez mais dos objetivos reais do negócio.Ao longo do episódio, você vai ouvir reflexões sobre:- reputação como ativo estratégico;- o impacto da IA generativa na percepção das marcas;- a relação entre comunicação, confiança e geração de demanda;- o papel da liderança na construção de valor reputacional;- e casos práticos do Innovation Lab de PR da Infor.Um episódio para quem quer entender por que, daqui para frente, falar de comunicação é também falar de crescimento, risco, confiança e competitividade.

Revitalization Rewards found in this episode:1. Dedicated Space - Set apart, that is not a multi-use space.2. Prayer Saturated Space.3. Tools for Thinking that are simple.4. Restricted Access - Not for everyone.5. Usage - Scheduled Times

Walk into an expensive "Innovation Lab" or High Tech Cutting Edge University Makerspace, and you'll probably see a laser cutter, a 3D printer or two, all kinds of expensive technology and the adjacent software and screens that make it possible. That's cool. But that's also a high barrier to entry. Does it really have to be that way? And how did the maker movement come to sit so deep in pricey STEM territory? You probably know I've always admired the work of Angela Stockman, writing makerspace pioneer. She's been on this podcast several times, and I love what she shares around having students build ideas across modes, using free or inexpensive materials to help them construct concepts, characters, and storylines. In our interview a few years ago, she said: "When we ask kids to build, they typically come up with ideas they wouldn't have otherwise. When we ask kids to build and then talk about what they have built, the complexity of their ideas is usually higher." These feel like very worthwhile goals to me - kids coming up with innovative, complex ideas. But let's be clear, we don't have to ask kids to build on a 3D printer or learn to code in order to help them extend and amplify their thinking through maker tools. Angela has always said that, but the proliferation of high tech makerspaces can be hard to drown out when thinking about this issue. Making is not about having one specific tool. It's about what making can give to kids in terms of their development of ideas, as Stockman suggest above, and in their development as learners too (Cohen). When students make, they make choices, they make mistakes, they recover. Ideally, they develop new skills at the same time that they develop a growth mindset around iterating. Today on the podcast, let's talk about a fun new free tool I've created for you to help your students build their ideas. Sign up for the free block kit: https://spark-creativity.kit.com/2195ef8920  Go Further:  Explore alllll the Episodes of The Spark Creativity Teacher Podcast. Get my popular free hexagonal thinking digital toolkit Join our community, Creative High School English, on Facebook. Come hang out on Instagram.  Enjoying the podcast? Please consider sharing it with a friend, snagging a screenshot to share on the 'gram, or tapping those ⭐⭐⭐⭐⭐ to help others discover the show. Thank you!  Sources: Ackermann, E. (2001). Piaget's Constructivism, Papert's Constructionism: What's the Difference? Future of Learning Group Publication, 5(3), 1-11. Cohen, J. D., Jones, W. M., & Smith, S. (2018). Preservice and early career teachers' preconceptions and misconceptions about making in education. Journal of Digital Learning in Teacher Education, 34(1), 31-42. J, Jessie. "Price Tag." Spotify Lyrics. https://open.spotify.com/track/2vR1oGQdPfwJe4EVh8uNGc  Kretchmar, Jennifer. "Seymour Papert and Constructionism." EBESCO: https://www.ebsco.com/research-starters/religion-and-philosophy/seymour-papert-and-constructionism. 2021. Potash, Betsy (Host). (2018, September 6). The Power of the Writing Makerspace, with Angela Stockman (No. 47). [Audio Podcast Episode]. In The Spark Creativity Teacher Podcast. https://nowsparkcreativity.com/2018/09/the-power-of-writing-makerspace-with.html Smith, S. (2018). Children's Negotiations of Visualization Skills During a Design-Based Learning Experience Using Nondigital and Digital Techniques. Interdisciplinary Journal of Problem-Based Learning, 12 (2). Available at: https://doi.org/10.7771/1541-5015.1747 Stockman, Angela. (2016).  Make Writing: 5 Teaching Strategies That Turn Writer's Workshop into a Maker Space. Hack Learning Series. TEDxTalk. (2013, January 10). Reimagining learning: Richard Culatta at TEDx Beacon Street [Video]. YouTube.  

Most families think their job is to protect what's been built.But the ones that last know how to grow people inside it.In this episode, we explore what it means to turn a family into an environment for development—not just preservation. Because legacy isn't sustained by structure alone—it's sustained by individuals who are growing, contributing, and choosing their role inside it.You'll learn:Why overprotection and under-challenge both limit the next generationHow family environments either expand or restrict growthThe difference between giving access and developing capabilityWhat it looks like to create space for real contribution and ownershipIf you want what you've built to last, it's not just about what you pass down—it's about who you're developing along the way.#familyleadership #legacy #familyenterprise #businessfamily #relationshipadvice #leadershipmindset #growthmindset #healthyrelationships #generationalleadership #entrepreneurship

Most families think their job is to protect what's been built.But the ones that last know how to grow people inside it.In this episode, we explore what it means to turn a family into an environment for development—not just preservation. Because legacy isn't sustained by structure alone—it's sustained by individuals who are growing, contributing, and choosing their role inside it.You'll learn:Why overprotection and under-challenge both limit the next generationHow family environments either expand or restrict growthThe difference between giving access and developing capabilityWhat it looks like to create space for real contribution and ownershipIf you want what you've built to last, it's not just about what you pass down—it's about who you're developing along the way.#familyleadership #legacy #familyenterprise #businessfamily #relationshipadvice #leadershipmindset #growthmindset #healthyrelationships #generationalleadership #entrepreneurship

What happens when you stop focusing on human resources and start focusing on the human experience? Technology is advancing faster than human nature can keep up. If you want to stay relevant, you need a fundamental cultural reset, not just new software.  In this interview, Laura Cushing, the Chief People Experience Officer at Pacific Life, discusses the evolving intersection of organizational culture, employee engagement, and artificial intelligence. She emphasizes a strategic shift toward accountability and transparency as the balance of power moves back toward employers in a post-pandemic landscape. To prepare for an AI-driven future, Pacific Life has implemented a Gen AI Academy and Innovation Labs to demystify technology and help staff reimagine their workflows. Cushing highlights the rising importance of "power skills"—human-centric abilities like coaching and visionary leadership—which remain essential as technical tasks become automated. Ultimately, she argues that HR leaders must cultivate deep business acumen and proactive trust-building to successfully guide their workforces through digital transformation. Watch the full video on YouTube ---------- Start your day with the world's top leaders by joining thousands of others at Great Leadership on Substack. Just enter your email: ⁠⁠https://greatleadership.substack.com/ Quick heads-up: my new book, The 8 Laws of Employee Experience, is a practical playbook for building an environment where people do their best work—order a copy here: https://bit.ly/8exlaws

What if your airport was a living laboratory for innovation? Listen in as we sit down with Brian Cobb, Chief Innovation Officer at Cincinnati/Northern Kentucky International (CVG) Airport, to explore how CVG operates as a “city within a city,” deploying emerging technologies to improve passenger experience, strengthen operational resilience, and reimagine the modern airport. Discover how CVG is advancing innovation through real-time TSA wait times, robotics testing, and the growing role of AI, autonomy, and cybersecurity. Get even more insight as Brian takes the stage as keynote speaker at the 25th anniversary of AeroTech® this June. He'll explore what's next for aviation innovation, from connected robotics and autonomous workflows to the standards and cross-industry collaboration needed to make it all work safely. To learn more and register, visit sae.org/events/aerotech.   We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today—a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen—and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube.Follow host Grayson Brulte on LinkedIn, X, and Instagram.

In this Skip the Queue podcast episode, our guest host Ruth Read, Director of blooloop and greenloop, is joined by Vero Celis, CEO and Founder of Valumia and Sustainability Advisor at Skutek Consulting, and Marie Rayner, Director of Project Development and Sustainability Lead at Storyland Studios, to discuss sustainability in the attractions industry, focusing on practical climate action, key risks, and how small, data-driven steps can create meaningful progress. Topics Discussed: what sustainability and climate action mean for attractions how to get started using existing data and simple steps integrating sustainability into storytelling and guest experience designing attractions with biodiversity and long term impact in mind attractions as spaces to test and showcase sustainable innovation risks of not acting including climate impacts and infrastructure challenges supply chain risks and ESG considerations growing guest expectations around sustainability practical operational improvements and quick wins barriers to progress including cost, alignment, and lack of clarity circular design and reducing waste across projects engaging and educating guests through visible sustainability efforts   Show references:    Guest Host:  Ruth Read, Director at blooloop, the go-to source for attractions news and its sustainability platform greenloop. https://blooloop.com/ https://www.linkedin.com/company/blooloop/about/ https://www.linkedin.com/in/ruthread/ Join the greenloop newsletter. https://mailchi.mp/blooloop.com/greenloops-reasons-to-be-cheerful   Veronica Celis Vergara, CEO and founder of Valumia and Sustainability Advisor at Skutek Consulting https://skutek-consulting.de/ https://www.valumia.com/ https://www.linkedin.com/in/veronica-celis-vergara/   Marie Rayner, Director of Project Development and Sustainability Lead at Storyland Studios https://www.storylandstudios.com/ https://www.linkedin.com/company/storyland-studios/about/ https://www.linkedin.com/in/marie-r-138b181b/   Skip the Queue is brought to you by Merac. We provide attractions with the tools and expertise to create world-class digital interactions. Very simply, we're here to rehumanise commerce. Your guest host is Ruth Read. If you like what you hear, you can subscribe on Apple Podcasts, Spotify, and all the usual channels by searching Skip the Queue or visit our website SkiptheQueue.fm. If you've enjoyed this podcast, please leave us a five star review, it really helps others find us. And remember to follow us on LinkedIn. Credits: Written by Emily Burrows (Plaster) Edited by Steve Folland Produced by Emily Burrows and Sami Entwistle (Plaster) Download The Visitor Attractions Website Survey Report - https://www.merac.co.uk/download-the-visitor-attractions-survey We have launched our brand-new playbook: ‘The Retail Ready Guide to Going Beyond the Gift Shop' — your go-to resource for building a successful e-commerce strategy that connects with your audience and drives sustainable growth. Download your FREE copy here

Join us this week for The Tech Leaders' Podcast, where Gareth sits down with Jane Mustoe, Senior Technical Director and Head of Innovation Labs at Tesco. Jane talks about her love of transformative technologies, and how Tesco are actively applying them. On this episode, Jane and Gareth discuss drones, robotics, staff less stores, and how AI will augment, not replace humans. Timestamps: Introduction and the Credit Crunch (2:25) Tesco Innovation Labs: Magic Tills and Digital Assistants (17:58) Innovation Culture and Digital Twins (23:10) AI Applications: Robotics, Dynamic Pricing and Waste Reduction (30:53) AI Usage, Governance and Hiring (38:50) The Future of Tech, and Advice for 21-year-old Jane (47:45) https://www.bedigitaluk.com/

Social Media:Folgt TatWort auf InstagramFolgt TatWort auf FacebookFolgt TatWort auf YoutubeFolgt TatWort auf TikTokWebsite:www.tatwort-podcast.de Hosted on Acast. See acast.com/privacy for more information.

oaching Innovation Lab -  https://coachinginnovationlab.com/coachingforcoachesBecome a Dream Life Coach - www.DeniseWalsh.com Join the FREE Fb group - https://www.facebook.com/groups/dreamlifecoachDenise is a Clinical Psychologist turned Dream Life Coach and knows that she is making more of an impact now, as an online life and business coach than she did in the corporate world.She has a 12 week overcoming self-sabotage course that helps clients align their Dream Life Pathway - gain clarity for what they want next, clear the cobwebs of the heart, and create the daily habits that will lead them to success. As we retrain your subconscious brain and reprogram limiting beliefs, the changes you experience in 90 day u turn will support you (and your family) for years to come. Join the next round of 90 Day U Turn - www.90DayUTurn.com #stopsabotaging #becomeacoach #leadership #growth #healingSupport the showJoin our free FB Group Here - https://www.facebook.com/groups/dreamlifecoach www.DeniseWalsh.com Facebook - https://www.facebook.com/thedenisewalsh Instagram -https://www.instagram.com/thedenisewalsh/

Do naszego studia zaprosiliśmy jednego z wyróżniających się absolwentów III rundy programu Unicorn Hub Innovation Lab – Szymona Ciamagę. Jego pomysł na biznes został uznany za najlepszy podczas DemoDay kończącego rundę. I właśnie o tym pomyśle, czyli Diagniso, rozmawiamy zSzymonem. W jaki sposób Unicorn Hub Innovation Lab pomógł Szymonowi w zweryfikowaniu swojego pomysłu na wirtualnego diagnostę dla warsztatów samochodowych? Jak mentorzy programu wsparli go w procesie szlifowania rozwiązania? Jak eksperymenty w trakcie UHIL przełożyły się na rynkową gotowość Diagniso?Naszą rozmówczynią jest też Agata Koprowska z Fundacji OIC Poland, która opowiada, jak Unicorn Hub Innovation Lab zapewnia przestrzeń początkującym przedsiębiorcom na testowanie swoich rozwiązań i weryfikowanie ich w oparciu o rynkowe kryteria i potrzeby odbiorców.Podcast nie mógłby się odbyć bez udziału Jarka Dudy - eksperta ds. marketingu B2B i budowania strategii wzrostu, konsultanta ds. przywództwa i rozwoju biznesu, który w programie Unicorn Hub Innovation Lab był mentorem Szymona, wspierając go w rozwoju Diagniso.Odcinek został zrealizowany we współpracy z OIC POLAND Fundacją Akademii WSEI, która koordynuje projekt Unicorn Hub Innovation Lab, współfinansowany ze środków FunduszyEuropejskich dla Nowoczesnej Gospodarki, w ramach Priorytetu II Środowisko sprzyjające innowacjom, Działanie FENG.2.27 Laboratorium Innowatora.

We bring you a special edition of Conqu'ring Heroes this week, as Jon presided over a panel discussion at Saturday's Spring Game Celebration Donor Appreciation Event. The discussion focused on the Human Performance & Sport Science (HPSSC) Athlete Innovation Lab, a new laboratory that is integrated within the Michigan Athletics Department as part of a strategic framework to elevate the sport science experiences at U-M to a level that will position the institution as an international leader in educational and research opportunities in this rapidly-advancing field. Panel participants included Ken Kozloff, PhD (HPSSC Co-Director), Nora Boerger (Assistant Coach, Women's Lacrosse), Laila Kostorowski (student-athlete, Women's Lacrosse), and Skylar Reynolds (master's student who works in the HPSSC lab).See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Episode SummaryWhat do you do when you can't stop the thing you're trying to fix?Three returning guests sit down for one of the most honest conversations about public sector modernization we've had on the show.From the latest on SF's 911 cloud migration, to what it means to modernize Harry Reid International Airport in real time while simultaneously designing the technology architecture for a second commercial airport 20 miles south, to Seguin's workforce transformation from one staff member with a degree and zero certifications in 2018 to 13 degrees and 27 certifications today - join us for this powerful conversation about leadership, community, and what it means to let others carry the message.FeaturingMichelle Geddes CIO San Francisco Department of Emergency ManagementRishma Khimji CITO Clark County Department of Aviation (Harry Reid International Airport) (now CIO Greater Orlando Aviation Authority)Shane McDaniel CIO City of Seguin, TX | TAGITM Past PresidentTimestamps(04:55) - The LinkedIn Banter Origin Story(07:20) - Michelle: SF's 911 Cloud Journey & Hybrid Architecture(09:25) - ESInet, Copper Lines Failing in LA & State Partnership(11:14) - AI for Multilingual 911 Dispatch(12:47) - Rishma: Harry Reid's Second Airport - 20 Miles South(14:31) - Using General Aviation Airports as Innovation Labs(16:40) - Computer Vision at Checkpoints & the 3-Year Rolling Stack(18:23) - Shane: Seguin's Workforce Story - 0 to 27 Certifications(23:48) - The Amazon Warehouse Hire & The Best Buy Delivery Driver(36:12) - TAGITM: The Solution Is in the Room(39:13) - Leadership, Community & Letting Others Carry the MessageListen now: YouTube x Apple x SpotifyWhenever you're ready, there are 3 ways you can connect with TechTables:1.

What if the experience and connections you've built over a lifetime are actually the key to unlocking your next opportunity?In a world where job boards feel like a black hole and AI is changing the rules overnight, it's easy to wonder where you fit—and how to move forward with confidence.In this episode of Smart Women Talk, Katana sits down with Katie Fortunato, co-founder of Higher Innovations, to cut through the noise and reveal what's really driving opportunity today—and how you can use it to your advantage.You'll discover:Why most jobs are never posted—and how to access the hidden marketHow your network can open doors faster than any résumé ever willThe real role of mentorship—and how to step into it at any stageWhat AI actually means for your career (and how to stay relevant)Simple ways to turn your experience into meaningful work and incomeThis isn't about starting over. It's about building forward—with everything you already have.Tune in now and start seeing your next chapter in a whole new way.Katie Fortunato is Co-founder and EVP of Platform Innovation and Strategy at Hire Innovations, a global leader in human-centered AI talent technologies. She drives innovation across a portfolio of brands—including Recruitics, Talivity, and Jobstream—helping the world's largest organizations modernize hiring while keeping people at the center.In 2023, Katie launched Talivity, one of the fastest-growing communities for people leaders, and established the company's Innovation Lab. Her latest venture, Jobstream (2026), is pioneering a new category in job discovery—connecting talent and opportunity through social platforms with transparent creator monetization.Prior to entrepreneurship, Katie built brand experiences for MTV, AOL, and The Wall Street Journal, where she led the publication's first live video brand experience and developed strategic growth partnerships to drive new revenue streams.Katie also serves as President of the Onward & Upward Foundation and sits on the Board of the American Red Cross Metro NY North Chapter. She splits her time between New York City and Greenwich, Connecticut.You can connect with Katie at GetJobStream.com.

Healthcare startups rarely fail because of bad technology. They fail because the system won't let them in.In this episode, Andreas speaks with Helen McShane, who leads the Innovation Lab at Young Lives vs Cancer, and Zoe Peden, Partner at impact venture firm Ananda, about a new experiment in healthcare innovation: a charity investing directly in startups.After more than 60 years supporting children and families affected by cancer, Young Lives vs Cancer has deep insight into where the system works — and where it doesn't. Through its Innovation Lab, the charity is now deploying mission capital to support startups building solutions for young cancer patients.Their first investment: £30,000 into Little Journey, a platform designed to help children prepare for medical procedures.Helen and Zoe explore how charities can combine institutional knowledge with venture discipline to help startups navigate complex healthcare systems and accelerate adoption where it matters most.In this episode:Why healthcare startups struggle with adoptionWhat “mission capital” means in practiceHow charities can support startup innovationWhy credibility and partnerships often matter more than cheque size

March 12, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Nike brings back After Dark Tour for 2026, expanding women-focused nighttime race series to seven global cities after drawing 50K+ participants Microsoft reports handling 50M+ daily health questions across Bing and Copilot, with 40% focused on symptoms and 1 in 5 involving personal health management Health Innovation Lab returns to West Palm Beach in November 2026, expanding to 250 attendees with dedicated HIL day ahead of Eudimonia Summit. Learn more at https://joinhil.com Today's episode is brought to you by AIIR — a modern communications and experiential agency for health, wellness, fitness, and performance brands. From earned media to events and creator-led campaigns, AIIR helps companies sharpen their story, earn attention, and build trust that compounds. Visit https://aiir.agency to learn more. More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co

The 21st Theater Sustainment Command. cuts the ribbon March 13 on its Innovation Lab at Kaiserslautern, Germany, built to help Soldiers tackle problems closest to the mission. The lab ties frontline needs to Army Development Command's ability to prototype and test new concepts. We look at what Soldiers can build, how ideas move through the pipeline, and what success looks like in year one with Major Ron White, Chief Innovation Officer of the Army's 21st TSC. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today on the podcast we're digging into student agency and the lessons Dr. Annalies Corbin has learned from her work pioneering microschools and portable innovation labs. Who is Dr. Corbin, you ask? Well, she's the CEO and founder of the PAST Foundation. "From a single school partnership in 2006, Annalies has grown PAST's supporters across the nation, building a reputation for both transforming teaching and learning by understanding tomorrow's education needs. In 2015, Annalies' commitment to transforming schools led to the development of PAST Innovation Lab. Connecting directly with teachers through online professional development courses, MAEd program and on-site workshops, PAST Innovation Lab impacts more classrooms and expands learning opportunities for teachers and students everywhere. In 23 years, PAST has impacted more than 2,300,000 students, over 20,000 teachers across 42 states, hosting nearly 20,000 visitors and building hundreds of partnerships" (Foundation Website). To connect further after the show, you can find Annalies hosting the podcast, Learning Unboxed, or read her new book, Hacking Schools: Five Strategies to Link Learning to Life. I think you're going to be really intrigued by the programs the PAST foundation has put into action, and the ways they can be applied to ELA. So let's dive in. Connect with Dr. Annalies Corbin: Find out more about her new book, Hacking School, here.  Learn about The Past Foundation. Say hello to Annalies on Instagram. Go Further:  Explore alllll the Episodes of The Spark Creativity Teacher Podcast. Snag three free weeks of community-building attendance question slides Join our community, Creative High School English, on Facebook. Come hang out on Instagram.  Enjoying the podcast? Please consider sharing it with a friend, snagging a screenshot to share on the 'gram, or tapping those ⭐⭐⭐⭐⭐ to help others discover the show. Thank you!   

Taka Ariga hits all the right notes for AI at scale: clarity of purpose, strong foundations, sustainable innovation, engaged ownership, and a confident workforce.    Taka and Kimberly discuss going beyond novel AI prototypes; the limits of automation; context building; data sovereignty and integrity; the unstructured data deluge; the unique sensitivities and needs of public agencies; valuing ownership and viable ways to scale; plagiarizing for good; foundations for AI success; wanting innovation without change; rethinking governance; enabling confident AI use; making space for reinvention; and being a skeptical AI advocate.Taka Ariga is a heretical technologist and the founder of Sol Imagination. He focuses on AI strategy design, implementation, and value capture. Taka served the US Office of Personnel Management (OPM) as CDO and CAIO and the US Government Accountability Office (GAO) as Chief Data Scientist and Director of the Innovation Lab. Related Resources:Sol Imagination (company)                  https://sol-imagination.ai/  A transcript of this episode is here.   

This episode of The New Abnormal podcast features Deborah Hayek, who leads Foresight & Innovation at Servus Credit Union, in Canada.She brings over a decade of experience integrating strategic foresight into business strategy within major financial institutions. Previously, at the forefront of Manulife's Global Innovation team, Deborah served as Head of Strategic Foresight, driving foresight initiatives across business lines and shaping innovation strategy through macro trend analysis and immersive engagement tools. Prior to that, she built and led the future-focused R&D team within Desjardins's Innovation Lab.In parallel, Deborah designs and teaches executive education at HEC Montréal, equipping senior leaders with the methods and mindset to navigate uncertainty and seize future opportunities. Through bilingual instruction, she aims to make strategic foresight both practical and accessible to leaders shaping tomorrow's economy. She discusses all of the above in our discussion, which I hope you find as interesting as I did. And you can read her newsletter on corporate foresight 'The Sparkline' on Substack... 

Artificial intelligence is advancing at an extraordinary pace, and education is being reshaped whether we are ready for it or not. In this episode, we discuss a new and fascinating book on this topic — Artificial Intelligence in Education: The Intersection of Technology and Pedagogy. The contributors are experts from around the world who are both educators and technically proficient. I'm joined by the editors of the book, who are leading experts in the field of learning technologies. Dr. Peter Ilic is a Senior Associate Professor in the Center for Language Research at the University of Aizu in Japan. Dr. Imogen Casebourne is the research lead at the Innovation Lab at the Digital Education Futures Initiative (DEFI) at Cambridge University. Prof. Rupert Wegerif is Professor of Education in the Faculty of have Education at the University of Cambridge and the founder and academic director of the Digital Education Futures Initiative (DEFI) at Hughes Hall, Cambridge University. The book and this conversation sit at the intersection, and sometimes the tension, between technologists and educators. Historically, educational technologies promised transformation but often end up reinforcing outdated models of learning. AI poses a new challenge that is fundamentally changing education. Together, we explore why simply adding AI to existing systems doesn't work, why dialogue between technology and pedagogy is now urgent, and how approaches like design-based research can help us develop educational AI more responsibly. We also discuss what it might mean to move toward a more dialogic understanding of education, one focused less on the transmission of knowledge and more on collaboration, problem-solving, and learning with both people and technology. At its core, this episode is a call for collaboration between educators, technologists, and policymakers and for taking an active role in shaping the future of AI in education, rather than being shaped by it. Links: Book: Artificial Intelligence in Education: The Intersection of Technology and Pedagogy https://link.springer.com/book/10.1007/978-3-031-71232-6 Dr. Peter Ilic: https://u-aizu.ac.jp/research/faculty/detail?lng=en&cd=90119 Dr. Imogen Casebourne: https://www.deficambridge.org/people/imogen-casebourne/ Prof. Rupert Wegerif: https://www.educ.cam.ac.uk/people/staff/wegerif/

In this teaser, Henrik and Jeremy debrief their conversation with Humza Teherany, Chief Strategy and Innovation Officer at MLSE. They reflect on how Humza rebuilt his technical fluency, why he believes leaders can't delegate this moment, and what it actually looks like to launch an internal AI lab that ships in 24 hours. Full episode out next week.Full episode LIVE NOW. For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:Henrik: https://www.linkedin.com/in/werdelinJeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

In this episode, Humza Teherany breaks down how he bridges deep technical fluency with strategic leadership at MLSE, home to the Raptors, Maple Leafs, and more. He shares how a vacation turned into an AI reawakening and how that hands-on immersion led to a fundamental shift in how his organization builds and experiments.Humza walks through MLSE's build in a day practice, their internal AI platform, and why speed to prototype now unlocks more than just efficiency. It changes who gets to shape the future. He, Jeremy, and Henrik explore the limits of traditional enterprise AI rollouts and how to build spaces for superusers that enable company-wide transformation. The conversation covers how technical literacy impacts credibility, why idea execution is the new differentiator, and how Humza's five-year-old inspired a bedtime story app powered by AI.Whether you're a CTO, a founder, or just figuring out where to start, Humza makes a compelling case. The best leaders don't delegate this moment. They build.Key TakeawaysLeaders should not delegate the AI momentHumza, Henrik, and Jeremy agree that this is a moment for leaders to be hands-on. The ones who build and explore the tools themselves are the ones unlocking real impact.Technical fluency builds credibility and better decisionsHumza's return to his technical roots has changed how he leads. Understanding how AI works helps leaders earn trust and make smarter, faster choices.Speed enables inclusionMLSE's build in a day model allows more people to contribute ideas and see them turned into real prototypes. Moving fast isn't just efficient - it changes who gets to participate.Empower your superusers firstRather than starting with enterprise-wide training, Humza focuses on enabling the small group already eager to build. That early energy helps drive broader culture change.MLSE: mlse.comLinkedIn: Humza Teherany - LinkedIn00:00 Intro: Humza Teherany and MLSE00:27 The Role of C-Suite Leaders in AI01:08 Reconnecting with Technical Skills02:08 Diving Deep into AI Tools03:03 The Importance of Hands-On Learning04:25 Progression from Consumer to Technical AI Tools07:28 Building a Business Case for AI10:03 Creating a Culture of Innovation14:00 Implementing AI in Business Operations21:05 Challenges and Strategies in AI Adoption26:17 Organizational Structure for AI Success32:02 The Importance of Reviewing and Planning Code33:01 The Future of Solo Developers and New Technologists34:58 Reimagining Company Structures with AI38:55 Key Skills for Future Technology Leaders41:19 Personal AI Experiments and Innovations46:52 Encouraging Creativity in Children with AI49:11 The Debrief

Change is emotional. Even when the strategy is solid, people still feel uncertain, skeptical, or overwhelmed, especially when the vision feels huge and the path feels unclear.In this revisited episode of The Visual Lounge (originally Episode 168), Matt sits down with Jake Gittleson, who leads McKinsey's Learning Research and Innovation Lab. Jake shares why storytelling is one of the most effective tools L&D teams have for supporting change inside organisations.Instead of trying to persuade people in one big moment, Jake explains why change stories should be shared over time, through small experiments, human insights, and incremental updates that meet people where they are. He also breaks down practical ways to gather stories through interviews, outline your narrative, and use video and audio to create connection, without needing expensive gear or a polished production setup.Learning points from the episode include:00:00 - 01:21 Introduction01:21 - 02:03 Jake's background02:03 - 04:14 How Jake started using audio and video04:14 - 07:01 What does a successful change look like07:01 - 08:45 Creativity as a tip for using video at work08.45 - 11:55 Jake's role and expertise in change and innovation11:55 - 15:11 Why human connection matters in change15:11 - 18:13 Operationalizing storytelling without big budgets18:13 - 21:13 Building the right stories21:13 - 27:10 Visual approaches to telling stories27:10 - 30:21 Capturing real voices30:21 - 39:51 Speed round39:51 - 40:46 Jake's final take40:46 OutroImportant links and mentions:Connect with Jake on LinkedIn: https://www.linkedin.com/in/jake-gittleson/Check out The Learning Geeks podcast: https://www.learninggeekspod.com/Listen to Jake's first appearance on The Visual Lounge in episode 168: https://player.captivate.fm/episode/ee9c311f-7f51-4a6c-a749-c2d7090a1274

On this episode I chatted with Jennifer Hankins, Jenn is the managing director of Tulsa Innovation Labs. She joined the founding TIL team in January 2020 and brings more than 10 years of direct economic development experience to the organization. Working to convene stakeholders across multiple industries, she is responsible for setting TIL's strategic direction, organizational mission, and most importantly, is responsible for leading a dynamic and high-performing team and the broad portfolio of work currently underway. Prior to joining Tulsa Innovation Labs, Jennifer was the vice president of Entrepreneurship and Small Business at the Tulsa Regional Chamber, where she helped grow the regional entrepreneurial ecosystem and managed the Chamber's business incubator for high-growth startups. Jennifer also served as manager of business retention and expansion on the Greater Oklahoma City Chamber of Commerce's Economic Development team. Prior to her time in Oklahoma, Jennifer worked in the Kansas City region for the Wyandotte Economic Development Council as investor relations coordinator and for Catholic Charities of Kansas City-St. Joseph as development manager. She is a native of Kansas City, Missouri and holds a Bachelor of Political Science degree from Oklahoma State University. www.tulsainnovationlabs.com Huge thank you to our sponsors. The Oklahoma Hall of Fame at the Gaylord-Pickens Museum telling Oklahoma's story through its people since 1927. For more information go to www.oklahomahof.com and for daily updates go to www.instagram.com/oklahomahof The Chickasaw Nation is economically strong, culturally vibrant and full of energetic people dedicated to the preservation of family, community and heritage. www.chickasaw.net Dog House OKC - When it comes to furry four-legged care, our 24/7 supervised cage free play and overnight boarding services make The Dog House OKC in Oklahoma City the best place to be, at least, when they're not in their own backyard. With over 6,000 square feet of combined indoor/outdoor play areas our dog daycare enriches spirit, increases social skills, builds confidence, and offers hours of exercise and stimulation for your dog www.thedoghouseokc.com #ThisisOklahoma #ThisisOklahoma   

In this episode of The Greener Way, host Michelle Baltazar interviews Candace Coppere, head of Climate and Innovation Lab, Sustainability Business at ISS STOXX (parent company of FS Sustainability) about the latest in measuring and monitoring physical risks for real asset investors.Coppere explains how fund managers and super funds can leverage geospatial AI to turn coarse global climate data into localised and more granular information. For example, AI and machine learning tools today can help convert wildfire projections from 100km grids to 300-meter estimates Read the full report here: https://insights.issgovernance.com/posts/measuring-and-monitoring-physical-risk-for-real-asset-inve…). *ISS STOXX is the parent company of FS SustainabilityThis podcast uses the following third-party services for analysis: OP3 - https://op3.dev/privacy

In this episode, Wendy sits down with Jessica Reynolds, Senior Director for the Office of International Investment and Trade at the Maryland Department of Commerce, to uncover how Maryland has become a powerhouse for helping companies scale internationally.  Jessica breaks down the state's innovative programs — from the Global Gateway soft-landing initiative to its publicly sponsored Innovation Lab — and shares real stories of startups, exporters, and industry leaders who transformed with the right support. Whether you're entering the U.S. market, exploring foreign expansion, or navigating fears around IP, regulations, culture, or investment, this conversation gives you a roadmap for taking the leap with confidence. You'll learn: How Maryland attracts and accelerates global companies through programs like Global Gateway, Soft Landing Exchange, and its state-run Innovation Lab: the first of its kind in the U.S. Common pitfalls and fears companies face when going international (IP protection, regulations, readiness, investment hesitations) and the real resources available to overcome them. Where businesses should start when expanding abroad or entering the U.S. market, and why there's “no wrong door” when tapping into export councils, government agencies, and trade partners

For episode 316 of the Glass and Out Podcast we welcome CEO of Sports Innovation Lab Angela Ruggiero. She is best known as a 4x Olympian and member of the Hockey Hall of Fame. She was a senior in high school when she played for Team USA and won gold at the 1998 Olympics in Nagano. In 2004 she won the Patty Kaizmeir Award as the top player in college hockey. She was also the first woman to play in a regular season professional hockey game in North America at a position other than goalie when she suited up for the Tulsa Oilers in a Central Hockey League game. However, her list of achievements extend well beyond her career as an athlete.  That includes time as a member of the International Olympic Committee from 2010 to 2018. She served as a member of the Executive Board of the IOC after being elected Chairperson of the IOC Athletes' Commission, the body that represents all Olympic athletes worldwide, a post which she held from 2016 to 2018. She also has an MBA from Harvard and was a senior management associate with Bridgewater Associates, the largest hedge fund in the world. Listen as she shares why learning to love adversity is key to success in life, why utilizing data in sports has to be built on trust, and why athletes are the best opportunity for corporations in the world.

On Episode #190 of the PricePlow Podcast, Mike and Ben host a special round-robin format from SupplySide Global 2025, recorded live at the ONE Innovation Labs booth. This episode showcases the collaborative power of the supplement industry, featuring conversations with Dr. Pedro Perez and Anthony Armas (ONE Innovation Labs), Katie Emerson and Karen Todd (Kyowa Hakko), Dan Force (Prinova), Zain Saiyed (Lonza), Dr. Benjamin Weeks (Adelphi University), and Nathan McWherter (Sports Research). The Miami-based ingredient innovator behind PureWay-C® is making waves with their LiposoMax® liposomal delivery platform, and this episode demonstrates how pharmaceutical-grade technology is transforming supplement formulation across multiple categories. From Setria® Glutathione to creatine monohydrate, from DUOCAP® capsule-within-capsule technology to ready-to-drink applications, this conversation reveals how genuine innovation creates opportunities throughout the supply chain. Beyond technical discussions about phospholipid complexes and cellular uptake kinetics, this episode tells the story of how ingredient suppliers, distributors, technology partners, and brands collaborate to bring differentiated products to market. If you're a formulator seeking to understand how delivery technology can justify premium positioning, or a brand builder looking for genuine scientific differentiation, this episode provides a masterclass in strategic partnerships that create value at every level. https://blog.priceplow.com/podcast/one-innovation-labs-liposomax-190 Video: ONE Innovation Labs Round Robin at SupplySide Global 2025 https://www.youtube.com/watch?v=NEU-PmSV6qs Detailed Show Notes: Pharmaceutical-Grade Innovation Meets Industry Collaboration (0:00) – Introduction: The Innovators Behind PureWay-C® (4:45) – The LiposoMax® Platform: True Liposomal Technology (10:45) – Kyowa Hakko: Elevating Setria® Glutathione with LiposoMax® (14:45) – Topical Applications and the Recycling Properties (20:30) – Prinova Partnership: Bringing LiposoMax® Creatine to Market (25:15) – The Science Behind LiposoMax® Creatine (28:45) – Clinical Data: Two Times Cellular Absorption (32:00) – Creatine's Mainstream Moment (37:00) – Format Flexibility: From Beverages to Soft Gels (39:30) – Lonza Partnership: The DUOCAP® Revolution (44:15) – Advanced Release Profiles and Formulation Strategies (48:00) – Benjamin Weeks' Cellular Research Methodology (51:30) – Magnesium Innovation and Niacin Parallels (54:30) – Lonza's Enzyme Delivery Research (58:30) – Sports Research: Commercial Success with Liposomal Vitamin C (1:02:00) – Authenticity in Liposomal Claims Where to Follow and Learn More ONE Innovation Labs ONE Innovation Labs on LinkedIn Anthony Armas on LinkedIn Dr. Pedro Perez on LinkedIn Erne... Read more on the PricePlow Blog

Kathleen Peters brings a background with digital communications companies and tech startups to her role as Experian's chief innovation officer. On this episode, Kathleen shares a bit about Experian's Innovation Lab, outlining some of its projects and explaining how the recent democratization of generative AI tools has made even more innovative thinking possible, both for tech experts and for contributors who have other core competencies. Read the episode transcript here. Guest bio: As Experian's chief innovation officer, Kathleen Peters explores new ways to solve market challenges in identity, risk, and fraud detection. She and her team define business strategies and investment priorities while incubating new products, analyzing industry trends, and leveraging the latest technologies to bring ideas to life. Peters joined Experian in 2013 to lead business development and global product management for its newest fraud products. She later led its Fraud & Identity business in North America until being named chief innovation officer for decision analytics in 2020. Peters has twice been named a “Top 100 Influencer in Identity” by One World Identity (now Liminal), which annually recognizes influencers and leaders in the identity space. Peters is regularly quoted in prominent media outlets, including Forbes and Bloomberg, and she frequently shares her insights on innovation, AI, and fraud prevention at industry events. Me, Myself, and AI is a podcast produced by MIT Sloan Management Review and hosted by Sam Ransbotham. It is engineered by David Lishansky and produced by Allison Ryder. We encourage you to rate and review our show. Your comments may be used in Me, Myself, and AI materials.

Mert Can is based out of Istanbul,We will talk about his career in full,Mert Can is the founder of Verilogy,He helps clients navigate the newest technology!

What does it mean to be an artist? In Artists At Work: Rethinking Policy for Artistic Careers (Stanford UP, 2025) Joanna Woronkowicz, the co-founder of the Center for Cultural Affairs and co-director of the Arts, Entrepreneurship and Innovation Lab at Indiana University Bloomington and currently based at Copenhagen Business School, tells the story of cultural work and cultural policy in the USA. Offering a broad definition of artists and creatives, the book offers a deep dive into the demographics of the arts workforce, their working patterns, the places where they work and live, and their education and training. The analysis also shows the range of challenges confronting the contemporary arts workforce, and crucially demonstrates what policy can do to help. Rich with empirical detail, as well as being clear and accessible, the book is essential reading across the humanities, social sciences, and for anyone interested in the arts today! Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

What does it mean to be an artist? In Artists At Work: Rethinking Policy for Artistic Careers (Stanford UP, 2025) Joanna Woronkowicz, the co-founder of the Center for Cultural Affairs and co-director of the Arts, Entrepreneurship and Innovation Lab at Indiana University Bloomington and currently based at Copenhagen Business School, tells the story of cultural work and cultural policy in the USA. Offering a broad definition of artists and creatives, the book offers a deep dive into the demographics of the arts workforce, their working patterns, the places where they work and live, and their education and training. The analysis also shows the range of challenges confronting the contemporary arts workforce, and crucially demonstrates what policy can do to help. Rich with empirical detail, as well as being clear and accessible, the book is essential reading across the humanities, social sciences, and for anyone interested in the arts today! Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/critical-theory

What does it mean to be an artist? In Artists At Work: Rethinking Policy for Artistic Careers (Stanford UP, 2025) Joanna Woronkowicz, the co-founder of the Center for Cultural Affairs and co-director of the Arts, Entrepreneurship and Innovation Lab at Indiana University Bloomington and currently based at Copenhagen Business School, tells the story of cultural work and cultural policy in the USA. Offering a broad definition of artists and creatives, the book offers a deep dive into the demographics of the arts workforce, their working patterns, the places where they work and live, and their education and training. The analysis also shows the range of challenges confronting the contemporary arts workforce, and crucially demonstrates what policy can do to help. Rich with empirical detail, as well as being clear and accessible, the book is essential reading across the humanities, social sciences, and for anyone interested in the arts today! Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/art

What does it mean to be an artist? In Artists At Work: Rethinking Policy for Artistic Careers (Stanford UP, 2025) Joanna Woronkowicz, the co-founder of the Center for Cultural Affairs and co-director of the Arts, Entrepreneurship and Innovation Lab at Indiana University Bloomington and currently based at Copenhagen Business School, tells the story of cultural work and cultural policy in the USA. Offering a broad definition of artists and creatives, the book offers a deep dive into the demographics of the arts workforce, their working patterns, the places where they work and live, and their education and training. The analysis also shows the range of challenges confronting the contemporary arts workforce, and crucially demonstrates what policy can do to help. Rich with empirical detail, as well as being clear and accessible, the book is essential reading across the humanities, social sciences, and for anyone interested in the arts today! Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/public-policy

This week, we catch up with Nicole Papantoniou, the Director of the Good Housekeeping Kitchen Appliances and Innovation Lab. We hear from Nicole about what it's like running one of the preeminent testing labs for kitchen appliances in the world, her personal journey to her current position and what she's excited about in terms of kitchen innovation. Enjoy the podcast! Learn more about your ad choices. Visit megaphone.fm/adchoices

1018: How is Experian transforming from a data company into a technology-first innovator? In this episode, Kathleen Peters, Chief Innovation Officer at Experian, joins Peter High to reveal how her team is scaling Generative AI, agentic AI, and behavioral analytics to fight fraud, streamline decisioning, and make advanced modeling accessible to everyone. Kathleen shares how Experian's global Innovation Labs evolved from blue-sky R&D to delivering real-world impact, from the Experian Assistant, a natural-language AI that can build models in minutes, to cutting-edge behavioral analytics that distinguish humans, bots, and AI agents in real time.

On Episode #183 of the PricePlow Podcast, Mike and Ben sit down with Raza Bashir, Chief Innovation Officer at MuscleTech and Iovate, for an in-depth conversation about supplement quality control, manufacturing excellence, and the exciting innovation pipeline that has him more energized than ever in his nearly 20-year career. Broadcasting from MuscleTech's laboratory facility, Raza provides unprecedented transparency into the rigorous processes that ensure every product meets the highest standards across 140 countries. This conversation goes far beyond typical brand discussions, diving deep into the nuts and bolts of quality assurance: how MuscleTech manages relationships with 28+ contract manufacturers worldwide, the extensive third-party testing protocols that validate every batch, and the end-to-end oversight that distinguishes legacy brands from newcomers. Raza shares insights from recent Consumer Reports testing that validated MuscleTech's mass gainers as the only products without concerning levels of heavy metals, demonstrating how comprehensive quality systems deliver tangible consumer protection. The discussion then shifts to innovation, with exclusive reveals of breakthrough effervescent technology launching through the EFF'N Series at GNC, new peptide formulations combining cutting-edge ingredients like dileucine with HMB and essential amino acids, and the evolution of stimulant technology through EuphoriQ and the revolutionary Stacked pre-workout featuring Hydronox citrulline hydrochloride. Throughout the conversation, Raza's passion for both scientific rigor and consumer experience shines through, explaining why MuscleTech continues setting industry standards after three decades. https://blog.priceplow.com/podcast/muscletech-quality-control-183 Video: Raza Bashir Discusses MuscleTech's Quality Control and Innovation Pipeline https://www.youtube.com/watch?v=-efByKU-cyk Detailed Show Notes: Quality Systems and Innovation at MuscleTech (0:00) – Introductions: Inside MuscleTech's Innovation Lab (0:45) – Raza's Journey: From Passionate User to Innovation Leader (4:00) – The Manufacturing Network: Managing 28+ Global Partners (6:45) – The Qualification Process: Rigorous Audits Before Production (10:30) – Ingredient Qualification: Testing Before Approval (14:00) – The Branded Ingredient Advantage: Established Supply Chains (17:15) – Production Consistency: Managing Multiple Manufacturers Per Product (22:15) – The Multiple Manufacturer Strategy: Service and Supply Chain Resilience (24:15) – Third-Party Testing and Consumer Reports Validation (27:00) – Protein Testing and Nitrogen Analysis Methods (32:00) – The Creatine Conundrum: Testing Above Label Claim (36:30) – The Transparency Advantage: Avoiding Proprietary Blends (39:00) – Innovation Without Over-Engineering (40:15) – Business Resilience and Continued Innovation (43:30) – The Innovation Risk: Pioneering New Ingredients (47:00) – EuphoriQ Evolution: Responding to Consumer Feedback (52:00) – The Leaner Business Philosophy: Focused Excellence (55:00) – The EFF'N Series: Revolutionary Effervescent Technology (59:00) – Theolim: Novel Metabolism Enhancement (1:04:00) – EFF'N Energy: Genius Pure and Yohimbe (1:06:00) – The Effervescent Experience: Dissolutio... Read more on the PricePlow Blog

Political violence expert Cynthia Miller-Idriss joins Margaret Hoover to discuss the assassination of conservative activist Charlie Kirk and the rising threat of political violence in America.Miller-Idriss, author of the new book “Man Up: The New Misogyny and the Rise of Violent Extremism,” details some of the factors fueling radicalization, including online gaming and social media. She also explains why young men have proven particularly susceptible to extremist influences amid a crisis of masculinity in society.As conservatives cast blame on the left and demand vengeance for Kirk's death, Miller-Idriss warns of vigilanteism and suppression of free speech. She says there is “unquestionably” a danger of further violence if rhetoric is not toned down.Miller-Idriss, the founding director of the Polarization & Extremism Research & Innovation Lab at American University, also talks about potential solutions to radicalization and what she has learned from talking to students about these issues.Support for Firing Line with Margaret Hoover is provided by Robert Granieri, The Tepper Foundation, Vanessa and Henry Cornell, The Fairweather Foundation, and Pritzker Military Foundation.

A team of researchers at American University in Washington D.C. has shifted their approach to look at domestic extremism as a public health problem. The researchers work with the Polarization and Extremism Research and Innovation Lab, or PERIL. Minnesota is no stranger to these types of incidents, with the shooting of two lawmakers and their spouses in June and the recent mass shooting at Annunciation Catholic Church and School. Hala Furst, PERIL's director of strategic partnerships and Rabbi Seth Limmer, director of public affairs, join MPR News host Nina Moini to talk about their work.

Looking for daily inspiration?  Get a quote from the top leaders in the industry in your inbox every morning.   What's the one premier event that brings the global attractions industry together? IAAPA Expo 2025, happening in Orlando, Florida, from November 17th through 21st. From breakthrough technology to world-class networking and immersive education, IAAPA Expo 2025 is where you find possible.  And, just for our audience, you'll save $10 when you register at IAAPA.org/IAAPAExpo and use promo code EXPOAPROSTEN. Don't miss it — we won't!   Coen Bertens is the owner of Coen Bertens Consultancy, where he partners with leisure and hospitality operators on operations, leadership, and guest experience. After beginning his career in banking, Coen joined Efteling in the Netherlands, where he moved from finance to operations, ultimately serving as director/CEO of the park. During his tenure, Efteling earned national recognition for guest friendliness and advanced a long-term, story-driven resort vision. In this interview, Coen talks about starting with people, shifting culture, and creating one fan a day. Starting with people “How you treat your people is how you treat your guests… you have to start with your people and change them into ambassadors.” Coen explains that Efteling's transformation didn't begin with guest-facing tactics—it began by equipping employees. Guided initially by advice from Lee Cockerell, the team built a “personal compass,” a single digital place where employees sought and shared feedback, identified talents, and aligned those talents to both personal growth and organizational contribution. Rather than pushing a hospitality script, leadership focused on pride, ownership, and talent development so that frontline teams would naturally deliver better experiences. That shift also meant moving decision-making closer to the work. Managers stopped “running and doing all the tasks,” and responsibilities—like resolving complaints on the spot—moved to the frontline. The results compounded: ideas surfaced faster, confidence grew, and service recovery became immediate instead of hierarchical. Shifting culture “We knew that if you want to be the most guest-friendly company… it's about changing the culture.” Culture change started with clarity of vision. A survey revealed that only a small slice of leaders could articulate Efteling's vision; nearly everyone else operated without clear goals. Coen's team distilled the vision into a simple, memorable “nine-plus organization”—akin to striving for a five-star standard—and recruited 50 internal ambassadors to spread it. Leaders repeated the vision constantly and connected it directly to tools like the personal compass so it lived in daily routines, not just on a wall. Empowerment mechanisms reinforced the shift. An Innovation Lab replaced the “idea box,” inviting students and staff to pitch solutions onstage to a centralized steering team. One standout idea—using VR to let guests with disabilities experience the Dreamflight dark ride alongside their families—came from a student, not management. Coen also shares a pivotal New Year's Eve story: when buses failed to arrive after midnight, employees self-organized to drive hundreds of guests home. That response—spontaneous, generous, and owned by the frontline—became a living metric of culture more powerful than any dashboard. Creating one fan a day “Keep it simple: create one fan per day… everyone has the time to create one fan per day.” A hospitality professor's advice became a durable operating principle: small, intentional moments scale culture. With ~800 employees a day, one fan per person translates into more than a million fan moments annually. Crucially, it's not about giveaways; it's about personal attention. In Efteling's Fairytale Forest, for example, an employee simply walks a parent and child to the restroom through winding paths, turning wayfinding into a warm, human interaction. Coen ties these moments to financial outcomes with a simple restaurant story: when service anticipates needs: right table, timely drinks, favorite refills, guests happily spend more and tip more. The message to teams is direct and doable: limit training topics, interact far more than you lecture, gamify learning, and repeat small behaviors daily until they become instinct.   For inquiries and further information, connect with Coen on LinkedIn—he welcomes messages and is happy to share tips. This podcast wouldn't be possible without the incredible work of our faaaaaantastic team:   Scheduling and correspondence by Kristen Karaliunas   To connect with AttractionPros: AttractionPros.com AttractionPros@gmail.com AttractionPros on Facebook AttractionPros on LinkedIn AttractionPros on Instagram AttractionPros on Twitter (X)

I sit down with two innovation leaders from one of America's oldest and most trusted consumer brands. Leah Fischman Hunter, Director of the Innovation Lab, and Ginny Fahs, Director of Product R&D at Consumer Reports, join me to unpack how a 90-year-old nonprofit is building modern tools for an online world filled with AI hype, dark patterns, and data brokers. I share a personal connection at the top. In 2004, I helped launch Consumer Reports WebWatch in the press, when most sites hid executive names, contact details, and return policies. That early effort to bring transparency to the Internet in the 1990s is why this episode matters so much to me.  Two decades later, the stakes are even higher, with scams in our inboxes, consent buried in legalese, and AI systems shaping what we see and buy. CR has always had our backs and I wanted you to hear how they are doing it again. Leah and Ginny explain how Consumer Reports blends advocacy with product building. Their team translates privacy laws into something people can actually use. We dig into Permission Slip, a free app that lets you reclaim your data and tell companies to stop selling it. We discuss the reality of an opt-out culture in the United States, why people feel powerless regarding data, and how CR's independence and mission enable it to prioritize the public interest. We also explore Ask CR, an advisor grounded in tested ratings and reporting, rather than ads or affiliate commissions. We zoom out to the bigger shift happening with AI. I raise the worry that conversational agents often deliver a single definitive answer, while consumers still need choice and transparency. Leah and Ginny describe early work with academic partners on pro-consumer agentic systems and what duty of care and duty of loyalty could look like in software built for people, not just profits. We explore why online evidence needs clearer authorship, how to consider deleting data from platforms you rely on, and where education must catch up quickly. If you care about your privacy, your wallet, and the truth behind the products you buy, this one is for you. You will walk away with a clearer picture of what rights you already have, how to exercise them without hiring a lawyer, and why organizations like Consumer Reports still matter when technology moves faster than the rules that govern it.  

In this episode: Randy Lipps is Chairman, President, Chief Executive Officer, and Founder of Omnicell, a leader in transforming the pharmacy care delivery model. Under his leadership, Omnicell has grown from a single product offering to delivering the most comprehensive portfolio of medication management solutions across the continuum of care.Mr. Lipps founded Omnicell in 1992 after observing how inefficiently medical supplies were managed when his daughter was hospitalized at birth. Inspired by his work in airline industry operations and logistics, he sought to enable better management of supplies and medications to help improve patient care.Omnicell became a publicly traded company in August 2001, and today healthcare systems worldwide leverage the company's automation and advanced services offerings to maximize clinical and operational outcomes.In 2014 Mr. Lipps was elected to the Bellwether League Hall of Fame, an industry organization that honors healthcare supply chain innovators, pioneers, and visionaries.Mr. Lipps has made giving back to the community a priority at Omnicell. Omnicell Cares, the company's formalized charitable efforts program, translates this into action, making a positive difference by fostering opportunities for volunteerism, charitable giving, and raising awareness for critical topics and issues. Mr. Lipps and his wife, Kathy, have focused their own philanthropy on poverty, nursing and public education, pharmacy research, youth groups, and local community efforts. Mr. Lipps serves as a member of the Board of Trustees of the American Nurses Foundation.Prior to founding Omnicell, Mr. Lipps was Assistant Vice President of Sales and Operations for a division of American Airlines. He holds Bachelor degrees in both Economics and Business Administration from Southern Methodist University. Topics to discuss –Introduction to Randy and Omnicell and his journey leading the digital transformation of medication management Staffing shortages and employee retention are top problems facing employers today, especially hospitals and health systems. In the pharmacy, labor shortages have far-reaching impacts including reduced quality of patient care, increased workloads for staff, slow delivery of medications, growing operational costs and process inefficiencies that lead to medication errors. Automation for pharmacies could be the key to addressing these issues and optimizing hospital staff's efficiency to reduce labor gaps.How the Innovation Lab allows customers to get a firsthand look at how automation technologies can make their healthcare operations more efficient and enable nurses and pharmacists to spend more time caring for patients. The autonomous pharmacy vision. How pharmacy automation reduces medication errors through accurate dispensing, streamlines inventory management and real-time tracking, ensures controlled substance security and regulatory compliance, and gives nurses more time to focus on higher-value tasks that directly impact patient care.Guest - Randall Lipps is Chairman, President, Chief Executive Officer, and Founder of OmnicellSocial Media:LinkedIn: https://www.linkedin.com/in/randall-lipps-a76412195/Website: https://www.omnicell.comYouTube: https://www.youtube.com/@Omnicell1Host - Hillary Blackburn, PharmD, MBAhttps://www.linkedin.com/in/hillary-blackburn-67a92421/  ★ Support this podcast on Patreon ★